VOLUME 12, ISSUE 6, JUNE 2023

Abstract: Augmented reality (AR) technology has potential to revolutionize the way we interact with and visualize the world around us. One area where this technology has great potential is in the field of furniture design and home decor. The goal of the Augmented Reality Furniture project is to develop a platform which allows users to virtually preview and interact with furniture in their own home environments using AR technology. Using a smartphone or other AR-enabled device, users will be able to see how different furniture pieces will look and fit in their own living spaces before making a purchase. The project will involve the use of 3D models of a variety of furniture items, as well as the development of AR software that can accurately place and scale these models in the user's environment. The platform will also include a user-friendly interface for browsing and selecting different furniture items, as well as tools for customizing and arranging the items in the virtual space.

Keywords: Augmented-reality, Furniture, AR core, AR Application, 3D model, AR technology.

Abstract: This work includes the analysis and design of the floating column and non floating column structures by using software ETABS-2015 and compares the result with STAAD-Pro v8i Software. In today’s jet age, we have a host of construction techniques at our disposal. Steel structures, R.C.C. structures, Core and hull type of structure (combination of steel & R.C.C construction). At times this choice available leads to confusion. The best way is to select the type of construction, depending on the circumstances and type of structure.Load transfer path has a great importance in case of structural stability in very major earthquake. The load distribution on the floating columns and the various effects due to it is also been studied in the paper. The importance and effects due to line of action of force is also studied. In this paper we are dealing with the comparative study of seismic analysis of multi-storied building with and without floating columns

Abstract: For land use planning, management/assessment, geodisaster risk mitigation, as well as post-disaster reconstructions, accurate landslip detection and mapping is crucial. The most common methods for mapping landslides up to this point have been visual interpretation and field survey. These methods are frequently criticised for being labor-intensive, time-consuming, and expensive. The deep-learning-based strategy for landslip detection and mapping has received a lot of interest due to its major benefits over the conventional techniques in light of the quick development of artificial intelligence. However, the use of a deep-learning-based approach [1] in landslip identification from satellite photos has long been limited by a lack of sufficient training samples. Studies comparing the suggested approach's viability and robustness to those of ResUNet and DeepUNet showed that it has significant potential for use in the emergency response to natural catastrophes. H5 keras model was developed and adopted. We have also considered earthquake dataset all over the world and with the help of cloud computing the impact of disaster by earthquake will be predicted.

Keywords: Cloud computing, heroku cloud, Multichannel output with cascading, H5 Convolutional Neural Network model, Convolution Neural Network (CNN) architecture, geodisaster, earthquake, landslide, ResUNet and DeepUNet.

Abstract: A technique used for hidden communication between two covert parties. It is an art of hidden communication. It also relates to the areas like network protocols and security for practical data hiding in communication networks using Transmission Control Protocol/Internet Protocol (TCP/IP). Network steganography uses communication protocols such as TCP/IP. Such methods make it harder to detect and eliminate. In a typical steganography using network the modification of a single network protocol occurs. Such modification can be to the Protocol Data Unit. Network steganography shelters a broad spectrum of techniques.

Keywords: Network Protocols, Covert Communication, Storage-based Covert Channel, System Security, Network Security, Steganography, Encryption, TCP/IP, IPv4, Scapy

Abstract: This paper researches blockchain technology applications for the banking sector. Blockchain is a decentralized ledger used to securely exchange digital currency, perform deals and transactions. Each member of the network has access to the latest copy of encrypted ledger so that they can validate a new transaction. Blockchain ledger is a collection of all Bitcoin transactions executed in the past. Basically, it's a distributed database which maintains a continuously growing tamper proof data structure blocks which holds batches of individual transactions. The completed blocks are added in a linear and chronological order. Each block contains a timestamp and information link which points to a previous block. Bitcoin is peer-to-peer permission-less network which allows every user to connect to the network and send new transaction to verify and create new blocks. Satoshi Nakamoto described design of Bitcoin digital currency in his research paper posted to cryptography listserv in 2008. Nakamoto's suggestion has solved long pending problem of cryptographers and laid the foundation stone for digital currency  

Keywords: Blockchain, Decentralized, Bank.

Abstract: Smart medicine box is an innovative technology that combines traditional medication storage with advanced features such as reminders, automatic pill dispenses, etc. These smart medicines box is a new invention in the field of medical department. This Smart medicine box enhance medication management. These smart medicines boxes help patients to improve their health by taking medicines on time with proper dosage.

Keywords: smart medicine box, automatic pill dispense, reminders.

Abstract: When trying to access Java classes from a Python script, one of the main problems is that the required Java modules may not be present in Python. This means that the Python script cannot natively interact with the Java code. One solution to this problem is to use Py4J. Py4J establishes a bridge between Python and Java, allowing Python to access Java classes and methods. Py4J is a Python module that enables Python programs to communicate with Java applications. One of the main benefits of using Py4J is that it allows access to Java classes and libraries that are not natively available in Python. The Py4J module establishes a bridge between Python and Java, allowing Python code to call Java methods and access Java objects. This can be particularly useful in situations where a Java library provides functionality that is not available in Python or when it is more convenient to use a Java library that is already available. This study mainly focuses on the potential use cases and the advantages it has over the conventional methods.

Keywords: java, python, modules, classes, objects, libraries.

Abstract: In this project, we developed a student data chatbot using Dialogflow, a cloud-based natural language processing platform. The chatbot was designed to assist students in finding information about their grades, schedule and other relevant academic data. The project involved defining intents, entities, and responses within the Dialogflow Agent console, testing the chatbot with sample queries, and integrating it with various platforms such as Facebook Messenger and Slack. The results of this project showed that the student data chatbot was successful in providing students with quick and easy access to the information they needed. The chatbot was able to accurately interpret and respond to student queries in natural language, and the integration with various platforms allowed for seamless communication between the student and the chatbot. The development of a student data chatbot using Dialogflow offers a promising solution for improving student engagement and access to academic information. Further improvements could be made by incorporating machine learning algorithms to enhance the chatbot's ability to understand and respond to student queries.

Keywords: Student data chatbot, Dialogflow, Natural language processing, Intents, Entities, Responses, Grades, Schedule, Academic data, Student engagement, Access to academic information.

Abstract: — The successful management of resources is crucial for maintaining financial stability in business activities. In this project, we explore effective resource management strategies specifically tailored to cost calculation in various projects within a business setting. Managing costs is a fundamental aspect of any organization's operations. It involves assessing and tracking expenses related to projects, ensuring optimal allocation of resources, and maintaining financial stability. By implementing efficient cost calculation methodologies, businesses can accurately estimate, control, and optimize their project expenditures, ultimately enhancing their overall performance and profitability. This project focuses on developing strategies that address the challenges faced in cost calculation for projects within a business environment. We delve into various aspects of resource management, such as identifying cost drivers, monitoring expenses, and analysing cost trends. By analysing historical data, we aim to create reliable models for estimating project costs, enabling businesses to make informed decisions and allocate resources effectively.

Keywords: Resources, Financial stability, Cost Calculation, Optimal allocation, Financial performance.

Keywords: Deepfake, Deep Learning, Deep fake Technology, Deep fake Detection, Forensic Verification, Fake Images, Fake Image Detection,Etc.

Keywords: Blockchain, Voting, Cryptography, API

Keywords: Sign to text, Single Shot Detector, TensorFlow (TF), Tensorflow Object Detection API

Abstract: In networked systems, distributed denial-of-service (DDoS) assaults have proliferated. The extent of the problem is best indicated by the enormous number of variations in both the types of DDoS assaults and their mitigating techniques. This article will analyse current DDoS assaults and the accompanying mitigation strategies. To show the degree of application and effectiveness of the solution techniques, the study tries to highlight the strengths and shortcomings of the existing defences. To accomplish these goals, the study makes the assumption that DDoS attacks may be compared based on similar qualities, which in turn helps to compare defence strategies. There is a developing issue with distributed denial-of-service (DDoS). It's astonishing how many and how different the defense strategies are from the attackers. Researchers will have a better knowledge of the issue and the available solution space thanks to the paper's presentation of two taxonomies for categorizing attacks and defenses. The selection of the attack classification criteria was made to draw attention to commonalities and significant characteristics of attack techniques that define problems and guide the development of defenses. The defense taxonomy organizes the corpus of existing DDoS defenses into categories according to design choices, and it then illustrates how these choices determine the benefits and drawbacks of suggested solutions.

Abstract: In this article, we outline the planning and execution of a 360-degree radar scanning system. The system is designed to scan a full 360-degree area and provide real-time data about the objects present in the scanned area. The system uses a rotating platform that can rotate in a full circle to scan the entire area. The radar system is mounted on the platform, and it scans the area continuously while the platform rotates. The system is designed to be compact and portable, making it easy to move and set up in different locations. The radar system uses a phased-array antenna to scan the area in a circular pattern, providing high-resolution data about the objects present in the area. The system is also equipped with a data processing unit that analyzes the radar data and provides real-time feedback about the objects detected.  

Keywords: Rotating platform, Object detection, Real-time feedback.

Keywords: pre-processing, feature extraction, recognition.

Abstract: —This paper presents an analysis of email-based monitoring and alerting systems, including their architecture, functionality, and implementation. The benefits and potential drawbacks of using such a system are discussed, along with a case study of its implementation in a real-world business application. The study examines the different components of the system, including monitoring agents, email servers, and alerting engines, and the different types of alerts that can be generated. The benefits of using an email-based monitoring and alerting system are highlighted, including reduced downtime, increased system availability, improved response times, and enhanced customer satisfaction. A comprehensive analysis of existing email-based monitoring and alerting systems is also presented, along with a survey of their features and capabilities. The effectiveness of these systems in different environments is discussed, and potential drawbacks, such as false alarms and ongoing maintenance, are examined.

Keywords: Email-based monitoring, AWS CloudWatch, Apache Kafka, Monitoring and analytics

Abstract: This study takes a low-cost, all-encompassing approach to the issue of water quality monitoring for both consumer sites and drinking water distribution networks. We intend to construct sensor nodes for on-the-spot pipe monitoring, calculation of water delivery, and water quality measurement. Multiple in-pipe electrochemical and optical sensors make up the principal sensor node, which places a focus on cheap cost, light weight implementation, and reliable long-term operation. This method enables a sensor network method for delivering spatiotemporally rich data to water customers, water businesses, and regulators and is ideal for large-scale deployments. A sensor array is constructed based on chosen characteristics, together with various microsystems for equivalent signal conditioning, processing, and logging.

Keywords: IoT, NodeMCU ESP8266, Arduino, pH sensor, TDS sensor, Solenoidal valve.

Abstract: The okra crop, commonly called lady’s finger, is a commercial crop grown by farmers. But when these crops are infected, they wreak havoc on the life of farmers as well as on the economy. Plant disease issues are due to problems arising in agricultural activities and climate change. It causes money problems and losses to profits, farmers, and to whole industry depending on okra. If disease identification is done without appropriate techniques, then the problem remains the same. Various research has proposed several techniques to overcome these infections. CNN, Deep learning, and many other machine-learning techniques are used for detecting and classifying plant diseases.

Keywords: Okra, CNN, Deep learning, Machine learning, detecting, classifying

Abstract: Brain tumour is one of the most challenging medical conditions to diagnose and treat. Accurate and timely detection of brain tumour is critical for effective treatment planning and improving patient outcomes. With recent advancements in machine learning and artificial intelligence (AI), there has been a growing interest in using AI for brain tumour detection. However, the opaque nature of AI models has raised concerns about their trustworthiness and reliability in medical settings. Explainable AI (XAI) is a subfield of AI that aims to address this issue by providing clear and intuitive explanations of how AI models make their decisions. XAI-based approaches have been proposed for various applications, including healthcare, where the interpretability of AI models is crucial for ensuring patient safety and building trust between medical professionals and AI systems. In this paper, we review recent advances in XAI-based brain tumour detection, focusing on techniques for generating explanations of AI model predictions. We also discuss the challenges and opportunities in implementing XAI in clinical settings and highlight the potential benefits of XAI for improving medical decision-making and patient outcomes. Ultimately, the objective of this paper is to provide a comprehensive overview of the state-of-the-art in XAI-based brain tumour detection and to encourage further research in this promising field. In the project CNN architectural model has reported best accuracy of 99%.

Keywords: Explainable AI, Convolution Neural Network

Abstract: This paper discusses how machine learning techniques can be used to detect credit card fraud. It covers the types of fraud, challenges, and various ML algorithms used. The steps for building an ML-based fraud detection system are explained, and current and future trends are examined. Overall, ML-based fraud detection systems can significantly improve accuracy and efficiency, leading to better customer protection and reduced financial losses.

Keywords: Machine Learning, Deep Learning, Logistic Regression, CNN.

Abstract: The mining industry is inherently hazardous, with workers facing risks such as cave-ins, falling debris, exposure to harmful gases, and machinery accidents. To mitigate these risks, smart helmets have been developed as a safety solution for the mining industry. Smart helmets are high-tech safety helmets equipped with sensors, cameras, GPS tracking, communication systems, and augmented reality technology. These helmets can detect potential safety hazards in the environment and alert workers to take necessary precautions. A wearable helmet is exhibited in this study, for the risks in the mining area. This prototype provides real-time monitoring of harmful gasses, temperature, humidity, and worker’s heart-rate.

Keywords: Mining, Helmet, Safety, Sensors, Arduino Nano, LCD Display, Alarm, Tracking, Vital sign monitoring.

Abstract: In order to identify potential threats, a network alert system often combines monitoring technologies including intrusion detection systems (IDS), firewalls, and antivirus software. A crucial tool for managing the performance, security, and availability of computer networks is a network alert system. When events or anomalies take place, it continuously monitors network traffic, devices, and services and creates alerts or notifications. The stability and integrity of networks are crucially maintained by this mechanism, especially in complex and dynamic contexts. A network alert system's capacity to give early issue detection is one of its main features. It may detect and report on potential issues including security threats, performance bottlenecks, and device failures by continually monitoring network activity.

Keywords: Network Alert ,Network Intrusion, Artificial Intelligence, Cloud, DNS Alert, DHCP Alert, Authentication Alert

Abstract: In today’s fast-paced world, managing finances has become a tedious task. In order to streamline the process of expense management, an innovative finance application is developed, with Excel sheets keeping track of expenses according to various condition is not possible for large number of employees. So an App is developed which can manage the finance of teams and Squads and visualize financial data and draw financial outcome. Spring boot usually consists of a lot of boilerplate code that has to be simplified. The advantages of such a simplification are many: a decrease in the number of artifacts that we need to define and maintain, consistency of data access patterns, and consistency of configuration. Spring Security is a framework that helps secure enterprise applications.

Keywords: Spring Boot, Spring JPA(Java Persistence API) , Spring Security, Database Views.

Abstract: Adversarial attacks on artificial intelligence (AI) are a growing concern in information science. These attacks manipulate input data to deceive AI systems into producing inaccurate or unexpected results. The purpose of this project is to investigate the impact of adversarial attacks on various AI systems and develop effective defence mechanisms to counter them. The project will begin by selecting a neural network model to attack and using various attack methods, such as gradient-based attacks and decision-based attacks, to generate adversarial examples. The attack's effectiveness will be evaluated by testing the adversarial examples on the target model and measuring the success rate and degree of perturbation needed to generate the examples. To defend against the attack, The project will modify the neural network architecture or training data and apply defensive techniques such as adversarial training or input sanitization.The project aims to contribute to developing secure and reliable AI systems that can resist adversarial attacks. By exploring different attack methods and defence mechanisms, hope to identify effective strategies to mitigate the risks of adversarial attacks in critical applications such as autonomous vehicles, medical diagnosis, and financial fraud detection. The project findings will be valuable to researchers, engineers, and practitioners working in the field of AI and information science to develop robust and secure AI systems.

Keywords: Adversarial attacks, Artificial intelligence (AI), Defence mechanisms, Neural network models, Attack methods, Robust AI systems

Abstract: The enthusiasm for creating a lightweight material has been the subject of a study tested by both researchers and specialists. Testing in creating lightweight concrete reduces density while maintaining quality and without negatively impacting cost. Combining new aggregates with a general mixing scheme is a typical way to reduce the thickness of concrete. Use of natural aggregate in such a rate leads to a question about the preservation of natural aggregates sources. In addition, operations associated with aggregate extraction and processing are the principal causes of environmental concerns. In light of this, in the contemporary civil engineering construction, using alternative materials in place of natural aggregate in concrete production makes concrete as sustainable and environmentally friendly construction material. Coconut shell is one of the main contributors of pollution problem as an agricultural waste. Coconut shell used as coarse aggregate in concrete encouraged sustainable and environmentally helpful material in the construction field. The main concern of this research is the environment, and the construction and building technology to improve natural world and building materials.

Abstract: The impact of carbide waste,CW on the strength of concrete made with cement partially replaced with Rice Husk Ash,RHA for use in rigid pavement was investigated. Oxide composition analysis of CW and RHA confirm their status as non pozzolanic material rich in CaO component and pozzolanic materials, respectively. The large amount of space taken on landfills by waste, the constant release of environmental polluting gases like CO₂ into the atmosphere and the high cost involved in cement production has led to the search for alternative binding materials that are cheap, ecofriendly and will help contribute to waste management.

Keywords: Calcium Carbide Residue; Wood ash; Supplementary Cementitious Materials; Pozzolanic reaction; CO2 emission.

Abstract: Virtual assistants have become an integral part of our daily lives, significantly influencing how we perform various activities. With the rising prominence of AI, we aim to explore this emerging field through our project, which focuses on AI-based workout assistants. Introducing Workout assistant, our application is designed to detect users' exercise poses, count repetitions for specified exercises, and provide personalized recommendations to enhance their form. By utilizing MediaPipe, the application accurately identifies a person's pose and analyzes the pose's geometry using both dataset information and real-time video. This analysis enables Workout assistant  to precisely count repetitions for specific exercises and offer detailed guidance to users on improving their exercise technique. Our goal is to leverage AI technology to create a comprehensive virtual workout assistant that empowers users to track their progress, ensure proper form, and gain valuable insights for their fitness journey.

Keywords: AI, Virtual assistant, CNN, workout assistant, Pose estimation, Blazepose, OpenCV.

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

Abstract: Over the past few decades, augmented reality (AR) and virtual reality (VR) have experienced a significant surge in popularity and adoption. However, the precise distinctions and defining characteristics of these technologies are often not well understood or discussed. This paper seeks to delve deeper into the nature of AR and VR, shedding light on their fundamental principles and workings. To begin with, augmented reality (AR) refers to a technology that overlays digital content onto the real world, enhancing the user's perception and interaction with their surroundings. By utilizing various sensors, such as cameras and motion trackers, AR systems gather real-time information from the environment and merge it with computer-generated elements. This seamless blending of virtual and real elements allows users to see and interact with digital objects in their physical environment. Examples of AR applications range from simple smartphone filters and games to more advanced industrial and medical training simulations. On the other hand, virtual reality (VR) creates an entirely immersive digital environment that transports users to a simulated reality. By wearing a VR headset, users are visually cut off from the real world and are instead presented with a computer-generated 3D environment. VR systems typically employ headsets with built-in displays and motion-tracking capabilities to accurately replicate the user's movements within the virtual space. This immersive experience can be enhanced further with additional sensory feedback, such as haptic feedback gloves or spatial audio systems. VR finds applications in gaming, education, architecture, and even therapy, providing users with a compelling sense of presence and interaction within the virtual realm.

Abstract: The risk of data misuse is growing in tandem with the exponential growth in data storage and utilisation in the modern world. As a result of this, data kept on controllers, mobile devices, or computers, whether it was acquired by humans or machines, is susceptible to numerous cyber-attacks. There are several digital forensic tools available nowadays that assist with conducting investigations by gathering evidence using various techniques. The numerous digital forensic tools that businesses, governments, and individuals employ to gather, extract, and present the information gathered are thoroughly examined in this paper. In this work, we also assess the forensic tools using several parameters so that customers may quickly choose the tool that best suits their requirements .We also briefly talk about some of the challenges people face in using digital forensic tools.

Abstract: This project's major goal is to develop a facial recognition-based attendance monitoring system for educational institutions to improve and modernize the current attendance system and make it more effective and efficient than it was previously. It has numerous advantages and can be used for security, identification, and authentication. The goal of this system is to create a face recognition-based class attendance system. The manual attendance system is time-consuming and cumbersome to maintain. This system consists of four phases- database creation, face detection, face recognition, and attendance updation. The database is created by the images of the students in class. Face detection and recognition are performed using the Haar-Cascade classifier and Local Binary Pattern Histogram algorithm respectively. Faces are detected and recognized from live streaming video of the classroom. Key Words: Face Recognition, Face Detection, Haar- Cascade classifier, attendance system.

Abstract: Agriculture must complete a huge effort that involves finding plant diseases. This is something that the economy is extremely dependent on. Due to the prevalence of plant illnesses, finding infections in plants is a crucial task in the agriculture industry. To detect illnesses in the leaves, and plant must be continuously examined. This constant inspection of the plants is labor-intensive and time-consuming since it involves many people. Simply said, some sort of deliberate strategy must be used to monitor the plants. The detection of Program-based diagnosis of diseases makes it easier to identify damaged leaves as well as save time and labour. The suggested method can more correctly categorise diseased plants by identifying their symptoms.

Keywords: In order to extract features from and categorization in plants disease species, CNN and deep learning techniques are used.

Abstract: Accurate prediction of stock market values is a critical task in financial analysis, empowering investors to make informed decisions. Machine learning has emerged as a powerful approach to enhance the authenticity and effectiveness of stock market forecasting. This research paper focuses on investigating the potential of regression models and LSTM-based machine learning techniques for predicting stock values. By comparing the performance of these models in stock market valuation, we aim to uncover their strengths and limitations. Our study leverages comprehensive historical stock market data from diverse sources, which undergoes meticulous preprocessing to extract pertinent features such as price trends, trading volume, and market sentiment. Regression models such as linear regression, polynomial regression, and support vector regression are implemented and rigorously evaluated to assess their predictive capabilities in estimating stock prices accurately. Additionally, we explore the potential of LSTM-based deep learning models in capturing intricate temporal dependencies and patterns in the data.

Keywords: stock market , forecasting, price prediction ,machine learning.  

Abstract: Near Field Communication (NFC) technology has gained significant attention in recent years due to its ability to enable seamless wireless communication between devices in close proximity. This research paper aims to explore the potential applications and advancements of NFC technology across various domains. The paper reviews existing literature, discusses the underlying principles of NFC, and examines its key features and capabilities. Additionally, it investigates the security aspects and challenges associated with NFC implementation. Furthermore, the paper highlights real-world use cases of NFC, including contactless payments, transportation ticketing, and data sharing. The research conducted includes an analysis of NFC's advantages and limitations, as well as a comparison with other wireless technologies. The findings of this study contribute to a deeper understanding of NFC technology and provide valuable insights for researchers, developers, and industry professionals seeking to leverage its benefits.

Keywords: Near Field Communication, NFC, wireless communication, contactless payments, transportation ticketing, data sharing, security, wireless technologies.

Abstract: Identity identification is often carried out via traditional authentication methods using biometric data, such as fingerprints, or user information verification, such as inputting a password. When using simply these authentication techniques, there are security hazards. Everyone considers security to be a big problem while they are apart from their family. There is currently no satisfactory solution for the aforementioned problem. Here, an electronic security architecture is introduced. Security has always been a worry in our homes, offices, stores, etc., and it continues to be so today. Everyone worries about someone breaking into their house or workplace without their permission. For instance, it may be difficult to tell if the person typing the password is authorised if the password has been hacked. The development of a two-factor RFI and biometric fingerprint authentication-based security scheme is described in this work. It can offer effective control facilities to prevent entry of an unauthorised user at any workplace, posing risks and disrupting work-flow. Passive approaches, communication alerts, the Internet of Things, machine learning, and database management systems have all been explored in relation to various technologies. This initiative focuses on protecting customers against unauthorised use of products, thefts, and constant notification for the benefit of society.

Keywords: Fingerprint sensor, RFID, Signal Processing, Micro-controller, Data Acquisition, Communication,Machine Learning,

Abstract: -Renewable energy generation and electric vehicles (EVs) have recently received increased attention in the smart grid. This paper describes a grid-connected solar-wind hybrid system for supplying Battery's electrical load demand. Battery monitoring systems for E-vehicles are a new development in the automotive and electrical industries. There are currently no large-scale monitoring systems for batteries in India. It has not progressed from a small-scale personal project to a large-scale application. Aside from that, there are existing methodologies that rent the batteries to the user as such and rely entirely on the user's timely payment in person. A battery supervision scheme is an automatic system that protects a rechargeable battery, for example, by preventing it from functioning outside of its safe operational area, monitoring its state, planning inferior statistics, commenting on that data, monitoring its situation, confirming it, and/or supplementing it. The BMS will also order the battery to be recharged by redirecting the improved energy back into the battery pack. It is only used for battery charging and discharging at rummage sales. With our proposed system, the EV battery is charged by both renewable and non-renewable sources, such as a solar PV plate. The battery management system can be linked to the monitoring structure, which can manage, monitor, and log data to an online database. This system monitors battery parameters such as voltage, current, temperature, power, and charge state. These parameters are then transmitted and stored in a database over the internet, which is then displayed to the user via an Android app. When a large enough dataset is available in the database, intelligent machine learning algorithms can be used to predict the life cycle of the battery and make recommendations to the user about the time and duration of each charge cycle, the battery's health, and other factors. When used in battery rental companies, the battery can only be charged if the user pays the rent on time. Index Terms—Charging station, electric vehicle (EV), solar photovoltaic (SPV) panels,

Abstract: In cities like Nagpur, Pune and Mumbai the busses are the nerves of the city. But they are behaving as an open invitation for evil minds to do mishaps as there is no maintenance of data of passenger. The tickets cost being of odd amounts and many other different problems while buying tickets. Also in this advance world we are dependent on the paper tickets and we even cannot book the ticket in advance doesn’t seems fare so here is a solution.In this proposed system ticket can be bought with just a smart phone application and, where users or passengers can issue ticket pass and carry pass tickets in his smart phone as a QR (Quick Response) code. The bus passes generating system using QR code can be brought easily any time anywhere and the ticket will be present in the passenger phone in the form of QR. Also the ticket checker is provided with a checker application to search and check for the user's ticket for checking purposes

Keywords: Login, Apply, Payment, Generation

Abstract: The rationale of this paper is to make a schedule stumbling component. For a three stage framework, the yield of our venture resets for the brief blame, whereas, in case of changeless blame, it trips the framework. These inadequacies are recognized by our contraption and it actually isolates the stock to keep absent from blast/fire hurt which may influence the control prepares within the sub-stations. The lurching system is made by utilizing 3, 1-stage transformers which have both data and surrender in star affiliation, and 3 transformers in delta affiliations with commitment of 220 volt and surrender of 12 volt. Here moo voltage testing is appeared. For both temporal and long span imperfections 555 clocks are utilized. To enact stumbling component, switches are utilized which makes the three sorts of blame in moo voltage side. Transient/Short term blame gives a fast recuperation as a brief trip though longer length of issues gives a lasting trip. This strategy, on the off chance that expanded may offer assistance in IOT based applications for SMS based administrations to clients as well as utilities for fault detection.

Keywords: IoT,Fault,.

Keywords: Technology Acceptance Model, E-learning, Covid-19 pandemic, Online education, primary school, Saudi Arabia Education. Works Cited: Ghadeer Aljizani, Farrukh Saleem "Impact of Covid-19 Pandemic on Primary Education in Saudi Arabia: TAM Implementation on MADRASATI System", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 6, pp. 1-16, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12601

Abstract: The increasing volume of data generated by various sources necessitates the development of efficient data warehousing solutions for effective data processing and reporting. This paper presents the design, development, and performance evaluation of a cloud-based data warehousing solution aimed at addressing these challenges. The solution leverages cloud computing technologies, including AWS services, to provide scalable and reliable data storage, management, and analysis capabilities. The paper discusses the key architectural components of the solution, including data ingestion, integration, transformation, and reporting modules. It highlights the use of AWS services such as S3, Lambda, Step Functions, and CloudWatch in enabling seamless data processing workflows. The implementation follows agile methodologies, ensuring iterative development, frequent testing, and user feedback incorporation. Performance evaluation of the solution demonstrates its efficiency in handling large volumes of data, delivering fast response times, and maintaining high data quality and consistency. The evaluation includes metrics such as data processing speed, query performance, and system scalability.

Keywords: Cloud-based data warehousing, Data processing, Reporting, Scalability, AWS services, Data integration

Abstract: In computer vision, the fundamental task of background modelling entails removing the static background from a scene in order to extract the foreground objects. Several computer vision applications, including object tracking, motion detection, and video surveillance, require this process as a prerequisite. For background modelling, a variety of techniques have been put forth, from straightforward threshold-based strategies to complex deep learning models. This paper presents a method that includes the K.M.M. baseline model pipeline followed by two pre-processing techniques that address the varying illumination problem. We also go over the difficulties associated with background modelling, including lighting variations, camera jitter, and PTZ, and we highlight some potential future research directions in this area. Finally, we compare the various methods based on their computational complexity, robustness, and MIOU score, and we offer some guidelines for picking the best method for a particular application.

Keywords: Background Subtraction, Foreground Detection, OpenCV, KNN

Keywords: Complaints, Crimes, Investigations etc.

Abstract: The COVID- 19 epidemic has needed lesser nanosecond- to- nanosecond urgency of patient treatment in ferocious Care Units( ICUs), rendering the use of Randomized Controlled Trials( RCTs) too slow to be effective for treatment discovery. There's a need for dexterity in clinical exploration, and the use of data wisdom to develop prophetic models for patient treatment is a implicit result. We propose the use of an nimble data wisdom frame grounded on the Scrumban frame used in software development. Scrumban is an iterative frame, where in each replication larger problems are broken down into simple do- suitable tasks for data. The two sides unite nearly in formulating clinical questions and developing and planting prophetic models into clinical settings. What's truly demanded are data scientist and croaker hookups icing close collaboration between the two sides in using these tools to develop clinically useful prophetic models to meet the demands of the COVID- 19 healthcare geography.

Keywords: Agile Scrumban, Minimal Viable Model,Cloud Computing, Predictive model, Amazon Web Services

Abstract: Diabetes is a chronic disease that spread over the past decades in abundance. It is a metabolic disease that may affect the entire body. Diabetes is classified are three types, which are type 1 diabetes (T1D), type 2 diabetes (T2D), and gestational diabetes (GD), where each type has specific causes. This research study aims to find out the most common behaviours that lead to diabetes and measure the relationship between human biometrics and the likelihood of behaving T2D.The study aimed to develop a machine learning prediction model by investigating five machine learning algorithms which are Support Vector Machine, Logistic Regression, K-Nearest Neighbour, Decision Tree, and Random Forest. This model was developed by Python using google colab, Random Forest algorithm outperformed in perform highly accurate behavioural prediction with 98% compared with other algorithms. The outcome from this research study would assist the medical practice and medical community with a tool that can early predict T2D.

Keywords: Behaviours, Diabetes mellitus, Machine Learning, Prediction, Type 2 diabetes. Works Cited: Samah Alzahrani "An Intelligent Model for early prediction of Type 2 diabetes likelihood using human behaviors and biometrics among adults in Saudi Arabia", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 6, pp. 328-338, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12657

Abstract: Cloud Appian BPM (Business Process Management) has gained significant attention in the healthcare industry as organizations seek to streamline and optimize their complex processes. This abstract explores the usage of Cloud Appian BPM in the healthcare industry and its potential benefits. Cloud Appian BPM is a platform that enables healthcare organizations to automate, manage, and optimize their critical business processes. In the healthcare industry, where efficiency, accuracy, and compliance are paramount, Cloud Appian BPM offers several advantages. Firstly, Cloud Appian BPM provides healthcare organizations with a centralized platform to design, model, and execute workflows, enabling them to automate manual and paper-based processes. By automating tasks such as patient admissions, claims processing, and inventory management, organizations can reduce errors, improve operational efficiency, and enhance patient care. Additionally, Cloud Appian BPM facilitates collaboration and communication among healthcare professionals. It enables seamless information sharing, task assignment, and real-time status updates, thereby enhancing interdepartmental coordination and promoting effective teamwork. This is particularly beneficial in scenarios where multiple stakeholders are involved, such as care coordination or discharge planning. Furthermore, Cloud Appian BPM offers advanced analytics and reporting capabilities, allowing healthcare organizations to gain valuable insights into process performance and identify areas for improvement. By analysing process data and key performance indicators, organizations can optimize workflows, reduce bottlenecks, and enhance overall process efficiency. In terms of compliance, Cloud Appian BPM provides features such as audit trails, access controls, and regulatory compliance templates, ensuring that healthcare organizations meet industry-specific regulations and standards. It facilitates adherence to HIPAA (Health Insurance Portability and Accountability Act) and other data privacy and security requirements, mitigating the risk of data breaches and non-compliance penalties.  

Keywords: Healthcare processes, Workflow automation, Patient onboarding, electronic health records (EHR), Care coordination, Claims management Works Cited:

Arjun Reddy Kunduru " Cloud Appian BPM (Business Process Management) Usage In health care Industry", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 6, pp. 339-343, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12658

Abstract: In this paper, room temperature deposition of CdS thin film on antimony doped tin oxide (ATO) substrate through spray pyrolysis technique. The size of thin film is estimated through field-emission scanning electron microscope (FESEM) FESEM and TEM (Quanta 200 and HITACHI SU8010) have been used to analyze surface morphology and particle size. The AFM(NT-MDT,INTEGRASpectra) was employed to analyze the topography and roughness of CdS thin film. The elemental analysis of the film was done by EDS spectra. The thickness of CdS film is also measured by AFM technique.  

Keywords: Spray pyrolysis, CdS, FESEM, TEM, AFM, EDS etc.

Abstract: Cloud storage technology has become a critical component in modern-day data storage and management, providing a scalable and reliable solution for organizations of all sizes. This paper provides an extensive review of cloud storage technology, covering its history, types, models, core technologies, architecture, and applications.The paper traces the evolution of cloud storage and explains the different types and models of cloud storage solutions, including public, private, and hybrid models. It then provides an in-depth analysis of the core technologies of cloud storage, focusing on the Google File System (GFS) architecture and the Hadoop Distributed File System (HDFS). The paper also examines the key features and benefits of cloud storage solutions, along with an assessment of their advantages and disadvantages. Overall, this paper serves as a valuable resource for students, researchers, and practitioners seeking to understand the current state of cloud storage technology and its potential impact on the future of data storage and management.

Keywords: Cloud Storage Models, GFS, HDFS, Amazon S3, Amazon EC2.

Abstract: Blockchain is among the most pivotal technological inventions in recent times. Blockchain is a transparent plutocrat exchange system that has revolutionized the way a business was conducted preliminarily. Companies and crackers have started investing significantly in the blockchain request and it's anticipated to be a net worth of further than 3 trillion dollars in the forthcoming times. Its fashionability has increased exponentially because of its impeccable security and capability to give a complete result to digital identity issues. It's a digital tally on a peer-to-peer network. This paper gives sapience into Blockchain technology, its history, its armature, how it works, its advantages and disadvantages, and its operation in different diligence.

Abstract: 5G stands for fifth-generation wireless technology. It's the rearmost replication of cellular technology that has three main features lesser speed, lower quiescence, and the capability to connect a lot further bias contemporaneously. A marketable 5G wireless network is anticipated to be stationed by 2020. This paper provides a brief preface to 5G wireless technology.

Abstract: The proposed method involves the use of an electronic notice board that can be controlled through an Android device, allowing messages to be displayed on it. In the past, notice boards required manual maintenance and daily sticking of notices, which was a tedious process. To address this issue, the project introduces an electronic display notice board that is connected to an Android device via Bluetooth. Messages are sent from the Android device to an Arduino board through Bluetooth. Notice boards are commonly used in places such as organizations, institutions, and public areas like parks, bus stations, and railway stations to convey important information. On the other hand, the process of stick various notices daily can be challenging. This project aims to solve this problem by developing an advanced wireless notice board system. The system utilizes an Android application installed on smartphones or tablets to instantly update and display the latest information.

Abstract: This research paper explores the concept of Web 3.0 and its implications for the future of the internet. Web 3.0 represents the next phase of web evolution, focusing on decentralization, user control, and increased utility. The paper highlights the evolution of the internet from Web 1.0 to Web 2.0, where user-generated content and interactivity became prominent.Web 3.0 is described as a decentralized and open internet powered by blockchain, artificial intelligence, and the Internet of Things. The paper discusses the potential of Web 3.0 to revolutionize data ownership, privacy, and security. It emphasizes the importance of hardware components in supporting Web 3.0 applications, such as computing power, secure storage, crypto wallets, network connectivity, and IoT devices...

Abstract: This system offers the very efficient manner of placement of students. on this device the student does their registration quite simple way the position officer can without difficulty get the statistics of college students. The gadget gets easy get right of entry to to the eligible scholar. in step with that device notify the scholars. what number of students receives placed so as to be shown through the various graphs. The system also enables the college to keep records of present day and former college students also. The system offers records to college for alumni meets. Placement officer prepares the agenda of all activities regarding the position. the corporation offers the standards who college students are eligible robotically they notify. scholar, TNP staff, TPO get the required facts. those students have registered they all are eligible for this system.

Keywords: education & Placement cellular device, Android, college students, college, Modules.

Abstract: A new frame is being used currently in order to develop cross platform operation since it's extremely clumsy to form operations for colourful platforms specifically due to the complications of using Java, Objective- C or Swift. therefore, the Ionic frame which lets the inventors make operations for multiple platforms by simply using web development language to produce full- fledged mobile operations. Ionic frame is an open- source UI toolkit used for high- quality mobile apps, desktop apps and web apps using web technologies like HTML, CSS, JavaScript. It give platform for inventors to make formerly and run far and wide. This Ionic Framework created by Max Lynch, Ben Sperry, and Adam Bradley of Drifty Co. in 2013. The first beta interpretation of the Ionic frame was released in March 2014. [1] Ionic is a great choice for creating introductory native functionalities within an operation which can run on multiple bias and operating system. The main purpose of the Ionic frame is that we can make the app only formerly and that can emplace in different bias. Because of this app development done briskly and it's also cost-effective. It also reduces the need for conservation.[2]

Abstract: Future 5G wireless networks will face new challenges and increasing demands for network capacity to support many devices using applications that require high data and alwaysavailable communication connectivity; support business wireless network very well. open again. New challenges offer new solutions and include changes in network connectivity, management and operational planning for future 5G wireless networks equivalent to existing wireless networksOne of the main goals of 5G wireless networks in the future is to use a combination of cloud storage and wireless/telephone equipment to provide a service as service for the various services available from various service providers and/or employees.

Keywords: future, 5G, wireless, capacity. The5G wireless system represents the fifth generation of mobile communications and promises significant advances over its predecessors.

Abstract: With the advent of cloud computing, it has been proved to be quite useful technology for development and delivery of ERP software. There is often need to customize an ERP software to fit the business process variation of an organization. Many research studies published during 2013 to 2022 acknowledges that deep customization of Cloud ERP System continue to be major challenge especially with public cloud-based edition. There is lack of further study to use server-less architecture to address deep customization needs. This study will present use of open FaaS to call function’s client specific implementation via API gateway so to provide deep customization capability in ERPsoftware. This is supported by a proof of concept with reference application of pricing element determination. Keyword: ERP, Customization, SaaS, Server-less runtime, Function-as-a-Service

Abstract: Blockchain, the foundation of Bitcoin, has received extensive attentions recently. Blockchain serves as an immutable ledger which allows transactions take place in a decentralized manner. Blockchain-based applications are springing up, covering numerous fields including financial services, reputation system and Internet of Things (IoT), and so on. However, there are still many challenges of blockchain technology such as scalability and security problems waiting to be overcome. This paper presents a comprehensive overview on blockchain technology. We provide an overview of blockchain architecture. Furthermore, technical challenges and recent advances are briefly listed. We also lay out possible future trends for blockchain.

Keywords: Blockchain, decentralization, transaction, scalability

Abstract: - This study introduces a home automation system based on Bluetooth and Arduino technology. Its purpose is to enable users to remotely monitor and control various household appliances using their Bluetooth-enabled mobile devices. The system comprises a Bluetooth connectivity model, an Arduino board, and powerful sensors. The Arduino board acts as the central management component, receiving commands from the user's mobile devices and transmitting signals to the corresponding appliances. Through Bluetooth connectivity, the system facilitates wireless communication between the Arduino boards and the user's mobile device, enabling the detection and control of multiple home applications such as air conditioning and lighting. The system is designed with user-friendliness, cost-effectiveness, and energy efficiency in mind. By employing this technology, users can enjoy the convenience and comfort of managing their home appliances remotely. Experimental results demonstrate that the proposed approach is both reliable and effective in regulating and monitoring home appliances using Bluetooth technology. In summary, this Bluetooth-based Arduino home automation system has the potential to significantly enhance daily life by offering convenient and comfortable control over various household appliances. It can find practical applications in the realm of smart homes.[2] Keyword: Arduino, Home automation, Bluetooth, Smart phone, Security

Abstract: With the increasing popularity of social media platforms like YouTube, monitoring and regulating user-generated content has become a significant challenge. In particular, detecting hate speech and hate crimes within YouTube comments is a critical task to ensure user safety, foster inclusive online communities, and comply with legal regulations. This abstract presents an overview of a research study focused on hate crime detection in YouTube comments using machine learning (ML) techniques. The objective of the study is to develop an automated system capable of identifying and flagging hate speech and potential hate crimes within user comments. The research involves collecting a large dataset of YouTube comments labeled for hate speech, hate crimes, and non-offensive content. Our work encompasses two main contributions. Firstly, we have developed a detailed taxonomy for classifying hateful online comments, considering both the types of hate speech and the targets of such comments. This taxonomy enables a more granular understanding and analysis of hate speech occurrences in online social media. Secondly, we have conducted an extensive machine learning experiment using various algorithms, including Logistic Regression, Decision Tree, Random Forest, Adaboost, and Linear SVM. The goal was to create a multiclass, multilabel classification model capable of automatically detecting and categorizing hateful comments within the realm of online social media. To ensure the reliability of our model, we performed validation tests to assess its predictive capability. Additionally, this research has provided valuable insights into the distinct types of hate speech prevalent on social media platforms.

Keywords: Hate, YouTube, social media, offensive, Muslim, jihad, fool.

Abstract: Hate crimes on social media platforms pose significant challenges in maintaining a safe and inclusive online environment. This research paper proposes a machine learning approach to detect and mitigate hate crimes on Facebook. By leveraging advanced natural language processing techniques and deep learning algorithms, the study aims to develop an automated system that can effectively identify and address hate speech, discriminatory content, and harmful activities on the platform. The research highlights the importance of proactive measures in combating hate crimes, protecting user well-being, and fostering a positive online community.

Abstract: Voice morphing techniques have gained significant attention due to their applications in speech processing and multimedia systems. This study presents a wavelet-based approach for voice morphing, which enables the transformation of a source speaker's voice to resemble a target speaker while maintaining intelligibility and naturalness. The proposed method utilizes the wavelet transform to decompose the source and target speech signals into different frequency subbands. By modifying the wavelet coefficients in these subbands, the spectral and temporal characteristics of the source speech are altered to match those of the target speech. The morphed speech is then synthesized by performing an inverse wavelet transform on the modified coefficients. Objective and subjective evaluations demonstrate the effectiveness of the proposed approach in achieving accurate voice morphing while preserving the linguistic content. The waveletbased voice morphing technique presented in this study offers potential applications in speech synthesis, voice conversion, and entertainment systems.

Keywords: Voice Morphing, Speech Processing, Wavelets, Signal Decomposition, Voice Conversion

Abstract: The current system for maintaining crime records and criminal data lacks comprehensive information on offences and perpetrators, making it less secure and susceptible to fraud. This study proposes the implementation of a blockchain-based system to safeguard data and improve security measures. By utilizing blockchain technology, crime investigators can access the database using their authentication credentials and view reports authored by police and witnesses. Investigators also possess the authority to update and modify data, expediting investigations and aiding in the identification of criminals. While previous research has focused on centralizing digital evidence management, this approach poses a risk of compromising confidential operational and investigative information if the central server is compromised. To mitigate this risk, a distributed system environment using blockchain technology is proposed for managing digital evidence and investigation data. However, the storage of massive volumes of data, such as evidence films, in a blockchain can adversely impact performance. Therefore, a three-tier blockchain architecture is proposed, consisting of hot and cold blockchains. The cold blockchain stores non-changing content and files, while the hot blockchain stores frequently changing information.

The study evaluates the system's performance in terms of digital crime evidence storage and query processing, highlighting the capabilities and benefits of the multi-level blockchain architecture. Implementing this three-tier blockchain architecture can significantly enhance the security and efficiency of digital crime evidence management, thereby improving the investigative process and aiding in the fight against crime.

Keywords: Crime Record, Blockchain, Security, Cryptography.

Abstract: Diabetes is a disorder that causes an increase in blood glucose levels due to a lack of insulin and affects 425 million persons globally. Diabetes is the most common cause of retinopathy. The retina is the photosensitive tissue that lines the inside of the eye. Hyperglycemia (high blood sugar) can cause retinal vascular damage. Diabetic Retinopathy (DR) is a diabetic eye condition that causes the blood vessels of the retina to enlarge and leak fluids and blood. If left uncontrolled, it might cause partial or total blindness. The sustained eyesight can be treated, but it cannot be restored to its former state. The disease's prognosis worsens with age. This paper presents a detailed review of various retinopathy detection methods. A comparative study is conducted with their merits and demerits for identifying the challenges in those techniques and then this paper is concluded with suggestions of solutions for enhancing the efficiency of deep learning models.

Keywords: Diabetic Retinopathy, Principal Component Analysis, Convolutional Neural Network, Deep Learning.

Abstract: Suicide is a significant global issue, with approximately 800,000 people taking their own lives every year. Detecting individuals at risk of suicide remains a challenging task, as highlighted in numerous suicide studies. However, with the widespread use of social media platforms, we have observed that individuals often express their suicidal thoughts or experiences in public on these networks. Therefore, it is crucial to identify people who may be prone to suicide at an early stage.  In this paper presents a novel approach for detecting suicidal content in social media platforms using natural language processing (NLP) and machine learning techniques. The proposed method combines keyword-based detection, sentiment analysis, and NLP-based approaches to identify posts that may indicate suicidal ideation. By analyzing the language and sentiment used in social media posts, the method aims to identify content that suggests a person may be at risk of suicide. The method is developed by training it on a carefully curated dataset of labeled posts, which includes examples of both suicidal and non-suicidal content. Through rigorous evaluation using metrics such as precision, recall, and F1-score, the effectiveness of the proposed method is assessed. The results demonstrate that the method achieves a high level of accuracy in identifying suicidal content. The implications of this research are significant. Social media platforms can incorporate the proposed method as an automated tool to flag potentially concerning content for further review. Trained mental health professionals can then examine the flagged posts and provide appropriate support and intervention to individuals in need. By leveraging this technology, timely interventions can be initiated to prevent suicides and offer assistance to those who may be at risk. Overall, the proposed method offers a promising solution to the challenge of detecting suicidal content in social media platforms. By leveraging NLP and machine learning techniques, it provides a proactive approach to identify individuals who may be in distress, enabling timely intervention and potentially saving lives.

Abstract: Breast cancer is the leading cause of death among women worldwide. Approximately 8% of women are diagnosed at some point in their lives. In order to limit the number of people dying from cancer, early diagnosis is necessary to stop the growth of cancerous cells. Deep Learning (DL) algorithms are now widely employed in the categorization of breast cancer. They have a high degree of classification accuracy as well as diagnostic capabilities. As a result, in Classification of Breast Cancer, we utilize transfer learning approaches to classify breast cancer utilizing a Deep Convolutional Neural Network (CNN).

Keywords: Deep Convolutional Neural Network, transfer learning, Breast Cancer, Deep Leaning

Abstract: ATM or Computerized Teller Machines are generally utilized by individuals these days. Performing cash withdrawal exchange with ATM is expanding step by step. ATM is vital gadget all through the world. The current traditional ATM is helpless against violations as a result of the fast innovation improvement. A sum of 270,000 reports have been accounted for with respect to check card extortion and this was the most detailed type of data fraud in 2021. A protected and effective ATM is expected to expand the general insight, ease of use, and comfort of the exchange at the ATM. In this day and age, the area of PC vision is progressing dangerously fast. The new advancement in biometric ID strategies, including finger printing, retina filtering, and facial acknowledgment has put forth an extraordinary attempt to save what is happening at the ATM. In particular, the objective of this venture is to give a PC vision strategy to tackle the security risk related with getting to ATM machines. This task proposes a programmed teller machine security model that utilizes electronic facial acknowledgment utilizing Profound Convolutional Brain Organization. In the event that this innovation turns out to be broadly utilized, appearances would be safeguarded as well as their records. Face Check Misleading content connection will be created and shipped off ledger holder to confirm the character of unapproved client through a few devoted fake savvy specialists, for distant certificate. In any case, clearly man's biometric highlights can't be repeated, this proposition will go quite far to take care of the issue of record security making it feasible for the genuine record proprietor alone approach his records. This wipes out the chance of extortion coming about because of ATM card burglary and replicating by utilizing this continuous dataset, the proposed framework accomplishes the most noteworthy precision with 97.93%.

Keywords: Software Development, SVM Classifier, Reverse Engineering, Python Code, Software Design, Python Classifier, SVM Approach, Application Software.

Abstract: Elections are a crucial part of any democratic system, and it is essential to ensure that the voting process is conducted in a fair and transparent manner. In traditional paper-based elections, the process is time-consuming, resource-intensive, and prone to errors. In this context, the use of technology can significantly improve the efficiency, security, and accuracy of the voting process. In this project, we propose an online voting system that uses face recognition technology to identify and authenticate voters. The proposed system allows voters to cast their votes remotely, eliminating the need for physical ballot boxes and reducing the cost and time involved in the voting process. The system works by capturing the facial image of the voter and passing it to the server unit for verification. The server compares the facial image with the information stored in the database and verifies the identity of the voter. If the identity is verified, the voter is allowed to cast the vote; otherwise, an error message is displayed on the screen, and the person is not allowed to poll the vote. The system is designed to be secure and tamper-proof. The use of face recognition technology makes it difficult for anyone to impersonate another person and cast a fraudulent vote. The system also ensures that each voter can cast only one vote, and the voting process is conducted in a transparent and fair manner. Overall, the proposed online voting system offers numerous advantages over traditional paper-based voting systems, including increased efficiency, reduced cost, improved security, and transparency

Abstract: Cyberbullying has emerged as a pervasive and harmful phenomenon in the age of technology, affecting individuals of all ages and across various online platforms. As the prevalence of cyberbullying continues to grow, the need for effective detection systems becomes crucial to protect and support victims. A comprehensive survey on methodologies and challenges related to cyberbullying detection systems is presented in the paper. The survey explores a diverse array of techniques and approaches employed in cyberbullying detection, including machine learning, natural language processing, social network analysis, and sentiment analysis. Various data sources and features utilised for detection are examined, such as text-based content, user behaviour patterns, and social interactions. Additionally, the paper discusses the challenges faced by cyberbullying detection systems, such as the evolving nature of cyberbullying tactics, the contextual complexity of online interactions, and the ethical considerations surrounding privacy and bias. The study expands on prospective topics for further research and development and points out the shortcomings of the approaches now in use.

Keywords: cyberbullying detection systems, social network analysis, sentiment analysis, natural language processing

Abstract: The rise and subsequent crash of the blockchain era have transformed cryptocurrencies into investment assets, necessitating accurate forecasts to guide investment decisions due to their highly unpredictable nature. While existing studies have utilized machine learning to predict Bitcoin prices with improved accuracy, few have explored the applicability of different modelling techniques to diverse data formats and dimensional attributes. This research focuses on categorizing Bitcoin prices into daily and high-frequency intervals, with the goal of anticipating cryptocurrency prices at various frequencies using machine learning techniques. For daily price prediction, a comprehensive set of high-dimensional aspects, including property and network characteristics, trading and market indicators, attention metrics, and gold spot prices, are leveraged. On the other hand, 5-minute interval price prediction relies on fundamental trading data obtained from cryptocurrency exchanges. Given the influence of major organizations on price control and the volatile nature of the cryptocurrency market, precise forecasting methods that consider factors such as market capitalization, maximum supply, volume, and circulating supply are essential. Deep learning techniques, such as recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent units (GRU), are employed as effective models for training the data. The proposed approach is evaluated using benchmark datasets and implemented in Python. The results demonstrate the efficacy of the suggested methodology in achieving accurate predictions. Consequently, neural networks, as intelligent data mining technologies, have gained widespread adoption in various sectors over the past decade, offering valuable insights into cryptocurrency price forecasting.

Keywords: LSTM, RNN, Cryptocurrency, blockchain.

Abstract: In our rapidly evolving world, technology advancements continue to shape our daily lives, prompting a shift towards modern and simplified techniques to accommodate our busy lifestyles. This project focuses on the utilization of machine learning algorithms, including linear regression, logistic regression, decision trees, SVM, Naive Bayes, KNN, K-means, Random Forest, dimensionality reduction algorithms, gradient boosting algorithms, and AdaBoosting algorithms, to streamline analytical work and prediction tasks. The system offers a user-friendly web interface that facilitates the loading of CSV and Excel data, allowing users to select and apply their preferred algorithm to suit their specific requirements. The system cleans the received data using data cleaning algorithms, and the user is presented with a list of options to assign algorithms to specific columns in the file. Graphs and charts generated by Google Charts based on the output of the predictions can be downloaded by the user. Additionally, the system enables users to visually compare two Excel or CSV files using charts, aiding in data analysis and comprehension. The application is developed using Django, Google Charts, Pandas, NumPy, and a MySQL database. Users can maintain distinct accounts to access their previous analytical work history conveniently. The application supports transforming various types of data into charts, allowing users to select and download the required charts.

Keywords: Machine Learning, Big Data, Charts, Data Analysis.

Abstract: With the growing demand for reliable and efficient power systems, the integration of cloud computing technology has emerged as a promising solution. This paper presents a unique perspective on the adoption of cloud computing in the power industry, exploring novel approaches and considerations for leveraging cloud infrastructure and services. Drawing from in-depth research and industry expertise, this paper delves into the transformative potential of cloud technology in power systems. It goes beyond the traditional discourse by examining unconventional use cases, such as dynamic load management, predictive maintenance, and demand response optimization, where cloud computing offers significant advantages. Addressing the concerns of power system practitioners, the paper explores the challenges and risks associated with cloud adoption, emphasizing the need for robust security measures, data privacy, and regulatory compliance. It provides novel insights and practical recommendations to guide industry professionals in navigating the complexities of cloud implementation while maintaining system integrity. By analyzing the potential economic and environmental benefits, this paper demonstrates how cloud computing can contribute to achieving a greener and more resilient power grid. It showcases innovative approaches, such as edge computing and distributed intelligence, that leverage the cloud to enable real-time monitoring, predictive analytics, and optimized resource allocation. Ultimately, this paper aims to inspire power industry professionals to embrace the transformative power of cloud computing. It encourages a forward-thinking mindset and promotes collaboration across sectors to unlock new possibilities for enhancing the efficiency, reliability, and sustainability of power systems.

Keywords: Cloud Computing, Power Systems, Dynamic Load Management, Predictive Maintenance, Demand Response Optimization, Security, Data Privacy, Regulatory Compliance, Collaboration, Interoperability, Scalability, Adaptability, Sustainable Energy, Edge Computing, Distributed Intelligence, Efficiency, Reliability, Sustainability.

Abstract: The demand for sustainable agricultural practices has led to the development of advanced technologies in precision agriculture. This research presents the design and implementation of a simple follow-me bot that serves as a seeder and uses ultrasonic sensors, Arduino microcontrollers, and servo motors. The goal of this research is to automate the process of seed placement, increase efficiency, and reduce human labor. The proposed system uses ultrasonic sensors to detect obstacles and determine the optimal path for the Follow-Me-Bot. By integrating the Arduino microcontroller with the sensors, real-time data acquisition and processing is achieved, allowing the bot to navigate through the designated area while avoiding potential obstacles. In addition, servo motors are used to actuate the seed placement mechanism and ensure precise and consistent seed placement. This research outlines the methodology used to design the follow-me bot and provides a detailed description of the hardware components and their interconnections. The programming logic and algorithms used to control the bot movement and seed delivery mechanism are also discussed, highlighting the seamless integration of the ultrasonic sensors and servo motors with the Arduino platform. Experimental results demonstrate the effectiveness of the proposed system, as the Seed Sower bot successfully navigates through an obstacle course while accurately sowing seeds. The bot's ability to adapt to changing environmental conditions and efficiently perform seeding demonstrates its potential for automating agricultural operations and improving productivity. This research is significant as it lays the foundation for the development of advanced autonomous agricultural systems. The integration of sensor-based navigation and precise seed application mechanisms paves the way for future innovations in precision agriculture and provides a sustainable solution to optimise crop production while reducing labor-intensive processes. Keywords: follow me bot, seed drill, ultrasonic sensors, Arduino microcontroller, servo motors, automation, precision agriculture, sensor based navigation, autonomous agricultural systems.

Keywords: Performance prediction, running, athletes, Smart watches, supervised machine learning algorithms.

Keywords: Machine learning, detection, and classification; static analysis;

Abstract: The technological development and researches have been improving in our daily life, the human computer interaction has been becoming the must source in our everyday life. These technologies will help the visually impaired to take part in some of their social activities. So, in order to mix with the surroundings and society and also to be independent in doing their daily routine activities, this project has been initiated as a good start for the blind people. Therefore, there should be an assistive device for the visually impaired people which would allow the blind people to easily navigate or make use of the functionalities of the device to mingle with other people in the society. Cash Recognition and fake note detection for Visually Impaired is a project dedicated towards blind people living. In recent years, deep learning has become the most popular research direction. This project mainly trains the dataset through neural networks. There are many different models that can be used in this research project. Throughout these models, accuracy of currency recognition can be improved.

Abstract: The risk of fire is unavoidable, and it can seriously harm people's lives and property. Virtual reality fire detection can be used to detect fires more effectively while avoiding many of the problems that plague other fire detection techniques now in use. This method is more effective than before and helps shorten the time it takes to find fires. The researcher has sought to analyze virtual reality's use in fire detection through this study in an effort to learn more about its benefits, effectiveness, and results.  

Keywords: Fire Detection, Machine learning, advantages, efficiency, virtual reality, image

Abstract: Countless people use the roadways at all hours of the day and night. Sleep deprivation affects all sorts of drivers and long-distance travelers. Accidents occur as a result of this. The number of accidents caused by tiredness is substantially larger than the number of accidents caused by drunk driving, according to several research. As a result, precautions must be taken to avoid such mishaps. The Detecting Driver Drowsiness system is one such measure. Detecting Driver Drowsiness system is a car safety technology that helps to save the driver’s life by preventing accidents when the driver is getting drowsy. The system will use OpenCV with eyelid-related parameters to gather the images from the webcam. The system can identify face landmarks and extract eye landmarks before computing the Eye Aspect Ratio (EAR) and comparing it to a threshold...

Abstract: Pipelines are very significant tool as they are used in many different industries for various applications such as transportation of gas, water, fuel, oils, etc. Over time, they are prone to aging, corrosion, cracks, mechanical damage etc., and ignorance of these problems leads to accidents which incurs huge losses in terms of both economy and lives. This highlights the inevitable need to inspect pipes at a regular interval for the purpose of security and improved efficiency in industrial plants. Now there is many ways of inspecting pipes such as X-rays, magnetic particle inspection method etc., but these methods do not give a full proper internal inspection of pipes. This pipe inspection robot aims at detecting the exact location of leakage and clearing the blockages and thus removing human factor from labour intensive and dangerous work, thereby reducing the number of accidents that happen due to the lack of regular inspection.

Keywords: Pipeline inspection, Crack detection and Blockage clearance.

Abstract: Keeping food fresh is essential for preserving food quality, cutting waste, and minimizing potential health hazards. Traditional techniques of evaluating food freshness frequently depend on subjective sensory evaluation, which can be labor-intensive and error-prone. But thanks to recent technological developments, there are now many methods for determining the objective freshness of food. The state-of-the-art techniques and tools for determining the freshness of food, such as spectroscopy, gas sensors, machine learning, and computer vision, are thoroughly reviewed in this study paper. The report also covers the advantages and difficulties of using these strategies in the food business.  

Keywords: Food Freshness Detection, Machine earning, advantages, efficiency, IOT, sensors

Abstract: Because of flexibility of application and highest performance, thrills, and middle end for an obtained extensive market. As a fundamental memory structure. The FIFO is widely used in FPGA based projects. But limited by the resources in chip and imperfections of development tools, the problem of insufficient memory while the overall capacity is often enough occurring in implementation of multi-channel FIFO. This project surveys various occasion applications of FIFO and puts forward the implementation of FIFO Memory Using Shift registers.

Keywords: FIFO, NoC, FPGA, Synchronous, Asynchronous,

Abstract: This project details the planning and design of a solar-powered electric vehicle charging station that solves the dual problem of expensive gasoline and harmful emissions. The number of countries where electric vehicles are on the roads continues to grow. Electric vehicles are not only environmentally friendly, but have proven to help reduce transportation costs by replacing expensive fuels with much cheaper electricity. Here, designing an electric vehicle charging infrastructure creates a novel and effective answer to this problem. Electric vehicles can be charged while driving, so there is no need to stop for charging. The system is powered by solar energy. No additional power supply required. Solar panels, batteries, transformers, regulator circuits, copper coils, AC/DC converters, atmega328P controllers and LCD displays are used to build this system. The technology is based on the idea that electric vehicles can be charged without stopping at a charging station. This technology therefore proves the feasibility of an on-road solar-powered wireless charging system for electric vehicles

Keywords: Wireless charging system, Electric vehicle, Solar power, Transmitting and receiving coil.

Abstract: The presence or absence of different pollutants in seawater, such as oil, sedimentation, sewage, nutrients, heavy metals, and thermal pollution, is referred to as water quality. The process of measuring the saltwater's temperature, salinity, density, light transmission, and amount of dissolved oxygen is known as water quality monitoring. This article presents this work, which focuses on an autonomous system that uses a solar-powered water boat to evaluate the quality of saltwater. The boat has sensors that can measure temperature, conductivity, pH, and water turbidity, this cutting-edge boat provides quick and accurate testing of saltwater quality. The evaluation emphasizes the importance of designing a system that is not only reasonable but also secure and dependable, harnessing the benefits of solar energy for long-term operation.  Key Words:  Solar boat, Water quality, Sensors

Abstract:  In this paper we present a novel approach for segmenting as well as estimating the volume of various brain tissues like white matter, gray matter, hippocampus Cerebro spinal fluid etc. In the study of brain morphometry, it is accepted that a relationship exists between brain structure and function, both normal and abnormal. One descriptor of morphometric structure is volume. Volumetric measures introduce a level of precision in the estimation of the size of white matter, grey matter, hippocampi and other tissues that is not available simply by visually inspecting a set of MR images. For that we need to perform MR image acquisition and image processing

Keywords: Threshold-based segmentation, Magnetic resonance, K-Means algorithm, Cavalieri’s estimator

Abstract: Nowadays, Most of IT industry preferred Cloud Computing technology to deliver data services at a nominal cost with little efforts and good level of scalability. During this exponential growth of cloud computing technology, security arisen as the major challenge. Researchers have made tremendous progress in comprehending the potential of blockchain technology for the cloud, finding risks, and suggesting mitigations. Even if blockchain has better security characteristics, its integration with cloud computing platforms must take into account its drawbacks, scalability issues, and privacy concerns. To further improve the security and usefulness of blockchain technology in the cloud, research and innovation must continue. Blockchain ensures security in all aspects as confidentiality and authenticity. The current review study looks at the function that block chains play in cloud-based security systems. However, there are a number of studies that make use of block chains, despite the fact that it has been noticed that block chains have significant limitations. On the other side, research that has been done on the cloud, has inadequate security features. It's possible that using blockchain technology with cloud computing can make cloud contents more secure.

Keywords: Blockchain, Security, Cloud computing, Nodes

Abstract: Plant pathology is the area of agricultural science that focuses on the investigation of plant diseases brought on by pathogens like nematodes, bacteria, viruses, and fungi. Plant disease identification is essential for efficient management and control since it allows for prompt action and stops crops from suffering additional harm. Growing technological sophistication and a better understanding of the interactions between plants and pathogens have led to major improvements in plant pathology and diagnosis methods in recent years. The objective of the plant pathology and diagnosis project is to develop and implement innovative techniques and tools for accurate and efficient detection, diagnosis, and management of plant diseases. The project's goal is to increase our capacity to recognize and lessen the effects of plant diseases on agricultural systems, which will help to improve crop health, production, and sustainability. Scope of the project is that it tries to address issues with newly emerging diseases, resistance to traditional control measures, and the influence of environmental variables on the onset of disease.

Abstract: SRAM, or Static Random Access Memory, is one of the fundamental elements of the digital world. In general, it uses a tremendous quantity of energy. A lot of SRAM research is thus being done in the areas of power dispersion, RAM chip size, and supply voltage requirements. This work considers SRAM analysis for low power applications in terms of Static Distortion Margin, The Information Retention Voltage, which is Read Margin (RM), and Write Margin (WM). One of the most crucial factors in memory design is static noise margin (SNM), which has an impact on both read and write tolerance. The threshold voltages of the SRAM cell's negative oxide metal transistor (NMOS) and positive metal oxide semiconductor (PMOS) components are correlated with SNM. High Write and Read Snr Margin are also major design obstacles. The challenge of the 6T SRAM project using 180nm, 90nm and 45nm technologies at Cadence Virtuoso is to address the scaling challenges of SRAM designs and explore the possibilities offered by different technology nodes. The focus is on optimizing the performance and energy efficiency of the 6T SRAM cells considering the effects of scaling and process variation. The purpose of this project is to analyze the trade-offs between power consumption, access time, and stability at each technology node, identify optimal design configurations, and develop guidelines for efficient and reliable 6T SRAM design. By leveraging Cadence Virtuoso's capabilities, this project aims to provide valuable insight into the development of robust, high performance SRAM cells in 180nm, 90nm and 45nm technologies.

Keywords: Noise Margin, Read Margin, SRAM, 6T-SRAM, Virtuoso, Write Margin

Abstract: An overview of the study and application of object identification utilising LiDAR (Light identification and Ranging) technology is provided in this project review paper. For object detection and localization in a variety of applications, including autonomous vehicles, robotics, and environmental monitoring, LiDAR has emerged as a potent sensing technique. The report also includes real-world case studies and applications where LiDAR-based object detection has been used successfully. In order to develop the field of object detection utilising LiDAR technology, it highlights active research projects, future directions, and prospective areas for improvement. For researchers, engineers, and practitioners interested in learning about and using LiDAR for precise and trustworthy object detection, this project review paper is a great resource.

Abstract: Measuring air quality is a crucial step in raising public awareness of the need to protect future generations' right to a healthy existence. This study underlines the need of measuring air quality using sensors while concentrating on how to accomplish so.  Based on this, the Government of India has already taken some steps to outlaw motorcycles powered by single- and two-stroke engines, which are comparatively releasing significant levels of pollution. Using various gas sensors, we are attempting to create the same system.

Keywords: PPM, VOC, LPG, AQI, MQ

Abstract: Power computation is crucial for evaluating and enhancing the energy efficiency of contemporary computer systems. Systems for bus encoding and decoding are becoming essential instruments for enhancing the accuracy and efficacy of power analysis. In order to improve the accuracy and efficiency of power analysis, this abstract offers a thorough review of bus encoding and decoding systems created especially for power calculations. Bus encoding algorithms for power computation modify data before it is transported across the bus in order to lower communication power consumption. Utilising encoding techniques such bus inversion coding, transition minimising codes, or differential signalling, redundant or repetitive data patterns are detected and efficiently represented to reduce power swings and dynamic power consumption. These encoding methods reduce bus activity and signal modifications. Keyword: CMOS, Bus Encoding & Decoding, Power Computation, Energy Efficiency.

Abstract: Due to the current miniaturization of wireless devices, the use of wearable textile materials as antenna substrates has been an attractive area of research. A crucial ally for remote health monitoring is wireless technology. A wearable antenna is a piece of clothing that is used for tracking and navigation, mobile and wearable computing, and public safety wireless communication applications. It can also be utilized to keep an eye on the healthcare system. The introduction of high-speed smartwatches with integrated Bluetooth antennas, smart glasses with integrated Wi-Fi, GPS, and IR antennas, body-worn action cameras with Bluetooth and Wi-Fi connectivity, and tiny sensor devices in sports shoes with Bluetooth and Wi-Fi connectivity are some of the key developments that have accelerated its growth.

Abstract: Millions of individuals worldwide are afflicted with the highly contagious infectious disease known as tuberculosis (TB). For the disease to be effectively treated and controlled, early and accurate TB detection is essential. Due to its low cost and wide availability, chest X-ray imaging is frequently used to diagnose tuberculosis. However, it takes skill and can take some time to analyse X-ray images . With the help of X-ray pictures and ResNet, a deep learning model renowned for its outstanding performance in image classification tasks, this study intends to create an automated system for the identification of tuberculosis. The suggested technique makes use of the detailed information seen in chest X-ray pictures to spot TB infection symptoms like the existence of pulmonary lesions, nodules, or cavities.

Abstract: Monitoring plant health and finding plant diseases are essential for sustainable agriculture. Disease identification and preventative strategies are a significant challenge because of the rapid rise in a variety of diseases and the low level of knowledge of these ailments. Early discovery gives more time to implement the proper preventative measures. Since arecanut plants are very vulnerable to a variety of pests and diseases, the suggested method is used to identify arecanut leaf diseases and divide them into four groups: healthy, diseased leaves with yellow spots, leaf blight, and yellow leaves. The project main goal is to create a YOLO model that analyses leaf images and can be used to find plant diseases.

Keywords: YOLO, Dataset, Epochs, Pixels.

Abstract: Coal mines are prone to a lot of fatalities. In order to reduce fatalities, it is better to keep the mine under constant monitoring which can send alerts when there is a change in parameters like temperature, fire and leakage of gases like methane which are likely a potential fatality. In order to avoid it we propose a monitoring system that can monitor basic safety measures and regulate such parameters. All the sensors can be assembled into a single unit and then placed in a coal mine for it to be monitored constantly.

Abstract: Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. Although the identity of a node can be verified through cryptographic authentication, conventional security approaches are not always desirable because of their overhead requirements. In this paper, we propose to use spatial information, a physical property associated with each node, hard to falsify, and not reliant on cryptography, as the basis for (1) detecting spoofing attacks; (2) determining the number of attackers when multiple adversaries masquerading as a same node identity; and (3) localizing multiple adversaries. We propose to use the spatial correlation of received signal strength (RSS) inherited from wireless nodes to detect the spoofing attacks. We then formulate the problem of determining the number of attackers as a multi-class detection problem. Cluster-based mechanisms are developed to determine the number of attackers. When the training data is available, we explore using Support Vector Machines (SVM) method to further improve the accuracy of determining the number of attackers. In addition, we developed an integrated detection and localization system that can localize the positions of multiple attackers. We evaluated our techniques through two testbeds using both an 802.11 (WiFi) network and an 802.15.4 (ZigBee) network in two real office buildings. Our experimental results show that our proposed methods can achieve over 90% Hit Rate and Precision when determining the number of attackers. Our localization results using a representative set of algorithms provide strong evidence of high accuracy of localizing multiple adversaries.

Abstract: One of the most important functional components in any processor is the Multiply-Accumulator (MAC). These functional blocks are currently employed in Fast Fourier Transform (FFT), Finite Impulse Response (FIR) filtering, convolution, and a variety of other DSP and DIP applications. Multiplication and addition are two basic operations that, when compared to other functional blocks, require more hardware resources and computing time. Speed of the multiplier and adder blocks determines the speed of the processor. In this work, a fixed-point power-efficient multiplier and an optimal delay adder block for 2D image convolution have been built and integrated into the MAC unit. Effective multiplication and accumulation units are required due to the demand for high-performance computing systems. The performance of the entire system can be greatly impacted by MAC unit delays, too. In order to find methods that efficiently reduce MAC unit delay, this study compares several adder and multiplier architectures using Xilinx software to handle this difficulty. The results of this study can help researchers and system designers choose optimal designs that balance performance and resource usage, which will ultimately result in increased efficiency and accelerated processing in a variety of computational domains.

Keywords: MAC, Delay, Adders, Multipliers.

Keywords: Face recognition, Object detection, YOLO, Image Processing

Abstract: A powertrain model is a simulation of the individual components of an electric vehicle propulsion system. It examines the interaction between electric motors, power electronics, batteries, and other auxiliary subsystems. The model uses mathematical equations and algorithms to provide insight into performance, efficiency and behavior. Engineers can use it to evaluate various factors, including battery capacity and engine specifications, and predict performance under various driving conditions. Powertrain models are useful tools for optimizing electric vehicle driving pleasure, energy efficiency and range.

Abstract: Advancements in wearable sensor technology have paved the way for innovative health monitoring systems capable of assessing multiple vital signs simultaneously. We introduce a comprehensive Health Monitoring System (HMS) integrating a pulse sensor, electrocardiogram (ECG) sensor, and pulse oximetry (SpO2) sensor to provide a comprehensive assessment of an individual's health status. The pulse sensor enables continuous measurement of heart rate and pulse waveform characteristics, providing insights into cardiovascular health. The ECG sensor records the electrical activity of the heart, allowing for the detection of abnormalities such as arrhythmias and myocardial ischemia. The SpO2 sensor measures blood oxygen saturation levels, providing crucial information about respiratory function and potential hypoxemia. The system allows healthcare professionals to remotely monitor patients' vital signs, facilitating early intervention and reducing the burden on healthcare facilities.

Abstract: This article describes an RF-to-DC energy harvesting circuit designed using a microstrip patch antenna, impedance matching network, and voltage doubler rectifier. The design was implemented in Keysight ADS software. The microstrip patch antenna operates at 915MHz, and the impedance-matching network ensures efficient power transfer. The voltage doubler rectifier achieves an output voltage of 2.305 V and a current of 0.006 mA and an input of 17 dBm and a Radiation intensity of 60%. This research demonstrates a promising method for collecting radio frequency and establishes the foundation for future research into low-power applications

Keywords: Antenna, Energy harvesting, Impedance matching, Impedance-matching network, Voltage doubler rectifier

Abstract: In this review study, the examination of battery charging and discharging focuses on methods to improve battery performance. Overcharging, undercharging, temperature regulation, and safety concerns are only a few of the challenges associated with battery charging that are discussed. The advantages, disadvantages, and applicability of various charging techniques for various battery chemistries are assessed. The effectiveness of the battery and the system as a whole is assessed in respect to various discharge techniques, and discharge-related difficulties are noted. Improvements in the charging and discharging processes are investigated, as well as battery integration with grid interactions and renewable energy sources. Overall, this assessment provides insightful data on the charging and discharging state of batteries and makes recommendations for enhancing battery performance across a range of applications.  

Keywords: Battery charging, Battery discharging, state of charge (SoC), state of health (SoH), battery status parameter, charging techniques, discharging strategies, battery monitoring, energy management, renewable energy integration, battery performance optimization.

Keywords:   IoT(Internet of things), Smart water bottles, Wellness device.

Abstract: This review paper focuses on the application of object detection techniques in e-waste management. Electronic waste (e-waste) poses environmental and health risks, necessitating efficient handling and disposal methods. Traditional approaches face challenges, highlighting the need for automated solutions. The paper explores the role of computer vision and object detection algorithms in identifying and categorizing e-waste items. Various techniques of deep learning models, are examined for their effectiveness in e-waste object detection. Advantages, such as increased accuracy and efficiency, are discussed. Additionally, the paper briefly touches upon the potential benefits of integrating object detection with robotic arm systems for enhanced e-wa ste separation processes. The review provides insight into current research advancements and highlights future prospects for object detection in large-scale e-waste management. These technologies offer promising avenues for automating e-waste identification and improving the overall efficiency of e-waste management systems.

Keywords: E-Waste,Object Detection,Robotic arm

Keywords: Supply World Wide Web (www), web attacks, web attacking http requests, HTML, JavaScript or SQL attacks.

Abstract: The main objective of medical image processing field is to design computational tools which will assist quantification and visualization of remarkable pathology and anatomical structure . A medical condition known as diabetic retinopathy occurs when fluids from blood vessels seep into there in a of the human eye , causing damage to the retina. The detection of the optic disc in pictures of the retinal fundus quantitative study of the evolution of its figure the next entplaya significant part in diagnosing different pathologies, and the abnormalities related to the retina of human eye. Maximum of there abnormalities which are related to optic disc may leads to a structural changes in theinner and the outer area the optical disc. The precise border of the optical disc is obtained by calculating the region of interest and applying an in no vative morphological transformation based adaptive thresholding. The presented technique helps to reduce the process area needed for segmentation techniques leading to a distinguished performance enhancement and reducing the amount of the needed computational cost for each retinal fundus image. The proposed technique has been evaluated on publicly available data sets of retinal images which are DIARETDB1, DRIVE, HRF, DRIONS-DB, IDRiD and STARE, and a remarkable improvement has been found over the existing techniques in terms of accuracy and processing time.

Keywords: Retinal image analysis, Regionof-Interest, CLAHE, optic disc, morphological operation, segmentation, and classification.