Archives - Page 2

  • MULTIMODEL EMOTION DETECTION IN VIDEOS USING PRE-TRAINED LLMS
    Vol. 1 No. 1 (2024)

    Abstract

    This paper presents a comprehensive system designed to detect emotions in videos using pre-trained Large Language Models (LLMs). The system integrates video processing and natural language processing techniques to analyze both visual and audio data, providing a robust solution for emotion detection. This paper details the system architecture, implementation, testing methodologies, and the tangible benefits observed during initial deployments.

    Index Terms

    Emotion Detection, Multimodal Analysis, Pre-trained LLMs, Video Processing, Natural Language Processing

  • Empowering Community Bonds through Resources and Sharing
    Vol. 1 No. 03 (2024)

    Abstract

    The project titled "Empowering Community Bonds through Resources and Sharing" seeks to explore and implement initiatives that foster a sense of unity and collaboration within a community. The project aims to identify and establish shared resources, such as community gardens, tool libraries, or communal spaces, to facilitate the exchange of tangible resources. Additionally, it delves into the exchange of ideas, experiences, and support systems, promoting a culture of inclusivity and understanding among community members.

    Through this project will investigate the impact of resource-sharing initiatives on community development, self-sufficiency, and resilience. The implementation of practical solutions, such as creating platforms for knowledge exchange and collaborative projects, will be a key focus. The project aims to contribute to the enhancement of community bonds by emphasizing the importance of collective well-being and interdependence.

    By conducting research, designing and implementing community-based interventions, and evaluating the outcomes to demonstrate the positive effects of empowering community bonds through resources and sharing. The findings and recommendations from this project can serve as a valuable guide for future community development initiatives, promoting a more cohesive and resilient society.

    Index terms

    Community empowerment, Resource-sharing initiatives, Community development, Self-sufficiency, Resilience, Knowledge exchange platforms, Collaborative projects, Collective well-being, Interdependence, Community-based interventions, Unity and collaboration, Inclusivity, Community bonds, Shared resources, Community gardens, Tool libraries, Communal spaces, Impact evaluation, Positive effects, Future community development initiatives.

  • Smart Hospital Management with REACT: A User-Centric Approach
    Vol. 2 No. 01 (2025)

    Abstract

    The project titled "Hospital Management using REACT" aims to develop a comprehensive and user-friendly web application for efficient management of hospital resources and operations. Leveraging the power of REACT technology, the project focuses on creating a responsive and interactive interface that facilitates seamless navigation and accessibility across various devices.

    The system will incorporate modules to handle key aspects of hospital management, including patient information, staff scheduling, and medical equipment inventory. Through the use of REACT components, the application will enable the creation of dynamic dashboards, real-time data visualization, and personalized interfaces tailored to different user roles within the hospital hierarchy.

    Key features of the proposed system include responsive design, ensuring optimal user experience on desktops, tablets, and smartphones. The state-of-the-art REACT state management will be utilized to handle complex data interactions, ensuring the accuracy and real-time updating of information.

    The project aims to address the challenges faced by hospital administrators in managing resources effectively, providing them with a powerful tool to streamline workflows and enhance decision-making. By adopting REACT in the development process, the project seeks to deliver a scalable, modular, and maintainable solution for hospital management that aligns with modern web development practices.

    Through this projectaims to showcase proficiency in web development, UI/UX design, and the utilization of cutting-edge technologies like REACT to solve real-world challenges in the healthcare sector. The resulting web application is anticipated to contribute to the improvement of hospital administration processes, ultimately benefiting both healthcare providers and patients.

     

    Index Terms

    Hospital Management, REACT Technology, Web Application, User Interface Design, Responsive Design, User Experience, Patient Information Management, Staff Scheduling, Medical Equipment Inventory, Dashboard, Data Visualization, User Roles, State Management, Workflow Streamlining, Decision-making Enhancement, Scalability, Modularity, Web Development, UI/UX Design, Healthcare Sector.

  • Satellite Image Classification using Inverse Reinforcement Learning and Convolutional Neural Networks
    Vol. 1 No. 05 (2024)

    Abstract

    Satellite image classification plays a pivotal role in various fields such as agriculture, urban planning, and environmental monitoring. This project proposes a novel approach to satellite image classification by integrating Inverse Reinforcement Learning (IRL) with Convolutional Neural Networks (CNN). The methodology involves training the model to understand complex spatial patterns and features inherent in satellite imagery through the extraction of relevant features using CNNs.

    In the proposed framework, the model learns from expert demonstrations, mimicking the decision-making process of human experts in order to infer the underlying reward structure guiding their actions. This application of IRL allows the model to generalize and make informed predictions on unseen satellite data, contributing to enhanced classification accuracy.

    The project aims to compare the results obtained from the IRL-based CNN approach with the accuracy achieved by traditional satellite image classification algorithms. Commonly used algorithms such as Support Vector Machines (SVM), Random Forests, and conventional CNNs trained with supervised learning will be considered for comparison. The evaluation will be based on metrics such as precision, recall, and F1 score, providing a comprehensive analysis of the proposed methodology's effectiveness.

    The findings from this project are expected to shed light on the potential advantages and improvements offered by integrating inverse reinforcement learning techniques with CNNs in the context of satellite image classification. This research contributes to the growing field of remote sensing and machine learning applications, offering valuable insights for future developments in satellite image analysis.

    Index Terms

    Satellite image classification, Inverse Reinforcement Learning (IRL), Convolutional Neural Networks (CNN), Spatial patterns, Feature extraction, Expert demonstrations, Decision-making process, Reward structure, Generalization, Prediction, Classification accuracy, Support Vector Machines (SVM), Random Forests, Supervised learning, Evaluation metrics, Precision, Recall, F1 score, Remote sensing, Machine learning applications.

  • Organ Donation Coordination Platform
    Vol. 2 No. 03 (2025)

    Abstract

    The project titled "Organ Donation Coordination Platform" aims to address the challenges associated with organ donation and transplantation processes by developing a comprehensive and efficient digital platform. The project focuses on creating a user-friendly interface that facilitates seamless communication and coordination among healthcare professionals, potential donors, and transplant recipients.

    The key objectives include the development of a centralized database to store donor and recipient information securely, implementation of advanced algorithms for quick and accurate compatibility matching, and the incorporation of technologies to streamline logistics and reduce delays in organ allocation. The platform will prioritize confidentiality and transparency, ensuring the secure exchange of medical information while adhering to ethical standards.

    Through the utilization of emerging technologies and innovative solutions, the project aims to enhance the overall efficiency of the organ donation process. This includes features for initial donor evaluation, real-time updates on organ availability, and post-transplant monitoring. The platform ultimately seeks to contribute to the advancement of organ donation and transplantation, fostering collaboration and accessibility within the healthcare ecosystem.

    Index Terms

    Organ Donation, Transplantation Processes, Digital Platform, Healthcare Professionals, Coordination, User-Friendly Interface, Centralized Database, Compatibility Matching, Logistics Streamlining, Organ Allocation, Confidentiality, Transparency, Medical, Information Exchange, Emerging Technologies, Innovative Solutions, Donor Evaluation, Real-Time Updates, Post-Transplant Monitoring, Collaboration, Accessibility, Healthcare Ecosystem.

  • HYBRID MACHINE LEARNING MODEL FOR EFFICIENT BOTNET ATTACK DETECTION IN IOT ENVIRONMENT
    Vol. 1 No. 1 (2024)

    Abstract

    The advent of machine learning has significantly transformed various sectors, introducing advanced predictive capabilities and intelligent systems. This paper presents a hybrid machine learning model combining Federated Learning (FL) and Explainable Artificial Intelligence (XAI). Federated Learning enhances privacy and security by allowing data to be trained across multiple decentralized devices without centralizing the data, while XAI provides interpretability and transparency to the model's decisions. Our proposed model leverages these technologies to ensure robust, secure, and understandable machine learning outcomes. The effectiveness of this hybrid model is demonstrated through extensive experiments, showing improved accuracy and interpretability without compromising user data privacy.Furthermore, the model addresses the growing concerns around data breaches and the lack of transparency in AI decision-making processes. By implementing Federated Learning, data remains localized on user devices, reducing the risk of exposure during data transfer. The integration of XAI techniques ensures that users and stakeholders can comprehend the rationale behind model predictions, fostering trust and compliance with regulatory standards. This combination is particularly beneficial for applications in sensitive areas such as healthcare, finance, and autonomous systems, where both data privacy and model transparency are paramount.

    Index Terms

    Hybrid Machine Learning, Federated Learning, Explainable AI (XAI), Data Privacy, Model Interpretability, Decentralized Training, Machine Learning Security, Transparent AI, Data Security, User Trust, AI in Healthcare, AI in Finance, AI Governance, SHAP, LIME, Model Transparency.

  • Advanced Computer Vision Model for Real-Time Traffic Sign Classification in Autonomous Vehicles
    Vol. 1 No. 04 (2024)

    This project titled "Advanced Computer Vision Model for Aiding Automobiles in Traffic Sign Classification" addresses the imperative need for enhancing road safety and driving efficiency through the application of cutting-edge technologies. The project focuses on developing a sophisticated computer vision model equipped with advanced algorithms and deep learning techniques. This model aims to accurately identify and classify various traffic signs encountered by vehicles in real-time.

    The proposed solution leverages state-of-the-art technologies, including convolutional neural networks (CNNs) and advanced image recognition techniques, to enable swift and accurate analysis of visual data captured by on-board cameras. The system exhibits adaptability to diverse environmental conditions and lighting scenarios, ensuring robust performance under varying circumstances.

    The primary objective of the project is to contribute to road safety by providing an intelligent system capable of recognizing a wide range of traffic signs, including regulatory, warning, and information signs. Through continuous learning and refinement, the computer vision model evolves to optimize its classification accuracy, contributing to a safer and more efficient driving experience.

    This project not only serves as a practical application of computer vision principles but also aligns with the broader goals of advancing technology for societal benefit. The outcomes of this research aim to pave the way for the integration of intelligent systems into automobiles, thereby making significant strides towards creating a safer and smarter transportation ecosystem.

  • Advanced Threat Detection: Enhancing Weapon Identification and Facial Recognition with YOLOv8
    Vol. 2 No. 02 (2025)

    Abstract:
    The provided implementation code integrates several computer vision and machine learning techniques to detect and analyze the presence of persons and weapons in an image, utilizing pose estimation to further assess the relationship between detected persons and objects. It employs convolutional neural networks (CNNs), specifically a custom model for person detection and YOLO (You Only Look Once) for weapon detection. The pose estimation is performed using the Media Pipe library, which helps in identifying human poses by locating key points on the person's figure depicted in the image.

    The process initiates by loading the necessary models and preparing the image for detection tasks. Persons in the image are identified using a custom CNN model that processes images pre-processed to highlight facial features, while weapon detection is handled by YOLO, which scans for items classified as weapons. Following the detection, the application determines the spatial relationship between detected weapons and persons by analyzing key points from the pose estimation to see if a person is holding a weapon.

    Keywords- computer vision, machine learning, pose estimation, object detection, CNN, YOLO, MediaPipe, facial recognition, weapon detection, image processing, security applications, real-time analysis, convolutional neural networks, keypoints detection.

  • Sign Language Recognition System integrated with a mobile application
    Vol. 1 No. 05 (2024)

    Abstract

    This project aims to develop an innovative Sign Language Recognition System integrated with a mobile application, utilizing state-of-the-art machine learning techniques. The project addresses the communication challenges faced by individuals with hearing impairments by providing a real-time, efficient, and user-friendly solution for sign language interpretation.

    The proposed system leverages a comprehensive dataset for training a machine learning model, enabling it to accurately recognize a diverse range of sign language gestures. The mobile application acts as a seamless interface, allowing users to communicate through sign language effortlessly. The system's integration with a mobile platform enhances accessibility, enabling users to engage in conversations, access information, and participate in various daily activities.

    Key features of the project include real-time gesture recognition, continuous learning and updating of the machine learning model for improved accuracy, and a user-friendly interface for both sign language users and those interacting with them. The successful implementation of this Sign Language Recognition System is expected to contribute significantly to fostering inclusivity and accessibility for individuals with hearing impairments.

    Index Terms

    Sign language recognition, Machine learning techniques, Mobile applications, Communication challenges, Hearing impairments, Real-time gesture recognition, Dataset, Accessibility, Inclusivity, Continuous learning, User-friendly interface, Integration, Mobile platform, Daily activities, Improved accuracy.

  • AI-Powered Image Recognition for Early Detection of Bacterial Blight and Black Rot in Mustard Plants
    Vol. 2 No. 04 (2025)

    Abstract

    This project focuses on the development and implementation of an advanced image recognition system for the timely and accurate diagnosis of bacterial blight and black rot in mustard plants. Agricultural diseases such as bacterial blight and black rot can significantly impact crop yield and quality, necessitating swift and precise identification for effective management. Traditional methods of disease diagnosis are often time-consuming and rely heavily on manual inspection, prompting the need for automated and efficient solutions.

    The proposed system leverages state-of-the-art image processing techniques and deep learning algorithms to analyze high-resolution images of mustard plants. Through extensive training on diverse datasets encompassing various stages of infection, the model learns to discern subtle visual cues indicative of bacterial blight or black rot. Features such as leaf discoloration, lesion patterns, and overall plant health are systematically examined, contributing to the model's diagnostic accuracy.

    The project aims to provide a user-friendly interface for agricultural professionals, enabling them to capture and upload images for automated analysis. The system's ability to swiftly differentiate between healthy and infected mustard plants facilitates early disease detection, allowing farmers to implement timely intervention and management strategies.

    The successful implementation of this advanced image recognition system holds the potential to revolutionize the field of crop disease diagnostics, offering a valuable tool for mustard plant health monitoring and contributing to sustainable agriculture practices. The project aligns with the intersection of technology and agriculture, showcasing the significance of leveraging cutting-edge solutions to address real-world challenges in the agricultural sector.

     

    Index Terms

    Image recognition, Bacterial blight, Black rot, Mustard plants, Crop diseases, Agricultural diagnostics, Deep learning algorithms, Automated analysis, Disease detection, Intervention strategies, Sustainable agriculture, Plant health monitoring, Agricultural technology, User interface, Model training, Leaf discoloration, Lesion patterns, Agricultural professionals, Timely diagnosis, Precision agriculture

  • Air Pollution Monitoring Using Machine Learning for Environmental Sustainability
    Vol. 1 No. 02 (2024)

    Abstract

    Air pollution is a critical environmental issue with significant implications for public health and the well-being of ecosystems. This project focuses on developing an innovative solution for air pollution monitoring utilizing machine learning (ML) techniques. The primary objective is to design a system that can accurately predict, analyze, and monitor air quality in real-time, providing valuable insights for effective pollution control and management.

    The proposed system incorporates a network of sensors strategically placed in various locations to capture diverse air quality parameters such as particulate matter, nitrogen dioxide, sulfur dioxide, and more. The collected data is then processed through ML algorithms to identify patterns, correlations, and trends, enabling the system to make accurate predictions about air quality levels.

    The project aims to address the limitations of traditional monitoring systems by 

    leveraging the adaptability and self-learning capabilities of ML models. By continuously updating and refining the models based on incoming data, the system becomes more adept at providing precise and timely information on air quality fluctuations.

    This endeavor not only contributes to a deeper understanding of local air pollution dynamics but also empowers decision-makers with actionable insights for implementing targeted interventions. The integration of ML in air pollution monitoring represents a significant step towards creating sustainable and data-driven strategies for mitigating the adverse effects of pollution on human health and the environment.

    Index terms

    Air pollution, Machine Learning, Environmental Monitoring, Sustainability, Predictive Modeling, Sensor Network, Data Analysis.

  • Mental Health Monitoring Web Application Using Machine Learning
    Vol. 1 No. 04 (2024)

    The "Mental Health Monitoring Web Application" project aims to address the growing need for accessible and efficient tools to support mental well-being. In today's fast-paced world, individuals often face challenges in managing their mental health due to various stressors and lifestyle factors. This project proposes the development of a web-based application designed to empower users to monitor and enhance their mental well-being effectively.

    The application will offer a user-friendly interface allowing users to log their mood, activities, and thoughts regularly. By analyzing this data over time, the application will help users identify patterns, triggers, and trends impacting their mental state. Additionally, the application will provide personalized recommendations, resources, and self-care techniques tailored to each user's needs and preferences.

    Key features of the application may include goal setting, appointment scheduling, and connectivity with mental health professionals for guidance and support. Through the integration of evidence-based practices and intuitive design, the Mental Health Monitoring Web Application seeks to promote mental health awareness, resilience, and overall well-being among its users.

    This project not only addresses the technological aspect of web application development but also delves into the critical domain of mental health, contributing to the advancement of tools and resources for mental health support in today's digital age.

  • Smart Face Recognition with Database-Integrated Identity Verification
    Vol. 2 No. 02 (2025)

    Abstract:

     

    The abstract describes an integrated system developed to enhance security and surveillance measures through simultaneous face recognition and object detection. Utilizing advanced machine learning algorithms, the system efficiently processes video streams to identify individuals and detect potential threats or items of interest within a monitored environment. Face recognition is achieved using sophisticated convolutional neural networks that have been trained on extensive datasets to ensure accurate and reliable identification. Concurrently, object detection is conducted via a YOLO (You Only Look Once) model, known for its real-time processing capabilities, enabling the identification of various objects, including weapons, within the same frame as the face detection.This dual capability is particularly advantageous for applications requiring comprehensive monitoring solutions, such as public safety or secure access control in sensitive areas. The system provides real-time analytics and visualizations by annotating video frames with labels and bounding boxes, indicating the presence and identity of both individuals and detected objects. Designed with scalability in mind, the framework can be customized or expanded to accommodate specific security requirements, offering a robust toolset for modern surveillance needs. This integrated approach not only enhances the effectiveness of surveillance systems but also significantly contributes to proactive security management and incident prevention.

    Keywords-face recognition, object detection, video surveillance, security systems, machine learning, convolutional neural networks, YOLO, real-time processing, public safety, secure access control, TensorFlow, OpenCV, deep learning, video analytics, surveillance technology, threat detection, computer vision, data visualization, scalability, proactive security management.

  • Blockchain Based Pharmaceutical Supply Chain Management System
    Vol. 1 No. 03 (2024)

    Abstract—Thisinitiativewilluseblockchaintechnologyto addressissues and inefficiencies in the pharmaceutical supply chain. The traditional pharmaceutical supply chain is vulnerable to problems such as counterfeiting, data inconsistencies, and a lack of transparency. The suggested solution makes use of a decentralized and transparent blockchain technology to improve the overall traceability, security, and efficiency of the supply chain.

    The solution uses smart contracts to automate and enforce presetbusinessrules,loweringtheriskofmistakesandfraud. Each transaction,from medicine manufacturetodistribution and retail, issecurelydocumented inanimmutableledger to provide a tamper-proof and genuine record. This degree of openness gives stakeholders, including as producers, distributors, and pharmacies, real-time information on the status and placement of pharmaceuticals.

     

    Keywords—Counterfeiting, Data Inconsistencies,Lack of transperency,Decentralized,Traceability,Security,Efficieny

    ,SmartContracts,Automation,Fraudprevention,Immutable ledger,Tamper-proof.

  • CROP RECOMMENDATION SYSTEM USING ML ALGORITHMS
    Vol. 1 No. 02 (2024)

    Abstract - Agriculture is the backbone of India's economy, crucial for the well-being of its people. Ensuring the production of high-quality crops is essential for maintaining a healthy lifestyle. Analyzing environmental and soil conditions, including factors such as moisture and pH levels, temperature, and chemical composition, is vital for cultivating superior crops. Predicting crop yields has become increasingly challenging due to unpredictable weather patterns caused by global warming, resulting in crop destruction, food scarcity, and tragic consequences such as farmer suicides. This study aims to develop a website utilizing machine learning models for crop recommendations, taking into account inputs such as pH values, temperature, and soil parameters. Various machine learning algorithms, including SVM, logistic regression, naive bayes, and Random Forest, are utilized, with Random Forest demonstrating superior prediction capabilities. These systems carefully analyse diverse factors, including soil quality, climate data, and past crop performance, to suggest optimal crops tailored to specific locations. Accessible through user-friendly platforms, crop recommendation systems empower farmers to harness the benefits of technology and data, thereby enhancing agricultural productivity, financial gains, and global food security.

     

    Ultimately, these systems serve as a crucial tool in advancing modern agriculture, leveraging technology and data analysis to assist farmers in making decisions that contribute to food security and agricultural sustainability.

    Key Words: Random Forest, machine learning model, moisture and pH level, temperature, and chemical composition, recommendation system, factors.

  • Data Trustworthiness in Mobile Crowd Sensing
    Vol. 2 No. 01 (2025)

    Abstract

    The project "Data Trustworthiness in Mobile Crowd Sensing" addresses the pressing challenge of ensuring the reliability and authenticity of data collected through mobile crowd sensing (MCS) applications. With the proliferation of sensor-equipped smartphones and ubiquitous connectivity, leveraging crowd intelligence for data acquisition has become widespread. However, guaranteeing the trustworthiness of this data, contributed by diverse sources, remains a major obstacle.

    This project develops robust frameworks, algorithms, and privacy-preserving mechanisms to validate and authenticate data in MCS environments. By focusing on stringent data collection protocols, advanced anomaly detection algorithms, and user-centric privacy measures, the project aims to enhance data reliability while fostering trust among participants. The inclusion of adaptive trust models and interactive feedback systems further ensures data quality.

    This work contributes significantly to the advancement of mobile crowd sensing by establishing a transparent, secure, and collaborative environment. Outcomes from the project are expected to transform data collection practices across domains like environmental monitoring, urban planning, and healthcare.

     

    Index Terms

    Mobile Crowd Sensing, Data Trustworthiness, Data Authentication, Data Validation, Privacy-Preserving Techniques, Collaborative Filtering, Adaptive Trust Models, Anomaly Detection, Sensor-Equipped Smartphones, Quality Control, Transparency, User Engagement, Environmental Monitoring, Urban Planning, Healthcare.

  • Deep Learning-Powered Automated Detection of Abnormalities in Chest X-Rays
    Vol. 2 No. 04 (2025)

    Abstract

    Medical imaging plays a crucial role in diagnosing various diseases and abnormalities within the human body, with chest X-rays being one of the most commonly used modalities. In recent years, deep learning techniques have shown remarkable promise in automating the analysis of medical images, including the detection of abnormalities in chest X-rays. This project aims to explore the application of deep learning algorithms, particularly convolutional neural networks (CNNs), for the automated detection of abnormal findings in chest X-rays. The project will involve the collection and preprocessing of a diverse dataset of chest X-ray images, encompassing both normal and abnormal cases. Subsequently, deep learning models will be trained, validated, and fine-tuned using the collected dataset to accurately classify chest X-rays as either normal or abnormal based on the presence of various pathologies such as pneumonia, lung nodules, or pleural effusion. The performance of the developed models will be evaluated using standard metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). The outcomes of this project aim to contribute to the advancement of computer-aided diagnosis systems in healthcare, potentially aiding clinicians in making more accurate and timely diagnoses, thus improving patient outcomes.

    Index Terms

    Medical Imaging, Chest X-rays, Deep Learning, Convolutional Neural Networks (CNNs), Automated Detection, Abnormal Findings, Dataset Collection, Preprocessing, Pathologies, Pneumonia, Lung Nodules, Pleural Effusion, Performance Evaluation, Accuracy, Sensitivity, Specificity, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Computer-Aided Diagnosis Systems, Healthcare, Patient Outcomes.

     

     

  • Predictive Modeling for Undergraduate Engineering Branch Allocation Leveraging Machine Learning to Optimize Admissions
    Vol. 1 No. 04 (2024)

    The allocation of branches in the admission process of undergraduate engineering programs plays a crucial role in shaping the academic journey of students. With limited seats and diverse preferences among applicants, accurately predicting the branch allocation based on ranks becomes imperative for educational institutions. This project aims to develop a predictive model for branch allocation, leveraging historical data and machine learning techniques. By analyzing past admission trends, the project seeks to identify patterns and factors influencing branch preferences. Utilizing algorithms such as regression analysis and decision trees, the model will forecast the likelihood of a student being allocated to a specific branch based on their rank and other relevant parameters. The project will also explore the integration of student preferences and institutional requirements to enhance the accuracy of predictions. Ultimately, the proposed predictive model aims to assist admission committees in making informed decisions, optimizing branch allocation, and ensuring a fair and efficient admission process for aspiring engineering students.

  • Real-Time Vehicle License Plate Detection and Recognition Using YOLOv5
    Vol. 2 No. 03 (2025)

    ABSTRACT

    The project titled "Vehicle Number Plate Detection and Extraction using YOLO V5" focuses on developing an efficient system for automating the identification and extraction of license plates from images or video streams. The implementation utilizes the YOLO V5 (You Only Look Once) object detection model, known for its real-time processing capabilities.

    The project begins with the collection and preparation of a diverse dataset containing images of vehicles, ensuring adequate representation of various license plate types, sizes, and environmental conditions. This dataset is then used to train the YOLO V5 model, fine-tuning its parameters for accurate and robust license plate detection.

    Upon successful training, the model is deployed to analyze new input data. During the inference phase, the YOLO V5 model identifies the regions of interest corresponding to license plates within the images or video frames. Subsequently, a mechanism is implemented to extract the license plate information, including alphanumeric characters.

    KEYWORDS

    • YOLOv5
    • Vehicle number plate
    • Detection
    • Extraction
    • Computer vision
    • Deep learning
    • Image processing
    • Object detection
    • ANPR (Automatic Number Plate Recognition)

  • The CRIMINAL INVESTIGATION TRAKER AND SUSPECT DETECTION
    Vol. 1 No. 1 (2024)

    Abstract

    This paper presents a comprehensive system designed to assist law enforcement agencies in tracking criminal activities and detecting suspects efficiently using the MERN stack. The developed system integrates MongoDB, Express.js, React.js, and Node.js to handle large volumes of data with high responsiveness. This paper details the system architecture, implementation, testing methodologies, and the tangible benefits observed during initial deployments.

    Index Terms

    Criminal Investigation, Suspect Detection, MERN Stack, Real-Time Systems, Law Enforcement Technologies.

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