VOLUME 12, ISSUE 5, MAY 2023

Abstract: The Internet has become a necessity in today’s world. It is one of the most useful and harmful things depending upon its usage. In the world of crimes also internet plays a significant role in helping black hat hackers in committing cyber-crimes and steal personal information. Electronic devices and the internet are used in these types of crimes like cyber pornography, email booming, exposure to harmful content, grooming, harassment, sexual abuse, cyber-stalking, virus attacks etc. Educated as well as uneducated, both are the victims, and the most vulnerable part of society is children, women, and senior citizens. Children are among the newest victims of cyber-crime. Our study area is the cyber city Delhi, which is secondarily survey including a case study of Palam district. The present study is concerned with finding and examining the problems and impact of cyber-crime on children, women, and senior citizensby suggesting some mitigation measures to boost cyber security. Questionnaires are discussed and results show how much people are aware of cyber-crimes, the connection between cyber-crimes and children, women & senior citizens, and what kind of problems they face in dealing with such kinds of crimes. The findings suggest that people do know about this current problem, but more awareness is needed from a small level like school discussions should have this kind of topics, elderly people don’t use social sites much but receive fake calls from banks and companies which try to manipulate them. To reduce the level of cyber-crime, some suggestions and solutions are discussed at the end.

Keywords: Cybercrime, Internet, Child, Women, Senior Citizens, Delhi, Palam

Abstract: The importance of food for human survival has been discussed in several medical conferences. Consumers now have more opportunities to learn about nutrition patterns, understand their daily eating habits, and maintain a balanced diet owing to modern dietary evaluation and nutrition analysis tools. Due to the ignorance of healthy food habits, obesity rates are increasing at an alarming speed, and this is reflective of the risks to people’s health. People need to control their daily calorie intake by eating healthier foods, which is the most basic method to avoid obesity. However, although food packaging comes with nutrition (and calorie) labels, it’s still not very convenient for people to refer to App-based nutrient dashboard systems which can analyze real-time images of a meal and analyze it for nutritional content which can be very handy and improves the dietary habits, and therefore, helps in maintaining a healthy lifestyle. This project aims at building an application that automatically estimates food attributes such as ingredients and nutritional value by classifying the input image of food. This method employs a deep learning model (CNN) for accurate food identification and Food APIs’ s to give the nutritional value of the identified food.  

Keywords: CNN, GLCM.

Keywords: Detecting phishing attacks for cybersecurity that use Catboost, Adaboost, Random Forest, Support Vector Machine algorithms, Natural Language Processing and Machine Learning.

Abstract:  Gait recognition is typically alluded to as a human recognizable proof/individual by the house style or method of individuals stroll in picture groupings. Stride acknowledgment innovation is a biometric distinguishing proof technique that dissects and recognizes people in view of their strolling designs. This innovation has acquired critical consideration lately because of its expected applications in security, observation, and medical services. Stride acknowledgment frameworks catch a singular's step highlights utilizing camcorders and afterward use AI calculations to investigate these elements for recognizable proof purposes. This theoretical gives an outline of step acknowledgment innovation, its applications, and its constraints. It additionally talks about the difficulties related with stride acknowledgment, like ecological variables and changeability in human step, and features the requirement for additional exploration in this field to work on the exactness and dependability of walk acknowledgment frameworks. Keywords: Gait recognition, Silhouette Segmentation, Contour detection, computer Algorithm vision, Machine learning.

Abstract: Potholes are a major concern on roads and highways, causing accidents, vehicle damage, and traffic delays. Detecting potholes in a timely and accurate manner is crucial for ensuring safe and efficient transportation. Various technologies are being used for pothole detection, including laser-based systems, machine learning algorithms, computer vision techniques, and acoustic sensors. Laser-based systems use high-precision measurements to detect the road surface's irregularities, including potholes. Machine learning algorithms analyze images of road surfaces captured by cameras mounted on vehicles to identify potholes. Computer vision techniques analyze road surface images captured by cameras or drones to detect potholes. Acoustic sensors can detect the vibrations generated by vehicles driving over potholes. Overall, the use of these technologies has improved pothole detection accuracy and efficiency, enabling faster repairs and safer roads.

Keywords: Machine Learning, Deep Learning, IoT, Pothole.

Abstract: Heart disease describes a range of conditions that affect your heart. Diseases under the heart disease umbrella include blood vessel diseases, such as coronary artery disease, heart rhythm problems (arrhythmias), and heart defects you're born with (congenital heart defects), among others. According to World Health Organization (WHO), cardiovascular disease (CVD) is one of the most lethal diseases that leads to the most number of deaths worldwide. Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option for reducing and understanding heart symptoms of disease using the device's vital parameters like body temperature, heart rate, and blood pressure. This project proposes a Random Forest technique as the backbone of computer-aided diagnostic tools for more accurately forecasting heart disease risk levels and sending alert messages to the doctor ­­­and the guardian with the location details of the patient. Random Forest modeling is a promising classification approach for predicting medication adherence in CVD patients. This predictive model helps stratify the patients so that evidence-based decisions can be made and patients managed appropriately. The chi-square statistical test is performed to select specific attributes from the Cleveland heart disease (HD) dataset. The data visualization has been generated to illustrate the relationship between the features. According to the findings of the experiments, the random forest algorithm achieves 88.5% accuracy during validation for 303 data instances with 13 selected features of the Cleveland HD dataset.  

Keywords: CVD (cardiovascular disease), Random Forest algorithm, Machine learning, WHO (world health organization).

Abstract: This study reviews the Artificial Intelligence and Machine Learning approaches developed thus far for driver profile and driving pattern recognition, representing a set of macroscopic and microscopic behaviors respectively, to enhance the understanding of human factors in road safety, and therefore reduce the number of crashes. It provides a definition of the two scientific fields in terms of safety, and identifies the most efficient approaches used regarding methodology, data collection and driving metrics. Results show that K-means and Neural Networks are the most commonly used methodologies for driver profile identification, and Dynamic Time Warping for driving pattern detection. Most studies discovered driver profiles related to aggressiveness, considering mainly speed and acceleration as driving metrics. Based on the gaps and challenges identified, this paper provides a new framework for combining microscopic and macroscopic driving behavior analysis, instead of examining them separately as is the state-of-theart. Such combined results can potentially improve the development of traffic risk models, which could be exploited in applications that monitor drivers in real-time and provide feedback. These models will represent human behavior more accurately, which can eventually lead to the recognition of “optimal” human driving patterns that Automated Vehicles (AV) could ‘mimic’ to become safer.

Abstract: ATM or Automated Teller Machines are widely used by people nowadays. Performing cash withdrawal transaction with ATM is increasing day by day. ATM is very important device throughout the world. The existing conventional ATM is vulnerable to crimes because of the rapid technology development. A total of 270,000 reports have been reported regarding debit card fraud and this was the most reported form of identity theft in 2021. A secure and efficient ATM is needed to increase the overall experience, usability, and convenience of the transaction at the ATM. In today's w  kl, orld, the area of computer vision is advancing at a breakneck pace. The recent progress in biometric identification techniques, including finger printing, retina scanning, and facial recognition has made a great effort to rescue the unsafe situation at the ATM. Specifically, the goal of this project is to give a computer vision method to solve the security risk associated with accessing ATM machines. This project proposes an automatic teller machine security model that uses electronic facial recognition using Deep Convolutional Neural Network  (DCNN). If this technology becomes widely used, faces would be protected as well as their accounts. Face Verification Clickbait Link will be generated and sent to bank account holder to verify the identity of unauthorized user through some dedicated artificial intelligent agents, for remote certification. However, it obvious that man’s biometric features cannot be replicated, this proposal will go a long way to solve the problem of account safety making it possible for the actual account owner alone have access to his accounts. This eliminates the possibility of fraud resulting from ATM card theft and copying. The experimental results on real-time datasets demonstrate the superior performance of the proposed approach over state-of-the-art deep learning techniques in terms of both learning efficiency and matching accuracy. By using this real time dataset, the proposed system achieves the highest accuracy with 97.93%

Keywords: AI, ATM, CNN, DCNN, FVCL

Abstract: In cloud storage, how to search sensitive data efficiently and securely is a challenging problem. The searchable encryption technique provides a secure storage method without loss of data confidentiality and usability. As an important branch of searchable encryption, public-key encryption with keyword search (PEKS) is widely studied by scholars. However, most of the traditional PEKS schemes are vulnerable to the inside keyword guessing attack (IKGA). Resisting the inside keyword guessing attack is likely to become an essential property of all new PEKS schemes. For a long time, mitigating IKGA has been inefficient and difficult, and most existing PEKS schemes fail in achieving their security goals. To address the above problems, we define the notion of Dual-server Public-key Authenticated Encryption with Keyword Search (DPAEKS), which protects against IKGA by leveraging two servers that do not cooperate, and supports the authentication property. Then, we provide a construction of DPAEKS without bilinear pairings. Experimental results obtained using a real-world dataset show that our scheme is highly efficient and provides strong security, making it suitable for deployment in practical applications. Keywords: Cloud computing, security,DPAEKS adopts a dual-server framework, wherein the test functionality is split into two parts which are handled by two independent servers.

Abstract: Photonic technologies have changed the world of communications in the form of fiber optics, integrated optics, electro-optical components, and micro-photonics. They offer some compelling advantages compared with their traditional RF counterparts when considered for use in space applications. Thus, research and development of photonics technologies for the space, the applications in areas of communications, sensing, and signal processing has been a major them for several years. The use of the photonic technologies for the space applications has risen the problem related to ability of optoelectronic and optic components to withstand space environment as all optoelectronic and optic components come from the terrestrial applications. Thus, the development of photonic technologies for the space applications has made the selection and acceptance test criteria of all optoelectronic and optic components that are part of the photonic system imperative. This presents a summary of the experience to Alter Technology Group on the mechanical, thermal, radiation, and endurance testing of several photonics technologies. In addition, the paper describes an assessment relates to reliability of these parts to be usefull in the space applications.

Keywords: Tunable lasers, semiconductor lasers, and photonic integrated circuits.

Abstract: Web phishing is a social engineering cyber attack that is used to gain the credentials of a legitimate user to achieve unauthorized access to the victim’s account using stolen credentials. Most of the phishing attacks happen on social media, E-commerce, net banking, and mobile platforms. Phishing website is one of the internet security problems that target human vulnerabilities rather than software vulnerabilities. Here, we improve the accuracy of the results. It aims to prevent online fraud and protect internet users from falling prey to phishing attacks. The project involves developing an automated system that can identify and flag websites that attempt to deceive users into divulging sensitive information such as login credentials, credit card details, and personal data.  The backend signature detection approach is based on machine learning algorithms that are trained to identify the patterns and characteristics of phishing websites. The system uses these algorithms to compare the identified websites against a database of known phishing sites and to determine the likelihood that a particular site is attempting to deceive users.  Overall, the Web Phishing Detection Based on Web Crawling and Backend Signature project is an important step towards improving online security and protecting users from the growing threat of phishing attacks.

Abstract: In the era most of the engineering students are known the theoretical concepts of electrical and electronic components used in the laboratory but they couldn’t experience or imagine the how each of the component is functioning and the electron current flows in the circuit. In this project, an application is developed to learn and experience the components and overcome the problem which was faced by the student community via augmented reality. Augmented reality provides new development ideas for the visualization of data information, and at the same time provides new development space for the convergence of digital visualization technology and multiple industry. Augmented reality makes the virtual object into the real scene. So the students can able to understand the concept practically through by visualizing the objects. Here we are trying to develop a learning tool for laboratory that uses electrical and electronics components using augmented reality. In this work, initially we are designing the electrical and electronic component through unity and to show the simulation of electrons in the electrical and electronic component through animation. This makes the student to understand the concept theoretically as well as practically. In this project implementing the super imposition augmented reality that makes the virtual object is placed on top the real object in real scene. unity which acts as an toolkit for making the 3D objects and makes the animation on the object. which makes everyone can able to understand the concept practically through virtualizing the virtual electrically and electronic components.

Abstract: Most of the countries are depends on agriculture, where Tamil Nadu is theland of agriculture. Here paddy cultivation is major source of earning. People in Tamil Nadu, consumes rice as main meal for three times in a day. Various factors such as diseases on paddy leaf, pest attack etc., the production of paddy will be affected approximately 40% to 50%, commonly rice related diseases should be detected in early stage to protect the paddy because it will destroy the entire farm land. If the diseases are identified in initial stage there is no need to spray a high dose fertilizer on the paddy crops. To overcome this, the proposed system uses pre-processing, transfer learning Inception_V3 method, neural network is trained by deep learning based Convolutional Neural Network(CNN) classification algorithm to identify the paddy leaf diseases like bacterial leaf blight, brown spot and rice blast. This method produces good accuracy. Scope of this project is to detect disease on paddy crops and to notify the types of diseases to farmer so that the farmers can take early action to protect the paddy crops. Index Terms: Convolutional Neural Network (CNN), Digital Agriculture, Internet-of-Agro-Things (IoAT), Machine Learning (ML), Deep Learning.

Abstract: Steel fibre for reinforcing concrete is defined as short, discrete lengths of steel fibres with an aspect ratio (ratio of length to diameter) from about 20 to 100, with different cross-sections, and that are sufficiently small to be randomly dispersed in an unhardened concrete mixture using the usual mixing procedures. Glass fibres reinforced polymer composites have been prepared by various manufacturing technology and are widely used for various applications. Glass fibres are having excellent properties like high strength, flexibility, stiffness and resistance to chemical harm. Objective of this to compare the check performance of glass and steel fibres, different percentages of 0.25%, 0.5%, 0.75%, 1%, and 1.25% were used. 1.5% 1.75% And 2% for concrete of M30 grade. To compare the fresh properties of fibre-reinforced concrete to those of conventional concrete. To compare fibre-reinforced concrete with ordinary concrete in terms of compressive strength and split tensile strength. Studying the behaviour of reinforced concrete blocks fortified with steel and glass fibres is the main goal and the methodology of this to research if steel and glass reinforcement for concrete is appropriate. The study used a comprehensive approach that included selecting materials, preparing concrete mixes, testing physical and mechanical properties, and evaluating environmental sustainability. The results were analysed using statistical methods to determine the significance of the differences observed between the various concrete mixes. The rigorous experimental approach and statistical analysis provided reliable data to support the study's findings.

Keywords: steel and glass fibre, compressive strength, split tensile strength.

Abstract: Solar energy is one of the most promising renewable sources of energy, and solar cells are the key components for converting sunlight into electricity. However, traditional silicon-based solar cells are limited by their relatively low efficiency and high manufacturing costs. Thin film technologies offer a potential solution to these problems by providing a more efficient and cost-effective alternative to traditional solar cells. In this report, we review recent developments in thin film technologies for improving the efficiency of solar cells. We discuss different types of thin film materials, including cadmium telluride, copper indium gallium selenide, and perovskites, and their potential for improving the efficiency of solar cells. We also review different manufacturing techniques for thin film solar cells, including physical vapor deposition, chemical vapor deposition, and solution-based methods. Finally, we discuss the challenges and opportunities associated with thin film technologies and their potential to revolutionize the solar energy industry. Overall, thin film technologies have the potential to significantly improve the efficiency of solar cells and reduce the cost of solar energy, making it a more viable and sustainable source of energy for the future.

Abstract: Hand gesture recognition is a challenging problem in computer vision, with various applications in fields such as robotics, human-computer interaction, and sign language recognition. The ability to recognize hand gestures in real-time can enable seamless communication between humans and machines, making human-computer interaction more intuitive and natural. In this project, we propose a system that can recognize hand gestures by classifying the fingers using open-source computer vision technology. The proposed system uses a combination of image processing techniques and machine learning algorithms to classify the fingers and recognize hand gestures. The input data is captured from a webcam and pre-processed using techniques such as skin colour detection and hand tracking. The hand is then segmented, and the fingers are extracted based on their location and orientation. The extracted finger images are then classified using a convolutional neural network (CNN) architecture. The CNN model is trained on a large dataset of finger images and hand gestures to achieve high accuracy in classification. The dataset comprises images of various hand gestures, including open, closed, and partially closed hands. The CNN model is trained to recognize the fingers' positions and orientations in the input images and classify them into their respective categories. The model is fine-tuned using transfer learning techniques to improve its accuracy and generalizability. The proposed system is evaluated using a variety of hand gestures, including thumbs up, thumbs down, okay, and rock-on, among others. The system achieves high accuracy in recognizing different hand gestures in real-time, with an overall accuracy of 95%. The proposed system's robustness and accuracy make it suitable for various applications, including sign language recognition, human-computer interaction, and gaming. In conclusion, this project proposes a system that can recognize hand gestures by classifying the fingers using open-source computer vision technology. The system achieves high accuracy in real-time hand gesture recognition and has potential applications in various fields. Future work can explore the use of deep learning algorithms and more extensive datasets to improve the system's accuracy and performance. Additionally, the proposed system can be extended to recognize hand gestures in different lighting conditions and backgrounds

Abstract: Non-invasive glucometers are devices that measure blood glucose levels without the need for a blood sample. These devices have the potential to revolutionize the management of diabetes by providing a more convenient and painless method of glucose monitoring. This abstract will discuss the technology behind non-invasive glucometers and their potential benefits and drawbacks. Non-invasive glucometers work by measuring glucose levels in body fluids other than blood, such as saliva, sweat, or interstitial fluid. This is done using various technologies, such as infrared spectroscopy, Raman spectroscopy, or optical coherence tomography. The device then calculates the blood glucose level based on the measurement of the body fluid. One of the main benefits of non-invasive glucometers is the reduction in pain and discomfort associated with traditional blood glucose monitoring methods. This can lead to increased compliance with glucose monitoring and ultimately better diabetes management. Additionally, non-invasive glucometers may reduce the risk of infection or other complications associated with finger pricks. However, non-invasive glucometers are not without their drawbacks. They may be less accurate than traditional methods, particularly in situations where glucose levels are rapidly changing and expensive than traditional methods. Keywords: Non-Invasive; NIR Spectroscopy; Hyperglycemia, Hypoglycemia.

Abstract:  This paper proposes a blockchain-inspired Internet-of-Things architecture for creating a transparent food supply chain. The architecture uses a proof-of-object-based authentication protocol, which is analogous to the cryptocurrency’s proof-of-work protocol. The complete architecture was realized by integrating a radio frequency identification (RFID)- based sensor at the physical layer and blockchain at the cyber layer. The RFID provides a unique identity of the product and the sensor data, which helps in real-time quality monitoring. For this purpose, a small feature size 900-MHz RFID coupled sensor was fabricated and demonstrated for real-time sensor data acquisition. The blockchain architecture aids in creating a tamper-proof digital database of the food packages at each instance. A detailed security analysis was performed to investigate the vulnerability of the proposed architecture under different types of cyber-attacks. Keywords: Food Supply Chain, Blockchain, radio frequency identification (RFID), tamper-proof.

Abstract: Stream flooding is a trademark wonder that can devastatingly influence human life likewise, monetary incidents. There have been various systems in considering stream flooding; in any case, lacking agreement and confined data about flooding conditions defeat the improvement of balance and control measures for this trademark wonder. This includes one more technique for the assumption for water level in relationship with flood earnestness using the gathering model. Our philosophy involves the latest headways in the Internet of Things (IoT) and AI for the robotized assessment of flood data that might be useful to prevent devastating occasions. Investigation results show that gathering learning gives a more strong gadget to expect flood earnestness levels. Keywords: Internet of Things , LSTM ,Machine to Machine Innovation

Abstract: Tourism is a rapidly growing industry, and with the rise of machine learning techniques, it is possible to make personalized tourist recommendations. Machine learning models can analyse a large amount of tourist data, including historical tourist trends, demographic information, user preferences. To develop a personalized tourist recommendation system, one approach is to use a combination of machine learning algorithms. The proposed system helps in guiding users with all the information regarding tourist places. In this paper the system also provides a personalized experience to tourists by taking into account their individual preferences. The proposed system helps user to give rating and reviews on the places they visited.

Keywords: Recommendation Web Application, Machine learning, Collaborative filtering algorithm, Content-based filtering, Knowledge-based filtering.

Abstract: In the present world due to urbanization and industrialization, huge amounts of plastic are generated every year. Hence the disposal methods and management of plastics like burning them in the open air cause hazardous problems in the atmosphere. Though there have been numerous attempts of technologies to repossess plastics, it primarily emits greenhouse gases and the usage of power for the entire recycling process is high rise. Plastic to fuel conversion is a promising technology that has the potential to reduce the amount of plastic waste in the environment and provide an alternative source of fuel. Hence the Pyrolysis technique is one of the nature-friendly attempts to convert plastic to inflammable gas. But this is a process that needs high energy for the anaerobic decomposition of plastic in the presence of the catalyst and at very high temperatures. To minimize the consumption of excess energy nature, renewable technologies are taken in to provide extreme temperature levels to enable pyrolysis. This technology is converting plastic waste into fossil fuel through decomposition.

Keywords: Urbanization, Hazardous, greenhouse gases, pyrolysis, anaerobic, Fossil fuel.

Abstract: Data are presently processed relatively in an systematic way due to the advancement of machine learning procedures. Such plans for information extraction are very often employed in a diversity of contexts, counting trade, social media, wagering, voting, predicting, and more. Healthcare in Body Sensor organize is one of these key areas where displaying and data analysis are exceedingly utilized. The information that is captured and fabricated in this organize is utilized to follow a person's regular exercises, check that the data is exact, determine when a medical emergency is appropriate, and more. There are abundant studies based on such examination; some acted their own methodology while others utilized pri-defined procedures such as Machine Learning, Deep Learning, Neural Systems and more. In order to analysis the sensor information, several methodologies that have been stated in some preferred research articles are compared in this document. Both the analysis strategies and the study's discoveries are very distinct and have numerous distinctive characteristics. The differentiate study contributes a comprehensible exhibit of these procedures and angle.  

Keywords: Healthcare, Body Sensor Network, Deep Learning, Neural Networks, Human Activity Recognition. 

Abstract: Health complications during the gestation period have evolved as a global issue. These complications sometimes result in the mortality of the fetus, which is more prevalent in developing and underdeveloped countries. The genesis of machine learning (ML) algorithms in the healthcare domain have brought remarkable progress in disease diagnosis, treatment, and prognosis. Around 800 women die every day due to pregnancy and childbirth-related issues. Maternal health and fetal health are closely associated with each other Every year approximately 3 million new born babies die because of miscarriage So there is a need for proper care including the prediction of risk levels before, during pregnancy for the safety of both mother and child. Data mining is a commonly used technique for processing enormous data. Researchers apply several data mining and machine learning techniques to analysis huge complex data, helping health care professionals to predict fetal health. In this project we used different algorithms are compared and the best model is used for predicting the fetal health.

Keywords: Machine learning, Fetal health

Abstract: Recently, terahertz spectroscopy has received a lot of attention because of its unique properties such as biosafety, fingerprint spectrum, and good penetration. In this review, we focus on the research progress of terahertz spectroscopic techniques for the detection and recognition of substances. First, we describe the fundamentals of terahertz spectroscopy. Then, we outline the applications of terahertz spectroscopy in biomedicine, agriculture, food production, and security inspection. Subsequently, metamaterials, which have recently received extensive attention, are also investigated for their applications in terahertz spectroscopic detection and recognition of substances is illustrated. Finally, the development trend of terahertz spectroscopy for substance detection and recognition has also been prospected.

Abstract: Quantum communication is built on a set of disruptive concepts and technologies. It is driven by fascinating physics and by promising applications. It requires a new mix of competencies, from telecom engineering to theoretical physics, from theoretical computer science to mechanical and electronic engineering. First applications have already found their way to niche markets and university labs are working on futuristic quantum networks, but most of the surprises are still ahead of us. Quantum communication, and more generally quantum information science and technologies are here to stay and will have a profound impact on the twentieth century. Hence, this technology plays a vital role in modern day communication.

Keywords: college placement portals, employment prospects, graduates, job search, recruitment processes, chat bot.

Abstract: This paper presents the design and development of a mechatronic emergency ventilator for treating breathing ailments. The system was developed to address the increasing demand for ventilators due to the COVID-19 pandemic. The ventilator consists of a mechanical system that provides the necessary air pressure and volume to the patient, and an electronic control system that regulates the respiratory rate and tidal volume. The system was designed with simplicity, portability, and cost-effectiveness in mind, using off-the-shelf components whenever possible. The stepper motor is interfaced with a stepper drive in order to impose a "PUSH-PULL" mechanism to run the AMBU bag, and Arduino is used to interface all of the sensors, motor, and supply. The "PUSH-PULL" technique is used to equalize air pressure. This meets every demand and requirement for the patient with Covid-19.

Keywords: Covid-19, Ventilators, PUSH-PULL, Stepper motor, Stepper drive, Arduino.

Abstract: Educational institutions play a crucial role in the growth and development of any nation. Therefore, it is imperative to find a suitable college for pursuing higher education. Our proposed system utilizes data analysis and data mining techniques, with a recommendation system being a critical part of it. The system uses data mining techniques to filter data and present relevant information. It caters to the needs of students, parents, and educationalists who seek guidance while searching for admission in engineering colleges. Many students with impressive scores miss out on their preferred colleges or courses due to lack of proper information. Therefore, we propose a recommendation system for engineering students that considers college NAAC grade and NBA grade.[2] this system will assist students and parents in selecting the desired college. The recommendation system is divided into three modules - Student, College, and Parent login modules, each with unique functions. Parents can also search for the best colleges based on different criteria.

Keywords: Educational institutions, growth and development, suitable college, higher education, proposed system, data analysis, data mining, recommendation system, students, parents, educationalists, admission, engineering colleges, college NAAC grade, NBA grade, missed opportunities, unique functions, parent login module, student module, college module.

Abstract: 3D holographic projection technology is a cutting-edge technology that utilizes computer graphics to create virtual 3D images that appear to be floating in the air. This technology works by projecting light onto a screen or other surface to create the illusion of a three-dimensional image. The images are created using specialized software that generates a virtual model of the object, which is then projected using lasers, mirrors, or other optical devices. The result is a stunning visual display that can be used for a variety of applications, including advertising, entertainment, and education. This technology has the potential to revolutionize the way we interact with the world around us, offering new and exciting ways to communicate and experience information. With continued advancements in computer graphics and hardware, we can expect to see even more impressive applications of 3D holographic projection technology in the years to come.

Abstract: In today's world scenario personal safety is one of the most important issues to be addressed in our country. Whenever a person encounters any kind of harassment, sexual abuse or molestation. They need urgent help at that time, proper reachability is not present for them. Apart from being aware about the significance of personal safety, it is essential that they are provided with protection during those critical times. The previous current system is helpful in detecting the person's location after the criminal offense has been committed. In this project we introduce a new technique via smart device, this device makes use of wireless sensors to communicate and send the message to the predefined authorities. The project idea is to provide a quick responding and reporting safety device for everyone. It reports a situation just by pressing button on the smart device. The device mainly works on the concept of emotion recognition which can achieve through CNN machine learning algorithm. The captured image is tested whether it is fake or genuine before sending it to the authorities.

Keywords: Machine Learning, CNN Algorithm, Deep Learning, IoT.  

Abstract: Flooding is one of the major disasters occurring in various parts of the world. Due to the high density of buildings, flash floods are prevalent in cities. In such scenarios, there is a possibility of a drainage overflow. Therefore, implementation of an intelligent analysis of drainage leakage detection during flood using sensors are necessitated for the field of research in Disaster management. Floods in india hence became the huge obstacle in achieving economic growth in the country. Unplanned urbanization also has a huge impact on drainage and garbage disposal system. Overflow of drainage water onto the grounds are day to day problems that one living in the cities usually face. Hence to decrease all these effects onto the human life, we come up with this proposed system.

Keywords: Sensors, microcontroller, Blynk cloud, Barriers.

Abstract: This project aims to assess the quality of various rice samples by processing, enhancing, and analyzing their digital images in the spatial domain. The images are subjected to techniques such as image reduction, enhancement, and increment, as well as object recognition to determine the size, color, and overall quality of the grains of rice. Traditionally, grain quality evaluation has been performed manually, but this approach is relative, time-consuming, and may yield inconsistent results, making it costly. Using image processing and edge detection algorithms, the grain size and shape are evaluated by identifying the boundaries and endpoints of each grain. Additionally, the length and breadth of rice grains are measured. The use of image processing greatly reduces the time required for evaluation. Keywords - Grading, Rice grain, Quality, Image processing, grain evaluation

Abstract: Healthcare is a core of human’s life. Being healthy is one of the main objectives of life ever since BC. Health is maintained and improved by the lifestyles, social, happiness and even willingness to live. Predicting a person's mood tomorrow, from data collected unobtrusively using wearable sensors and smartphones, could have a number of beneficial clinical applications; While accurately predicting mood and wellbeing could have a number of important clinical benefits, traditional Machine Learning (ML) methods frequently yield low performance in this domain. An integrated system to make human lives more easily and to help people in terms of mental health. The main objective of this project is to classify a new data and inform whether a person with stress is affected from acute stress or chronic stress. We will use the heart rate data taken for months and analyzes the data and find the Heart rate variance that constantly related with the stress. After that a variable is found, which is used as an input to the ensemble classifier which includes the Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) approaches. The best classifier will be selected by voting process.

Abstract: Software-defined  network  (SDN)  is  a  network architecture  that used to build, design the hardware components  virtually. We can dynamically change the settings of network connections. In the traditional network,  it's  not possible to change dynamically, because it's a fixed connection. SDN is a good approach but still  is  vulnerable  to  DDoS  attacks . The  DDoS  attack  is  menacing  to  the  internet.  To  prevent  the  DDoS attack,   the machine learning  algorithm  can  be  used.  The  DDoS  attack  Is  the  multiple  collaborated  systems  that   used  to  target  the  particular  server  at  the  same  time.  In  SDN  control  layer  is  in  the  center that  link   with  the  application  and  infrastructure  layer,  where  the  devices  in  the  infrastructure  layer controlled  by  the  software.  In  this  paper,  we  propose  a  machine  learning  technique  namely  Decision   Tree  to  detect  malicious  traffic.  Our  test  outcome  shows  that  the  Decision  Tree  detects  whether  the attack  is  safe  or  not.

Abstract: ACG - Ai Content Generator is a web application which helps us to generate contents for various scenarios such as (product description, job description, cold emails etc..) This software is making use of GPT-4. GPT-4, or the fourth generation Generative Pre-trained Transformer, is a neural network machine learning model trained using internet data to generate any type of text. Developed by OPENAI, it requires a small amount of input text to generate large volumes of relevant and sophisticated machine-generated text. The process goes like the user can choose what content they have to generate so based upon their customized request, the user can choose and give key words in the search box corresponding to it . So once it is done then, then the GPT-4 API will be activated with the help of the openai key . Then the request will be handled by the GPT-4 and the desired content will be generated

Keywords: Artificial Intelligence, Machine Learning, BERT, API Key, GPT 4 (Fourth generation ,Generative Pre-trained Transformer),

Abstract: The development of touchless interaction devices has improved important observation in recent years, especially in the circumstances of large displays. One such system is gesture-based touchless interaction, which enables users to interact with large displays using natural gestures without physically touching the screen.  From this paper, we benfit  an abstract for gesture-based touchless interaction with large displays. I relate the technical components for the technique, including the sensors, recognition algorithms, and user interface. We also discuss the  application possibilites of such a system, including in public spaces, education, and entertainment. Finally, we address some of the challenges related with the implementation and use of gesture-based touchless interaction with large displays, such as accuracy, ergonomics, and user privacy. Overall, we argue that gesture-based touchless interaction is the good way for the development of large display interaction systems, offering a more intuitive and engaging user experience. Keywords: gesture-based, touchless interaction, large displays, sensors, recognition algorithms, user interface, public spaces, education, entertainment, accuracy, ergonomics, user privacy.

Abstract: In order to meet the needs of paperless offices and greatly improve word efficiency, it is necessary to research and implement a handwritten digit recognition system. Handwritten digit recognition plays and important role in large-scale data statistics and the financial business, such as industry annual inspection, population census, tax statements and checks, etc. This project proposes a new type of handwritten digit recognition system based on convolutional neural network (CNN). In order to improve the recognition performance, the network was trained with a large number of standardized pictures to automatically learn the spatial characteristics of handwritten digits. For model training, according to the loss function, the convolutional neural continuously updates the network parameters with the data set in MNIST, which contains 60,000 examples. For model test, the system uses the camera to capture the pictures composed of the images generated by the test data set of MNIST and the samples written by different people, then continuously processes the captured graphics and refreshes the output every 0.5 seconds. With the trained deep learning model, we got a recognition accuracy of 99.3% in test process. Good performance in this experiment shows that our system can automatically recognize the handwritten digital content appearing in the target area and output the content label in real time.  

Keywords: Digit Recognition, Deep Learning, Convolutional Neural Network (CNN), MNIST data set, Real-time recognition, Image processing, Machine learning

Abstract:  A national identity document is an identity card with a photo, usable as an identity card at least inside  the country, and which is issued by an official authority. The most common applications for these smart  cards are smart to travel documents, electronic IDs, electronic signatures, municipal cards, key cards used  to access secure areas or business infrastructures, social security cards, etc. These documents have several  security features which mitigate and combat document forgery. As these security systems are difficult to  circumvent, criminal attacks on ID verification systems are now focusing on fraudulently obtaining genuine  documents and the manipulation of the facial portraits. Trusted identity is a vital component of a well functioning society. To reduce risks related to this fraud problem, it is necessary those governments and  manufacturer of IDs continuously develop and improve security measures. With this in mind, we introduce  the first efficient steganography method – StegoCard – which is optimized for facial images printed in  common IDs. StegoCard is an end-to-end facial image steganography model that is formed by n Deep  Convolutional Auto Encoder, that can conceal a secret message in a face portrait and, hence, producing the  stego facial image, and a Deep Convolutional Auto Decoder, which is able to read a message from the stego  facial image, even if it is previously printed and then captured by a digital camera. Facial images encoded  with our StegoCard approach outperform the StegaStamp generated images in terms of their perception  quality. Peak Signal-to-Noise Ratio, hiding capacity and imperceptibility results on the test set are used to  measure the performance.

Abstract: In the present world, the data security is the major problem. The data should be secured in such a way that only the sender and the receiver should be able to view the original data. Most of the traditional techniques that are being used today uses the generic functions, random key generators, or algorithms to generate the keys. But the keys generated using the traditional techniques will not be unique to each and every individual. So, the Biometric Cryptosystems can be used to achieve the data security. The Biometrics, such as fingerprints can be used for data security. Since the fingerprints of each and every individual in the world is unique, the keys generated using the fingerprints will be unique. In this work, the fingerprint of the individual is taken as the input to generate the prime numbers using the minutiae points. The generated prime numbers are then given as the input to the RSA algorithm to generate the keys. The generated keys are then used for the encryption and the decryption process.  

Keywords: Fingerprint feature, Minutiae points, RSA algorithm, Cryptography, Data security

Keywords: Convolutional Neural Network, Transfer Learning, Face Recognition, Artificial Intelligence.

Abstract: Diabetic Retinopathy (DR) is a disease that damages retinal blood vessels and leads to blindness. Usually, colored fundus shots are used to diagnose this irreversible disease. The manual analysis (by clinicians) of the mentioned images is monotonous and error-prone. Hence, various computer vision hands-on engineering techniques are applied to predict the occurrences of the DR and its stages automatically. The VGG16, spatial pyramid pooling layer (SPP) and network-in-network (NiN) are stacked to make a highly nonlinear scale-invariant deep model called the VGG-NiN model. The proposed VGG-NiN model can process a DR image at any scale due to the SPP layer’s virtue

Abstract: In Network filtering is a technique used to control the flow of data in a network by selectively allowing or blocking traffic based on predetermined rules or criteria. There are various technologies available for implementing network filtering, each with their own strengths and weaknesses. One commonly used technology is firewall filtering, which is based on predefined rules that determine what traffic is allowed or blocked. Another technology is intrusion prevention systems (IPS), which use deep packet inspection to detect and block potentially harmful traffic. Content filtering is another popular technology that filters data based on its content, such as blocking websites containing specific keywords or categories.

Abstract: Background radiation is an important aspect of environmental monitoring, as it can have significant impacts on human health and safety. In recent years, advances in technology have made it possible to measure background radiation more accurately and efficiently. One promising approach involves the use of drones, which can cover large areas quickly and provide high-resolution data. The key challenge in this approach is to develop a reliable method for collecting and analysing the data. To address this challenge, this paper presents a novel method for measuring background radiation using drones equipped with wireless transfer capabilities and custom software. The drones are flown at predetermined altitudes and collect data using radiation sensors. The data is then transmitted wirelessly to a ground station, where it is processed and analysed using custom software. The results of our experiments show that this method is highly effective in measuring background radiation over large areas. The data collected by the drones is accurate and reliable, and the custom software provides a powerful tool for analysing and visualizing the data. This approach makes it easier and more efficient to gather data and identify potential health risks.

Keywords: Background Radiation, ESP8266, GPS, Geiger Counter, UAV, IoT

Abstract: Distracted driving is any activity that deviates an individual’s attention from driving. Driver inattention and distraction are the main causes of road accidents, many of which result in fatalities. Driver distraction is a major cause of road accidents. Distracting activities while driving include text messaging and talking on the phone. Currently, distraction detection systems for road vehicles are not yet widely available or are limited to specific causes of driver inattention such as driver fatigue. Research efforts have been made to monitor drivers' attention states and provide support to drivers. Both invasive and non-invasive methods have been applied to track driver's attention states, but most of these methods either use exclusive equipment which are costly or use sensors that cause discomfort. The existing work of distracted driver detection is concerned with a limited set of distractions(Mainly cell phone usage).In this paper, a robust driver distraction detection system that extracts the driver's state from the recordings of an onboard camera using Deep Learning based Faster Region Convolution Neural Network (FRCNN).This project uses the state farm distracted driver detection, which contains four classes: calling, texting, looking behind, and normal driving The main feature of the proposed solution is the extraction of the driver's body parts, using deep learning-based segmentation, before performing the distraction detection and classification task. Experimental results show that the segmentation module significantly improves the classification performance. The average accuracy of the proposed solution exceeds 96% on our data set. The class activation map (CAM) of our proposed method is subjectively more reasonable, which would enhance the reliability and explain ability of the model.

Keywords: Alert Message, DD, FRCNN, Face Detection,

Abstract: The world population is expected to grow by another two billion in 2050, according to the survey taken by the Food and Agriculture Organization, while the arable area is likely to grow only by 5%. Therefore, smart and efficient farming techniques are necessary to improve agriculture productivity. Agriculture land suitability assessment is one of the essential tools for agriculture development. Several new technologies and innovations are being implemented in agriculture as an alternative to collect and process farm information. The rapid development of wireless sensor networks has triggered the design of low-cost and small sensor devices with the Internet of Things (IoT) empowered as a feasible tool for automating and decision-making in the domain of agriculture. This research proposes an expert system by integrating sensor networks with Artificial Intelligence systems such as neural networks and Multi-Layer Perceptron (MLP) for the assessment of agriculture land suitability. This proposed system will help the farmers to assess the agriculture land for cultivation in terms of four decision classes, namely more suitable, suitable, moderately suitable, and unsuitable. This assessment is determined based on the input collected from the various sensor devices, which are used for training the system. The results obtained using MLP with four hidden layers is found to be effective for the multiclass classification system when compared to the other existing model. This trained model will be used for evaluating future assessments and classifying the land after every cultivation

Abstract: With increase in the population, accident rates are also increasing rapidly, the main reason is drowsiness of the driver. Such lethal incidents can be prevented if the driver is warned in time. To implement this technology, we proposes a smart alarm system to detect the drowsiness of driver using facial expressions and eye movements. Here open computer vision is used to detect driver’s eye movements for a long time. We propose an approach based on Convolutional Neural Networks (CNN) that describes the object detection problem as sleepy detection. Based on the drivers' real-time video feed, it can detect and identify whether the eyes are open or closed. The technology used in this object detection challenge is the cellular CNN architecture with a single-shot multi-box detector. A different algorithm is used based on the output produced by the SSD_MobileNet_v1 architecture. A dataset of approximately 4,500 photographs of yawning, non-yawning, eyes-open, and eyes-closed subject faces was labelled to train the SSD_MobileNet_v1 network. The trained model is tested on about 600 randomly selected photos. The suggested strategy will guarantee improved computing efficiency and accuracy.

Keywords: single shot multi-box detector, Deep learning,  Smart alarm, eye tracking, drowsiness detection.

Abstract: Hand gestures are a type of non-verbal communication that can be utilized in many contexts, such as deaf-mute communication. The automatic interpretation of sign language is a research area that has not gotten much attention, despite the fact that it is essential for hearing impaired and silent persons to live independent lives as sign language is their primary mode of communication. Numerous methods and algorithms have been created in this field with the help of artificial intelligence and image processing. To recognize the signs and translate them into the necessary patterns, any system that understands sign language has undergone considerable training. This will help deaf people to communicate with the outside world easily. This proposed technique helps vocally disabled people to communicate.

Keywords: Human-Machine Interaction, Gesture Recognition, Machine Learning, Neural Networks, Convolutional Neural Network.

Abstract: In this paper, for detecting alive humans in disaster susceptible areas using an mobile robot is proposed. The soldiers are sent to save the victims who are struck in disaster. This causes risk to the soldiers lives also. Therefore, this mobile robot is developed. It consists of RFID to identify soldiers who are present in rescue operation. The unknown Human is detected using Passive-Infrared Sensor. The notification about the soldiers and other people in the disaster can be sent to control room through GSM module. The movement of the robot can be controlled from control room itself. The live video of surrondings is also sent to the control room through wireless camera.

Keywords: RFID,Mobile Robot,PIR sensor,Wireless Camera.

Abstract: The system would likely involve multiple components, such as charging stations equipped with wireless charging technology, EVs that are outfitted with compatible hardware and software, and a central system that manages and tracks the charging and location data. The wireless charging would allow EVs to recharge their batteries without physical connections or cables, which could make charging more convenient and efficient for users. The real-time location tracking would enable the central system to check the location of each EV, which could be useful for managing charging demand, identifying trends in usage patterns, and optimizing the distribution of charging resources. Overall, this paper would likely require a combination of hardware and software expertise,as knowledge of wireless charging and location tracking technologies. Additionally, there may be regulatory and safety considerations to the use of wireless charging systems in public areas, which would need to be carefully managed and addressed.

Abstract:  Sight and touch are the basic sensory systems for human interaction with the environment. For blind amputees, one of the key challenges is how to quickly and intuitively convey information about the environment to restore their daily life abilities. Inspired by the ability of human auditory localization, we constructed a virtual scene almost identical to reality and at the same time added a virtual sound source to the interactive object. Using the spatial sound rendering (SAR) method, the three-dimensional movement of a virtual sound source can be simulated live in real time. Finally, a myoelectric prosthetic control system was developed to assist blind amputees in their daily activities. The Fitts' law test for target localization was performed on both SAR and voice guidance (VP) guidance methods, the results indicate that SAR significantly improves the information transfer rate. Prosthetic control test results show that SAR reduces the completion time by half compared to VP while restoring the natural grasp path. With the advantage of intuitive and rich perception, SAR demonstrated potential applications for blind amputees to reconstruct control and sensory loops.

Keywords: Object and Obstacle detection, Convolutional Neural Network (CNN)

Abstract: i-Speculum aims to enhance the concept of smart mirrors by incorporating interactive features such as voice inputs and outputs, gesture control, and touchscreen capabilities. The smart mirror's software, powered by Raspberry Pi, recognizes users through facial recognition or voice control, allowing easy access to personalized information such as emails, smart home controls, and daily news updates. By presenting information in an intuitive and accessible format, smart mirrors improve productivity and convenience while reducing reliance on traditional computing devices. The combination of a traditional mirror's reflection and a computer display's versatility makes smart mirrors a promising technology for the future.

Keywords: I-speculum, Smart Mirror, Raspberry Pi, Interactive Features

Abstract: Brain tumor segmentation from 3D images is one of the most important and challenging tasks in the field of medical imaging. Manual classification can lead to false predictions and diagnoses. Moreover, this is a difficult process when the supporting data is enormous.. Extracting brain tumour regions from MRI images becomes challenging due to the great variety of appearances of brain tumours and how similar they are to normal tissues. In this article, we designed a modified U-Net architecture under a deep learning framework for brain real images for medical imaging and computer-assisted interventions provided by the BRATS 2020 dataset. Test accuracy of 99.4% has been achieved. A comparative review with other papers shows our model using U-Net performs better than other deep learning-based models.

Keywords: Deep learning,brain tumor classification and segmentation,3d unet architecture.

Abstract: Scrap collecting robots are becoming increasingly important as waste management and recycling become critical environmental issues. This paper presents a comprehensive review of the literature on scrap collecting robots, focusing on recent advancements, technologies, applications, and future directions. The types of scrap collecting robots, advantages, and technologies used are discussed, including the use of sensors, artificial intelligence, and machine learning. The paper also presents the applications of scrap collecting robots in residential, commercial, industrial settings, and disaster response. The need for continued research and development is emphasized, as scrap collecting robots are poised to play a crucial role in addressing environmental challenges in waste management and recycling.

Abstract: Recently, the increase in the number of sport lovers in games like football, cricket, hockey etc. has created a need for digging, analyzing and presenting more and more multidimensional information to them. Different classes of people require different kinds of information and this expands the space and scale of the required information. Tracking of ball movement is of utmost importance for extracting any information from the ball based sports video sequences. Detection is the first step of tracking. Dynamic and unpredictable nature of ball appearance, movement and continuously changing background make the detection and tracking processes challenging. Main intention is to evaluate the quickest way to detect the ball in any sport event in order to develop sports, AI without spending million dollars on techor developers by comparing different technologies.

Abstract: Nowadays, kidney stone has become a major problem and if not detected at an early Stage, then it may cause complications and sometimes surgery is also needed to remove the stone. This study presents an ultrasound speckle suppression method to detect the stones in the human kidney. An initial image is first improved using image enhancement techniques, which are used to change the image’s intensities. Next, median filters smooth the picture and eliminate noise. Pre-processed images are segmented using a thresholding technique. The suggested approach locates stones using location coordinates. The suggested scheme has been assessed by different performance measuring parameters. Physicians are likely to benefit from the research in terms of clinical diagnosis and educational training. Based on 30 test cases, the proposed plan was correct 96%. Key Words: Kidney stone detection, image processing, wavelet processing, ultrasound images, median filter, canny edge detection.

Abstract: A decent existence requires access to quality healthcare. It is extremely important to our day-to-day existence. Nevertheless, getting a doctor's appointment for each health issue is exceedingly difficult. The goal is to create a medical chatbot using computer science that will propose a doctor who specializes in a certain ailment. This can make it easier to increase access to medical data via chatbots. Chat bots are software applications that converse with users by using language. The chatbot keeps the data within the data to identify the sentence keywords, create a call to action, and respond to the question. Conversational User Interfaces (CUIs) let users interact with computers in a direct, human-like way. The way we engage with computers and applications is radically altered.CUI is utilized in this study to train data and a variety of packages that help us provide suitable satisfactory outcomes For the purpose of recovering findings, we will combine machine Learning with Natural Language Processing in this chat bot. Care plays an important role in our daily lives; whenever someone is ill, they visit their general practitioner or a nearby clinic to learn more about the issues they are facing. In recent years, a number of organizations and businesses have worked with hospitals to produce support that could help doctors and medical staff deal with patients in a better manner and reduce their labor by using technology; not only does this help the project's main goal is to help you convey information that you have mined more effectively (information) using Access to timely, easy, contextual information is required for both customers and staff.

Keywords:  Chat Bot System which can make suggestions to help people with correct decision making without making a failurre of treatment in initial stages of curing disease for hospital and people benefits.Making Faster Progress and results of health development in good side.

Abstract: To analyse the tumours and help patients receive the appropriate treatment according to their classifications, it is essential to have a thorough understanding of brain disorders such as classifying Brain-Tumors (BT). There are many imaging techniques for BT detection, including magnetic resonance imaging (MRI), which is frequently used due to the higher image quality and fact that it uses non-ionizing radiation. With the help of two datasets and a Gaussian Convolutional Neural Network (GCNN), this research suggests a method for identifying different BT types. To categorise tumours into pituitary, glioma, and meningioma, one of the datasets is employed.

Keywords: Deep learning, brain tumor classification, Gaussian convolutional neural network

Abstract: Human Identification Based on Free-Style Activities is a system designed to identify individuals by analyzing their unique patterns of free-style activities, such as walking, running, or gestures. The system aims to provide a reliable and efficient method of identification that goes beyond traditional biometric measures. By leveraging advanced algorithms and machine learning techniques, it captures and analyzes the distinctive characteristics of an individual's activities to establish their identity. This abstract presents the concept and potential benefits of Human Identification Based on Free-Style Activities, highlighting its potential applications in security, surveillance, and forensic investigations.

Keywords: Free-Style Activities, Activity Recognition, Unique Patterns, Machine Learning.

Abstract: The main objective of this proposed system is to detect Glaucoma in the retinal image. Glaucoma is an eye condition that can’t be healed once it happens. In case the corrective therapy does not continue, it causes a permanent visual disability so it cannot be ignored. Treatment will be helpful when the disease is identified at an early stage. Most of the research describes different techniques widely incorporated in the detection of Glaucoma disease. In this proposed system, the detection of Glaucoma is identified through Image Pre-processing and SVM algorithm. Pre-processing operators like Segmentation, Enhancement, Binarization, and Thresholding are used to extract the optic cup and optic disc from the retinal image to find the CD R. This proposed technique is based on OTSU’s segmentation method to locate the Optic cup and disc. Calculating only the CDR (Cup-to-Disc ratio) does not help to distinguish all the images as Glaucomatous or normal. Thus, RDR (Rim-to-Disc ratio) is considered another feature for Glaucoma assessment. The SVM (Support Vector Machine) algorithm plays an important role.

Keywords: Cup to Disc Ratio (CDR), Rim to Disc Ratio (RDR), Support Vector Machine (SVM), Optic Disc (OD), Optic Cup (OC), and Region of Interest (ROI).

Abstract: The various "Night Vision" techniques are referred to as invention that gives us the mysterious phenomenon of vision in all out dimness and vision adjustment in low light conditions. This invention is an amalgamation of a few distinct strategies each with their own different focal points and inconveniences. Low- Light Imaging, Thermal Imaging and Illumination are the most commonly known techniques. various night vision gadgets (NVDs) that allow images to be produced in levels of light moving towards adding up to darkness, as well as clarifies various applications where innovation in night vision is used to care for of different issues because of low light conditions . Pedestrians and animals have the greatest risk in night time traffic due to darkness, the ability to identify such objects should be the key performance requirement, and the device should remain successful when facing oncoming vehicles 'headlights. The infrared system has been shown to be superior to the near infrared system. Near infrared images have been identified as having substantially higher visual clutter compared to far- reaching infrared images. The visual clutter is shown to correlate with reduced pedestrian detection distance. Far- infrared images are thought to be more peculiar and hence more difficult to view, although the presence of the image is presumably related to the lower visual clutter.

Abstract: Traffic jams have become one of the biggest problems any metropolitan city faces in today’s time. This paper suggests implementing a smart traffic detector using OpenCV. The density of vehicles on the road keeps increasing to a higher amount these days. In traffic signal, people waste much time particularly during the peak hours of the day. In order to solve this problem of high traffic pressure, it is indispensable to solve traffic congestion. The frustration that is faced by people during traffic jams could also lead to mishaps such as accidents. Thus an idea of monitoring the traffic congestion using real-time image processing techniques, through this software has been proposed. The theme is to determine the traffic density on each side of the road by calculating the number of vehicles at the traffic signal zone. In this, an input image of traffic surveillance is shown to our trained machine which declares whether there is traffic or not by judging via the number of vehicles seen. After the image acquisition, the image undergoes various image pre-processing, image enhancement, and edge detection techniques. This project has been customized to be used in the future to control the traffic signals as well as monitor violators and avoid inconvenience and accidents as much as possible.  Keywords: Traffic Sign, Arduino UNO, Node MCU, Camera 

Abstract: As we are seeing much better growth in technology but we don’t see that its level is being properly utilized in the voting system. The present voting system is highly unsecured and it’s not efficient in utilizing the current technology i.e., It can’t determine that the person who come for voting is eligible or not, it just depends on the voting in-charge officer in the booth. Here there is also a possibility to boost the vote number as the vote count lies within the piece of equipment and if the in-charge officer is corrupted, he has the chance to do it, even while transporting the machines to the strong room. Hence we can’t rely on it any more. In the projected system, i.e., “Global wireless e-voting system”, machine is made smart that it can find out whether the voter is qualifiedfor voting or not with the help of scanning the eye pattern of the voter and also the vote count is not maintained in the machine itself .Vote count is made to be stored in a remote server by converting them into radio waves. Hence there won’t be any scope of escalating the vote count. Even the machine fails; there won’t be any problem to the votes that are casted as they are saved in the server. By this we can reduce many problems regarding the present EVM’s.

Abstract: India's economy is based largely on agriculture, which supports nearly 70% of the population. To meet the global population's demand for food, agriculture's yield needs to be rapidly increased. The Wireless Sensor Network (WSN) is a modern technology used to address a variety of current issues. In many fields, including transportation, medicine, the military, mobile phones, home appliances, and others, WSN is crucial. All living things rely on agriculture as one of their major food sources. Agriculture crops, however, are now impacted by numerous environmental changes. WSN plays a significant role in the field of agriculture to combat this. WSN is used in agriculture for monitoring, temperature measurement, irrigation system measurement, water supply measurement, and other tasks. WSN enables the farmer to increase crop production while lowering yield costs. Climate change, environmental change, and natural disasters all have an impact on agriculture. The management of soil and water can be done using WSN. Here, wireless sensors are utilised, resulting in very low implementation costs. In this study, the crops are observed using wireless sensor nodes. Sensors can be used to measure temperature, humidity, and other theft indicators. Agriculture's productivity is boosted as a result. Automatic processes reduce the need for human effort and encourage farmers to develop their farmland. Using GPS, the location of the farmland can be transmitted. The use of sensors, Wi-Fi, cameras, and other devices helps to make agriculture as smart as possible. All collected data are kept in memory or the cloud. Thus, this system helps the agriculturists, landowners and research experts to monitor these parameters at the base station without going to the field site. The measured data is appropriately archived in the database and displayed using a GUI tool as well. The system's design incorporated the IRIS mote, MDA100 data acquisition board, and MIB520 USB interface board. We use Microsoft Visual Studio to create the GUI tool and the TinyOS operating system to create the wireless node software. For node-to-base station communication, the direct topology and ZigBee IEEE 802.15.4 protocol are utilised. Finally, we also talk about potential future research areas.  

Abstract: Crop prediction using Machine Learning (ML) and Internet of Things (IoT) based solutions is promising approach to guarantee food security and sustainable agriculture. Algorithms based on Machine Learning have shown promising results in predicting crop yields based on various environmental elements such as weather, soil conditions, and irrigation patterns. The project presents an alternative approach for crop prediction using ML. This approach involves integration of various sensors and IoT devices to collect data on various ecological factors that are known to affect crop yields, such as temperature, humidity, rainfall, and soil nutrient levels. To evaluate our approach, we collected data on crop yields and environmental factors for several years from multiple farms in different regions. The data is pre-processed and used to train the ML model, and its accuracy in predicting crop yields for a specified set of environmental conditions is tested. The outcomes reveal that this approach outperforms traditional methods of crop prediction, such as statistical regression models. The ML model was able to precisely predict crop yields with an average error rate of less than 4%. This demonstrates the potential of ML algorithms in improving crop yields and ensuring food security. In conclusion, this approach of crop prediction using ML is a promising method for improving agriculture and food security. By leveraging the power of ML algorithms and collecting data on various environmental factors, it can accurately predict crop yields and optimize agricultural practices. This may significantly affect food production worldwide and contribute to feeding the world's expanding population.

Keywords: Crop Prediction, ML Algorithm, Environmental Factors, IoT based Solutions, Food Security, sustainable agriculture.

Abstract: This paper aims to predict the direction of Bitcoin price in USD using machine learning techniques and sentiment analysis. Social media platforms such as Twitter and Reddit have been explored as sources of public sentiment, and we have applied sentiment analysis to tweets and Reddit posts related to Bitcoin. Supervised machine learning principles have been used to develop a prediction model, and we have analyzed the correlation between Bitcoin price movements and sentiments in tweets...

Abstract: Abstract The techniques of machine vision are extensively applied to agricultural science, and it has great perspective especially in the plant protection field, which ultimately leads to crops management. The paper describes a software prototype system for rice disease detection based on the infected images of various rice plants. Images of the infected rice plants are captured by digital camera and processed using image growing, image segmentation techniques to detect infected parts of the plants. Then the infected part of the leaf has been used for the classification purpose using neural network. The methods evolved in this system are both image processing and soft computing technique applied on number of diseased rice plants. We proposed in this project to detect the blast disease in rice through image segmentation, HOG (Histogram of Gradient) feature extraction and classify the disease in high evaluated pattern recognition model called SVM (support vector machine). The experimental result shows in MATLAB in accurate manner

Abstract: Artificial intelligence (AI) has been playing an increasingly important role in the development of new technologies across various domains. One such domain is law enforcement, where AI-based tools can be used to improve the efficiency and accuracy of suspect identification. In this project, we propose a system that generates facial images of suspects based on input text descriptions using the Stable Diffusion AI model. The existing systems for suspect identification rely on eyewitness accounts, sketches, and/or composite images, which can be unreliable and time-consuming. Our proposed system uses AI-based image generation to provide law enforcement agencies with a more efficient and accurate method for generating facial images of suspects based on input text descriptions. The proposed system consists of four modules: input processing, image generation, user interface, and database. The input processing module receives the text description of the suspect's appearance and pre-processes it to remove any unwanted characters. The image generation module uses the Stable Diffusion AI model to generate a latent vector ...

Abstract: Ultra-wideband (UWB) transmission has recently received great attention in both academia and industry for applications in wireless communications. It was among the CNN’s top 10 technologies to watch in 2004. A UWB system is defined as any radio system that has a 10-dB bandwidth larger than 20% of its center frequency, or has a 10-dB bandwidth equal to or larger than 500 MHz, The recent approval of UWB technology by Federal Communications Commission (FCC) of the United States reserves the unlicensed frequency band between 3.1 and 10.6 GHz (7.5 GHz) for indoor UWB wireless communication systems. It is expected that many conventional principles and approaches used for short-range wireless communications will be reevaluated and a new industrial sector in short-range (e.g., 10 m) wireless communications with high data rate (e.g., 400 Mbps) will be formed. Further, industrial standards IEEE 802.15.3a (high data rate) and IEEE 802.15.4a (very low data rate) based on UWB technology have been introduced.

Keywords: K-means clustering, Artificial Neural Network.

Abstract: Electronic voting systems have gained significant attention due to their potential to streamline the voting process, increase accessibility, and reduce fraud. However, ensuring the integrity, security, and transparency of the voting process remains a critical challenge. Blockchain technology has emerged as a promising solution to address these concerns by providing a decentralized, immutable, and transparent ledger system.This abstract presents an e-voting system that leverages blockchain technology to enhance the security, transparency, and efficiency of the voting process. The proposed system utilizes a permissioned blockchain, ensuring that only authorized participants can participate in the consensus and validation process.Each eligible voter is assigned a unique cryptographic identity and can securely cast their vote through a user-friendly interface. The vote is recorded as a transaction on the blockchain, which includes a digital signature and timestamp to ensure its authenticity and immutability. The votes are anonymized to preserve voter privacy while maintaining the ability to verify the integrity of the overall election process.  Keywords: electronic voting, e-voting, blockchain, e-government, verifiable voting

Abstract: The leading cause of death worldwide over the past few decades has been heart disease. Thus, regular monitoring and early detection of cardiac disease can lower the death rate. An vast amount of data has been continuously being generated by the exponential increase of data from various sources, including streaming systems, wearable sensor devices used in Internet of Things health monitoring, and others. A breakthrough in technology, streaming big data analytics and machine learning, has the potential to revolutionise the healthcare industry, particularly in the area of early heart disease detection. This technology might be more affordable and more potent. This research suggests a real-time cardiac disease prediction system built on Apache Spark to address this problem.

Keywords: big data, spark, distributed machine learning, heart disease, real-time

Abstract: A customised adaptive cruise control (ACC) system based on model predictive control (MPC) and driving style identification to accommodate various driving styles while ensuring car-following, comfort, and fuel-efficiency performances. A series of real-world vehicle experiments are carried out to gather the driving data of 66 randomly selected drivers in order to determine the controller parameters that correspond to various driving styles. The experimental data is then clustered using an unsupervised machine learning technique. A driving style classifier is created using supervised machine learning on the basis of this information, and it can be used to identify drivers' driving styles online. The control issue with the customised ACC system is thus defined as a multi-objective optimisation issue that may be resolved using the MPC approach. The simulation findings demonstrate that the suggested personalised ACC system can provide varying performances and cater to the needs of various driving styles.

Abstract: Because of new technology, smart phones have become very important for communication and are now a big part of our daily lives. In Country like India, people are having trouble finding and hiring local technical professionals to fix things in their homes and offices. This also makes it difficult for new workers to find jobs nearby. To solve this problem, we need to create a platform that helps connect technical workers with people who need their services. This platform should be easy to use and work well with the latest technology. One idea is to create an android app called Task -Tracer, as well as a website. Task -Tracer would be a great way to start in developed Countries. It's an app that lets users and experts with different technical skills communicate. It uses Google Maps to help with searching and hiring based on location. Users can see a map with markers showing all the available workers in their area. Right now, the app has four categories of experts: Decorators, Electricians, Mechanics, and Plumbers. But more categories can be added in the future.

Keywords: Android recruitment system, local technical l professionals, Maintenance and Repair system, Geo based home services

Abstract: The internet of things is a system of connecting devices to the internet. It interacts with the real world with wide range of applications. Nowadays, in many home, user can remotely access and control a variety of home devices such as smart curtains, lights etc. despite providing convenient services including home monitoring, temperature management and daily work assistance. A smart home can be vulnerable to malicious attacks because all messages are transmitted over insecure channels. Moreover, home devices can be a target for device capture attacks since they are placed in physically accessible locations. But, IOT has a disadvantage that it has less security. To provide security for protecting information to be delivered and communication through the use of codes we use cryptography. In cryptography we are using AES algorithm, in this message will be passed in turns of block ciphers. To connect IOT and cryptography we require MQTT (message queuing telemetry transport) broker to publish and subscribe system takes place, in which we can publish and receive message as a client. This encrypted message cannot be decoded until it has decryption key, so the device can secure from attacks.  

Keywords: Internet of Things (IoT), Message Queuing theory telemetry transport (MQTT), Advanced Encryption Standard (AES), Amazon web service (AWS), Light Emitting Diode(LED).

Abstract: Nowadays, most of the business enterprises are running through web application. But the major drawback is that they fail to provide a secure environment. To overcome this security issue in web application, there are vulnerability detection tools are available at present. But these tools are not proactive and consistent as it unable to track vulnerabilities. Vulnerability assessment reports play a vital role in ensuring the security of an organization’s application, computer systems and network infrastructure.it is a process of identifying, classifying and prioritizing security vulnerabilities in IT infrastructure. It is a systematic review of security weakness in an information system. It evaluates if the system is susceptible to any known vulnerabilities, assign severity levels to those vulnerability and can help to identify problem area. A comprehensive vulnerability assessment evaluates whether an IT system is exposed to known vulnerability. Web application vulnerability scanners are automated tools that scan web application, normally from the outside, to look for security vulnerabilities such as cross-site scripting, SQL injection, command injection, path traversal and insecure server configuration. It is a software command-line vulnerability scanner that scans webservers for dangerous file/CGIs, outdated server software and other problems. It performs generic and specific type checks.

Keywords: Command-Line Interface(CLI), Graphical User Interface(GUI).  

Abstract: Brain excrescence is the growth of abnormal cells in brain some of which may leads to cancer. The usual  system to  descry brain excrescence is glamorous Resonance Imaging( MRI)  reviews. From the MRI images information about the abnormal towel growth in the brain is  linked. In  colorful  exploration papers, the discovery of brain excrescence is done by applying Machine Learning and Deep Learning algorithms. When these algorithms are applied on the MRI images the  vaticination of brain excrescence is done  veritably  presto and a advanced  delicacy helps in  furnishing the treatment to the cases. These  vaticination also helps the radiologist in making quick  opinions. In the proposed work, a  tone- defined Artificial Neural Network( ANN) and complication Neural Network( CNN) is applied in detecting the presence of brain excrescence and their performance is anatomized.  

Keywords: Image Segmentation; Support Vector Machine; Self-Organized Mapping; MRI

Abstract: This paper introduces a user-friendly face recognition system that includes liveness detection. The system's core programming is implemented in Python, which provides access to powerful machine learning libraries such as TensorFlow and Keras. These libraries are known for their high-level neural network capabilities, which greatly assist in training the dataset. In terms of hardware interfacing, a basic setup is employed to demonstrate the functionality of the liveness system. This setup incorporates a mechanism that automatically opens and closes a door when a recognizable face is detected. The face recognition system outlined in this paper aims to provide a straightforward and accessible solution for identifying individuals. By integrating liveness detection, it enhances security by verifying that the detected face is not a still image or a video playback. The choice of Python programming language for the core implementation offers several advantages. Python is widely used for machine learning tasks due to its simplicity and extensive libraries. TensorFlow and Keras, in particular, provide a comprehensive set of tools for neural network-based tasks, including facial recognition. Overall, this paper presents a practical and effective approach to face recognition, combining the power of Python and machine learning libraries to achieve accurate and reliable results while incorporating liveness detection for enhanced security applications.  Keywords— liveness, neural networks, Biometrics, training, Python, ESP controller.

Keywords: Deep Learning, Feature Extraction, Fusion, Enhancement, Discrete Wavelet Transform, Biometrics, Modalities, Matlab, Digital Image Processing.

Abstract: Breast cancer is the leading cause of cancer death in women. Early detection and diagnosis are the most effective strategy to control tumour progression. The currently recommended imaging method for early determination and diagnosis of breast tumours. Classifications of still a big challenge and play a crucial role in assisting radiologists in accurate diagnosis this project, we propose a convolution neural network-based classification technique which is one of the deep learning techniques. The architectural model of CNN is used for the classification of breast cancer into normal and abnormal. Pre-processing is performed on the input mammogram image to remove unwanted elements. Segment the tumour region using morphological operations, and highlight the region on the original mammogram image. If the mammogram image is normal, it indicates that the patient is healthy. BC patients and healthy patients are classified using Random Forest (RF) Classifiers.

Keywords: Deep learning, Cancer Detection, CNN, Feature Extraction.

Abstract: This research paper aims to investigate the impact of website quality on online purchase intention for an E-commerce website named Laptop Store. The study examines the factors that influence customers' online purchase intentions and their behaviour towards online shopping. The research also explores the effect of social media on consumers' online shopping behaviour and the moderating role of product type and review valence on purchase intention. The research methodology includes a quantitative approach, with a survey questionnaire distributed among a sample of Indian consumers. The data collected was analysed using structural equation modelling (SEM) and the results indicated that website quality has a significant positive impact on customer satisfaction and behavioural intentions. Furthermore, the study found that social media has a significant impact on consumers' online shopping behaviour. The research contributes to the existing literature by providing insights into the factors that influence online purchase intentions and the effect of social media on consumers' behaviour towards online shopping. The findings of this research can be useful for E-commerce website owners and marketers in enhancing website quality and social media strategies to improve customers' online shopping experiences and increase purchase intentions.

Keywords: E-commerce, Online shopping, Website quality, Purchase intention, Consumer behaviour, Social media, Product type, Review valence, Customer satisfaction

Abstract: This paper proposes massive and growing burden imposed on modern society by depression has motivated investigations into early detection through automated, scalable, and non-invasive methods, including those based on speech. However, speech-based methods that capture articulatory information effectively across different recording devices and in naturalistic environments are still needed. This article presents a novel multi-level attention-based network for multi-modal depression prediction that fuses features from audio, video, and text modalities while learning the intra and inter modality relevance. Multi-level attention reinforces overall learning by selecting the most influential features within each modality for decision-making. We perform exhaustive experimentation to create different regression models for audio, video, and text modalities. Evaluations of both landmark duration features and landmark n-gram features on the DAIC-WOZ and SH2 datasets show that they are highly effective, either alone or fused, relative to existing approaches.  

Keywords: Depression classification, landmark n-grams, speech articulation, smartphone speech, naturalistic environments

Keywords: Machine learning, Naïve Bayes, support vector machine-nearest neighbour, random forest, bagging, boosting, neural networks.

Abstract: It is critical to diagnose liver illness early on in order to receive the best therapy possible. Machine learning algorithms is growing rapidly such as SVM, K-mean clustering, KNN, Random Forest, Logistic regression, and others. The input is usually numerical data of various factors, and the output findings are obtained in real-time, predicting whether or not the patient has a liver problem. In this project, used a variety of supervised machine-learning methods before deciding which one is best for the model. Existing systems rely on classical deep learning models, which are inefficient and imprecise. They aren't precise enough. This proposing model is to use classification algorithms to identify liver patients from healthy individuals. Here, we choose the algorithm in this module that serves as the best fit. The dataset is taken from the Kaggle dataset. The advantages of the proposed model are that it shows high accuracy, is fast processing and is highly scalable. With the effective use of the presented model, practitioners can make intelligent clinical decisions.

Keywords: Bivariate analysis, correlating columns, MSE Loss Function, Removing Null values, Replacing Non-acceptable zero values, Univariate analysis

Abstract: Deep learning is a sub field of machine learning that focuses on training artificial neural networks to learn and make predictions or decisions without being explicitly programmed.In this paper, we present a convolutional neural network-based model for the detection of humans in aerial images of mountain landscapes acquired by unmanned aerial vehicles (UAVs) used in search and rescue operations.. By using drones in SAR applications, it is desirable to minimize the cost and time spent on SAR operations. In this paper, we present a convolution neural network-based model for the detection of humans in aerial images of mountain landscapes acquired by unmanned aerial vehicles (UAVs) used in search and rescue operations. Detection of humans in aerial images remains a complex task due to various challenges such as pose and scale variations of humans, low visibility, camouflaged environment, adverse weather conditions, motion blur, and high-resolution aerial images. Due to imaging from high altitudes, in most high-resolution aerial images captured by UAVs, only 0.1 to 0.2 percentage of the image represents humans. To solve the problem of low coverage of the object of interest in high resolution aerial images, we propose to implement a deep learning-based object detection model. In this paper, we propose a novel method for the detection of humans in aerial images based on the Efficient DET architecture and ensemble learning. The method has been validated on the HERIDAL image datasets. By implementing the proposed methodologies, we achieved an map of 95:11%. To the best of our knowledge, this is the highest accuracy result for human detection on the HERIDAL datasets.

Keywords: Search and Rescue Operations, Unnamed Aerial Vehicles, Heridal Image Dataset, Ensemble learning.

Abstract: The electronic voting machine verifies voters using finger print scanners. A voting system that uses fingerprint identification eliminates the requirement for the user to carry an ID that provides the necessary information. Web-based system enables voter to cast their votes from anywhere in the world. Online website has a prevented IP address generated by the government of India for election purpose. People should register the name and address in the website. Election commission will collect the fingerprint and face image from the voters. The database or server will store the images. , keeping track of voter decisions is not a difficult task, however, in situations where there are hundreds of thousands of voters, keeping a precise record of voter decisions becomes important and more difficult. The advancements in blockchain technology provide a potential solution to the record-keeping problem of contemporary voting procedures, as blockchain technology by design, excels in applications where multiple users are working on immutable data. this project provides the best solution to avoid false voting. The electronic voting machine was connected with the computer. The computer is having the full database list of the peoples who are having the eligibility to vote. For each polling, the corresponding person identity was deleted. So it avoids false voting. A touch screen is used, so it is user-friendly. Keywords:  Voter ID, Fingerprint module, Pattern Matching, Machine Learning, Image Processing..

Keywords: IOT, ML, Healthcare, ECG, Temperature, Sensors.

Abstract: Deep learning, a subfield of machine learning, has revolutionized various domains by enabling computers to learn from large amounts of data and make accurate predictions or decisions. . The new method does not force the person's physical appearance to vote, which makes the things easier. This paper focusses on a system where the user can vote remotely from anywhere using his/her computer or mobile phone and doesn’t require the voter to got to the polling station through two step authentication of face recognition and OTP system. This project also allows the user to vote offline as well if he/she feels that is comfortable. The face scanning system is used to record the voters face prior to the election and is useful at the time of voting. The offline voting system is improvised with the help of RFID tags instead of voter id. This system also enables the user the citizens to see the results anytime which can avoid situations that pave way to vote tampering.

Keywords: Online voting system, Face recognition, OTP authentication, Biometric authentication, Secure voting, Fraud prevention, Voter verification, Identity authentication, Electronic voting, Democracy, Election security, Digital voting, Biometric recognition, Two-factor authentication.

Abstract: The rising frequency of Distributed Denial of Service (DDoS) attacks is mostly due to botnets, as their weaknesses in the Internet of Things (IoT) make them an excellent target for these attacks. As DDoS attacks have become more frequent, it is critical to address the implications they have for the IoT industry, which is one of their primary causes. The purpose of this study is to offer an examination of attempts to stop DDoS attacks, mostly at the network level. These solutions effectiveness in addressing IoT risks serves as a gauge of their sensitivity. This analysis makes it clear that there isn't yet a perfect answer for IoT security and that there are still lots of prospects for research and development in this area. Keywords— Denial of Service (DoS), Distributed Denial of Service(DDoS), Internet of Things (IoT), Attacks, Technology, Botnets.

Abstract: The general rise in environmental and anthropogenically induced greenhouse gas emissions is driven by worldwide population growth and a growing appetite for clean energy, industrial production and consumer consumption. Moreover, well-established, developed and emerging countries are looking for fossil fuel and petroleum resources to support their aviation, power utilities, industrial sectors and consumer processing needs. As emerging technological advances in clean energy technologies progress, there is a growing trend to overcome these challenging concerns and achieve the Paris Agreement priorities. Hydrogen is expected to be implemented in various manufacturing applications as a fundamental fuel in the development and production processes of future energy carrier materials. This paper summarizes recent developments and hydrogen technologies in fuel refining, hydrocarbon processing, materials manufacturing, pharmaceuticals, aircraft construction, electronics and other hydrogen applications. It highlights the existing industrialization scenario and describes expected innovations including theoretical scientific advances, production of green raw materials, potential exploration and renewable resource integration. Furthermore, this article discusses some of the future socioeconomic implications of hydrogen as a green resource.

Abstract: A tropical crop called areca nuts, sometimes referred to as betel nuts, is grow. India is the world's second-largest producer and consumer of areca nuts. The early monsoon winds from the Indian Ocean and the Bay of Bengal bring heavy rain, which causes a range of illnesses to afflict the areca nut throughout its life cycle, including Yellow Leaf, Nut Split, and Fruit Rot. The only method of disease detection currently available to farmers is observation with the naked eye, and they must periodically examine each crop carefully in order to identify any diseases. Furthermore, without a farmer who is well-versed in these diseases and areca nuts, it will be difficult to detect diseases. This system incorporates several machine learning and image processing principles that will make this vision a reality. As the system concentrates on early detection so the issue might be eliminated at the starting stage in order to avoid the barriers later, it may accept inputs from areca nuts (including the tree) and transport them there for pre-processing. Otherwise, it poses a serious risk.

Keywords: Areca nut, yellow leaf, fruit rot, machine learning, image processing.

Keywords: Traditional voting system, decentralized nature of blockchain, Key, Poof-of-Individuality(POI), confidentiality of vote, uniqueness of each vote, etc

Abstract: The recent advances in embedded processing have enabled the vision based systems to detect fire during surveillance using convolutional neural networks (CNNs). However, such methods generally need more computational time and memory, restricting its implementation in surveillance networks. In this research article, we propose a cost-effective fire detection CNN (YOLO Object Detection) architecture for surveillance videos. The model is inspired from GoogleNet architecture, considering its reasonable computational complexity and suitability for the intended problem compared to other computationally expensive networks such as “AlexNet”. To balance the efficiency and accuracy, the model is fine-tuned considering the nature of the target problem and fire data. Experimental results on benchmark fire datasets reveal the effectiveness of the proposed framework and validate its suitability for fire detection in CCTV surveillance systems compared to state-of-the-art methods. We plan to overcome the shortcomings of the present systems and provide an accurate and precise system to detect fires as early as possible and capable of working in various environments thereby saving innumerable lives and resources. Keywords: Convolutional Neural Networks (CNNs), GoogleNet architecture, Fire Detection, CCTV Surveillance Systems, etc.

Abstract: According to research, 8 lakh people die from waterborne diseases in most of developing and undeveloped countries. The flips in the sewage treatment cause the improper treatment of water, which in turn affects the lives dependent on that water. The automation of these treatment processes will help improve the treatment of water. Every year over a million people suffer from waterborne diseases and a number of them are mainly due to improper treatment of water. Water pollution has been an increasing problem over the last few years. Hence, it is a necessity to deal with this problem and to make sure most of the swage is treated before use. The treatment plant is automated with the help of IoT and IoT-based sensors. These sensors are used to monitor the quality of the water and control the flow of water. This data can also be used to process and store this information for future research and processing. This sewage treatment plant automation aims to achieve this by making use of IoT to reduce power consumption and gives better water quality. On a large scale, the implementation and sewage treatment plant automation will be way cheaper than the traditional methods.

Keywords: Water quality testing, Water treatment, Arduino, Arduino sensors.

Keywords: Automatic Pet Feeder, Arduino IDE, Smart Home, Digital Image Processing, Neural networks.

Abstract:  Flight ticket prices fluctuate based on factors such as flight timing, destination, and duration. To address the challenge of determining the optimal time to purchase tickets, this proposed system aims to develop a predictive model using machine learning algorithms. By analyzing historical flight data, our project focuses on identifying underlying price trends in India and providing recommendations for the best time to buy tickets.This project seeks to validate or debunk myths surrounding the airline industry, comparing different models to predict the optimal timing for ticket purchases and potential cost savings. Notably, price trends vary significantly depending on the route, month, day, time of departure, whether it's a holiday, and the airline carrier. For highly competitive routes like major business destinations (e.g., Mumbai-Delhi), prices tend to increase as the departure date approaches. However, other routes, such as tier 1 to tier 2 cities like Delhi-Guwahati, have specific time frames when prices are at their lowest. Additionally, the collected data reveals two distinct categories of airline carriers in India: the economical group and the luxurious group. In most cases, the lowest-priced flights belong to the economical group. Furthermore, the data confirms that certain periods of the day are associated with higher expected prices. Expanding the scope of this project to cover various routes can lead to significant savings when purchasing domestic flight tickets in the Indian market..

Keywords: Flight ticket, Optimal timing, historical data, competitive routes, Indian Domestic Airline market

Abstract: This project deals with an innovative rather an interesting manner of intimating the message to the people using a wireless electronic display board which is synchronized using the Bluetooth technology. Now-a-days information displaying is going digital with a high speed. This will help us in passing any message almost immediately without any delay just by sending a SMS which is better and more reliable than the old traditional way of passing the message on notice board. This proposed technology can be used in colleges many public places, malls or big buildings to enhance the security system and also make awareness of the emergency situations and avoid many dangers. Using Bluetooth module display the message onto the display board. In the last couple of decades, communication technology has developed by leaps and bounds. It has already established its importance in sharing the information right from household matters to worldwide phenomena. In this paper, we present the development of an SMS controlled E-notice board which can be updated automatically and remotely.The system was implemented using a BLUETOOTH Module IC controlled by a Microcontroller and an LCD display. The BLUETOOTH module receives the message to be displayed as data, then transmits the message through the COM port to the microcontroller displays the message on the LCD display.

Keywords: LBPH, OpenCV, Haarcascade,Face recognition, Face detection,Spreadsheet.

Abstract: The term "cybersecurity" refers to the practice of protecting electronic information by reducing information risks and vulnerabilities. Data threats can be caused by a variety of factors, including intentional attacks (such as hacking) or unintentional events (such as power outages). Cyber security includes measures to prevent unauthorized access to electronic information and systems and prevent hijacking of information and systems. Some of the latest artificial intelligence (AI) developments have implications for cybersecurity. For example, machine learning techniques can be used to identify malware and natural language processing can be used to decrypt large volumes of text for security-related information. Artificial intelligence can also be used to create new approaches to endpoint security, network security, and user authentication.

Keywords: cybersecurity, hacking, artificial intelligence, malware.

Abstract: This research paper delves into the intricacies of designing and developing an integrated learning platform that effectively harnesses academic assets to augment the learning experience for both students and educators. The primary objective of the project is to create a dynamic website that seamlessly facilitates the exchange of home-made notes, offers a comprehensive array of courses spanning various subjects, empowers users to pose inquiries across a broad spectrum of topics, and furnishes valuable updates on professional opportunities. By leveraging these multifaceted features, the website serves as an all-encompassing platform for the exchange of knowledge and collaborative endeavors, fostering an immersive and interactive learning environment. The study undertakes a meticulous exploration of the underlying design principles, meticulous development methodologies, and astute implementation strategies employed in creating this innovative platform. Furthermore, it evaluates the efficacy of the website in augmenting the learning experience through the scrupulous analysis of user feedback and insightful observations. The findings derived from this comprehensive analysis contribute to the advancement of our comprehension regarding the optimal utilization of academic assets to optimize learning outcomes and promote lifelong learning. Looking towards the future, the research endeavor envisages expanding the repertoire of available courses to encompass a wider range of subjects, integrating more interactive features to enhance user engagement, and perpetually refining the platform in accordance with evolving user requirements and emergent trends in the realm of education.

Keywords: self made  notes, ask and learn, online courses, hiring updates

Abstract:  The use of technology in mental health has grown significantly in recent years, with the emergence of mental health tracker web applications. These applications allow individuals to track their mood, symptoms, and behaviors, and may offer resources and tools for managing mental health. This paper discusses proposed mental health tracker systems, which typically include features such as mood tracking, symptom tracking, and behavior tracking. Additionally, this paper highlights the importance of conducting a literature survey to better understand the current state of research on mental health tracker web applications, including their effectiveness and potential benefits and drawbacks. A literature survey could provide valuable insights into the most effective features and interventions for these applications, and help identify areas where further research is needed. Overall, mental health tracker web applications have the potential to improve mental health outcomes and offer a valuable tool for managing mental health conditions.  

Keywords: Machine Learning, Track Mental Health, Flutter, Mental Health Dataset.

Abstract: The proposed smart medical box is designed to address medication adherence challenges, particularly among the elderly. It consists of a companion phone application for initial setup and remote operation, allowing users to specify pills and schedule them at varying times throughout the day. An Arduino board/ESP 8266 controls the operations and connectivity functions of the box, which only unlocks for a short time in the specified slot. Notifications for missed or low-stock medication are sent to the app, and the system logs usage statistics. Implementation on a larger scale using IoT-based platforms can benefit institutions such as old-age homes and nursing homes, improving medication management, sanitation, and safety, and reducing drug overdependence. The proposed system offers a potential solution to medication mismanagement challenges by providing visual and auditory cues to reduce confusion and improve medication adherence. By using technology to regulate pill intake and generate medication adherence reports, the smart medical box has the potential to significantly enhance medication management for patients, especially the elderly, and improve overall healthcare outcomes. The records can be sent to the administrator who can further notify the participants.

Keywords: Smart Medical Box, Medication adherence, Companion phone application, Pill scheduling, Arduino, IoT- based, Healthcare outcomes.

Abstract: Lakes and water bodies have a huge impact on the local ecosystem and environment. Unfortunately, water bodies today are in very bad state all over the country. In most cases, they have become dumping place for household and industrial waste. In many places, they are also filled with garbage. The result is that water bodies are dying, and this makes the water crisis faced by the communities worse. Water pollution can be attributed to one of four sources sewage discharges, industrial activities, agricultural activities, and urban runoff including storm waters. Sources of water pollution are either point sources or non-point sources. Point sources have one identifiable cause, such as a storm drain, dumping of industrial waste, sewage treatment facilities, illegal dumping water treatment plan or an oil spill. Non-point sources are more diffuse, such as agricultural runoff. The first step towards water body revival is to survey for the garbage dumped in waterbodies. So, we have come up with a solution to do a survey of plastic waste in the waterbodies using Biomimetic ROV and IoT i.e., raspberry pi, TDS sensor, Turbidity sensor, Ultra-sonic sensor, GPS sensor. Using camera we will detect the plastic bottle, plastic cup, plastic bag through object detection, the data from the sensors are sent to firebase real-time database and that data will be displayed in the website in real-time. This will help us know the amount of garbage in the waterbodies and that data can be used to clean that garbage, which will reduce the water pollution crisis.

Keywords: Water Pollution Crisis, Biomimetic ROV, IoT, Plastic Garbage, Object Detection.

Keywords: Artificial intelligence, feature classification, machine learning, supervised learning, Mediapipe, SVM.

Abstract: In particular for a spelling programme application, this research offers an optimisation technique for steady state visual evoked potential (SSVEP)-based brain computer interface (BCI). Stimulator, signal processing, and application (spelling programme) are the minimum number of components required to construct a comprehensive BCI application in this application. To achieve the best performance, the three components should ideally operate on independent processing units. However, combining those three parts into a single computer system has other benefits, such as improving concentration and simplifying system setup. The spelling system and the jerky animation are the two key components that require optimisation. We will concentrate on the display driver technology and programming factors while optimising the flickering animation. The layout and representation of the letter matrix will be the main areas of emphasis for spelling system optimisation. We put our program's frequency range, frequency resolution, and frequency stability to the test across many computers. Conclusion: Using a computer monitor as the stimulator, it can be concluded that, regardless of the software technology used (DirectX or OpenGL), the maximum synthesizable stimulator frequency is always half of its minimum refresh-rate. With a frequency resolution of 0.11 Hz, the highest synthesizable frequency of up to 30 Hz is attained. 106 people participated in our system testing at CeBIT 2008 in Hannover, Germany. The spelling system's average accuracy is 92.5%. Therefore, without substantial expert assistance or pricy hardware, the optimisation approach outlined here resulted in a stable and dependable system that performed well across the majority of subjects.

Abstract: Mobile Wireless Sensor Networks (MWSNs) are an important part of the Internet of Things (IoT), where many sensors are connected through wireless channels. However, there are challenges related to energy consumption, connectivity, scalability, and security in MWSNs. Adding mobility makes it even more challenging to find a good way to improve MWSN performance. This paper introduces a new routing protocol called "Triple Tier Cluster-Based Routing" (TTC-BR) that improves MWSN performance. It does this by dividing the network into virtual zones and using a triple-tier clustering approach. The virtual zones cover the entire network area using three levels: the main connectivity zone (MCZ), candidate cluster zone 1 (CCZ1), and candidate cluster zone 2 (CCZ2). The protocol selects the best sensor node to act as the Cluster Head (CH) for each zone.TTC-BR outperforms other routing protocols such as DDR, MCCA, LEACH-MEEC, and LEACH-M, and can improve network lifetime by 9% to 48%. Our study suggests that TTC-BR is an efficient solution to improve the performance of MWSNs, particularly for large networks and many sensors. Keyword: Mobile wireless sensor network (MWSN), cluster-based routing, cluster head (CH), virtual zone, energy-efficient.

Abstract: Agricultural robotics technology helps to produce qualitative products at higher speeds and with fewer errors. Robotics helps in various fields like agriculture, medicine, mining and space researches. The major disadvantage of driverless machines for agriculture is liability. This technology can completely change cultural or emotional appeal of agriculture. Robotics in agriculture could play a very important role in automizing the several processes. Agriculture consists of grass cutting, Ploughing, Seed Sowing, Seed watering and Crop cutting for early growing plants/crop.  These operations can be achieved by means of solar operated multifunctional vehicle. Current methods for off-road navigation using vehicle and terrain models to predict future vehicle response are limited by the accuracy of the models they use and can suffer if the world is unknown or if conditions change and the models become inaccurate .In this paper, an adaptive approach is presented that closes the loop around the vehicle predictions. This approach is applied to an autonomous vehicle known as field robots used in agriculture Agricultural Robotics is the logical proliferation of automation technology into biosystems such as agriculture, forestry, green house, horticulture etc. Presently a number of research are being done to increase their applications. Some of the scientist contributions are mobile robot, flying robot, forester robot, Demeter which are exclusively used for agriculture.

Keywords: Robotics, Navigation, Field Robots, Proliferation, Demeter

Abstract: Electric vehicles (EVs) are an innovative technology that could transform the transportation industry, and they are seen as a crucial step towards achieving a sustainable transport sector. EVs have become known for producing low carbon dioxide, low noise, high efficiency, and flexibility in grid operations and integration. Despite these benefits, the adoption of the delayed adoption of EVs is a result of inadequate charging infrastructure and extended charging periods. To overcome this challenge, hybrid charging mechanisms that utilize wind and solar power are developed to improve the charging while also monitoring the battery. The management and monitoring system of EVs is an IoT-based solution that provides real-time data on the battery's status, capacity, and charging and consuming current. This information is made available to users through an application, allowing them to make knowledgeable choices on how to charge their automobiles. Moreover, during the mobility of vehicles, the energy generated by solar and wind power used to charge the battery. This results in a sustainable charging process that uses green energy and complements the existing charging infrastructure. Overall, hybrid charging mechanisms that utilize wind and solar power, along with the IoT-based management and monitoring system, could help overcome the challenges facing the adoption of EVs. By utilizing more green energy and offering efficient charging, these mechanisms can help shift leading to a sustainable future where EVs play a central role in the transportation sector.

Keywords: Electric vehicles, sustainable transport, IoT-based solution, hybrid charging, green energy, sustainable charging.

Abstract: Digital transformation means the adaptation of the power of new technology to create a better experience for users. As part in the digital world our project aims to create a payment system for students inside the college. So, we have developed a payment system that enables students to pay with their ID cards inside the campus like stationary, canteens, food courts and hostellers can pay their mess bill. Our project utilizes RFID technology and Raspberry Pi to create a seamless payment experience for students using their NFC-enabled student ID cards. Students and cashier can also track their transaction in mobile application that are developed. Also, Students can easily recharge their ID cards using the mobile application. This system provides a user-friendly, efficient, and secure payment option that reduces the need for physical exchange of cash or sensitive payment information while using third party apps.

Keywords: NFC Reader, Digital transformation, Radio Frequency Identification.

Abstract: Diabetes Mellitus, also known as diabetes, causes persistently high blood sugar levels that can lead to diabetic retinopathy, which has been linked to damage to the retina's tiny blood vessels. The retina must first register light before the optic nerve can transmit signals to the brain. Until diabetic retinopathy begins to advance towards Proliferate DR/PDR, treatment is frequently postponed. As Diabetic Retinopathy (DR) worsens, more regular comprehensive dilated eye exams are required. Severe non-proliferative diabetic retinopathy patients are at a high risk of developing PDR and may require a thorough dilated eye exam every two to four months. [1] Therefore, in our work, we constructed a model called "retina.model" that can recognise even the slightest variation between each stage of DR and is 100% reusable with a growing amount of cognition over time as the computer tries to learn new patterns.

Keywords: matrix handling, Diabetes , American Optometric Association (AOA), Deep Learning, CNN architecture, Diabetic Retinopathy (DR), Image Classification, retina of the eye, Optometrist, Gaussian filters, Mild DR and Moderate DR, "retina.model", Severe non-proliferative diabetic retinopathy and Cotton wool spots.

Abstract: Drones can be defined as powered aerial vehicle that does not carry any human operator. As, remote control of a drone is difficult for operators having less technical knowledge which may take a long time for operator to gain control over the drone. Use of gestures over remote control is a unique method of gaining control over the drone, which makes it easy for an operator with less or no technical knowledge to gain control over the drone within few minutes. Gesture control of a drone has been implemented by various researchers and these methods are highly expensive as well as they are affected by various parameters like wavelength of light, unbalanced forces on accelerometers, bulky apparatus etc. The aim of the project is to develop a system that uses hand gestures as a method to control the flight of a drone. In this system, the drone’s absolute position is not being monitored or recorded. Instead, the drone is being told to move relative to its current position based on the detected motion of the user. In order to enable fully autonomous flight, an extended Kalman filter (EKF) based procedure is used to control and adjust all six DOF (degrees of freedom) of the drone. The EKF used the readings of the pre-mounted accelerometer and gyroscope sensors on the drone as well as a supplementary optical flow sensor and a time-of-flight (TOF) sensor. The estimator uses an extended aerodynamic model for the drone, where the sensor measurements are used to observe the full 3D airspeed. To detect the motion of the user, a nearfield sensor is measuring the disturbance of an electric field due to conductive objects, like a finger. Finally, to combine these systems, code will be developed on a Raspberry Pi to facilitate communication from the sensor to the drone and convert from the input X, Y, Z sensor values to the values compatible with the drone system.

Keywords: EKF, DOF, TOF, Gyroscopic.

Keywords: Deep Learning, CNN, ResNet50, VGG16.

Abstract: Authentication is an important security aspect in modern digital systems, and traditional methods such as passwords and PINs are vulnerable to security threats. Therefore, there is a need for innovative and efficient authentication solutions that can overcome these challenges. A possible authentication solution is to use Rubik's Cube, which is highly secure, unique, and difficult to guess or crack. This approach uses one side of the Rubik's Cube as a unique password, and playing the same side again results in successful authentication. The faces of the Rubik's Cube can be read by the camera and the code can be written in Python using the cv2 module. The purpose of this study is to evaluate the feasibility, effectiveness, and usability of Rubik's Cube authentication approaches in various areas such as access control, security, and authentication. The study also examines the security implications and potential vulnerabilities of the approach, and suggests mitigation strategies to strengthen security. Using the Rubik's Cube as a potential solution for authentication provides insights for designing more secure and efficient authentication systems. This research contributes to research into innovative and efficient authentication solutions, which can be used as an alternative or supplementary authentication method to existing methods such as biometrics and multi-factor authentication.  

Keywords: Authentication, Security, Python cv2

Abstract: Driver drowsiness detection is an essential component of modern vehicle safety systems. In this project, we propose a novel method for detecting driver drowsiness using a web camera. Our system captures video footage of the driver and applies computer vision algorithms to track facial features and determine the level of drowsiness. The system uses a combination of facial landmarks detection, eye- tracking, and machine learning techniques to determine the driver's level of alertness. Our experiments show that the proposed system can accurately detect driver drowsiness and alert the driver in real-time. The proposed method has the potential to enhance road safety and reduce the number of accidents caused by driver drowsiness.

Abstract: The Stocks Market has had a significant impact on the global economy, including the stock market. Traditional stock market prediction algorithms may not be accurate in predicting stock prices in the current scenario due to the unpredictable nature of the Market. This paper proposes the use of Stocks analysis to improve traditional stock market prediction algorithms. We analyze Stocks data, such as the number of cases, hospitalizations, and deaths, to get a better understanding of how the Market is affecting various industries and the overall economy. This information is then incorporated into traditional stock market prediction algorithms to provide a more accurate forecast of stock prices. We also use machine learning algorithms to analyze Stocks data and predict stock prices. By analyzing large amounts of data, machine learning algorithms can identify patterns and trends that may not be apparent to human analysts. Our results show that incorporating Stocks analysis into traditional stock market prediction algorithms can provide a more accurate forecast of stock prices in the current Market scenario.

Keywords: Stocks, stock analysis, svm, classification, Machine Learning

Abstract: Due to growing enemy attacks, monitoring of military regions is vital in today's world. The fundamentals of robotics, including sensors and actuators, provide an overview of robotic building. The drone is controlled via a reliable and long-range remote-control system. The robot will serve as a surveillance robot, both during the day and at night. The robot will also drop explosives in certain locations to give stealth during a war-like situation. In some situations, if the robot is spotted by opposing troops, it will also operate as a suicide bomber. This robot is better suited for military purposes like monitoring a certain region. It will provide a tactical edge in hostage situations or on unfriendly terrain. It is capable of walking on surface and monitoring a large region. This will be useful in applications such as civilian and military robots.

Keywords: BLDC motors, Propellers, High-resolution camera, Real-time videos, Actuators.

Abstract: This paper presents a novel areca nut removal technique using a wheel-based, completely autonomous tree climbing robot. The system consists of a base and two arms with four wheels, and can be moved as a differential drive robot. It drives up while hugging the tree until just the wheels are in contact with the surface, making the removal of areca nuts much simpler and quicker.

Keywords: Rollers, Battery, Fruit Plucker, Robotic arm.

Abstract: - The main motive of introducing these “Virtual Treatment and consultation system” is to promote online health care services. Because lack of specialists is major problem in small towns. But Virtual Treatment provides online services like online treatment. It will be beneficial for all those peoples located in small towns and the patients who have to take regular treatment, travel a lot where there is a lack of medical facilities. Virtual Treatment and Consultation System is a web-based project system which deals with online check-up through video conferencing & doctor gives online prescription. The project is very helpful to doctor, receptionist and public. People can book appointments online by approaching the website of Virtual Treatment and Consultation System. And People can discuss their health-related issues via video conferencing and get doctor useful prescription Virtual Treatment system is a computerized system designed and programmed to deal with day-to-day records like appointment, interaction and management activities. It also maintains patients records.

Abstract: Brain tumor is a very serious brain cancer. It is present or become due to the separation of the brain cells. It can be life threatening to a person. In the recent field of this study, tells us that deep learning will help in health industry of medical diseases imaging in the Medical Diagnostic of all the diseases. CNN is mostly used in this Machine learning algorithm. In this project CNN algorithm, image processing and data augmentation of the brain images of cancerous and are not cancerous. This project will require less computational power due to the transfer learning compared to the old CNN model. This has good accuracy results than the old pre-trained models. With the help of this project the brain tumour can be easily identified.

Abstract: By employing a scanned image of the check and information from a Magnetic Ink Character Reader (MICR) device, the Cheque Truncation System (CTS) technique simplifies the handling of checks electronically without actually exchanging or moving the financial instrument. 1,50,000 branches are covered at the time. As of September 2020, 1,219 non-CTS clearinghouses (ECCS centres) had been converted to CTS. Currently, there are around 18,000 bank branches that lack any kind of formal clearing agreement. This study offers an automatic fix for the aforementioned issues that any Indian commercial bank could use. All banks that are interested in taking part must join to the proposed block chain-based system in order to offer their clients faster cheque clearance. The proposed system is based on the block chain. One of the key technologies used to build the system was Ethereum. It strengthens the system's integrity and benefits both the bank and the customer by accelerating and simplifying the clearing of checks while boosting security. Additionally, it contributes to a quicker and more precise system for detecting cheque fraud, which benefits both the user and the bank by providing a safe, effective, and environmentally friendly solution. Last but not least, it makes it possible for the payer and payee to clear checks directly in a continuous stream.

Keywords: blockchain, cheque, fraudster, image processing, OTP

Abstract: Cricket is one of the most popular sports that is played in many countries and have audience who like it in huge numbers. And in the technology field, Artificial Intelligence has gained lots of interest from people. So, implementing Artificial Intelligence in the field of sports, especially cricket, has brought many advancements which has helped in decision making, ball tracking and many visualizations. In this project we have implemented a model which classifies the cricket shots using the pose of the player and also based on the shot obtained we predict the runs that can be scored. As per the survey many algorithms and techniques have been proposed which include Artificial neural network, Computer vision, Deep convolutional neural network, Pose detection, Long short term memory, Recurrent Neural Network and Deep neural network. The Project has been implemented using Convolution neural network based algorithms which can be used for self-paced training and to predict score from a shot. In other words, the model identifies the shot of the player which can be related to the pose structure, by which the players can improve their shot pose, helping in training themselves. The dataset has been obtained from Kaggle where each shot has around 1000 images, with a total of around 4000 images. The results are obtained from the CNN models that are VGG-16 and ResNet-50, where the better results are obtained using ResNet-50. The dataset has been divided into 80% and 20% for training and testing purposes respectively. The score prediction for shot classification is done using the Linear regression model. This can help batsmen to improve their shot, bowlers to ball so that they can take wickets or reduce the runs and also would be helpful in training the players.

Keywords: Cricket shot, Convolution neural network, Resnet-50, VGG-16, Linear regression.

Abstract: This research paper proposes the development of a cardless ATM system that aims to provide a more secure and convenient banking experience for users. The system includes two types of accounts, individual and joint, and requires login through mobile OTP, face recognition, and fingerprint authentication. The face and fingerprint recognition technology has been developed using Convolutional Neural Network (CNN) algorithms, ensuring accurate and reliable authentication.Upon successful login, users can perform various banking operations such as withdrawing and depositing money. The system also provides notification alerts to joint account holders upon the completion of any transactions.The proposed cardless ATM system aims to reduce the risk of fraud and enhance user convenience through the implementation of advanced authentication and transaction management features.

Keywords: Cardless ATM, CNN, Mobile OTP, Face Recognition, Fingerprint, Fraud detection.

Abstract: With increase in new products, there is always a problem of counterfeits in almost every industry. It is essential to have a system which can check details of product and identify whether the product is genuine or counterfeit. Counterfeit plays vital role in today’s world as it can affect many industries resulting in loss of sales, reputation, profits, ideas. In order to fight these counterfeit products, we have used blockchain technology. Blockchain technology is a decentralized, distributed ledger that stores transactions in the form of blocks in many databases that are interlinked to each other via chains. Blockchain is stable and secure as it has immutable property due to which the data once stored in blockchain nodes cannot be modified. By using blockchain technology, there is no need for customers to rely on third party for confirming product originality. Our project uses QR (Quick Response) codes to combat the problem of counterfeit. QR code scanner will be used by the buyer to scan the product for genuineness, as the QR code of each product is connected to our blockchain. System will check product’s unique code with the entries in our blockchain database. If code matches, the notification will pop out to customer stating product is genuine. If not, customer will receive notification that product is counterfeit.

Keywords: Blockchain, Counterfeit, Supply chain, QR codes.

Abstract: By enabling on-demand access to computer resources and services through the internet, cloud computing has completely transformed the IT sector. Yet, because of the centralised architecture of cloud services, the security and privacy of data have grown to be significant issues. Blockchain technology has been suggested as a potential remedy to address these problems by offering a secure and decentralised foundation for cloud computing. This review article overviews current work on a blockchain-based integrated architecture for preserving security in cloud computing. This study examines the many elements of the integrated architecture, such as security standards, blockchain technology, and cloud computing. The survey also examines the various methods for integrating permissioned and permissionless blockchains, smart contracts, and consensus mechanisms into cloud computing. The study research also covers the benefits and difficulties of incorporating blockchain technology into cloud computing, including interoperability, scalability, and data protection. The report also provides a comparative review of the current methods for cloud computing integration of blockchain technology based on several factors including security, performance, and cost-effectiveness. The survey paper's conclusions discuss the directions that research in this area should take going forward, emphasising the need for more investigation into how blockchain and cloud computing technologies can work together to improve the security, privacy, and effectiveness of cloud computing services. Keywords — Blockchain, Cloud Computing, Decentralized, Smart Contracts, Data Privacy, Security, Scalability, Permissioned Blockchain

Abstract: For a very long time, law enforcement and search and rescue organizations have struggled to locate missing people. However, recent developments in artificial intelligence (AI) have made it possible to create more effective strategies for locating the missing person. One possible tactic is to use AI algorithms to examine a variety of data sources, like as social media posts and surveillance camera footage, in order to build a detailed profile of the whereabouts and conduct of the missing person. By combining these numerous data sources, AI systems might help detectives uncover trends and anomalies that might be indicative of the missing person's travels or activities. Face recognition has emerged as a popular and difficult problem in the image processing field, which is currently one of the technology trends. Finally, depending on past information and other important factors such as facial features, AI-powered predictive models can be created to assist authorities in anticipating where missing persons may be located. These models can support search and rescue teams. The application of AI technologies has the potential to completely transform the way missing people are tracked and located, offering quicker and more accurate findings while requiring less time and money to conduct a search.

Keywords: Artificial intelligence, image processing, CNN, missing person, open cv

Abstract: Flight delays have been extensively studied in recent years. The rising demand for air travel has led to a rise in flight delays. Commercial scheduled flights regularly encounter delays as a result of clogged airspace, a rise in passengers each year, maintenance and safety concerns, unfavourable weather, and the delayed arrival of the aircraft that will be used for the next flight. In order to considerably reduce expenses, academics are looking at how to anticipate and analyse flight delays because it has become a serious problem in the US. The recommended approach therefore makes use of machine learning to predict flight arrival and delay. We have developed a model that implements different machine learning algorithms to predict whether a flight will be delayed or not based on certain characteristics. These characteristics include weather data, past flight data and flight details. We have analysed numerous algorithms based on past research and settled on the Support Vector Machine or the SVM algorithm. The SVM algorithm is a supervised machine learning algorithm which is majorly used for classification as well as regression problems. We also aim to help passengers in their stay in the vicinity of the airport in situations where their flights are delayed.

Keywords: Flight delay prediction, Supervised Machine Learning, Classification, Prediction, Support Vector Machine, Air traffic management, predictive analytics.

Abstract: Vehicle Speed Detection and Estimation is an essential task for many traffic management and safety systems. In this research, we offer a novel computer vision-based method for real-time vehicle speed estimate and detection. To identify the cars and determine their speeds, our system first analyses video footage of the moving vehicles using image processing algorithms. On a dataset of actual traffic scenarios, we test the suggested algorithm, and the results show that it performs rather well in terms of accuracy. Potential uses for the suggested approach include traffic control, law enforcement, and intelligent transportation systems. Overall, this research helps to create methods for detecting and estimating vehicle speed that are accurate and efficient, which may greatly enhance traffic management and safety. The issue description, suggested solution, study methods, and findings are all briefly summarized in this abstract. It emphasizes the contribution to the area and draws attention to the research's importance and prospective uses. The abstract is succinct and informative, giving the reader enough details to comprehend the study and its significance to the subject.

Keywords: Image processing, computer vision, Nodejs, OpenCV, Image Processing, Moving Object Detection.

Abstract: In a state of natural psychic equilibrium, tension may be viewed as a disturbance in general. A person's mental health will be put under stress if they cannot balance the expectations placed on them with their ability to cope with them. There are many different kinds of challenges. Psychological equilibrium disruption is a broad description of depression. Depression detection is one of the main areas of biomedical engineering study because it may be simple to avoid depression with the right measures. There are many bio signals accessible, including Mri, Rgb, oxygenation, and Frs. which can be used to determine depression levels because they show unique changes in depression induction. Due to the readily available recording, we use ECG as the top candidate in this endeavor. Multiple SVM model types have been examined by changing the function number and kernel type.

Keywords: Preprocessing, Segmentation, SVM Algorithm, Machine Learning. Feature Extraction, Classification CNN Algorithm.

Abstract: Bedridden individuals often face a significant challenge when it comes to performing daily activities due to their inability to move. Such individuals have to rely on others for assistance with even the most basic tasks, which can lead to a feeling of dependency and a reduced sense of autonomy. This lack of independence can negatively impact their mental and emotional well-being, and can cause a decline in their overall quality of life. To address this issue, we designed and implemented a robot to assist bedridden individuals in performing various tasks. This robot combines a mobile robot with a versatile robotic arm, which is controlled through the Blynk platform. The Blynk platform is a mobile application that allows users to remotely control devices that are connected to the internet. With the help of the ESP8266 microcontroller and L298N Motor driver, the robot can be easily maneuvered. The robotic arm utilizes servos to perform a wide range of angular movements, making it capable of performing many activities that were previously impossible for bedridden individuals. This technology allows bedridden individuals to perform tasks like object manipulation and relocation, providing them with a newfound sense of independence and significantly improving their quality of life.

Keywords: NodeMCU, Mobile robot, Robotic arm, Blynk IoT

Abstract: COVID-19, which is caused by SARSCoV2 (Severe Acute Respiratory Syndrome Coronavirus 2), has wreaked widespread havoc in recent years. Almost immediately after the epidemic, experts at nearly every public health center began investigating all possible sources of the virus. The spread surged dramatically in the early phases, which became a big concern in the medical community. Researchers employed numerous ML approaches in the computer age to study the causes and patterns of diffusion. As a result, several studies utilizing machine learning and artificial intelligence have been conducted in this field. This article examines the essential elements in predicting severity due to COVID-19 and summarizes new studies on predicting severity using machine learning. This study’s reviewed research papers were published using various search techniques.

Keywords: COVID-19 Pandemic, Machine Learning, Feature Selection, Severity Risk, ML Techniques

Abstract: Accurate prediction of crop prices is crucial for farmers to make informed decisions about agricultural production and trade. It enables them to optimize their planting decisions, determine the harvest time, and plan their sales strategy to maximize profits. Given the increasing volatility of agricultural markets due to climate change and other factors, the significance of crop price prediction has grown in recent years. As a result, sophisticated models that can analyze large amounts of data to provide accurate price forecasts are in high demand. Nowadays, farmers seek to leverage analytics for obtaining the data they need to make actionable insights and informed decisions. Automated farming is becoming more popular among farmers in many countries. Crop price estimation and evaluation are critical to minimize losses and manage the risk of price fluctuations when farming a specific crop type. Falling prices can lead to significant losses for farmers. In this study, we employed the Random Forest Algorithm to analyze past data, predict prices for new data, and estimate crop prices.