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 .
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.
Copyright (c) 2025 Indian Journal of Engineering Research Networking and Development (Online ISSN:3048-7676)
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
ParkCam – Smart Vision-Based Parking Management
The "Smart Parking System using Computer Vision" project aims to revolutionize traditional parking management by incorporating advanced computer vision technology. This project focuses on the development of a system that utilizes real-time video feeds from cameras installed in parking areas. The project employs sophisticated image processing algorithms to detect and track vehicles, accurately identifying available parking spaces. Through the integration of computer vision, the system distinguishes between occupied and vacant parking spots, providing users with instant updates on parking availability. The project also incorporates machine learning algorithms to enhance accuracy and adaptability, allowing the system to learn from patterns and optimize its performance in different parking scenarios. The user interface, accessible through a mobile application or web platform, provides real-time information on parking availability and guides users to the nearest vacant spot. This project not only streamlines the parking experience for users but also contributes to efficient traffic flow and reduced congestion in parking areas. The "Smart Parking System using Computer Vision" project showcases the potential of cutting-edge technology in addressing urban mobility challenges and enhancing the overall parking management landscape. Index terms Smart Parking System, Computer Vision, Real-time Video Feeds, Image Processing Algorithms, Vehicle Detection, Parking Space Tracking, Occupied and Vacant Parking Spots, Machine Learning Algorithms, User Interface, Mobile Application, Web Platform, Parking Availability, Traffic Flow, Congestion Reduction, Urban Mobility, Parking Management, Technology Integration, Efficiency Optimization.
SafeCam: Real-Time Accident Detection & Alerts
The project "Safe Cam: Real-Time Accident Alerts" aspires to revolutionize road safety through the development of an innovative technological solution. By harnessing advanced camera and sensor systems, the project's primary aim is to detect accidents as they occur in real-time, providing users with immediate and accurate alerts through a user-friendly application interface. Through this proactive approach, drivers gain the ability to make informed decisions regarding their routes, ultimately reducing the risk of accidents and navigating around potential traffic congestion more efficiently. At its core, the significance of the "Safe Cam" project lies in its potential to enhance the driving experience by prioritizing safety through technology. By empowering drivers with timely information about accidents, the project not only fosters a culture of safer driving habits but also emphasizes the pivotal role of technology in addressing critical road safety concerns. Through the seamless integration of real-time accident detection and user notification systems, "Safe Cam" aims to contribute to a safer and more efficient driving environment, underscoring the importance of leveraging technological advancements to mitigate road hazards effectively. Index terms Safe Cam, Real-time accident alerts, Road safety, Technological solution, Advanced camera systems, Sensor systems, Accident detection, User-friendly application interface, Proactive approach, Informed decision-making, Traffic congestion, driving experience, Safer driving habits, Technology integration, User notification systems, Driving environment, Technological advancements, Road hazards, Safety prioritization.
Smart City App for Citizen Complaint Management
The project titled "Smart City App for Citizen Complaint Management" aims to develop a comprehensive mobile application to streamline and enhance the process of citizen complaint resolution within urban environments. The project focuses on creating a user-friendly interface that enables citizens to easily report issues such as potholes, streetlight outages, and other civic concerns. Key features of the app include a simple and intuitive complaint submission system, allowing users to provide detailed information about the nature of the problem. The application assigns a unique reference number to each complaint, facilitating efficient tracking and monitoring by municipal authorities. The project also emphasizes the integration of a centralized dashboard for municipal officials, enabling them to prioritize and manage complaints effectively. The app incorporates automation to categorize and prioritize complaints based on urgency and severity, optimizing resource allocation for timely issue resolution. Transparency is a key aspect of the Smart City app, with real-time updates and notifications keeping citizens informed about the status and progress of their submitted complaints. Users are encouraged to provide feedback on the resolution process, promoting continuous improvement in municipal services. By addressing the challenges of citizen complaint management, the project contributes to the development of smarter and more responsive urban environments. The Smart City app aims to foster a collaborative relationship between citizens and municipal authorities, ultimately leading to a more efficient and engaged community. Index Terms Smart city, Citizen complaint management, Mobile application, Urban environments, Potholes, Streetlight outages, Civic concerns, User-friendly interface, Complaint submission system, Municipal authorities, Centralized dashboard, Automation, Issue resolution, Transparency, Real-time updates, Notifications, Feedback, Municipal services, Collaborative relationship, Community engagement.
Integrated E-Commerce Platform for Agriculture Enhancing Supply Chain Efficiency and Farmer Empowerment
The " Integrated E-Commerce Platform for Agriculture Enhancing Supply Chain Efficiency and Farmer Empowerment" is a comprehensive technological solution designed to address the evolving needs of the agricultural sector. This project aims to create a user-friendly online platform that facilitates seamless transactions and collaboration between farmers, agribusinesses, and suppliers. The portal integrates various modules, including inventory management, order tracking, and payment processing, to streamline the agricultural supply chain. The primary focus is on providing farmers with easy access to a diverse range of agricultural products and services, such as seeds, fertilizers, and equipment. The portal also serves as an information hub, offering valuable insights on best practices, market trends, and innovative farming techniques. By fostering a digital marketplace, the project aims to empower farmers, enhance their decision-making processes, and contribute to the overall efficiency and sustainability of the agricultural ecosystem. Through this Integrated E-Commerce Portal, the project envisions creating a connected and collaborative environment for stakeholders in the agricultural value chain. This initiative not only supports farmers in making informed choices but also promotes transparency and efficiency in agricultural transactions. Overall, the project aims to leverage technology to uplift the agricultural community, fostering growth, sustainability, and improved productivity in the sector. Index terms Integrated E-Commerce Portal, Agriculture, Technological solution, User-friendly platform, Seamless transactions, Collaboration, Farmers, Agribusinesses, Suppliers, Inventory management, Order tracking, Payment processing, Agricultural supply chain, Agricultural products, Services, Seeds, Fertilizers, Equipment, Information hub, Best practices, Market trends, Farming techniques, Digital marketplace, Empowerment, Decision-making processes, Efficiency, Sustainability, Stakeholders, Value chain, Transparency, Growth, Productivity.
Integrated E-Commerce Platform for Agriculture: Enhancing Supply Chain Efficiency and Farmer Empowerment
Few-Shot Learning for Efficient and Accurate Crop Disease Detection in Agriculture
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.