Archives - Page 3

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  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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  Enhancing Climate Resilience through Machine Learning-Driven Insights

  Vol. 2 No. 06 (2025)

  Abstract

  Climate change poses significant challenges to communities worldwide, necessitating innovative solutions to enhance resilience against its impacts. This project focuses on leveraging Machine Learning (ML) techniques to strengthen climate resilience. The project aims to develop and implement ML algorithms to analyze diverse datasets related to climate patterns, extreme weather events, and environmental conditions.

  The primary objectives include identifying patterns and trends within the data, enabling the prediction of climate-related risks and the optimization of resource allocation. The project will explore the application of ML in various domains, such as agriculture, infrastructure planning, disaster preparedness, and resource management, to develop adaptive solutions for communities.

  Through the integration of ML, the project seeks to contribute to more effective decision-making processes and the development of proactive strategies to address climate-related challenges. The continuous learning and refinement of ML models will enable the creation of sustainable and adaptive systems, enhancing the resilience of communities to the dynamic impacts of climate change.

  This student project not only provides an opportunity to apply theoretical knowledge in real-world scenarios but also underscores the significance of technology in fostering climate resilience for a sustainable future.

  Index terms

  Climate change, Machine Learning (ML), Resilience, Predictive modeling, Climate patterns, Extreme weather events, Environmental conditions, Resource allocation, Agriculture, Infrastructure planning, Disaster preparedness, Resource management, Decision-making processes, Adaptive solutions, Sustainable development, Proactive strategies, Community resilience, Data analysis, Model refinement, Real-world applications, Technology integration.

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  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.

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  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)
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  Integrated E-Commerce Platform for Agriculture: Enhancing Supply Chain Efficiency and Farmer Empowerment

  Vol. 2 No. 04 (2025)

  Abstract

  The " Integrated E-Commerce Platform for Agriculture Enhancing Supply Chain Efficiency and Farmer Empowerment" is a comprehensive technological solution designed to address the evolving needs of the agricultural sector. This project aims to create a user-friendly online platform that facilitates seamless transactions and collaboration between farmers, agribusinesses, and suppliers. The portal integrates various modules, including inventory management, order tracking, and payment processing, to streamline the agricultural supply chain.

  The primary focus is on providing farmers with easy access to a diverse range of agricultural products and services, such as seeds, fertilizers, and equipment. The portal also serves as an information hub, offering valuable insights on best practices, market trends, and innovative farming techniques. By fostering a digital marketplace, the project aims to empower farmers, enhance their decision-making processes, and contribute to the overall efficiency and sustainability of the agricultural ecosystem.

  Through this Integrated E-Commerce Portal, the project envisions creating a connected and collaborative environment for stakeholders in the agricultural value chain. This initiative not only supports farmers in making informed choices but also promotes transparency and efficiency in agricultural transactions. Overall, the project aims to leverage technology to uplift the agricultural community, fostering growth, sustainability, and improved productivity in the sector.

  Index terms

  Integrated E-Commerce Portal, Agriculture, Technological solution, User-friendly platform, Seamless transactions, Collaboration, Farmers, Agribusinesses, Suppliers, Inventory management, Order tracking, Payment processing, Agricultural supply chain, Agricultural products, Services, Seeds, Fertilizers, Equipment, Information hub, Best practices, Market trends, Farming techniques, Digital marketplace, Empowerment, Decision-making processes, Efficiency, Sustainability, Stakeholders, Value chain, Transparency, Growth, Productivity.

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  Data Trustworthiness in Mobile Crowd Sensing

  Vol. 1 No. 03 (2024)

  Abstract

  The project, "Data Trustworthiness in Mobile Crowd Sensing," aims to address the critical issue of ensuring the reliability and authenticity of data collected through mobile crowd sensing applications. In the rapidly evolving landscape of sensor-equipped smartphones and ubiquitous connectivity, leveraging the collective intelligence of a crowd for data acquisition has become increasingly popular. However, the inherent challenges of ensuring the trustworthiness of data gathered from diverse sources pose significant obstacles.

  This project focuses on developing robust mechanisms and algorithms to validate and authenticate data in the context of mobile crowd sensing. The research encompasses the design and implementation of stringent data collection protocols, authentication measures, and quality control mechanisms to filter out inaccurate or fraudulent data points. The goal is to enhance the overall reliability of information collected from various contributors.

  In addition to technical aspects, the project emphasizes the importance of creating a transparent and collaborative environment. Privacy-preserving techniques and clear communication regarding data usage policies are integral components to foster trust among contributors. By addressing these aspects, the project aims to establish a framework that ensures the anonymity and privacy of participants while building a foundation of trust in the mobile crowd sensing ecosystem.

  Ultimately, the outcomes of this project are expected to contribute significantly to the advancement of reliable data collection practices in mobile crowd sensing applications, fostering innovation in areas such as environmental monitoring, urban planning, and healthcare.

  Index Terms

  Mobile Crowd Sensing, Data Trustworthiness, Data Authentication, Data Validation, Reliability, Authenticity, Sensor-equipped Smartphones, Ubiquitous Connectivity, Collective Intelligence, Data Collection Protocols, Quality Control Mechanisms, Fraudulent Data, Privacy-preserving Techniques, Data Usage Policies, Transparency, Collaboration, Privacy, Anonymity, Environmental Monitoring, Urban Planning, Healthcare.

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  Innovative Dual Authentication Protocols for Cloud Data Storage and Sharing

  Vol. 2 No. 01 (2025)

  Abstract

  As cloud computing continues to revolutionize the way data is stored and shared, the security of sensitive information becomes a paramount concern. This BTech project delves into the design and implementation of a robust dual access control framework for cloud-based data storage and sharing. The proposed system aims to enhance the security posture of cloud environments by incorporating a two-tier authentication mechanism.

  The dual access control system combines traditional authentication methods, such as usernames and passwords, with an additional layer of security, such as biometrics or multi-factor authentication. This two-pronged approach ensures that only authorized users can access and share data stored in the cloud, minimizing the risk of unauthorized access and potential data breaches.

  The project will involve the development of a prototype system, including the integration of authentication mechanisms and the establishment of secure data sharing protocols. Through rigorous testing and evaluation, the effectiveness of the dual access control system in safeguarding sensitive information will be assessed.

  The outcomes of this project are expected to contribute to the advancement of cloud security practices, providing a valuable solution for organizations and individuals seeking enhanced protection for their data in cloud environments. The project aligns with the growing demand for innovative approaches to address the evolving challenges of securing information in the era of cloud computing.

  Index Terms

  Cloud computing, Data storage, Data sharing, Security, Dual access control, Authentication mechanisms, Two-tier authentication, Biometrics, Multi-factor authentication, Prototype development, Secure data sharing protocols, Testing and evaluation, Sensitive information, Data breaches, Cloud security practices, Innovative approaches, Organizational security, Individual security, Evolution of challenges.