Indian Journal of Engineering Research Networking and Development (Online ISSN:3048-7676)
Indian Journal of Engineering Research Networking and Development aims to provide a comprehensive platform for researchers, academics, and professionals to publish, discover, and access high-quality scholarly journals across Computer Science disciplines. Our mission is to facilitate the dissemination of knowledge and foster collaboration within the academic community. Key Features: 1. User-friendly Publication Platform: Indian Journal of Engineering Research Networking and Development offers a user-friendly interface for authors to submit their research papers, articles, and manuscripts. The submission process is streamlined, allowing authors to easily upload their work and track the status of their submissions. 2. Peer Review System: All submissions undergo a rigorous peer review process to ensure the quality and credibility of published content. Our platform enables efficient peer review management, with tools for reviewers to assess submissions and provide constructive feedback to authors. 3. Open Access Publishing: We support open access publishing, making research freely accessible to readers worldwide. Authors have the option to publish their work under open access licenses, promoting greater visibility and impact for their research. 4. Advanced Search and Discovery: Users can easily discover relevant research articles using our advanced search and filtering options. The website provides tools to explore journals, browse articles by category, keyword, or author, and stay updated with the latest publications in their areas of interest. 5. Community Engagement: Indian Journal of Engineering Research Networking and Development fosters a vibrant academic community by facilitating discussions, collaborations, and networking among researchers, scholars, and practitioners. Users can engage with content through comments, ratings, and sharing features. 6. Responsive Design: Our website is optimized for seamless browsing across desktop, tablet, and mobile devices, ensuring accessibility and usability for users on the go. 7. Editorial Board: Each journal on Indian Journal of Engineering Research Networking and Development is managed by an esteemed editorial board comprising experts in the respective field. The editorial board ensures the integrity and scholarly rigor of published content. 8. Analytics and Metrics: Authors and journal editors have access to comprehensive analytics and metrics to track the performance and impact of their publications. Metrics include citation counts, download statistics. Future Developments: 1. Integration with ORCID: Implement integration with ORCID (Open Researcher and Contributor ID) to streamline author identification and authentication processes. 2. Enhanced Collaboration Tools: Develop features for collaborative research projects, such as shared document editing, project management, and communication tools for research teams. 3. AI-powered Recommendation System: Implement an AI-powered recommendation system to suggest relevant articles, journals, and researchers based on user preferences and browsing history. 4. Expanded Content Types: Introduce support for additional content types such as preprints, datasets, multimedia content, and supplementary materials to enhance the diversity of published research. 5. Global Outreach Initiatives: Launch initiatives to promote inclusivity and diversity in scholarly publishing, including translation services, mentorship programs, and partnerships with institutions in underrepresented regions. Indian Journal of Engineering Research Networking and Development is committed to advancing the dissemination of knowledge and empowering researchers worldwide through our innovative platform. Year of Starting:-2024 Format of Publication : Online Subject:-The Indian Journal of Engineering Research Networking and Development is a premier peer-reviewed journal dedicated to advancing research and knowledge in the fields of computer Science .Our mission is to provide a platform for researchers, practitioners, and academics to publish high-quality, innovative, and impactful research that contributes to the development .
AI-Powered Library Management System Using Model Context Protocol and Large LanguageModels
<span style="color: oklch(0.2642 0.013 93.9); font-family: pplxSerif, pplxSerif, ui-serif, Georgia, Cambria, 'Hiragino Mincho ProN', 'Yu Mincho', 'Songti SC', SimSun, 'Songti TC', PMingLiU, 'Songti TC', MingLiU_HKSCS, 'Songti TC', PMingLiU, AppleMyungjo, Batang, serif; font-size: 16px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 430; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; background-color: oklch(0.9798 0.005 78.3); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">This work presents the design and implementation of an AI-powered Library Management System (LMS) that combines the Model Context Protocol (MCP) with a cloud-based large language model to interact via a smart library assistant called <strong style="box-sizing: border-box; border-style: solid; border-width: 0px; border-color: oklch(0.2642 0.013 93.9 / 0.14); scrollbar-color: initial; scrollbar-width: initial; --tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-scroll-snap-strictness: proximity; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: #3b82f680; --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; font-weight: 630; color: oklch(0.2642 0.013 93.9); font-family: pplxSerif, pplxSerif, ui-serif, Georgia, Cambria, 'Hiragino Mincho ProN', 'Yu Mincho', 'Songti SC', SimSun, 'Songti TC', PMingLiU, 'Songti TC', MingLiU_HKSCS, 'Songti TC', PMingLiU, AppleMyungjo, Batang, serif; font-size: 16px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; background-color: oklch(0.9798 0.005 78.3); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">LibraBot<span style="color: oklch(0.2642 0.013 93.9); font-family: pplxSerif, pplxSerif, ui-serif, Georgia, Cambria, 'Hiragino Mincho ProN', 'Yu Mincho', 'Songti SC', SimSun, 'Songti TC', PMingLiU, 'Songti TC', MingLiU_HKSCS, 'Songti TC', PMingLiU, AppleMyungjo, Batang, serif; font-size: 16px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 430; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; background-color: oklch(0.9798 0.005 78.3); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">. Instead of rigid keyword-based search, the system offers a natural-language chat interface built on the Ollama Cloud LLM and the SmolAgents framework. Nine dedicated MCP tools cover core library workflows, including checking item availability, locating items, managing reservations, and recommending books based on content, all integrated with a catalog of over one hundred B.Tech engineering titles stored in SQLite and Excel and exposed through a multi-page Flask web interface. Personalized recommendations rely on a TF–IDF cosine-similarity model that operates purely on content features and does not require historical user interaction data. In 70 test scenarios, the system achieved a 100% success rate, with an average response time of 1.2 seconds and uptime reaching 99.5% under concurrent loads of up to 150 users, demonstrating that MCP-based tool integration with LLM reasoning can deliver a scalable, always-available alternative to traditional library management approaches.
Copyright (c) 2026 Indian Journal of Engineering Research Networking and Development (Online ISSN:3048-7676)
AI-Powered Automated Video Dubbing System with Multi-Language Support and Lip Synchronization
The exponential expansion of digital multimedia across international platforms necessitates efficient multilingual dubbing solutions. Conventional dubbing methodologies prove resource-intensive and economically prohibitive for widespread content localization. This research introduces an intelligent automated dubbing framework integrating advanced neural architectures for speech processing, translation, and synthesis. The system employs Whisper for acoustic modeling [1], NLLB-200 for cross-lingual translation [2], XTTS v2 for voice cloning [3], and Wav2Lip GAN for visual synchronization [4]. A novel segment-based processing approach ensures temporal precision between synthesized audio and source video. Experimental validation demonstrates superior naturalness and synchronization accuracy compared to existing methodologies. The framework addresses critical applications in educational technology, digital entertainment, corporate communication, and accessibility enhancement.
AI Diet Planner Using Neural Network with Knapsack Optimizer
Dietary management is a fundamental component of preventive healthcare, yet individuals often struggle to formulate nutritionally balanced meal plans that align with personal health objectives, physiological constraints, and budgetary limitations. This paper presents an intelligent diet planning system that integrates a feedforward neural network with a dynamic programming-based 0/1 Knapsack optimization algorithm to generate personalized daily meal schedules. The neural network is trained on a curated health and nutrition dataset to predict optimal macronutrient ratios—proteins, carbohydrates, fats, vitamins, and minerals—based on user-specific inputs such as age, body mass index, health goals, and known food allergies. Once target nutritional profiles are established, the Knapsack optimizer selects the most nutritionally dense food items from a structured food database while respecting caloric budgets and cost constraints. The system generates structured meal plans comprising breakfast, lunch, dinner, and snack components. A continuous feedback loop enables the system to adapt predictions based on user responses, improving personalization accuracy over successive days. Experimental evaluation demonstrates that the proposed hybrid model achieves a mean absolute error of 3.8% in nutrient prediction and outperforms conventional static diet applications in user satisfaction and adherence metrics. The system offers a practical and scalable AI-powered alternative to manual dietary consultation.
Explainable Deep Learning Models for Autonomous Vehicle Decision-Making
Autonomous vehicles (AVs) rely heavily on deep learning models for perception, planning, and control. While these models achieve high accuracy, they operate as black boxes, making their decision-making process opaque. Lack of interpretability in safety-critical environments limits trust, hinders debugging, and complicates regulatory approval. This paper proposes an explainable deep learning framework for AV decision-making by integrating state-of-the-art perception and control models with post-hoc explainability techniques, specifically SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations). Using simulated driving scenarios in the CARLA simulator and real-world datasets such as KITTI and nuScenes, the framework generates actionable explanations for vehicle actions, including lane changes, braking, and steering. Our results demonstrate that XAI techniques can highlight critical features influencing decisions, uncover model biases, and assist developers in improving AV reliability. The proposed framework enhances safety, transparency, and accountability, providing a practical path toward regulatory-compliant autonomous systems.
Copyright (c) 2025 Indian Journal of Engineering Research Networking and Development (Online ISSN:3048-7676)
AI-Driven Real-Time Phishing Detection Using Transformer-Based NLP Models
Phishing has become one of the most pervasive cyber threats, evolving from straightforward deceptive emails to complex social engineering attacks that exploit human trust and sophisticated obfuscation techniques. Existing detection mechanisms, such as rule-based or blacklist-oriented systems, often fail against zero-day attacks and cleverly disguised messages. To address this, we propose a Transformer- based Natural Language Processing (NLP) approach for real-time phishing detection that analyzes textual and semantic features of emails, messages, and web content. Our methodology leverages pre- trained language models such as BERT and RoBERTa, fine-tuned for phishing classification tasks. By capturing contextual embeddings and semantic intent, the model identifies phishing attempts that traditional techniques often miss. Experimental evaluation demonstrates an accuracy of 98.4%, surpassing conventional SVM, Naïve Bayes, and RNN-based classifiers. The proposed framework is designed for real-time integration with email servers and web browsers, offering scalable, adaptive, and practical cybersecurity defense.
Real-Time Anomaly Detection in Streaming Sensor Data Using LSTM Autoencoders
In the era of Industry 4.0 and the Internet of Things (IoT), billions of connected sensors continuously generate large volumes of real-time data streams. This sensor data is vital for decision-making in domains such as industrial automation, predictive maintenance, and critical infrastructure monitoring. However, these systems are susceptible to irregularities caused by sensor faults, environmental disturbances, or cyber intrusions. Detecting such anomalies in streaming data is challenging due to the velocity, volume, and evolving nature of the streams. This research introduces a Real-Time Anomaly Detection Framework using Long Short-Term Memory (LSTM) Autoencoders, designed specifically for processing continuous, high-velocity sensor data. Unlike conventional models that rely on static, offline data, the proposed model learns temporal dependencies dynamically and adapts to new patterns using an incremental sliding window mechanism. The LSTM Autoencoder reconstructs normal time-series sequences, and any significant deviation between input and reconstruction indicates an anomaly. The framework integrates with streaming platforms like Apache Kafka and Apache Flink, enabling low-latency inference. Experimental evaluations on real-world industrial datasets demonstrate that the proposed approach achieves superior precision (0.96) and F1-score (0.94) while maintaining latency below 100 milliseconds. The system adapts to changing patterns in real time, offering robustness against concept drift. This work contributes toward developing intelligent, adaptive, and explainable anomaly detection systems applicable to diverse real-time environments such as smart manufacturing, IoT-enabled grids, and autonomous systems.
Shaping the Ultra-Connected Future: The Road to 6G
Talk of “6G” is no longer an engineering thought experiment: it’s becoming a social and economic conversation. Vendors and standards bodies describe 6G as the platform that will move us from connected devices to connected intelligence and immersive experiences; governments are already funding research and patent drives; and citizens are thinking about towers, costs and health implications. This article surveys current views on 6G from industry white papers, policy bodies and early public reactions. It explains the potential benefits (new services, resilience, socio-economic uplift), explores the main cost levers (spectrum, densification, energy), and outlines social and governance challenges (acceptance, equity, environmental impact). The aim is to give a balanced, readable synthesis of what people think, what’s at stake, and how societies might prepare for the ultra-connected infrastructures 6G promises.
The Silent Shift: How Low-Code is Reshaping Work, Security, and the Software Mindset
Low-code and no-code (LC/NC) platforms promise to transform software development by enabling rapid application creation without traditional programming expertise. This article explores the dual themes of security and scalability in LC/NC platforms through a storytelling lens — tracing the excitement of innovation, the anxieties of security professionals, and the mixed emotions of developers and enterprises as they weigh convenience against control. The article examines challenges in adoption, long-term learning curves, the risk of losing traditional craftsmanship in coding, and the looming question of employment shifts. Finally, it presents public perceptions and industry reflections on whether LC/NC platforms represent empowerment or an over-simplification that may come at hidden costs.
Carbon-Conscious AI: Reducing the Environmental Impact of Deep Learning
Deep learning has achieved remarkable progress in recent years, powering applications ranging from natural language processing to computer vision and autonomous systems. However, this rapid growth has come with a significant environmental cost. Training large- scale models requires vast computational resources, which in turn lead to high energy consumption and substantial carbon emissions. The environmental footprint of AI research is increasingly recognized as a critical challenge, raising concerns about sustainability and ethical deployment. This paper examines the issue of carbon-conscious AI, focusing on strategies to reduce the environmental impact of deep learning. We provide a literature review of existing work on energy-efficient AI, discuss techniques such as model compression, neural architecture search, and efficient hardware utilization, and propose a framework for integrating carbon-awareness into AI development pipelines. Simulation-based evaluations show that adopting energy-aware training and inference strategies can reduce carbon emissions by up to 40% without compromising model accuracy. We conclude with future directions for building sustainable AI ecosystems through interdisciplinary collaboration among researchers, industry, and policymakers.
The Cyber-Physical Meta verse: Where Virtual Dreams Shape Real Life
The rise of cyber-physical metaverse systems (CPMSs) signals a new era where the physical and digital worlds are no longer separate but deeply intertwined. Unlike conventional virtual reality, CPMSs synchronize real-time data from sensors, machines, and human actions with immersive metaverse environments, creating a bidirectional bridge where changes in one world ripple into the other. This article adopts a science storytelling approach to explore how CPMSs are envisioned by researchers, industries, and communities. Through stories drawn from medicine, education, industry, and urban life, it highlights both the transformative potential and the challenges of this emerging infrastructure. Key opportunities include collaboration without boundaries, reduced risk in critical tasks, and sustainable design through digital twins. Yet, obstacles such as latency, privacy, costs, equity, and trust must be addressed for CPMSs to gain social legitimacy. Ultimately, this article argues that the road to cyber-physical metaverse systems is not just about technical readiness but about societal acceptance, ethical governance, and human-centered design.
Protecting Privacy in Community-Driven Sensing with Secure Aggregation
Mobile Crowd Sensing (MCS) leverages ubiquitous mobile devices to collect large-scale environmental, health, and urban data. While enabling powerful analytics, MCS faces significant privacy risks as user-contributed data often contains sensitive personal information such as location, health status, or behavior patterns. To ensure both utility and confidentiality, this work explores the integration of Differential Privacy (DP) and Secure Aggregation mechanisms for MCS platforms. We analyze privacy–utility trade-offs, present algorithms for local and global differential privacy, and design lightweight secure aggregation protocols suitable for resource-constrained mobile devices. The proposed framework ensures individual-level privacy, defends against inference attacks, and maintains high data utility for real-time analytics. Index Terms
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).
A Hybrid DeepFM Approach to Student Retention
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.
Empowering Architecture through Human-AI Co-Creation
This project explores the innovative realm of "User-Driven Generative AI Architectural Design." In this endeavor, the focus is on developing an interactive and user-centric architectural design system powered by generative artificial intelligence (AI). The project aims to empower users with the ability to actively shape and influence the architectural design process through intuitive interfaces and input mechanisms. The proposed system leverages cutting-edge machine learning algorithms to understand user preferences, constraints, and creative inputs. Through iterative feedback loops, the generative AI adapts and refines its output, ensuring a personalized and evolving design experience for each user. The goal is to establish a symbiotic relationship between human creativity and AI capabilities, fostering a collaborative environment where users actively contribute to the generation and refinement of architectural designs. Key aspects of the project include the development of an intuitive user interface, the integration of advanced generative AI algorithms, and the implementation of a learning mechanism to enhance the system's responsiveness to user input. The outcome of this project holds the potential to revolutionize architectural design processes, offering a dynamic and user-driven approach to generate innovative and personalized architectural solutions. Index terms User-Driven Design, Generative AI, Architectural Design, Interactive Interfaces, Machine Learning Algorithms, User Preferences, Creative Inputs, Feedback Loops, Personalization, Collaborative Design, User Interface Development, Advanced AI Algorithms, Learning Mechanisms, Responsive Systems, Innovative Solutions, Revolutionizing Design Processes.
Data Trustworthiness in mobile Crowd Sensing
The project, "Data Trustworthiness in Mobile Crowd Sensing," 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.
Air Quality Prediction Using Machine Learning Algorithms
The project titled "Air Quality Prediction Using Machine Learning Algorithms" endeavors to address the critical issue of air pollution through the application of advanced computational techniques. The project aims to develop a robust predictive model that can forecast air quality levels based on historical data, meteorological parameters, and relevant environmental features. Leveraging machine learning algorithms such as regression, decision trees, or neural networks, the project seeks to analyze complex relationships within the data and enhance the accuracy of air quality predictions. The methodology involves the collection and preprocessing of extensive datasets encompassing pollutant concentrations, weather conditions, and geographical information. The selected machine learning algorithms will be trained on this data to recognize patterns and correlations, enabling the model to make accurate predictions. The project also explores the integration of real-time data streams, satellite imagery, and sensor networks to improve the responsiveness of the predictive model.
Enhancing Image Security through Cryptographic Steganography
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.
A Machine Learning Framework for Cultural Speech Analysis
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.
Real-Time Bus Arrival Prediction Using Machine Learning and GPS
The proposed project aims to develop a robust Bus Arrival Time Prediction and Tracking system utilizing machine learning techniques. The primary objective is to enhance the efficiency and reliability of public transportation by accurately predicting bus arrival times based on historical data and real-time information. The project involves the creation of a machine learning model trained on a comprehensive dataset that includes factors such as traffic conditions, weather, and past bus performance. The system integrates GPS tracking technology and other relevant sensors to continuously update the model in real-time, ensuring precise predictions that adapt to dynamic conditions. The project aims to improve the overall commuter experience by minimizing waiting times and providing passengers with timely and reliable information. Key components of the project include data collection and preprocessing, machine learning model development and training, integration with real-time tracking technologies, and the implementation of a user-friendly interface for passengers to access predicted arrival times. The successful completion of this project will contribute to the advancement of smart transportation systems, fostering efficiency and user satisfaction in public bus services.
Inclusive Sign Language to Text and Speech Conversion via Multi-Dataset Integration
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.
OCR-Aided NLP for Automated Document Summarization
The project titled "Automated Text Summarization from Scanned Documents" aims to develop a system that streamlines the process of extracting key information from scanned documents and generating concise textual summaries. The primary focus is on leveraging Optical Character Recognition (OCR) technology to convert scanned images into machine-readable text, followed by the application of Natural Language Processing (NLP) techniques for effective summarization. The project involves the design and implementation of an intelligent algorithm that identifies important sentences, extracts key phrases, and summarizes the main ideas present in the scanned documents. Advanced NLP models and neural networks will be explored to enhance the accuracy and efficiency of the summarization process. The system's objective is to provide a time-efficient and accurate means of distilling relevant content from large volumes of scanned documents, thereby facilitating improved accessibility and aiding in efficient document management. The proposed solution has significant potential applications in various domains such as information retrieval, document categorization, and knowledge management. The successful implementation of this project will contribute to the advancement of automated summarization techniques, offering a valuable tool for individuals and organizations dealing with vast amounts of scanned textual data.
Block chain-Powered Digital Certificate Verification System
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.
A Tamper-Proof Certificate Validation System Using Blockchain Technology
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.
Enhancing Climate Resilience through Machine Learning-Driven Insights
Climate change poses significant challenges to communities worldwide, necessitating innovative solutions to enhance resilience against its impacts. This project focuses on leveraging Machine Learning (ML) techniques to strengthen climate resilience. The project aims to develop and implement ML algorithms to analyze diverse datasets related to climate patterns, extreme weather events, and environmental conditions. The primary objectives include identifying patterns and trends within the data, enabling the prediction of climate-related risks and the optimization of resource allocation. The project will explore the application of ML in various domains, such as agriculture, infrastructure planning, disaster preparedness, and resource management, to develop adaptive solutions for communities. Through the integration of ML, the project seeks to contribute to more effective decision-making processes and the development of proactive strategies to address climate-related challenges. The continuous learning and refinement of ML models will enable the creation of sustainable and adaptive systems, enhancing the resilience of communities to the dynamic impacts of climate change. This student project not only provides an opportunity to apply theoretical knowledge in real-world scenarios but also underscores the significance of technology in fostering climate resilience for a sustainable future. Index terms Climate change, Machine Learning (ML), Resilience, Predictive modeling, Climate patterns, Extreme weather events, Environmental conditions, Resource allocation, Agriculture, Infrastructure planning, Disaster preparedness, Resource management, Decision-making processes, Adaptive solutions, Sustainable development, Proactive strategies, Community resilience, Data analysis, Model refinement, Real-world applications, Technology integration.
A Comprehensive Defense Model Against DoS and DDoS Attacks
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.
Leveraging AI and Multimedia Analysis for Proactive Suicide Prevention
This project aims to address the critical issue of suicide prevention by leveraging machine learning techniques for early detection of potential suicidal tendencies. Suicide is a global public health concern, and timely identification of individuals at risk can significantly contribute to intervention and support. The project focuses on the utilization of diverse data sources, such as social media posts, online behavior, and multimedia content, to develop an intelligent algorithm capable of recognizing patterns indicative of suicidal thoughts or intentions. Natural language processing and sentiment analysis techniques are employed to scrutinize textual content for linguistic cues associated with distress. Furthermore, image and video analysis techniques, including facial expression recognition and body language analysis, contribute to a comprehensive approach in identifying visual indicators of self-harm or suicidal ideation. The research involves the development and training of machine learning models on diverse datasets to ensure robust and accurate detection capabilities. The project emphasizes the ethical considerations surrounding privacy and responsible use of technology, aiming to strike a balance between aiding mental health professionals and respecting individual privacy. The successful implementation of this project could lead to the creation of a valuable tool for mental health practitioners and support systems, enabling them to intervene proactively and provide timely assistance to individuals at risk of suicide.
Real-Time Facial Analytics: A Deep Learning Approach to Gender, Age, and Emotion Recognition
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.
AI-Powered Suicide Prevention Through Behavioral Analysis
This project aims to address the critical issue of suicide prevention by leveraging machine learning techniques for early detection of potential suicidal tendencies. Suicide is a global public health concern, and timely identification of individuals at risk can significantly contribute to intervention and support. The project focuses on the utilization of diverse data sources, such as social media posts, online behavior, and multimedia content, to develop an intelligent algorithm capable of recognizing patterns indicative of suicidal thoughts or intentions. Natural language processing and sentiment analysis techniques are employed to scrutinize textual content for linguistic cues associated with distress. Furthermore, image and video analysis techniques, including facial expression recognition and body language analysis, contribute to a comprehensive approach in identifying visual indicators of self-harm or suicidal ideation. The research involves the development and training of machine learning models on diverse datasets to ensure robust and accurate detection capabilities. The project emphasizes the ethical considerations surrounding privacy and responsible use of technology, aiming to strike a balance between aiding mental health professionals and respecting individual privacy. The successful implementation of this project could lead to the creation of a valuable tool for mental health practitioners and support systems, enabling them to intervene proactively and provide timely assistance to individuals at risk of suicide. Index terms Suicide prevention, Machine learning techniques, Early detection, Suicidal tendencies, Global public health concern, Timely identification, Intervention and support, Diverse data sources, Social media posts, Online behavior, Multimedia content, Intelligent algorithm, Recognizing patterns, Suicidal thoughts or intentions, Natural language processing, Sentiment analysis, Linguistic cues, Distress detection, Image analysis techniques, Video analysis techniques, Facial expression recognition, Body language analysis, Visual indicators, Self-harm, Suicidal ideation, Machine learning models, Ethical considerations, Privacy concerns, Responsible technology use, Mental health professionals, Support systems, Proactive intervention, Timely assistance.
CertiChain – A Blockchain-Based Framework for Digital Certificate Verification