Archives - Page 3

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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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  A Hybrid DeepFM Approach to Student Retention

  Vol. 2 No. 08 (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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  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.