Archives

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  Intelligent Fake Profile Detection for Instagram Using Machine Learning

  Vol. 2 No. 02 (2025)

  Abstract:

  The project entails the development of a Python-based system for detecting fake Instagram profiles using machine learning models and image processing techniques. The script employs libraries such as TensorFlow, Keras, Scikit-Learn, Pandas, Matplotlib, Seaborn, and OpenCV, facilitating extensive data handling, statistical analysis, visualization, and machine learning model training. The process begins by importing relevant libraries and datasets, followed by preliminary data exploration and visualization to understand the features and distributions within the data. The training dataset is preprocessed and scaled before being fed into a neural network model structured with multiple dense layers and dropout for regularization, optimized using Adam optimizer for categorical cross-entropy loss. The model's performance is evaluated using metrics like accuracy and loss progression during training/validation phases. Further, the script incorporates image processing techniques to analyze profile pictures using Haar Cascade classifiers for face detection, which contributes to determining the authenticity of profiles. Additionally, OCR (Optical Character Recognition) via Easy OCR is applied to extract textual data from images, providing a comprehensive understanding of profile components such as username, posts, followers, and other descriptive elements. Overall, this project integrates multiple computational techniques to create an automated tool that aids in identifying and analyzing fake profiles on Instagram, showcasing the practical application of AI and machine learning in social media management.

  Keywords- Instagram, Fake Profile Detection, Python, Machine Learning, TensorFlow, Keras, Neural Networks, Data Visualization, Image Processing, OpenCV, Haar Cascade, OCR, EasyOCR, Pandas, Matplotlib, Seaborn, Scikit-Learn, Statistical Analysis, Data Preprocessing, Model Evaluation, Text Extraction, Profile Analysis.

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  Brain Tumor Detection Using Convolutional Neural Network

  Vol. 1 No. 002 (2024)

  Abstract— Brain Tumor segmentation is one of the most crucial and arduous tasks in the terrain of medical image processing as a human-assisted manual classification can result in inaccurate prediction and diagnosis. Moreover, it is an aggravating task when there is a large amount of data present to be assisted. Brain tumors have high diversity in appearance and there is a similarity between tumor and normal tissues and thus the extraction of tumor regions from images becomes unyielding. In this paper, we proposed a method to extract brain tumor from 2D Magnetic Resonance brain Images (MRI) by Fuzzy C-Means clustering algorithm which was followed by traditional classifiers and convolutional neural network. The experimental study was carried on a real-time dataset with diverse tumor sizes, locations, shapes, and different image intensities. In traditional classifier part, we applied six traditional classifiers namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Logistic Regression, Naïve Bayes and Random Forest which was implemented in scikit-learn. Afterward, we moved on to Convolutional Neural Network (CNN) which is implemented using Keras and Tensorflow because it yields to a better performance than the traditional ones. In our work, CNN gained an accuracy of 97.87%, which is very compelling. The main aim of this paper is to distinguish between normal and abnormal pixels, based on texture based and statistical based features.

   

  Keywords— CNN, FCM, Medical Image, segmentation, SVM

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  CertiChain – A Blockchain-Based Framework for Digital Certificate Verification

  Vol. 2 No. 05 (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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  CCTV-Based Safety Gear Detection System for Enhancing Workplace Safety in Industrial Environments

  Vol. 1 No. 04 (2024)

  The implementation of CCTV surveillance for safety gear detection in industrial environments is becoming increasingly imperative to enhance workplace safety standards. This project aims to develop a robust system utilizing CCTV cameras to detect the presence or absence of essential safety gear worn by employees, such as helmets, goggles, and gloves. Through image processing techniques and machine learning algorithms, the system will analyze real-time video feeds to identify instances of non-compliance with safety regulations. The project will involve the design and development of algorithms for object detection and classification, as well as the integration of these algorithms into a user-friendly interface for industrial monitoring purposes. The anticipated outcome is a reliable CCTV surveillance system capable of proactively identifying safety gear violations, thereby mitigating potential hazards and promoting a safer work environment in industrial settings.

  Index terms

  CCTV surveillance, Safety gear detection, Industrial environments, Workplace safety standards, Image processing techniques, Machine learning algorithms, Object detection, Classification algorithms, Real-time video feeds, Non-compliance detection, Safety regulations, User-friendly interface, Industrial monitoring, Safety gear violations, Hazard mitigation, Proactive identification, Work environment safety, Robust system design.

   

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  LSTM-Based Telugu Text Generation: Enhancing NLP for Content Creation and Language Preservation

  Vol. 2 No. 04 (2025)

  Abstract

  In this project, a Telugu text generator utilizing Long Short-Term Memory (LSTM) neural networks is developed to facilitate the automatic generation of coherent and contextually relevant Telugu text. The LSTM architecture, known for its effectiveness in handling sequential data, particularly in natural language processing tasks, serves as the foundation for this text generation system. The project aims to address the growing demand for computational tools capable of generating Telugu text, catering to various applications such as content creation, language learning, and cultural preservation. Through extensive training on a large dataset of Telugu text, the LSTM model learns the underlying patterns, semantics, and grammatical structures of the language, enabling it to generate text that closely resembles human-written Telugu content. The project's implementation involves data preprocessing, model training, and evaluation phases, with a focus on optimizing the LSTM network's performance in generating fluent and coherent Telugu text. The developed Telugu text generator provides a valuable resource for Telugu speakers, researchers, and developers interested in leveraging natural language processing techniques for Telugu language applications.

  Index Terms

  Telugu text generation, Long Short-Term Memory (LSTM) neural networks, Sequential data processing, Natural language processing (NLP), Computational linguistics, Text generation systems, Language modeling, Data preprocessing, Model training, Evaluation metrics, Grammatical structures, Semantic analysis, Cultural preservation, Language learning, Content creation, Telugu language applications, Fluency assessment, Coherence analysis, Text similarity, Resource optimization.

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  Human Activity Recognition

  Vol. 1 No. 02 (2024)

  Abstract

  The project on "Human Activity Recognition" explores the implementation of sensor fusion and machine learning techniques to accurately identify and classify human activities in various scenarios. This research aims to contribute to the development of intelligent systems capable of understanding and responding to human behavior. The project focuses on utilizing data from diverse sensors, including accelerometers and gyroscopes, to capture intricate patterns associated with activities such as walking, running, sitting, and other movements.

  The proposed system employs advanced machine learning algorithms for the analysis of sensor data, aiming to enhance the accuracy and efficiency of human activity recognition. By integrating multiple sensor modalities, the project aims to create a robust model capable of handling diverse environmental conditions and activity contexts. The research explores the

  application of deep learning methodologies to extract meaningful features and patterns from the sensor data, enabling real-time and context-aware activity recognition.

  The significance of this project lies in its potential applications across various domains, including healthcare, sports analysis, and smart home environments. The developed system could find applications in health monitoring, personalized fitness tracking, and creating adaptive environments that respond to human behavior. The project not only contributes to the academic understanding of human activity recognition but also provides practical implications for the development of smart and responsive technologies in our daily lives.

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  A Hybrid Approach for Securing Digital Images Using Encryption and Steganography

  Vol. 2 No. 01 (2025)

  Abstract

  In today's digital age, ensuring the security and confidentiality of image data is paramount, particularly with the proliferation of online communication and storage platforms. This project proposes a hybrid approach for enhancing image security by synergistically integrating encryption and steganography techniques. The combination of these two methods aims to fortify the protection of digital images against unauthorized access and tampering. Encryption ensures the confidentiality of image content by converting it into an unintelligible form using robust cryptographic algorithms. Concurrently, steganography conceals the encrypted data within the image itself, embedding it imperceptibly into the pixels or metadata. The synergy of encryption and steganography not only safeguards the integrity of image data but also adds an additional layer of concealment, making it arduous for adversaries to detect and decipher sensitive information. Through this project, the efficacy of the hybrid approach will be evaluated through experimentation and analysis, with the ultimate goal of providing a comprehensive solution for image security in various applications and contexts.

   

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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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  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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  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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  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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  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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  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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  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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  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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  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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  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.