Archives

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  Zero-Shot Multilingual Sentiment Analysis Using Transformer-Based Models Exploring Feasibility and Effectiveness

  Vol. 1 No. 04 (2024)

  This project aims to explore the feasibility and effectiveness of zero-shot multilingual sentiment analysis using transformer-based models. Traditional sentiment analysis techniques often rely on language-specific models trained on large corpora of labeled data, making them impractical for analyzing sentiments across multiple languages. In contrast, transformer models, such as BERT and GPT, have shown promising results in natural language understanding tasks by leveraging large-scale pre-training and fine-tuning on specific tasks. This project proposes to extend the capabilities of transformer models to perform sentiment analysis across various languages without requiring language-specific training data. The project will involve pre-training a transformer model on multilingual text data and fine-tuning it on sentiment analysis tasks using transfer learning techniques. The effectiveness of the proposed approach will be evaluated on standard benchmark datasets in multiple languages, measuring the accuracy and robustness of sentiment predictions. The outcomes of this project have the potential to significantly enhance the applicability of sentiment analysis tools in multilingual settings, catering to diverse linguistic communities and enabling broader cross-cultural sentiment analysis applications.

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  AI-Driven Hybrid System for Personalized Movie Recommendations

  Vol. 2 No. 02 (2025)

  Abstract:

  This paper introduces a novel hybrid recommendation system that integrates content-based and collaborative filtering approaches using deep learning techniques to enhance movie recommendations. Our model merges the metadata of movies, including genres, cast, and crew from the MovieLens dataset with user ratings to construct a comprehensive feature set. We employ a Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer to extract content-based features and utilize Singular Value Decomposition (SVD) to derive collaborative filtering features, thereby addressing both user preferences and item characteristics.We further enhance the model by concatenating these features into a unified representation, which is then processed through a deep neural network to predict movie ratings. The network architecture consists of multiple dense layers with dropout regularization to prevent overfitting, ensuring robustness in learning complex user-item interactions. We evaluate our model on a standard dataset, focusing on mean squared error (MSE) as the performance metric to assess accuracy.The results demonstrate the effectiveness of our hybrid approach in providing precise recommendations by leveraging both the semantic content of movies and the historical interactions of users, thereby outperforming traditional methods that rely on singular recommendation strategies. This research contributes to the recommendation system community by showcasing a scalable and efficient method to improve recommendation quality and user satisfaction in multimedia services.

  Keywords- Recommendation systems, Deep learning, Hybrid models, Collaborative filtering, Content-based filtering, MovieLens dataset, TF-IDF vectorization, Singular Value Decomposition, Neural networks, User-item interaction, Multimedia services, Movie recommendations, Personalization, Machine learning, Artificial intelligence.

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  AI-Powered Mobile Application for Visually Impaired People

  Vol. 1 No. 03 (2024)

  Abstract

  This paper introduces an innovative mobile application powered by Artificial Intelligence (AI) technology, designed to significantly improve the daily lives of visually impaired individuals. The application incorporates advanced features such as object recognition, color recognition, offline functionality, currency notes recognition, barcode reading, text reader. These features collectively empower users by providing real-time information about their surroundings, facilitating safe and independent, enabling secure financial transactions, and enhancing overall accessibility. The integration of AI algorithms ensures accurate and efficient performance, making the application a valuable tool for improving the quality of life and promoting inclusivity for visually impaired individuals. This research contributes to the field of assistive technology by showcasing the potential of AI-driven solutions in addressing accessibility challenges and fostering independence among diverse user groups.

  keywords

  Artificial Intelligence, Mobile Application, Accessibility, Assistive Technology, Color Recognition, Offline Functionality, Object Recognition, Currency Notes Recognition , Barcode Reader , Text Reader , Scene Recognition, Voice Based Navigation

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  A Tamper-Proof Certificate Validation System Using Blockchain Technology

  Vol. 2 No. 06 (2025)

  Abstract

  This project aims to explore the application of blockchain technology in enhancing the verification and validation processes of digital certificates. As the digital landscape continues to evolve, the need for secure and reliable methods of validating credentials becomes paramount. Traditional methods of certificate verification often face challenges related to security and trust.

  The project focuses on leveraging the decentralized and tamper-resistant nature of blockchain to establish a robust framework for digital certificate verification. Through the implementation of smart contracts and cryptographic principles, the proposed system ensures the integrity and authenticity of digital certificates. The blockchain network acts as a distributed ledger, recording and validating certificate transactions across multiple nodes.

  Key objectives of the project include designing a user-friendly interface for certificate submission and verification, implementing secure cryptographic techniques for digital signatures, and integrating smart contracts to automate the validation process. The project also explores the potential scalability and efficiency benefits of blockchain technology in handling large-scale certificate verification scenarios.

  Through this endeavor, this project aims to contribute to the advancement of secure credential verification systems, addressing the challenges associated with traditional methods. The project aligns with the broader goals of enhancing digital trust and security, offering a practical and innovative solution for validating digital certificates using cutting-edge blockchain technology.

  Index Terms

  Blockchain technology, Digital certificates, Verification processes, Validation processes, Decentralization, Tamper-resistance, Smart contracts, Cryptographic principles, Distributed ledger, User-friendly interface, Cryptographic techniques, Digital signatures, Scalability, Efficiency, Credential verification systems, Digital trust, Security, Innovation.

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  Missing Child Identification System using Deep Learning and Multiclass SVM

  Vol. 1 No. 02 (2024)

  Abstract

  The "Missing Child Identification System using Deep Learning and Multiclass SVM" is a groundbreaking project designed to address the pressing issue of locating and identifying missing children. Leveraging advanced technologies in the realms of deep learning and machine learning, this project aims to create a robust system for facial recognition and classification.

  The deep learning component of the system utilizes state-of-the-art techniques to extract intricate facial features, generating comprehensive representations of each child. Simultaneously, a multiclass Support Vector Machine (SVM) is employed to classify and refine the identification process. The SVM acts as a classifier, distinguishing between different classes of facial features, thereby enhancing the accuracy of categorizing missing children.

  The integration of deep learning and multiclass SVM in this project facilitates a more effective and efficient means of matching facial characteristics with existing databases. The result is a powerful tool that streamlines the identification process, enabling authorities to quickly and accurately reunite missing children with their families.

  This project not only showcases the potential of cutting-edge technologies in addressing social issues but also underscores the significance of technology-driven solutions in humanitarian efforts. The "Missing Child Identification System" stands as a testament to the positive impact that technology can have on society, particularly in safeguarding the well-being and security of our most vulnerable population – our children.

  Index terms

  Missing Child Identification, Deep Learning, Multiclass SVM, Facial Recognition, Machine Learning, Social Impact, Humanitarian Technology, Child Safety, Database Matching, Technology Solutions, Vulnerable Populations, Reunification Efforts, Advanced Technologies, Facial Feature Extraction, Classifier Systems

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  Student Dropout Prediction using DeepFM Algorithm

  Vol. 2 No. 01 (2025)

  Abstract

  This project revolves around the development and implementation of a predictive model for anticipating student dropout utilizing the innovative DeepFM algorithm. DeepFM, a fusion of deep learning and factorization machines, proves to be a potent tool for unraveling complex patterns within diverse datasets. The project aims to leverage the algorithm's capability to analyze and interpret multifaceted features such as academic performance, attendance, and socio-economic factors. Through the integration of deep neural networks and collaborative filtering, DeepFM demonstrates proficiency in discerning intricate relationships, contributing to accurate student dropout predictions. The project's primary objectives include the exploration of DeepFM's suitability for handling sparse and high-dimensional student data, as well as its potential to offer proactive insights for educational institutions to address and mitigate dropout risks. The anticipated outcome is a robust predictive model that aids educational institutions in identifying potential dropout candidates, facilitating timely interventions and support mechanisms to enhance student retention.

  Index Terms

  Predictive modeling, Student dropout prediction, DeepFM algorithm, Deep learning, Factorization machines, Multifaceted features, Academic performance, Attendance, Socio-economic factors, Deep neural networks, Collaborative filtering, Sparse data handling, High-dimensional data, Dropout risk mitigation, educational institutions, Timely interventions, Support mechanisms, Student retention, Data analysis, Machine learning applications.

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  Few-Shot Learning for Efficient and Accurate Crop Disease Detection in Agriculture

  Vol. 2 No. 04 (2025)

  Abstract

  Crop diseases pose a significant threat to agricultural productivity, necessitating rapid and accurate detection methods. This project explores the application of N-shot/Few-shot learning techniques for crop disease classification and detection. The conventional challenge in agricultural datasets is the scarcity of labeled examples for various disease classes. Leveraging the principles of N-shot learning, the project aims to develop a model capable of accurately identifying and categorizing crop diseases with only a limited number of labeled instances per class.

  The project involves the implementation of a machine learning model that learns to generalize from a small dataset, thus overcoming the limitations of insufficient labeled samples. By adopting N-shot learning, the system becomes adept at recognizing patterns and features associated with different crop diseases, enabling it to make reliable predictions even in scenarios with sparse labeled data. The proposed solution holds promise for resource-constrained environments where obtaining extensive labeled datasets is challenging.

  Through the development and evaluation of the N-shot/Few-shot learning model, this project contributes to the advancement of efficient and cost-effective crop disease detection methods. The outcomes are expected to have implications for sustainable agriculture by providing farmers with a reliable tool for early disease identification, thereby aiding in timely and targeted interventions to safeguard crop yield and quality.

  Index terms

  Crop diseases, Agricultural productivity, N-shot learning, Few-shot learning, Crop disease classification, Disease detection methods, labeled datasets, Machine learning models, Generalization, Pattern recognition, Feature extraction, Sparse labeled data, Resource, constrained environments, Efficient detection methods, Cost-effective solutions, Early disease identification, Timely interventions, Sustainable agriculture, Crop yield, Crop quality.

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  A Comprehensive Defense Model Against DoS and DDoS Attacks

  Vol. 2 No. 06 (2025)

  Abstract

  This project explores the development and implementation of a mitigation strategy against Dos and DDoS attacks using a hybrid model.

  The project aims to analyze the intricacies of Dos and DDoS attacks, focusing on the hybrid model that combines both volumetric and application layer attack vectors. The research involves studying existing attack methodologies and understanding their impact on network infrastructure and application functionalities. Through this analysis, the project seeks to design and implement a comprehensive defense mechanism that addresses the challenges posed by hybrid Dos and DDoS attacks.

  Key objectives of the project include:

  Investigating various Dos and DDoS attack vectors and their implications on network security.

  Designing a hybrid model that integrates countermeasures against volumetric and application layer attacks.

  Developing and implementing a prototype system to demonstrate the effectiveness of the proposed hybrid defense mechanism.

  Evaluating the performance of the hybrid model under simulated attack scenarios and real-world conditions.

  The outcomes of this project are expected to contribute to the enhancement of network security measures, providing a more resilient defense against evolving Dos and DDoS threats. This research aligns with the broader goal of fortifying network infrastructures to ensure the availability and reliability of critical online services in the face of sophisticated cyber-attacks.

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  Empowering Community Bonds through Resources and Sharing

  Vol. 1 No. 05 (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.

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  AI-Driven Stock Market Forecasting: Leveraging News Sentiment and NLP for Predictive Analytics

  Vol. 2 No. 03 (2025)

  Abstract

  This project delves into the innovative realm of stock price prediction by leveraging the insights derived from news articles. In an era characterized by the abundance of financial information and the rise of natural language processing techniques, the project aims to develop a predictive model that harnesses the power of textual data to forecast stock price movements.

  The project involves the collection of relevant news articles from diverse sources, with a focus on financial news that could impact stock markets. Natural language processing algorithms are implemented to analyze the sentiment and content of these articles, extracting key features that may influence stock prices. The dataset is then utilized to train machine learning models, employing techniques such as sentiment analysis and time-series analysis.

  The predictive model's performance is evaluated using historical stock data, measuring its accuracy in forecasting price movements against actual market outcomes. The project also explores the impact of various external factors, such as market trends, economic indicators, and geopolitical events, on the accuracy of predictions.

  Through this project aims to contribute to the evolving field of financial technology by providing a comprehensive exploration of the feasibility and effectiveness of utilizing news articles for stock price prediction. The project's findings may offer valuable insights into the integration of natural language processing and machine learning techniques in financial analysis, paving the way for advancements in predictive modeling within the context of dynamic and unpredictable stock markets.

  Index terms

  Stock price prediction, News articles, Natural language processing (NLP), Financial information, Machine learning models, Sentiment analysis, Time-series analysis, Historical stock data, Market trends, Economic indicators, Geopolitical events, Financialtechnology (FinTech), Predictive modeling, Financial analysis, Integration of NLP and machine learning, Dynamic stock markets.

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  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

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  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.

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  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.

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  Smart City App for Citizen Complaint Management

  Vol. 2 No. 05 (2025)

  Abstract

  The project titled "Smart City App for Citizen Complaint Management" aims to develop a comprehensive mobile application to streamline and enhance the process of citizen complaint resolution within urban environments. The project focuses on creating a user-friendly interface that enables citizens to easily report issues such as potholes, streetlight outages, and other civic concerns.

  Key features of the app include a simple and intuitive complaint submission system, allowing users to provide detailed information about the nature of the problem. The application assigns a unique reference number to each complaint, facilitating efficient tracking and monitoring by municipal authorities.

  The project also emphasizes the integration of a centralized dashboard for municipal officials, enabling them to prioritize and manage complaints effectively. The app incorporates automation to categorize and prioritize complaints based on urgency and severity, optimizing resource allocation for timely issue resolution.

  Transparency is a key aspect of the Smart City app, with real-time updates and notifications keeping citizens informed about the status and progress of their submitted complaints. Users are encouraged to provide feedback on the resolution process, promoting continuous improvement in municipal services.

  By addressing the challenges of citizen complaint management, the project contributes to the development of smarter and more responsive urban environments. The Smart City app aims to foster a collaborative relationship between citizens and municipal authorities, ultimately leading to a more efficient and engaged community.

  Index Terms

  Smart city, Citizen complaint management, Mobile application, Urban environments, Potholes, Streetlight outages, Civic concerns, User-friendly interface, Complaint submission system, Municipal authorities, Centralized dashboard, Automation, Issue resolution, Transparency, Real-time updates, Notifications, Feedback, Municipal services, Collaborative relationship, Community engagement.

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  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.

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  Real-Time Facial Analytics: A Deep Learning Approach to Gender, Age, and Emotion Recognition

  Vol. 2 No. 06 (2025)

  Abstract

  This project aims to develop a sophisticated real-time face recognition system capable of extracting comprehensive insights such as gender, age, and emotion, while incorporating statistical analysis. The project leverages advanced deep learning architectures, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks, to achieve a multi-dimensional understanding of facial attributes.

  The Convolutional Neural Network is employed for its effectiveness in spatial feature extraction, enhancing the accuracy of gender and age estimation. Support Vector Machines contribute to refining classification boundaries, augmenting the overall precision of the recognition system. The inclusion of Long Short-Term Memory networks enables the model to capture temporal dependencies, facilitating nuanced emotion analysis in real-time scenarios.

  Additionally, the project incorporates statistical methods to provide valuable insights into the distribution and variability of demographic attributes and emotional states within the dataset. The holistic integration of these diverse approaches ensures a robust and efficient real-time face recognition system capable of delivering accurate and nuanced results across multiple dimensions. This project not only contributes to the advancement of facial recognition technology but also offers a valuable learning experience in the realm of deep learning and computer vision.

  Keywords

  Real-time face recognition, Gender estimation, Age estimation, Emotion analysis, Statistical analysis, Deep learning architectures, Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Long Short-Term Memory (LSTM) networks, Spatial feature extraction, Temporal dependencies, Demographic attributes, Emotional states, Dataset analysis, Robust recognition system, Computer vision, Deep learning, Facial attributes, multi-dimensional understanding, Nuanced results.

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  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.

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  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.

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  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.

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  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.

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  ParkCam – Smart Vision-Based Parking Management

  Vol. 2 No. 05 (2025)

  Abstract

  The "Smart Parking System using Computer Vision" project aims to revolutionize traditional parking management by incorporating advanced computer vision technology. This project focuses on the development of a system that utilizes real-time video feeds from cameras installed in parking areas. The project employs sophisticated image processing algorithms to detect and track vehicles, accurately identifying available parking spaces.

  Through the integration of computer vision, the system distinguishes between occupied and vacant parking spots, providing users with instant updates on parking availability. The project also incorporates machine learning algorithms to enhance accuracy and adaptability, allowing the system to learn from patterns and optimize its performance in different parking scenarios.

  The user interface, accessible through a mobile application or web platform, provides real-time information on parking availability and guides users to the nearest vacant spot. This project not only streamlines the parking experience for users but also contributes to efficient traffic flow and reduced congestion in parking areas. The "Smart Parking System using Computer Vision" project showcases the potential of cutting-edge technology in addressing urban mobility challenges and enhancing the overall parking management landscape.

  Index terms

  Smart Parking System, Computer Vision, Real-time Video Feeds, Image Processing Algorithms, Vehicle Detection, Parking Space Tracking, Occupied and Vacant Parking Spots, Machine Learning Algorithms, User Interface, Mobile Application, Web Platform, Parking Availability, Traffic Flow, Congestion Reduction, Urban Mobility, Parking Management, Technology Integration, Efficiency Optimization.

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  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.

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  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.

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  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

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  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.