VOLUME 13, ISSUE 8, AUGUST 2024

Abstract: This study explores the integration of Wireless Sensor Networks (WSN) and Machine Learning (ML) in smart farming to address critical agricultural challenges. By leveraging real-time data collection and advanced analytical tools, the research demonstrates the potential of ML algorithms—Decision Trees, Naive Bayes, Support Vector Machines (SVM), Logistic Regression, and Random Forests—in enhancing crop management, including yield prediction, soil quality assessment, and pest and disease detection. The study finds that Naive Bayes achieves the highest accuracy and balanced precision-recall metrics, while ensemble methods like Random Forests effectively reduce overfitting and improve prediction accuracy. Despite the promising results, the research identifies challenges such as data accessibility, model integration, and user interface design that must be addressed to fully realize the potential of smart farming technologies. Overall, the findings provide valuable insights into optimizing resource utilization, reducing crop losses, and promoting sustainable farming practices, thereby supporting global food security and economic stability.

Keywords: Smart Farming, Machine Learning, Supervised Learning, Data Drive Decision

Abstract: Diabetes affects millions of people around the world with a significant economic, physical, social and psychological burden leading to avoidable suffering, disability and sometimes mortality. This significant burden is exacerbated in less developed countries by the chronic and seemingly intractable dearth of qualified medical practitioners. Given accurate diagnoses and predictions, efficacious life-saving therapies could be developed. This paper presents a module for the diagnosis of diabetes within the framework of a modular, extensible and comprehensive artificial intelligence-driven healthcare system.

Keywords: Diabetes, Artificial Intelligence, Deep Learning, Healthcare System.

Abstract: The proposed web application aims to streamline the vehicle ownership transfer process and vehicle fitness certificate management by integrating the Regional Transport Office (RTO) and police departments. This integration will enhance security, transparency, and efficiency, leveraging modern encryption techniques and cloud storage solutions. The application will automate vehicle ownership transfers, providing a seamless process that links the RTO and police departments. A user-friendly interface will allow users to initiate and track transfer requests, ensuring secure document management and storage.The system will also automate the creation of vehicle fitness certificates in PDF format, encrypting them before storage in AWS S3. Users and authorities can verify the authenticity of these certificates. Keywords: AES Encryption, PDF File, AWS S3, Data Security, RTO, Police Department.

Abstract:  Education system plays a vital role in any one’s carrier. Students' health is an important research topic today because they are the cornerstone of our society. Researchers have used various technological breakthroughs to address schoolchildren's and college/university students' health issues, and machine learning is now frequently employed [1]. However, to understand the efficacy of machine learning and progress in student health research, a concise review of the influence of machine learning on student health is required, which the proposed work provides [3]. The primary objective is to examine which of the students' health concerns are efficiently addressed by machine learning algorithms and the outcomes of the approaches. The project also discusses what leads students to perform poorly in schools, colleges, and universities and if machine learning will improve student health in the future. The main aim of the project is to find how student health academic problems effects their performances. supervised learning algorithms applied to process the educational data and generates correlation between student health problems and academic performances. In this proposed system we develop automation for education sector [5]. Proposed system is a browser-based application meant for a college developed using Microsoft technologies such as Visual Studio, C# and SQL Server.

Keywords: mental health factor, academic performance.

Abstract: This work contains the classification of patients in an Emergency Department in a hospital according to their critical conditions. Machine learning can be applied based on the patient’s condition to quickly determine if the patient requires urgent medical intervention from the clinicians or not [1]. Basic vital signs like Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Respiratory Rate (RR), Oxygen saturation (SPO2), Random Blood Sugar (RBS), Temperature, and Pulse Rate (PR) are used as the input for the patients’ risk level identification [2]. High-risk or non-risk categories are considered as the output for patient classification. Machine learning techniques such as Gaussian NB, KNN or DT are used for the classification. We'll use a variety of supervised machine learning methods before deciding which one is best for the model. Existing systems rely on classical learning models, which are inefficient and imprecise. They aren't as accurate as the proposed model and take a little longer to process. There are many research works on this topic where they have built models and shown results generated using R language, Python language and data science tools. But all these works are just models, cannot be used as application useful in real time. In our project work we build an application with model that can predict high risk patients and low risk patients in an emergency department and provides doctors with the information of how to handle patients and treat better [5]. Proposed system is a real time medical system useful for hospitals and doctors and built using Microsoft tools such as Visual Studio tool and SQL Server tool.

Keywords: Patients, classification, high risk and low risk, doctor.

Abstract: Now a day’s patient deaths increasing rapidly because of many chronic diseases and many other factors like diseases, lack of medical facilities, resources, medicines etc. According to the number of mortality from public health statistics data of the Strategy and Planning Division, had been increasing consecutively every year, so health service is the most important task to reduce the mortality rate for the country population. It’s a challenging factor to reduce the death rates in a hospital. So we need a system which will automatically detect the reasons for death rates. The purpose of this project is to show an association between mortality rates and health services or resources by using unsupervised machine learning algorithms. This is what we are doing in the proposed system where we find the relationship between hospital resources and mortality rates. We build a real time system using Microsoft technologies such as Visual Studio and SQL server to help hospitals to reduce death rates. We consider many parameters like Neurologist, Cardiologist, Gynecologist, Orthopedics, Surgeon, Physician, Beds, ICU, Nurses and Mortality Rate. Also system finds the most important parameter which increases the death rates using ML algorithms.

Keywords: Data Science, Machine Learning, Association Learning, Mortality Rates, Visual Studio, SQL Server

Abstract: In The Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects movement, muscle control, and balance. Early diagnosis of PD is essential for timely intervention and management of the disease. This project proposes a novel PD detection system that leverages spiral drawings and convolutional neural networks (CNNs) for accurate and efficient diagnosis. The proposed system consists of two main components: data collection and analysis. Patients are instructed to draw spirals using a digital pen or touchscreen device, capturing the subtle motor impairments characteristic of PD. These drawings are then pre processed and fed into a CNN model for feature extraction and classification. The CNN model is trained on a dataset of spiral drawings from both PD patients and healthy individuals. Transfer learning techniques are employed to fine-tune a pre-trained CNN architecture, enhancing the model's ability to detect subtle patterns indicative of PD. The developed system is implemented using Python and Flask, providing a user-friendly web interface for data collection and analysis. The system aims to improve the accuracy and efficiency of PD detection compared to existing methods, offering a non-invasive and cost effective solution for early diagnosis and monitoring of PD patients.

Keywords: Parkinson, CNN, Deep learning, Spiral Drawings.

Keywords: Artificial Intelligence, Machine learning, Cloud security, Threat Defense, Encryption, Cybersecurity, Advanced Threats

Abstract: The proposed web application aims to address communication barriers faced by individuals with hearing impairments by converting spoken language into Indian Sign Language (ISL) visuals and vice versa. The system utilizes speech recognition via the Google Speech API, natural language processing (NLP) for text preprocessing, and dictionary-based machine translation to generate ISL visuals, which are displayed as images for real-time communication. Designed specifically for the Deaf community in India, this application empowers users by enabling smoother interactions and reducing social isolation. By promoting inclusivity, this project enhances access to essential services and fosters greater participation in everyday activities for those who rely on ISL Keywords: Indian Sign Language (ISL), Speech Recognition, Natural Language Processing (NLP), Machine Translation.

Abstract: This work presents a comprehensive analysis of Double-Gate Tunnel Field-Effect Transistors   (DG-TFETs) employing III-V semiconductor materials, utilizing the Non-Equilibrium Green’s Function (NEGF) formalism. TFETs, which operate based on quantum tunneling mechanisms, offer promising solutions for low-power electronic applications due to their potential for achieving steep subthreshold swings and high ON-state currents. The NEGF formalism, known for its robustness in modeling quantum transport phenomena, is applied to study the performance of DG-TFETs with III-V materials such as GaAs and InP, which are known for their high electron mobility and narrow bandgaps. Key performance metrics, including ON-state current (I_ON) and OFF-state current (I_OFF) are evaluated by utilizing different gate metal work functions. Our analysis demonstrates that electric field and drain current of III-V DG-TFETs exhibit significantly improved I_ON and reduced I_OFF through optimized band alignment. All simulation have been carried out using the SILVACO technology computer aided design (TCAD) tool.

Keywords:  Double-Gate Tunnel Field Effect Transistor (DG-TFET), Non-Equilibrium Green’s Function (NEGF), Work function, ON-State current, OFF-State current, Doping concentration.

Abstract: Brain monitoring systems have gained significant attention due to their potential in medical diagnostics, neuroscience research, and brain-computer interfaces. This paper presents a real-time brain monitoring system utilizing artificial intelligence (AI) to enhance the accuracy and efficiency of brain activity analysis. The system employs advanced machine learning algorithms to process electroencephalogram (EEG) signals, providing immediate insights into neural activity. Our results demonstrate that the AI-driven approach significantly improves the detection and classification of brain states compared to traditional methods. This study offers valuable contributions to the development of more responsive and precise brain monitoring technologies.

Keywords: Real-time monitoring, EEG, Artificial Intelligence, Brain activity, Healthcare

Abstract: Breast cancer is a dangerous illness that mostly affects women. Increasing survival rates requires early detection of the disease. Artificial intelligence developments, especially in deep neural networks, have greatly improved breast cancer detection. The suggested LMHistNet, a hybrid convolutional neural network model intended to categorise microscopic images of breast tumour tissue acquired during excisional biopsy, is one such development. Levenberg-Marquardt optimization and asymmetric convolutions are used by LMHistNet to categorise breast cancer images into eight subclasses and a binary (benign or malignant) category. To achieve adaptive feature refinement, the model integrates a convolution block attention module. Additionally, batch normalisation is used to normalise input features and speed up convergence. Additionally, this method reduces internal covariance shifts during training. The model's convergence is further improved by the application of a hinge loss function, which makes it a useful instrument for precise breast cancer detection. By extracting various features from histopathological pictures, the LMHistNet model effectively performs binary and eight-class classifications that are both independent and dependent on magnification. LMHistNet's efficacy was shown in a study containing 7,909 histopathological pictures, of which 2,480 were benign and the remainder malignant. Using loss and accuracy curves, the model's performance was assessed at several magnifications (40X, 100X, 200X, and 400X). According to the findings, the model scored 88% accuracy, 89% precision, 88% recall, and 88% F1 score for multiclass categorization into eight subtypes. The model achieved remarkable accuracy, precision, recall, and F1 score of 99% for binary classification, which discerns between benign and malignant tissues. These results highlight the model's strong performance in correctly categorising photos of breast cancer at various magnifications and classes.

Keywords: Breast cancer, convolutional neural networks (CNN), deep learning, histopathological images, Levenberg–Marquardt, transfer learning.

Abstract: Agriculture plays a vital role in global food security, and farmers constantly seek ways to optimize their crop selection to maximize yields and profits. However, selecting the most suitable crop for a specific region or environment can be challenging due to various factors such as climate, soil conditions, and water availability. The Crop Recommendation System addresses this challenge by leveraging machine learning algorithms to analyze environmental data and provide personalized crop recommendations.

Keywords: Crops, predict, Environment

Abstract: The "Vision For Blind Using YOLOv8 Algorithm" project is an innovative approach designed to enhance the autonomy and accessibility of visually impaired individuals. In India, where a significant portion of the population faces visual impairment, daily tasks such as currency identification and reading printed materials pose considerable challenges. This project addresses these issues through an Android application that leverages advanced deep learning techniques to provide multiple functionalities. The app employs the YOLOv8 algorithm for accurate and real-time detection of currency notes, enabling users to identify denominations through simple gestures. Additionally, the app includes features for summing the total value of multiple notes, detecting counterfeit currency using a Convolutional Neural Network (CNN), and converting printed text to speech for accessibility. This comprehensive tool not only empowers visually impaired individuals to manage their finances more independently but also offers them a means to access written information. The project's development involved meticulous planning, including data collection, model training, and extensive testing to ensure high accuracy and reliability. The use of intuitive swipe gestures makes the app user-friendly and accessible, enhancing its practical utility. By addressing critical daily challenges, this project represents a significant advancement in inclusive technology, providing visually impaired individuals with greater independence and confidence in their daily activities

Keywords: Machine learning, deep learning, NLP, you only look once, Convolution neural network Mobile, Android.

Abstract: The application for malls or e commerce platform that divide the customers into profitable and non- profitable customers plays a vital role in the marketing sector. The administrator within the shopping centers chooses to promote methods and client division points to form a relationship with the first profitable clients by arranging the foremost appropriate marketing procedure. Numerous methods are applied to separate the advertising, but outstandingly tremendous data is uncommonly effective in decreasing their adequacy. Many works used association rule learning is used to establish a relationship between variables. With the use of an appropriate algorithm in this, we find what items customers frequently buy together by generating the set of rules and can be used those rules for various market strategies. By using that rules, we also develop a recommender framework that will offer assistance the mall managers or e commerce managers to empowering the market strategies. This not only helps customers have a better choice but also gives advice to businesses selling products with reasonable prices. Customer segmentation is done based on their interest using association learning algorithms like Aprioir algorithm or ECLAT algorithm or SFIT algorithm.

Keywords: Clustering techniques used k means, Fuzzy c means algorithm

Abstract: Nowadays electronic gadgets play an important role in students' life as a source of learning. The Dependency of services provided by electronic gadgets has reached a large scale. Electronic gadgets like smart phones have a major impact on people in their day-to-day life [1]. Among all, students are the important one, as they rely on electronic gadgets for their academic activities. The major impact is that it can affect the students’ mental and physical health. Students are getting addicted to these electronic gadgets as it becomes inevitable [3]. This project work uses machine learning techniques to demonstrate how gadgets affect students' daily lives. Many parameters used to find association among use of gadgets and student academic performance. The parameters include how many electronic devices they use and how long they use them for, whether the usage of electronic gadgets shows any improvement in their academic performance. This system applies unsupervised machine learning (ML) techniques to discover which significant attributes that a successful learner often demonstrated in an academic course. Our project main goal is to find the correlation between the use of gadgets and the student academic performance. Many research works are there related to this topic, all works purely concentrated on building static models using machine algorithms but there is no real time application useful for educational sector[4][5][6]. In our New Proposed system we focus on this issue and our system major objective is to predict the student academic ups and downs based on the use of electronic gadgets. Efficient ML algorithms will be used either Apriori algorithm or ECLAT algorithm will be used. System build as real time cum browser based application useful for college. We use efficient tools such as VISUAL STUDIO and SQL SERVER, using these Microsoft technologies we can build attractive and impressive GUI based applications.

Keywords: Data Science, Machine Learning, Educational Sector, Supervised Learning, Student Data, Training Datasets. Naïve Bayes.

Abstract: Now day’s crimes are increasing rapidly and there is no region or city without crimes. So we require an automation for crime detection and prevention. System should stop crimes before it starts. As crimes are increasing, precautionary measures to be taken to stop crimes [2]. Finding frequent crimes and related correlations is a tedious and high effort task in the current crime sector. This issues needs to be addressed. Current real time system does not supports automation for crime prediction in real time. Machine learning or AI is the emerging technology to solve this issue. Efficient Unsupervised learning algorithms used to process crime training datasets and frequent crimes and their relationships are identified [5]. Proposed system build to reduce crimes and applicable to crime sector. We are building a real time application where it is useful for crime sectors to reduce the crimes. Currently none of the crime sector applications does this and we use efficient data science algorithms to predict crimes with better results.

Keywords: Data Science, Machine Learning, Apriori Algorithm, Apriori TID Algorithm, Crime Types, Patterns

Abstract: Data backups have immeasurable importance in data management. They help protect important data against human errors, viruses, and other such threats. Therefore, unauthorised access to the contents of a backup can prove extremely harmful. Finding vulnerabilities and points of exposure of the backups is crucial to ensure better security. Aim of this paper is to discuss the vulnerabilities regarding the accessibility of data backups, be it cloud or a physical drive. Through surveying the internet forums, we can find out various methods of entry via CVEs. The paper will list the techniques to access data backups as an adversary and then provide potential solutions to each technique.

Keywords: Common Vulnerabilities and Exposures (CVE), Backup, Encryption, Compression

Abstract: This project was to design and implement a Medicine Time Reminder System, aimed at helping patients remember to take their medications on time. The system was designed with a microcontroller as the central processing unit, which regulated and controlled all the system's activities. The microcontroller receives input from a Real-Time Clock module, then compared to the pre-set alarm time stored in its memory, The alarm is triggered when the two times align, causing the system to vibrate and initiate calls to the designated phone numbers. In the implementation process, a 3-by-4 keypad was connected to the microcontroller, allowing patients to set the alarm time and phone numbers. The OLED display was connected to the microcontroller via an I2C protocol and displayed system status, time, date, and other data. A GSM module was connected to the microcontroller via a serial communication protocol, allowing the system to make calls. A buzzer and vibrator were connected to the microcontroller through a BC547 transistor, providing sound and vibration notifications. The system was tested with 110 patients aged 17 years and above. The assessment results showed that the system was highly effective in ensuring the patients took their medications on time. Eighty-four percent of patients reported that the system effectively reminded them to take their medication at the right time. The medicine time reminder system utilizing sound, vibrators, and a GSM module offers a comprehensive solution for medication adherence. By combining auditory and tactile cues and incorporating Phone calls, this system improves the chances of timely medication intake and helps individuals stay on track with their prescribed dosage schedules.

Keywords: Microcontroller ATmege328p, Buzzer, GSM module, Real Time Clock, Vibrator, LCD, Keypad

Abstract: In today's fast-paced and regulated food industry, ensuring product authenticity and traceability is crucial. Our dynamic digital signature system for food packaging enhances security, transparency, and efficiency in the food supply chain through advanced technology. By integrating digital signatures, QR codes, simulation data, and MQTT protocols, this system provides a robust mechanism for tracking and verifying food products from production to consumption. This ensures compliance with quality and safety standards. The real-time monitoring capabilities enable distributors, hub staff, companies, and clients to access accurate product information instantly[2]. This system addresses the complexities of a global supply chain involving multiple stakeholders, thereby improving reliability and traceability. Ultimately, it empowers customers with precise product information, enhancing trust and satisfaction.

Keywords: Food Packaging, Digital Signature, Traceability, QR Code, Food safety, MQTT Protocol, Simulation Data.

Abstract: When we travel, it is important to be able to move freely without being weighed down by our baggage and luggage. There is a saying, "If you want to travel happy, you must travel light". A nagging problem for travelers is finding a place to stash their suitcases when they arrive in town too early to check into a hotel, or when they've checked out of their rooms and are planning adventures before heading to the airport. One possibility is carrying luggage to restaurants, museums or to a meeting with a potential client. Travel hands Free is an app that connects travelers looking for short-term bag storage with cafes, restaurants, gift shops and other businesses that have plenty of secure storage space. In addition to helping businesses turn unused or underutilized space into revenue-producing uses, the app also helps local economies by getting travelers to explore neighborhoods and reducing foot traffic.

Keywords: Hands-Free Travel, Local Economy Boost, Baggage Storage, Traveler Convenience.

Abstract: In this paper, we present an Vehicle maintenance can be daunting. With so much else going on in life, it’s difficult to remember to schedule things like oil changes and brake jobs. Vehicle mileage, health, and service tracking are crucial aspects of vehicle management. They involve monitoring and recording various parameters related to a vehicle’s performance and condition. Here’s a brief note on each aspect: In today's fast-paced world, logging information has become synonymous with efficiency and informed decision-making, making "Gofurthr" an indispensable app. In the face of growing environmental concerns and the need for resource optimization, this application provides a user-friendly platform for individuals and businesses to meticulously track and manage essential vehicle-related data, with a particular focus on fuel consumption and performance. In an era where every decision is increasingly data-driven, "Gofurthr" not only streamlines the often-cumbersome process of record-keeping but also empowers users with valuable insights into their driving habits, enabling them to make informed choices for optimizing fuel efficiency, reducing costs, and minimizing their environmental footprint. Through its seamless integration of technology, connectivity, and user-friendly design, "Gofurthr" emerges as a catalyst for a more connected, sustainable, and efficient approach to transportation management in our modern world.

Abstract: The proliferation of online job boards has concomitantly led to an upsurge in the prevalence of fraudulent job postings. These deceptive listings are designed to mislead job seekers by advertising non-existent employment opportunities or misrepresenting the details of legitimate positions. The repercussions of falling prey to such scams extend beyond wasted time and effort to potential financial losses for the affected individuals. This research project aims to develop a machine learning-based solution for the detection of fake job postings, thereby addressing this critical issue in the online job market.

Abstract: Cardiovascular diseases (CVDs) remain the leading cause of death and illness worldwide. Early diagnosis and intervention are vital to improving patient outcomes and reducing the burden on healthcare systems. Recent research indicates that alterations in retinal vascular structure may be linked to cardiovascular health. Retinal images offer a non-invasive approach to assess microvascular anomalies, making them a valuable source of data for predictive modeling. This study aims to develop a machine learning model using Recurrent Neural Networks (RNNs) to analyze retinal images and detect patterns that could signal heart disease. RNNs are particularly well-suited for processing sequential data, enabling better predictions by capturing temporal dependencies in retinal images.

Abstract: Serverless computing has rapidly gained popularity for its scalability and cost-efficiency, but it has also introduced significant security challenges, particularly in serverless containers. This paper presents a comprehensive security framework that addresses these challenges. Serverless containers, while flexible and agile, pose risks such as unauthorized access and data breaches due to their ephemeral nature and shared environments. The framework consists of three key components: enhanced identity and access management using JWT and OIDC, improved container isolation via Docker's Namespace feature and customizable firewall rules, and advanced threat detection techniques encompassing static and dynamic analysis. Experiments demonstrate the framework's effectiveness in enhancing security without compromising performance. This paper discusses the security challenges in serverless containers, outlines the proposed framework, and summarizes experimental results, contributing to a deeper understanding of serverless container security.

Keywords: Serverless Computing, Serverless Containers, Security Framework, Identity and Access Management, JWT (JSON Web Token), OIDC (OpenID Connect), Container Isolation Docker Namespace, Firewall Rules, Threat Detection Techniques, Static Analysis Dynamic Analysis, Vulnerability Scanning, Experimental Evaluation, Security Challenge Unauthorized Access, Data Breaches, Cross-Container Contamination, Ephemeral Containers, Shared Environment.

Abstract: Parkinson's disease (PD) is a progressive neurological disorder that significantly impairs motor functions due to the gradual degeneration of the central nervous system. Early detection is crucial to manage and potentially slow down the progression of PD. This study proposes a novel detection system that leverages spiral drawings and Convolutional Neural Networks (CNNs) to improve the accuracy and efficiency of PD diagnosis. Developed using Python and Flask, this system aims to offer a more precise and effective alternative to existing diagnostic methods, potentially enabling earlier intervention and better patient outcomes.

Abstract: Today, Augmented Reality and Virtual Reality technologies are making waves in the education sector. XR technologies have become key innovation areas in research where new learning tools that create new learning experiences for learners are being developed. These emerging and engaging technologies are fast supplanting textbooks by a large part. The technological capacity to overlay multimedia content virtually in 3D and augmenting objects onto the real world for interaction means that educational content can be availed to students instantly as they require it. Extended reality thus, is poised to enable the fruition of the objectives of competency-based curriculum. In Kenya, the development and implementation of a competency-based curriculum in the education system without the right educational technologies do not guarantee the acquisition of the pre-determined competencies. Hence, the successful implementation of technologies such as augmented reality and virtual reality could make the difference. Yet, there is little that has been done to marry the developments in augmented reality or virtual reality and curriculum in Kenya. The objective of this paper was to investigate curriculum content for grade 3 STEM subjects that can be integrated with XR technologies for simulation in regard to competence-based curriculum. The study population comprised of primary level schools in Chesumei Sub-County, Nandi County. A three-stage sampling method was used to arrive at the study sample from a population of 166 schools. The findings from this paper will be used in developing a prototype for XR gaming application in identified areas. These results will benefit actors in the education sectors such as policy makers, curriculum developers, implementors of CBC and other partners in education. These findings add to the body of literature on the use of educational technologies.

Keywords: Virtual Reality, Augmented Reality, CBC, educational technologies, simulation.

Abstract: Nowadays, wearing a helmet is crucial for safety, providing essential head protection and reducing the risk of severe injuries, potentially saving lives for motorbike and bicycle riders. Conversely, not wearing a helmet poses serious risks, including increased vulnerability to head injuries, fatalities, and elevated accident susceptibility due to reduced visibility and non-compliance with traffic regulations. A real-time system can leverage deep learning to detect instances of helmet non-compliance. By employing the YOLOv3 algorithm, helmet non-compliance can be detected with an accuracy ranging between 80% and 95%. Furthermore, the XML framework is utilized for precise number plate extraction from vehicles of violators, ensuring similar accuracy levels. The EasyOCR algorithm converts these number plate images into text, facilitating their storage in a database.The system ensures comprehensive documentation and monitoring by securely storing the data of helmet non-compliance, including vehicle numbers, in a database. This supports detailed record-keeping, reporting, and further analysis, ultimately contributing to improved road safety and enforcement of helmet regulations.

Keywords: Deep learning, YOLOv3, XML framework, Easy OCR, Helmet non-compliance , Number plate extraction.

Abstract: This paper explores the transformative impact of Artificial Intelligence (AI) on education enterprise systems. It examines various AI technologies, including machine learning, natural language processing, computer vision, and predictive analytics, and their applications in educational settings. The research highlights key benefits such as personalized learning experiences, automated administrative tasks, enhanced decision-making processes, improved student engagement, and early intervention capabilities. Through case studies of successful AI implementations in educational institutions, the paper demonstrates the potential of AI to address long-standing challenges in education while improving the quality and efficiency of educational delivery. The study also acknowledges the ethical considerations and challenges associated with AI adoption in education.[2]

Keywords: Artificial Intelligence, Education Enterprise Systems, Machine Learning, Natural Language Processing, Computer Vision, Predictive Analytics, Personalized Learning, Automated Administration, Student Engagement, Early Intervention

Abstract: Purpose: The purpose of this chapter is to introduce the reader to the theory and practice of cybersecurity, the main understanding of cybersecurity, the challenges faced globally, the security principles, and the security models for protecting the information security chain from various threats. In the second part of the chapter, we will introduce the critical aspects of the robust cybersecurity model, which was developed within the framework of the UPBAL project "Be Secure" and which gained further development in the F182 project on smart grid security. Design/methodology/approach: The theoretical part of the chapter is based on the General Systems Theory, on which the main understanding of cybersecurity appears, while the current results are based on the experience achieved within the framework of the two mentioned projects, evaluated in the Piloting Reports, and theoretical strategies of other cyber defense-related theories. The theoretical principles are illustrated by several real-life practical examples from the field of ICT security. Findings: As it is quite difficult to find any wider theories and practical documents that are oriented only to the needs of the development of the principles of a concrete area of cybersecurity, the concepts and strategic steps proposed could be of potential use to researchers and experts in this field, to discover the importance of developing a wider cybersecurity theory, find approaches within generalized methodology, as well as study and apply already developed criteria aspects and principles.

Keywords: Cybersecurity, Challenges, Security principles, Security models, Information security, Robust cybersecurity model, UPBAL project, Smart grid security, General Systems Theory, ICT security

Abstract: A method for detecting power transmission line bolts and their defects based on positional relationship. Using thermal image and their temperature to identify the problem in jumper bolts on transmission line. Thus existing method we are using which fuzzy c means clustering. In the proposed system first we are taken image deonising of transmission line jumper bolts. Then with help of image deonising we take more image segmentation of thermal image of healthy and faulty jumper bolts. After the image segmentation of transmission line bolts we also can feature extraction. Then the prominent feature selection is used to improve power quality in hybrid distributed power generating systems. The fault classification of the distributed power generating system already tested and it is named as thermos vision which is used to measure an object temperature. The heat energy is converted into thermal image.

Abstract: Skin cancer is a growing public health concern; while some types of skin cancer are deadly, such as Melanoma, early detection is crucial for effective treatment and improving patient survival rates [1,2,3,4]. In fact, Malignant melanoma accounts for only 2.3% of all skin cancers yet is responsible for more than 75% of skin cancer-related deaths. However, if it is detected at an early stage, it is highly curable; the 10-year survival rate is between 90% and 97% when the tumour thickness is less than 1 mm. Also, the treatment for an early detected cancerous mole is as simple as excision of the lesion, which can prevent metastasis and spread of cancer to other organs. In this research study, we introduce an approach for skin cancer classification using a state-of-the-art deep learning architecture that has demonstrated exceptional performance in diverse image analysis tasks. We have used two publicly available benchmark data sets for training and validating our results: HAM10000 and ISIC2018 datasets. These datasets consist of dermoscopic images captured using Dermatoscopes and carefully annotated by expert dermatologists. Preprocessing techniques, such as normalization and augmentation, are applied to enhance the robustness and generalization of the model. The proposed approach demonstrated the efficacy of extracting relevant features for accurate classification by leveraging Deep Object Detection models to identify the location of the Lesion, then using the Segment Anything Model (SAM) and MedSAM for extracting the border of the lesions, then finally using various pre-trained states-of-the-art Deep Convolution Networks for Classification. Comprehensive experiments and evaluations are performed in this research; the results demonstrate the effectiveness of using Zero-Shot Segmentation methods over traditional deep learning architectures in skin cancer classification.

Keywords: Skin Cancer, Computer vision, Cancer classification, image processing, Vision Transformer, image classification, Cancer Cell Segmentation.

Abstract: Diabetes is a chronic metabolic disorder characterized by persistently high blood glucose levels, either due to inadequate insulin production or the body's impaired response to insulin. Managing confidential healthcare data is crucial due to the widespread prevalence and significant health impacts of diabetes. Despite its global significance, there is a lack of real-time applications for early prediction and dietary guidance for diabetes this application seeks to address this issue by creating an application that provides early diabetes predictions and identifies potential reversals through advanced machine learning technique KNN offers tailored dietary plans. This real-time medical system, developed using Microsoft tools like Visual Studio and SQL Server offer significant advantages for hospitals, providing valuable support for their operations. doctors. This system achieves 92.2%accurate result in prediction of diabetes and reverse diabetes using KNN Algorithm. Keyword: KNN, Diabetes Chronic, Machine Learning

Abstract: Quantum computing is new advancement of computer technology. Now with quantum computing we will achieve great computational capabilities which can handle very complex tasks easily which are very difficult for regular computers. Quantum computing is transforming today’s technology by offering massive computational capabilities that can handle problems which are very difficult for regular computers. In this paper we will explain the influence of quantum computing on existing cryptography. Also, we will focus on both the obstacles and the positive prospects of quantum computing. We will cover many challenges of quantum computing like the need for effective error correction techniques, scaling the technology, preparing required hardware, and constructing innovative quantum algorithms. On the brighter side, quantum computing holds the great potential to improve cryptographic security through the formation of quantum-resistant algorithms, quantum key distribution, and enhanced problem-solving abilities. Furthermore, this paper addresses the ethical issues and security risks linked to quantum computing. It also uncovers insights into the future of quantum computing and its capacity to transform cryptography, and the pressing necessity to apply post-quantum cryptographic standards. We will talk about real world issues and what other sections of world are doing to defend against security risks due to quantum computing. This paper also details what kinds of research leading organizations are doing to take advantage of quantum computing. In addition to investigations aimed at enhancing cryptographic algorithms, efforts are being made to address concerns regarding the potential vulnerabilities of established cryptographic systems such as RSA, AES, and ECC.

Keywords: Quantum computing, Cryptography, Quantum Algorithms, Quantum Error Correction, Quantum Supremacy.

Keywords: cloud computing, cloud storage, data deduplication

Abstract: The primary objective of the study was to compare the Cardiovascular fitness between  Swimmers and football players. The data was collected through respondents in the form of different descriptive tests. The demographic information about, age, height, weight etc. was obtained before seeking training. In the present study, Total 200 players ( 100 Swimmers and 100 football  players) selected for present study  and their age ranged from 18 to 25 years. The physical conditions of the subjects were assessed by  the demographic information form . They were requested to co-operate and participate actively for the same. Resting heart rate of each subject was recorded. Electronic Blood pressure machine was used to measure the blood pleasure of sample. The results of the study shows that insignificant differences was found in Systolic blood pressure between footballers and swimmers. The results of the study show that insignificant differences were found in Diastolic blood pressure between footballers and swimmers. The results of the study shows that significant differences was found in Heart Rate between footballers and swimmers. The finding of the study indicates that footballers ware to found have lower heart rate as compared to swimmers. result reveals  a statistically significant difference of cardiovascular fitness was found between Football players and Swimmers. Football players were better Cardiovascular Fitness.

Keywords: Cardiovascular, Blood Pressure, Resting Heart Rate, Football, Swimming

Abstract: Citrus fruits and leaves are susceptible to a range of diseases that can significantly impact agricultural yield and quality. Traditional methods for disease detection rely heavily on manual inspection, which is both time-consuming and prone to human error. This paper presents a machine learning approach to automate the detection of diseases in citrus fruits and leaves. By leveraging computer vision and deep learning techniques, we develop a model that can classify and identify symptoms of various diseases from images. The approach involves preprocessing image data, extracting relevant features, and training a convolutional neural network (CNN) on a dataset of labelled images. Our model demonstrates high accuracy and efficiency in identifying disease symptoms, offering a scalable solution for early detection and management. The results indicate that integrating machine learning into disease monitoring systems can enhance precision, reduce labour costs, and improve overall crop health management.

Keywords: Citrus fruits, Disease detection, Machine learning, Computer vision, Deep learning, Convolutional neural network (CNN), Image preprocessing, Feature extraction, Accuracy, Crop health management.

Abstract: This research examines the development and application of an automated brain tumour detection system for robust imaging modalities such MRI and CT scan using convolutional neural networks for exploratory diagnosis. Medical images or anatomical imaging are derived from MRI and CT scans, which play a significant role in this method. The architecture involves a series of processes such as injecting features to medical images specifically, applying pre-processing to enhance discriminative power in the input data. Deep learning architectures entail the utilisation of several network architecture such as CNN and various other state-of-the-art models.

Abstract: Multivariate imaging advanced in recent years which prompted many applications for detailed understanding in the fields of satellite imaging, medical imaging, and microscopic imaging. To achieve more insights about it, various feature extraction techniques exist which utilize the ample spectral and spatial details in an image. But apart from feature extraction dimensionality reduction (DR) and efficient classification has become a key aspect in multivariate image analysis (MIA). Adding more and more variables in feature space of multivariate image results into high dimensionality which in turn increases the complexity in classification. Therefore, it becomes important to apply DR techniques before classification process. Most widely used DR method is Principal component analysis (PCA) which is linear DR method. The main disadvantage of PCA is that it does not consider the nonlinearity in data. The proposed new method is invariant to nonlinearity in data. To consider nonlinearity, Gabor filter is used to extract spatial features from multivariate data. Gabor filter based method performs dimensionality reduction of nonlinear multivariate images while improving the classification accuracy. Keywords - Multivariate image analysis (MIA), dimensionality reduction (DR), Gabor filter, Support Vector Machine (SVM), Convolutional Neural Network (CNN)

Abstract: The primary objective of the study is to determine the effects of meditation and pranayama on body mass index (BMI). The forty five female collegiate students  selected for the present study were divided into three equal groups called, Experimental group I ( Meditation Group), experimental II (Pranayama group) and Control group, consisting of 15 Female students in each group. They were the students of graduate Course and their age ranged from 18 to 25 years during the academic year 2016-17. The entire sample were directed to assemble in a multipurpose hall Padmpani College of Physical education to seek their willingness, to act as subjects. The result of the study reveals that there were  significant difference were found in Body Mass Index (BMI)  (F= P<.05) among Meditation , Pranayama  and Control  group .

Keywords: Meditation, Pranayama, BMI

Abstract: Cloud computing emerges as one of the hottest topic in field of Computer Science and  information technology through which Client can shared and access data and information via a web browser. The emergence of cloud computing is the computing equivalent of the electricity revolution of a century ago. It is the time and trend   of cloud computing and definitely upcoming 20-25 years it will be bring the revolution in the business as well as Information technology industry. Generally Cloud Computing is analogy of internet. Cloud computing is based on several other computing research areas such as HPC, virtualization, utility computing and grid computing. Cloud computing refers to the delivery of computing resources over the Internet. Instead of keeping data on your own hard drive or updating applications for your needs, you use a smart service over the Internet, at another location, to store your information or use its applications. Doing so may give rise to certain privacy Implications. Cloud computing provides a shared pool of resources, including data storage space, networks, computer processing power, and specialized corporate and user applications. Cloud Computing have many property that is why it is growing and cover all web application over the Internet. In this paper we will study evolution of cloud computing ,service providing by it, deployment model of cloud computing ,where it will be use ,what is main problem to implementing it and various characteristics which is different to all other computing over Internet. Currently we are starting time of cloud computing where it starts and it will grow to implement many to many wonderful and attracting  application which are still in development .This paper also describe some key point which is beneficial and helps  to implementing vast feature of cloud computing.

Keywords: Cloud Computing, SAAS, IAAS, PAAS, Virtualization, ASP, GAD.

Abstract: Agriculture plays a vital role in global food security, and farmers constantly seek ways to optimize their crop selection to maximize yields and profits. However, selecting the most suitable crop for a specific region or environment can be challenging due to various factors such as climate, soil conditions, and water availability. The Crop Recommendation System addresses this challenge by leveraging machine learning algorithms to analyze environmental data and provide personalized crop recommendations.

Abstract: Brain MRI segmentation is a crucial task in medical image analysis, offering vital insights for the diagnosis and treatment of various neurological disorders. This paper introduces an advanced deep learning-based method for the segmentation of brain MRI images, leveraging the power of convolutional neural networks (CNNs) to achieve precise delineation of brain structures. Our approach demonstrates significant improvements over traditional segmentation techniques, highlighting its potential as a reliable and efficient tool for clinical applications. The results underscore the robustness and accuracy of our model, paving the way for its integration into routine medical practice to enhance diagnostic accuracy and patient outcomes.

Abstract: Skin diseases represent a significant health challenge worldwide, impacting individuals across all age groups and demographics. For effective treatment and management of these conditions, timely and accurate diagnosis is essential. However, diagnosing skin diseases can be complex and time-consuming, often necessitating specialized expertise from dermatologists or other healthcare professionals.In recent years, advancements in computer science and artificial intelligence have transformed various sectors, including healthcare. Specifically in dermatology, automated methods have emerged for detecting and diagnosing skin diseases through visual data, such as images.

Abstract: This project aims to develop an innovative communication system that bridges the gap between deaf and hearing individuals by addressing both verbal and visual communication barriers. The system is implemented in two phases. In the first phase, the focus is on converting audio messages into Indian Sign Language (ISL). Audio input, either live or pre-recorded, is transcribed into text using advanced speech recognition technologies. This text is then mapped to predefined ISL images or GIFs, enabling seamless communication by making spoken language accessible to the deaf community through visual sign representations. The second phase enhances the system's ability to interpret visual information for the deaf. Images are collected and used to train a Multilayer Perceptron (MLP) model, achieving a 90% accuracy in recognizing and interpreting these images. The model processes the images and converts them into corresponding text or speech outputs, allowing deaf individuals to understand visual cues through textual or spoken descriptions. This dual-phase approach not only facilitates effective communication between deaf and hearing individuals but also enhances the interaction of the deaf community with their environment.

Abstract: The abstract of an interactive computer system provided with a gesture-based virtual mouse and keyboard is for technology that allows users to control computers using hand gestures as opposed physical input devices like a traditional mouse and keyboard. This technique is based on using computer vision or any other sensor mechanism to capture the hand movements of the user and command them for moving a virtual mouse &keyboard, displayed at that screen in addition it may provide clicking functions too. The system uses gesture recognition algorithms which enable it to understand the users' gestures precisely from a distance making them operate different functions inside graphical user interface (GUI). The virtual mouse provides cursor movement, left-clicking and double-clicking functionality as well as clicking-and-dragging an object within the interface while the virtual keyboard lets users type in text or trigger special key-combinations. The system was designed around the idea of providing an intuitive and natural way for interacting with computers, specifically in scenarios where conventional input devices are undesirable or impractical. It improves user mobility and can be useful when presentations, gaming or touchless environments are anticipated, if the end user is disabled due to physical impairments that make it difficult for him/her to use traditional means of input. With a combination of sophisticated gesture recognition algorithms, real time tracking and comprehensive user interface designs the system will attempt to offer an ideal all around experience for users.

Keywords: Machine Learning, GUI, Virtual mouse, Hand Gesture, Computer Vision.

Abstract: Implement a system for forecasting Foreign Direct Investment (FDI) inflows to India for the year 2025. The system will incorporate various influential sectors and apply sophisticated analytical methods to deliver precise predictions and insights. Evaluate a broad spectrum of sectors impacting FDI inflows, such as telecommunications, agriculture, automotive, computer software & hardware, non- fertilizer chemicals, power, media, electrical equipment, services, cement and gypsum products, paper and pulp, food processing, engineering, pharmaceuticals, glass, construction, metallurgy, tea and coffee, and precious metals and jewellery.Analyze historical data and trends within these sectors to assess their effect on FDI inflows. Apply linear regression techniques to establish the relationship between various factors and FDI inflows. Develop forecasting models to project FDI inflows for 2025 based on the analysed data.

Abstract: The suggested glaucoma diagnostic system incorporates segmentation of the eye nerve disc and cup of the visual nerve utilizing the U-Net framework, alongside glaucoma classification via the VGG16 model. This system aims to improve precision and effectiveness in detecting glaucoma, facilitating prompt treatment for patients. The system will utilize the U-Net framework to delineate the optic cup and disc areas inside retinal pictures. The VGG16 framework will be Utilized for two-class of grouping, glaucoma status, using the segmented optic regions as input. This model will discern between instances where glaucoma and those without. Clinical Use: The system is designed to support healthcare practitioners in precisely identifying glaucoma in its early stages. It serves as a diagnostic tool, offering clinician’s dependable information to enhance their decision-making process. The system requires labeled datasets of retinal images for training and evaluation. These datasets must include annotations for optic disc, optic cup, and glaucoma status.

Abstract: Sunflower cultivation is a vital agricultural practice in India, supporting the livelihoods of more than 350,000 families. Since the emergence of sunflower rust disease in 1983, these families have faced substantial difficulties in maintaining crop yield and quality. This study seeks to create a robust sunflower yield prediction system using machine learning techniques, specifically focusing on Decision Tree, K-Nearest Neighbor (KNN), and Linear Regression algorithms. The system leverages a dataset that includes weather conditions, soil properties, and historical yield data from seven taluks in the Mysuru district. Data preprocessing steps, such as handling missing values and data normalization, ensure the dataset's integrity. The study evaluates the performance of Decision Tree, KNN, and Linear Regression in predicting sunflower yield, with an emphasis on accuracy, precision, and recall metrics. The findings reveal that Decision Tree and KNN, with their classification capabilities based on proximity to nearest neighbors, deliver more accurate predictions compared to Linear Regression, which models the linear relationships between variables. The resulting system serves as a practical tool for farmers, helping them make informed decisions regarding crop management and yield optimization. The study highlights the significant potential of integrating machine learning in agriculture, particularly in predicting crop yields and addressing challenges related to agricultural planning and resource management.

Abstract: The goal of this project in analysing and predicting tobacco yield is to develop accurate models that forecast yield based on factors such as temperature, rainfall, humidity, soil pH, potassium, magnesium, and agricultural practices. These models aim to optimize resource utilization, enhance farming techniques, manage risks, and support data-driven decision-making, ultimately boosting productivity, profitability, and sustainability in tobacco farming. The project aims to optimize agricultural practices to maximize yield and resource efficiency while managing risks such as pests, diseases, and adverse weather conditions. It also includes providing decision support to farmers through actionable insights and validating models with real-world data to ensure their accuracy and reliability, ultimately enhancing productivity and sustainability in tobacco farming.

Abstract: This research paper introduces a novel Android application designed to assist visually impaired individuals in recognizing and summating Indian currency notes. The application leverages a Convolutional Neural Network (CNN) with the MobileNet architecture, trained on a comprehensive dataset comprising images of 10, 20, 50, 100, 200, 500, and 2000 Indian Rupee notes. The primary functionalities of the application include swipe-based interactions, allowing users to perform currency summation, trigger text-to-speech conversion, and initiate the currency identification process.The development process involves meticulous data preprocessing, model training, and optimization to accommodate the constraints of mobile devices. We address the challenges of real-time currency recognition by implementing features such as audio feedback to inform users of the identified denomination promptly. Additionally, the user interface incorporates accessibility features, including screen reader compatibility and voice command support, ensuring a seamless and intuitive experience for individuals with visual impairments.

Keywords: Include at least 4 keywords or phrases.

Abstract: The rapid advancement of natural language processing (NLP) technology has revolutionized human-computer interaction, particularly in the programming domain. This research paper presents the development and implementation of the "Code with VS Code using Natural Language Processing" project, aimed at simplifying the code writing and execution process through voice input and natural language understanding. The project encompasses a Flask-based web application that serves as an interface, enabling users to select programming languages like Python, Java, and JavaScript and generate code through two pathways: automated code generation using ChatGPT and manual code input supported by NLP techniques such as tokenization, lexing, parsing, and stemming. The system integrates voice input support and real-time code execution within the VS Code environment, enhancing accessibility and reducing cognitive load for developers. This innovative approach seeks to democratize coding, making it more intuitive and accessible for individuals with varying levels of technical expertise. The project faced several technical challenges, including ensuring accurate voice recognition and handling diverse programming constructs. Future directions include expanding the system to support additional languages and enhancing NLP capabilities to better understand and process complex code requirements. The "Code with VS Code using Natural Language Processing" project represents a significant step towards a more inclusive and efficient programming environment.

Keywords: Machine learning , deep learning, NLP, GenAi, syntax library, C, java, javascripts.

Abstract: The Hand Gesture Controller project presents an innovative solution in the field of Human-Computer Interaction (HCI) by enabling users to interact with computers using hand gestures. This project eliminates the need for traditional input devices like keyboards and mice, instead relying on gesture recognition technologies to perform various computing tasks. By leveraging OpenCV and MediaPipe, the system achieves accurate and real-time hand gesture recognition, allowing users to control the mouse pointer, perform clicks, scroll, and even execute complex commands like drag-and-drop or multiple item selection. This approach offers a more natural and intuitive interface, particularly beneficial in environments where traditional devices are impractical or for users with mobility impairments. The project emphasizes the use of a gesture recognition system that does not rely on Convolutional Neural Networks (CNNs), opting instead for simpler and more efficient methods suitable for real-time application. Throughout extensive testing, the system demonstrated high accuracy in gesture detection and responsiveness, offering a seamless user experience. This paper explores the development and implementation of the Hand Gesture Controller, its applications, and potential for further development, making a significant contribution to the field of HCI and assistive technology.

Keywords: Machine learning , deep learning, NLP, GenAi,syntax library,C,java,javascripts.

Keywords: Machine learning , deep learning, regression, classification.

Abstract: The "E-commerce Application for Hardware Part Selling" project aims to revolutionize the hardware retail industry by creating a dedicated online platform for purchasing hardware components like nuts, bolts, screws, and metal parts. Traditional methods of acquiring these parts often involve visiting multiple stores, facing limited availability, and lacking detailed product information. This project addresses these challenges by providing a comprehensive online solution that enhances user convenience and administrative efficiency. Utilizing PHP for server-side scripting, MySQL for database management, and hosting on AWS, the platform ensures scalability, security, and robust performance. Key features include a user-friendly interface, detailed product listings, efficient order management, and secure payment processing. The platform supports both user and admin modules, facilitating seamless interaction and management of products, orders, and user data. The system's design emphasizes a modular architecture, enabling future scalability and integration of advanced features like mobile applications, online payment gateways, and analytics. Through rigorous testing, the application demonstrates reliability and user satisfaction, positioning itself as a significant advancement in the hardware retail industry. The project's success highlights the potential of digital transformation in streamlining retail processes and enhancing customer experiences, with further potential for expansion into new markets and features.

Keywords: ecommerce, ajax, json, php, mysql.

Abstract: Maintaining a healthy body in modern society necessitates careful monitoring of calorie intake to achieve and sustain an optimal Body Mass Index (BMI). Traditional methods of calorie estimation, which are often manual and cumbersome, hinder the feasibility of regular use. This project introduces a novel, automated approach to calorie estimation using deep learning algorithms, specifically convolutional neural networks (CNNs), to classify and estimate the calorie content of food items from images. By leveraging Tensor Flow, a robust machine learning framework, the system is capable of detecting various food types, such as fruits and vegetables, and calculating their respective calorie values. Additionally, the integration of Google's Generative AI (Gemini) enhances the system by providing comprehensive nutritional information beyond calorie content, offering users insights into macronutrient composition and other health-related data. Techniques like prompt engineering and template patterns are employed to ensure that the generated information is accurate and contextually relevant to the user's dietary needs.

Abstract: The Internet of Things, or IoT, is all the rage these days; it links people's daily lives to small devices like sensors and offers smart applications. In order to analyze a patient's health in real time, smart healthcare applications need remote patient monitoring. Internet of Things (IoT) devices need high-power radio connection, including Fifth Generation (5G), for data transfer in order to provide real-time patient monitoring. The healthcare industry has been transformed by the growing engagement of 5G-enabled IoT technology, which has provided efficient and real-time monitoring and diagnosing capabilities. There are a number of issues that have come to light as a result of the widespread use of 5G-IoT in healthcare applications, a lack of energy-efficient resources; a lack of a standard framework for handling applications; and, most importantly, the potential for malicious data or the disclosure of sensitive medical information. This issue is addressed by proposing a four-module improved system structure. line of new products to improve the 5G-IoT smart healthcare application, the initial module suggests a system structure that gives the general technique. For efficient management of 5G-IoT applications, we outline the suggested smart healthcare system (SHS). It is possible to evaluate the system's efficiency with the use of an automated model called FLAML.

Keywords: Internet of Things, Healthcare, Patient, Monitoring, And Diagnosis.

Abstract: This paper presents a brain tumor detection system designed to assist early diagnosis using a multi-model machine learning approach. The system integrates MRI image analysis using Convolutional Neural Network (CNN) and symptom-based prediction using Random Forest. It combines both medical imaging and clinical data to improve accuracy and reliability. The system is implemented as a web-based application that allows users to upload MRI images or enter symptoms for preliminary screening. It targets healthcare support by providing fast, accessible, and effective tumor detection.

Keywords: Brain Tumor Detection, Machine Learning, Deep Learning, Convolutional Neural Network, Random Forest, MRI, Medical Image Analysis, Web Application.