Archives - Page 3

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  Enhancing Network Security through Intrusion Detection Systems

  Vol. 1 No. 03 (2024)

  Abstract

  The project titled "Enhancing Network Security through Intrusion Detection Systems" aims to explore and implement advanced techniques to strengthen the security posture of computer networks. In today's digital landscape, where cyber threats are ever-evolving, the role of Intrusion Detection Systems (IDS) becomes crucial in detecting and mitigating potential security breaches.

  The project focuses on the comprehensive understanding and deployment of both signature-based and anomaly-based IDS approaches. It delves into the development of a robust system capable of monitoring network traffic, identifying known attack patterns, and detecting deviations from normal behavior. By combining these methods, the project aims to provide a more effective defense against a wide range of cyber threats.

  Furthermore, the project incorporates the integration of machine learning algorithms within the IDS framework. This addition allows the system to learn and adapt to emerging threats, thereby improving its ability to detect previously unknown and sophisticated attacks. The implementation of machine learning contributes to a dynamic and intelligent intrusion detection mechanism, reducing false positives and enhancing overall accuracy.

  The outcomes of this project will not only contribute to the academic understanding of network security but will also provide practical insights into implementing advanced intrusion detection techniques. Ultimately, the project seeks to empower organizations with a more resilient defense against cyber threats, ensuring the confidentiality, integrity, and availability of their networked systems.

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  Data Trustworthiness in mobile Crowd Sensing With ML

  Vol. 2 No. 08 (2025)

  Abstract

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

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

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

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

   

   

  Index Terms

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

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  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.

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  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.      
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  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.

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  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.

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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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  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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  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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  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.

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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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  Cryptography beyond Quantum threats

  Vol. 2 No. 09 (2025)

  Abstract

  Quantum computers threaten many widely used public-key cryptosystems (RSA, ECC) by enabling Shor-style algorithms that efficiently solve integer factorization and discrete logarithms. Post-Quantum Cryptography (PQC) replaces vulnerable number-theory primitives with schemes based on problems believed hard for both classical and quantum adversaries (lattices, codes, hash-based, multivariate systems). This paper surveys the current PQC landscape, highlights the leading algorithms recommended for standardization, evaluates their performance and security trade-offs, and presents a practical methodology for adopting PQC (hybridization, crypto-agility, benchmarking and TLS integration). We report recent implementation and benchmark findings showing lattice-based schemes (e.g., CRYSTALS-Kyber for key-encapsulation, CRYSTALS-Dilithium/FALCON for signatures) provide strong security with practical performance for many real-world scenarios, while some alternative families (isogeny-based SIKE) were broken and illustrate the need for conservative migration strategies. Finally, we give deployment recommendations (hybrid KEMs in TLS, phased rollout, crypto-inventory) and an agenda for future research (parameter selection, side-channel resistance, efficient signatures).

  Index Terms

  Post-Quantum Cryptography (PQC), Quantum-Resistant Cryptography, Lattice-Based Cryptography, Learning with Errors (LWE), CRYSTALS-Kyber (Key Encapsulation Mechanism), CRYSTALS-Dilithium (Digital Signatures), FALCON Signatures, SPHINCS+ (Hash-Based Signatures), Code-Based Cryptography (McEliece), Multivariate Polynomial Cryptography, Isogeny-Based Cryptography (SIKE, CSIDH), Hybrid Key Exchange Protocols, TLS 1.3 and PQC Integration, Crypto-Agility, Side-Channel Attack Resistance, Secure Public Key Infrastructure (PKI), Long-Term Confidentiality, Harvest-Now-Decrypt-Later Attacks, Quantum Threat Models, Standardization (NIST PQC Project).