Archives

  • PERSONALIZED SKINCARE PRODUCTS RECOMMENDATION SYSTEM USING ML AND DL
    Vol. 1 No. 1 (2024)

    Abstract The personalized skincare product recommendation system leverages machine learning (ML) and deep learning (DL) techniques to provide tailored product suggestions based on individual skin profiles. The system utilizes a combination of user input, skin condition analysis, and product data to deliver recommendations that address specific skincare needs. By integrating advanced ML algorithms and DL models, the system aims to enhance user experience, improve skincare outcomes, and drive engagement with personalized recommendations. This paper details the design, implementation, and evaluation of the system, demonstrating its efficacy in delivering accurate and relevant skincare product recommendations.Index TermsPersonalized Skincare, Product Recommendation, Machine Learning, Deep Learning, Skin Analysis, Recommendation Systems, User Profiling, AI in Beauty, Skincare Data, PredictiveModeling, Natural Language Processing (NLP), Convolutional Neural Networks (CNNs), Recommender Systems, User Experience.1. IntroductionPersonalized skincare is an emerging field where individual skin characteristics and needs drive the selection of appropriate products. Traditional skincare product recommendations are often generic, lacking personalization and not accounting for unique skin conditions. Advances in machine learning (ML) and deep learning (DL) offer a transformative approach to building sophisticated recommendation systems that can analyze user data, such as skin type, concerns, and preferences, to suggest tailored skincare solutions.Machine learning algorithms enable the processing of large datasets, extracting patterns, and making predictions based on historical user data and product efficacy. Deep learning models, particularly those using convolutional neural networks (CNNs) and recurrent neural networks (RNNs), enhance the system's capability to understand complex relationships between user profiles and product features. This integration allows for a more precise and individualized approach to skincare recommendations.The need for personalized skincare solutions is driven by increasing consumer demand for products that address specific skin concerns such as acne, aging, or sensitivity. Traditional approaches to skincare often rely on broad categorizations that may not fully address individual needs. By incorporating ML and DL into the recommendation system, the proposed solution aims to bridge this gap, providing users with personalized product suggestions that are more likely to meet their specific requirements.This paper explores the development of a personalized skincare recommendation system, detailing the methodologies employed, thedesign of the recommendation algorithms, and the evaluation of system performance. The integration of ML and DL techniques into the recommendation process offers a novel approach to enhancing user satisfaction and optimizing skincare outcomes.      

  • Block chain-Powered Digital Certificate Verification System
    Vol. 2 No. 07 (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.

  • Development of an LSTM-Based System for Real-Time Toxic Comment Classification in Online Platforms
    Vol. 1 No. 05 (2024)

    Abstract

    The project aims to develop an efficient and accurate system for classifying toxic comments in online platforms using Long Short-Term Memory (LSTM) networks. With the exponential growth of online content, ensuring a safe and respectful environment for users is of paramount importance. Toxic comments, characterized by offensive, abusive, or harmful language, pose a significant challenge for content moderation.

    This project employs LSTM, a type of recurrent neural network, known for its ability to capture sequential dependencies in data. The LSTM model will be trained on a labeled dataset comprising both toxic and non-toxic comments. During training, the model learns to recognize patterns and contextual cues associated with toxic language, enabling it to make predictions on unseen comments.

    The implementation involves preprocessing textual data, constructing an LSTM architecture, and fine-tuning the model to achieve optimal performance. The trained LSTM model will be integrated into an interactive platform, providing real-time classification of comments. The project will also explore techniques for model evaluation, addressing challenges such as false positives and false negatives.

    The successful implementation of this project can significantly contribute to enhancing online content moderation, fostering healthier digital communication spaces, and mitigating the impact of toxic behavior. Additionally, the project offers valuable insights into the application of deep learning techniques, specifically LSTM, in addressing real-world social challenges related to online content.

    Index Terms

    Toxic comments, Online platforms, Long Short-Term Memory (LSTM) networks, Content moderation, Recurrent neural network, Sequential dependencies, Labeled dataset, Textual data preprocessing, Model fine-tuning, Real-time classification, Model evaluation, False positives, False negatives, Digital communication spaces, Deep learning techniques.

  • Digital Campus Tour Realistic 3D Walkthrough with Interactive Features
    Vol. 2 No. 03 (2025)

    Abstract

    This project aims to develop a Virtual Campus Walkthrough application utilizing the three.js library, offering an immersive and interactive experience for users to explore a digital representation of a campus environment. The application will allow users to navigate through the virtual campus using keyboard or mouse inputs, providing a simulated experience akin to physically walking through the real campus. Leveraging the capabilities of three.js, the application will render high-quality 3D graphics and textures, ensuring a visually compelling and realistic environment. Users will have the opportunity to interact with various elements within the virtual campus, such as buildings, landmarks, and informational points of interest. The project will focus on creating a user-friendly interface, optimizing performance for smooth navigation, and integrating interactive features to enhance the overall experience. Ultimately, the Virtual Campus Walkthrough aims to provide a valuable tool for prospective students, faculty, and visitors to explore and familiarize themselves with the campus remotely.

    Index Terms

    Virtual Campus Walkthrough, three.js library, Immersive Experience, Interactive Exploration, Digital Representation, Campus Environment, Navigation Controls, 3D Graphics, Textures Rendering, Realistic Environment, User Interaction, User-Friendly Interface, Performance Optimization, Interactive Features, Prospective Students, Faculty, Visitors, Remote Exploration.

  • 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.

     

  • 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.

  • brain

    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

  • 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.

  • 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.

  • Inclusive Sign Language to Text and Speech Conversion via Multi-Dataset Integration
    Vol. 2 No. 07 (2025)

    Abstract

    This project aims to develop an advanced system for Sign Language to Text and Speech Conversion by leveraging the power of multi-dataset integration. The primary objective is to enhance the accuracy and inclusivity of existing sign language conversion systems by incorporating diverse datasets.

    The project involves the integration of various sign language datasets, encompassing different signing styles, regional variations, and expressions. This approach ensures a comprehensive understanding of sign language, enabling the system to accurately translate a wide range of signs and gestures into written text and spoken words.

    By adopting a multi-dataset integration strategy, the system becomes more adaptable and inclusive, catering to the diverse needs of the signing community. The project addresses the limitations of traditional systems by providing a more robust and versatile solution for sign language users.

    Through the implementation of this innovative approach, the project aims to contribute to the advancement of communication accessibility for individuals who rely on sign language as their primary mode of expression. The outcomes of this research have the potential to positively impact the field of assistive technology, fostering better communication and understanding for the deaf and hard-of-hearing community.

    Index terms

    Sign language, Text and speech conversion, multi-dataset integration, Accuracy enhancement, Inclusivity, Signing styles, regional variations, Expressions, Adaptability, Communication accessibility, Assistive technology, Deaf and hard-of-hearing community, Communication technology, Translation systems, Gesture recognition, Accessibility technology, Assistive devices, Language processing, Communication aids, Technological innovation.

  • 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.

     

  • 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.

  • 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.

  • 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.

  • 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

  • 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.

  • 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

  • A Machine Learning Framework for Cultural Speech Analysis
    Vol. 2 No. 07 (2025)

    Abstract

    This project aims to explore the realm of detection across diverse cultures using advanced speech analysis techniques. In an era of increasing globalization, understanding cultural nuances is paramount, especially in the context of communication. The project focuses on developing a robust framework that harnesses the power of speech analysis to detect anomalies, deviations, or noteworthy patterns within different cultural contexts.

    The research involves the utilization of cutting-edge technology to analyze speech patterns and linguistic subtleties specific to various cultures. By integrating machine learning algorithms and cross-cultural expertise, the project aims to enhance the accuracy and adaptability of detection systems. The ultimate goal is to create a framework that can discern cultural variations in verbal expression, providing a nuanced understanding of communication styles within different societies.

    Throughout the project, BTech students will delve into the intricacies of speech analysis, exploring cultural norms, linguistic variations, and contextual factors that influence communication. The interdisciplinary nature of the research involves combining computer science, linguistics, and cultural studies to create a comprehensive detection system that respects and adapts to the diversity of human expression.

    This project not only contributes to the advancement of speech analysis technologies but also addresses the pressing need for culturally sensitive detection mechanisms in our interconnected world. Students will gain valuable insights into the challenges and opportunities of implementing such systems, fostering a holistic approach to technology development that respects cultural diversity.

     

     

    Index Terms

    Speech analysis, Cultural detection, Globalization, Communication nuances, Anomaly detection, Linguistic subtleties, Machine learning algorithms, Cross-cultural expertise, Verbal expression, Communication styles, Interdisciplinary research, Computer science, Linguistics, Cultural studies, Detection systems, Human expression, Technological advancement, culturally sensitive mechanisms, Technology development, Cultural diversity.

     

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

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

    Abstract

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

    Index Terms

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

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

    Abstract

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

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

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

    Index terms

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

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