International Journal of Scientific Research in Computer Science, Engineering and Information Technology
International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT) is peer-reviewed, online international journal published Bimonthly by Shauryam Research Institute. IJSRCSEIT is a highly-selective journal, covering topics that appeal to a broad readership of various branches of Computer Science, Engineering, Information Technology and related fields. The Journal has many benefits all geared toward strengthening research skills and advancing academic careers. Journal publications are a vital part of academic career advancement. To maintain a high-quality journal, manuscripts that appear in the IJSRCSEIT Articles section have been subjected to a rigorous review process. Email addresses for submitting articles are ijsrcseit@gmail.com, editor@ijsrcseit.com JOURNAL INFORMATION Title : International Journal of Scientific Research in Computer Science, Engineering and Information Technology Frequency : Bimonthly E-ISSN : 2456-3307 Publisher : Shauryam Research Institute Refereed : Peer Review Copyright : Technoscience Academy, India Starting Year : 2016 Subject : Computer Science, Engineering and Information Technology Language : English Distribution : Open Access Publication Format : PDF & HTML -> Online Document Identifier Type : DOI (https://doi.org/10.32628/IJSRCSEIT ) Email Id : editor@ijsrcseit.com, ijsrcseit@gmail.com Mobile No. : +1 (404) 860-9920 (WhatsApp support only) Website : https://ijsrcseit.com
AI-Powered Systems for Automated Detection and Classification of Medicinal Plants
Medicinal plants have been widely used for traditional and modern healthcare due to their therapeutic properties. Identifying medicinal plants correctly is crucial for their effective utilization. This paper presents an Android application that detects whether a plant is medicinal or not using Convolutional Neural Networks (CNN).The system allows users to upload an image of a plant, which is transformed using a CNN- based deep learning model trained on a Data repository of medicinal and non-medicinal plant images. The model classifies the plant based on its features and provides real-time critique to the user.The application is designed to be lightweight and efficient, ensuring fast and accurate classification. The CNN model is either embedded within the app using TensorFlow Lite or processed through a cloud-based back end for enhanced effectiveness. This project aims to assist researchers, botanists, and herbal medicine enthusiasts in fostering medicinal plants with ease.
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology https://creativecommons.org/licenses/by/4.0
Innovation in Strategy for Public Policy
As per the World Bank, health, education and employment are the pillars of economic development. In this study, parameters are chosen in a way that covers all the areas and spheres of economic growth. Several international economic policies and theories will be studied to understand whether those can be applicable in the present scenario. Special attention to be made on fiscal and monetary policy which of govt. has been taken as part of public policy. Research demonstrated the economy's extreme parameters in 3- dimensional space (nudge) and identified several public policies that can be implemented. But in reality, extreme conditions on all three dimensions generally do not sustain. Economics can be analysed in the following dimension: a. Macroeconomic factor: GDP deflator, CPI, WPI, FDI, continental labour transition, budget allocation v/s actual expenditure b. Micro-economic factor: supply and demand mechanism, fiscal policy, monetary policy Machine recommended public policy will be compared with man-recommended public policy based on parameters like accuracy, feasibility, effectiveness, efficiency, biasness, etc. This study will make a relative analysis of two mode of public policy recommendation.
Comparative Analysis of Affordable Housing Financing Models : United States and Sub-Saharan Africa
This abstract examines and contrasts affordable housing financing models in the United States and Sub-Saharan Africa, highlighting regulatory, institutional, and market-driven determinants that shape affordability outcomes. In the United States, a mature ecosystem of instruments Low-Income Housing Tax Credits (LIHTC), government-sponsored mortgage insurance programs, tax-exempt housing bonds, public housing authorities, and layered subsidy mechanisms supports supply-side production and preserves long-term affordability through legally enforceable covenants and deep capital markets. In Sub-Saharan Africa, financing remains constrained by shallow mortgage markets, limited formal lending, tenuous land tenure, and heavy reliance on informal savings, microfinance, remittances, donor grants, development finance institutions, and nascent public–private partnership arrangements. This comparative analysis evaluates models across key dimensions: scale and outreach, cost-efficiency, administrative complexity, fiscal sustainability, risk allocation, and inclusion of the lowest-income households. The findings indicate that U.S. instruments have achieved measurable scale and predictability because of secondary markets, tax incentives, and robust institutional capacity, yet they can generate spatial concentration of subsidized stock and entrenched subsidy burdens. Sub-Saharan African approaches display local adaptability and incremental housing pathways that suit informal urban expansion but suffer from high unit financing costs, currency and interest-rate volatility, inadequate risk pooling, and weak legal protections for tenants. Policy implications emphasize hybrid strategies: developing secondary mortgage mechanisms and mortgage-backed instruments where feasible, expanding blended finance and performance-based subsidies, clarifying land tenure and regulatory frameworks, and mobilizing concessional capital to lower unit costs. Transferable lessons include rigorous monitoring frameworks and demand-side targeting, while respecting locally driven incremental finance methods. These recommendations aim to improve affordability, resilience, and equitable access across divergent institutional contexts. Future research should employ comparative longitudinal data, randomized pilots of financing interventions, and stakeholder cost-benefit analyses to identify scalable models that balance fiscal prudence with social equity, enabling policymakers and practitioners to design context-appropriate financing pathways. globally.
Copyright (c) 2024 International Journal of Scientific Research in Computer Science, Engineering and Information Technology https://creativecommons.org/licenses/by/4.0
AI-Augmented Threat Intelligence for Autonomous Vulnerability Management in Cloud-Native Clusters
This research introduces an AI-augmented framework for proactive vulnerability management in cloud-native clusters, integrating real-time threat intelligence with automated DevSecOps workflows. The system continuously aggregates CVE data, analyzes exploit likelihood using machine learning models, and dynamically injects remediation recommendations into Terraform and ArgoCD pipelines. By coupling predictive analytics with infrastructure-as-code automation, it enables continuous risk scoring, autonomous patch deployment, and contextual prioritization of vulnerabilities. Empirical evaluations across AWS, Azure, and GCP clusters demonstrate a 73% reduction in mean time to remediation (MTTR) and an 89% increase in vulnerability detection accuracy, outperforming conventional reactive methods. The framework’s low operational overhead (<4%) and seamless integration with GitOps workflows establish its practicality for large-scale, multi-cloud environments. The proposed approach advances the state of cloud security toward self-healing, AI-driven resilience and continuous compliance within distributed containerized ecosystems.
Cloud-Native Data Analytics Platform with Integrated Governance : A Modern Approach to Real-Time Stream Processing and Feature Engineering
The exponential growth of streaming data from diverse sources including Internet of Things devices, web applications, and database change data capture systems has created unprecedented challenges in data management, analytics, and governance. Traditional batch-oriented data architectures struggle to meet the demands of real-time analytics while maintaining data quality, security, and compliance requirements. This research presents a comprehensive cloud-native data analytics platform that integrates Apache Kafka for distributed messaging, Apache Flink for stream processing, Delta Lake for medallion architecture storage, and Feast feature store for machine learning operationalization, all unified under a robust governance framework leveraging Great Expectations, AWS security services, and enterprise observability tools. The proposed architecture processes over 340,000 events per second across multiple data sources, implements a three-tier medallion storage pattern with automated quality validation, and achieves sub-10-millisecond latency for online feature serving while maintaining point-in-time correctness for machine learning applications. Experimental validation demonstrates 99.95% data quality compliance, 99.99% system availability across three availability zones, and successful integration of 2,000+ feature definitions supporting both batch and streaming machine learning workloads. The platform addresses critical gaps in existing approaches by combining real-time stream processing with comprehensive data governance, automated quality remediation, and scalable feature engineering capabilities. This work contributes a production-ready reference architecture for organizations seeking to modernize their data infrastructure while maintaining enterprise-grade governance, security, and operational excellence standards.
An Empirical Technique to Establish the Ideal Parameters for an Adaptive Neuro-Fuzzy-Based Qoe Optimized LAN Model
The expansion of real-time applications like video streaming, conferencing, and interactive services has made Quality of Experience (QoE) optimization in communication networks more crucial. An adaptive neuro-fuzzy-based QoE optimization model for local area networks (LANs) was developed and evaluated in this research. Multiple predictor combinations were assessed using experimentally generated throughput, delay, jitter, packet loss, and bandwidth datasets. The study finds that throughput, delay, jitter, and packet loss are the best predictors of network QoE using regression analysis and evaluation metrics including the Akaike Information Criterion (AIC), Adjusted R2, and Root Mean Squared Error (RMSE). Superior prediction accuracy was demonstrated by the chosen four variable model, which maintained strong explanatory power while achieving the lowest AIC of 12062.53 and RMSE of 100.12.This work incorporates a Neuro-Fuzzy framework in its experiment and analysis in contrast to traditional methods of regression exclusively. These results show how useful Neuro-Fuzzy modeling is for identifying nonlinear relationships between end user experience and network performance, providing a scalable framework for real-time QoE control in LAN settings. In order to establish a fusion of objective–subjective framework, future work will expand the framework to diverse networks, explore rule-reduction schemes to advance scalability, and incorporate more subjective QoE measurements. A strong and understandable AI-driven method for QoE optimization in network performance management is provided by this study.
Adaptive Neuro-Fuzzy Inference System (ANFIS)
Final Evaluation of a Real-Time Data Adaptation Tool for Automated Machine Learning
This paper presents a Real-Time Data Adaptation Tool designed to enhance the performance, stability, and automation of machine learning systems operating under continuously evolving data conditions. The proposed framework integrates automated preprocessing, concept drift detection, noise reduction, and dynamic hyperparameter optimization to maintain high model accuracy in real-time environments. Simulation results demonstrate that the tool achieves a significant 14–18% improvement in model performance, reduces latency to 45–60 ms, and outperforms recent state-of-the-art methods in accuracy, efficiency, and automation. Comparative analysis further validates the tool’s capability to support continuous learning pipelines with minimal manual intervention, making it suitable for modern AI-driven applications.
A Comparative Analysis of CNN, XG Boost, DNN and Res Net for Leukemia Classification Using Hybrid PSO Model
Major health concern is leukemia, a form of blood cancer marked by aberrant white blood cell development. Effective treatment depends on an early and precise diagnosis, and deep learning (DL) has become a game-changing tool in this field. A thorough examination of several deep learning models, such as ResNet, XG Boost, Convolutional Neural Networks (CNNs), and Deep Neural Networks (DNNs), is given in this review study. For prompt treatment, leukemia must be identified from peripheral blood smear images as soon as possible. Although medical image analysis is dominated by Convolutional Neural Networks (CNNs), recent research has investigated ensemble and gradient boosted techniques as XG Boost, Deeper ResNet, and fully connected DNNs. Four typical models—custom CNN, ResNet 50, feature fusion with XG Boost, and a multilayer DNN—are experimentally compared head-to-head on the public ALL IDB1, ALL IDB2, and C NMC 2019 datasets in this study. ResNet 50 had the highest macro F1 (96.8%), followed by CNN (94.3%), DNN (90.1%), and XG Boost (88.5%). The significance of ResNet's margin (p < 0.05) was validated by statistical testing. While XG Boost depended on global color histograms, ResNet concentrated on the shape of leukocyte nuclei, according to qualitative saliency analysis. The findings show the trade-offs between interpretability, accuracy, and computing cost and offer recommendations for implementing AI screening methods in labs with limited resources. .
A Systematic Review of Real-Time Adaptive Automation in Machine Learning Model Training
Real-time adaptive automation is rapidly transforming machine learning (ML) model training by enabling systems to make dynamic adjustments in response to evolving data, environmental changes, and performance feedback. This systematic review explores current advancements, architectures, and techniques that support adaptive automation during ML training, including dynamic hyperparameter tuning, online learning, automated data preprocessing, resource-aware training, and feedback-driven model optimization. By synthesizing findings from recent studies across domains such as autonomous systems, cybersecurity, industrial IoT, and real-time analytics, this review highlights the strengths and limitations of existing approaches. Key challenges—including concept drift, latency constraints, scalability issues, and limited interpretability—are examined in detail. The results indicate that real-time adaptive automation significantly improves model accuracy, responsiveness, and robustness in dynamic environments. However, standardized frameworks, benchmarking protocols, and unified evaluation metrics are needed to fully harness its potential. This review provides foundational insights and future research directions for designing next-generation adaptive ML training systems.
A Survey on Real-Time Water Surveillance Boat with Object Detection and Conveyor Belt Retrieval
Water pollution caused by floating waste such as plastics, bottles, and cans has become a growing environmental concern, posing serious threats to aquatic life and human health. Conventional cleaning methods are manual, labor-intensive, and inefficient for continuous or large-scale operations. To overcome these limitations, this paper presents a Real-Time Water Surveillance Boat with Object Detection and Conveyor Belt Retrieval, an autonomous system that integrates embedded control, computer vision, and mechanical automation for smart aquatic waste management. The system employs a Raspberry Pi as the central controller and a Pi Camera Module for real-time detection of floating debris using machine learning and computer vision algorithms. Upon detection, a motor-driven conveyor belt mechanism is activated to collect and store waste onboard, ensuring efficient and contactless retrieval. The system supports IoT-based monitoring for real-time data logging and remote supervision, enabling continuous tracking of debris detection events and system performance. Powered by rechargeable batteries with the provision for solar charging, the boat ensures energy- efficient operation suitable for lakes, rivers, and canals. This autonomous platform contributes to sustainable environmental management by reducing human effort, improving water quality, and promoting eco-friendly, intelligent waste retrieval.
Multi-Factor Authentication Model with Light Weight Encryption for Cloud IoT Systems
The quick development of Cloud-IoT systems has enabled large-scale automation, universal sensing, and intelligent decision-making across various application domains, including smart cities, industrial IoT, healthcare and transportation. However, the integration of resource-constrained IoT devices with cloud platforms familiarizes significant security encounters, predominantly in authentication and in data protection. Traditional cryptographic and authentication structures look to be computationally very intensive which makes them inappropriate for low-power IoT architectures. This research recommends a robust and energy effective security framework that integrates the Multi-Factor Authentication (MFA) with the lightweight encryption to ensure secure device access, user authentication and data confidentiality in Cloud-IoT environments. The proposed MFA model incorporates possession-based, knowledge-based and behavioral factors thereby minimalizing the threat of credential compromise and the unauthorized access. Lightweight encryption algorithms such as SPECK and PRESENT are labouring to produce cryptographically secure authentication tokens and protect announcement without overloading the constrained devices. Investigational evaluations validate better-quality resistance to replay, impersonation and brute-force attacks while achieving low computational overhead. The mixture of MFA and lightweight cryptography efficiently supports trust, enhances system resilience, and provides a scalable security architecture suitable for next-generation IoT deployments.
ABS-PoNR: Attribute-Based Signature scheme with Proof of Non-Revocation
In this paper, we introduce a novel Attribute-Based Signature scheme with Proof of Non-Revocation (ABS-PoNR) that enhances security and fine-grained access control in dynamic environments. Our scheme extends traditional attribute-based signatures by incorporating a non-revocation mechanism for attributes and unique user identifiers (UUIDs). It ensures that signatures remain valid only if the signer’s attributes and UUIDs have not been revoked. The proof of non-revocation allows verifiers to check the revocation status without compromising privacy and rejects signature if the proof is invalid or attributes are revoked. Utilizing bilinear pairing-based cryptography, the ABS-PoNR scheme enables efficient and secure attribute-based signing. Our security analysis is based on the Computational Diffie-Hellman (CDH) assumption and proves the unforgeability of the scheme which makes it computationally infeasible to forge valid signatures. We also discuss the efficiency and practical applicability of the ABS-PoNR technique in scenarios requiring dynamic attribute management and frequent updates to access rights. It marks a significant advancement in attribute-based cryptography.
AI-Enhanced Sustainable Infrastructure Planning for Mega Pilgrimages
Mega pilgrimages, such as the Hajj in Saudi Arabia, represent one of the most complex urban and infrastructural planning challenges in the modern world. These events bring millions of participants into condensed urban spaces, testing the limits of transportation systems, healthcare services, environmental sustainability, and public safety. Traditional infrastructure planning methods often fall short in responding dynamically to such scale and unpredictability. This paper explores how Artificial Intelligence (AI) can be leveraged to transform infrastructure planning for mega pilgrimages into a more sustainable, adaptive, and resilient process. Through a systematic literature review and the integration of cross-disciplinary insights, we propose a conceptual AI-enhanced infrastructure framework that addresses real-time urban mobility, environmental resilience, and healthcare delivery during mass gatherings. Focusing particularly on the context of Saudi Arabia and its Vision 2030 initiatives, this study demonstrates the capacity of AI technologies ranging from predictive analytics to autonomous systems to strengthen critical infrastructure, reduce environmental impact, and improve crowd management. Our findings advocate for the integration of AI-driven approaches into national planning agendas for mega events, emphasizing the need for ethical governance, data infrastructure, and inter-agency coordination.
A Soft Power Projection Framework: Education, Cultural Diplomacy, and Regional Development in Nigeria’s Foreign Policy
Nigeria’s foreign policy has increasingly incorporated soft power as a strategic tool for regional influence and international recognition. This review explores the development of a Soft Power Projection Framework that leverages education, cultural diplomacy, and regional development as key drivers of Nigeria’s foreign engagement. Education diplomacy—through scholarship programs, academic exchanges, and the export of Nigerian higher education—has served as an instrument for building transnational networks and promoting national image. Similarly, cultural diplomacy, expressed through Nollywood, music, and sports, enhances Nigeria’s cultural visibility and fosters mutual understanding across Africa and the global South. Regional development initiatives, particularly within the Economic Community of West African States (ECOWAS), demonstrate Nigeria’s leadership in promoting peace, stability, and shared prosperity. The paper synthesizes theoretical perspectives on soft power (Nye, 1990) with empirical evidence from Nigeria’s diplomatic practice to propose an integrated framework linking educational outreach, cultural identity, and developmental assistance. By evaluating institutional structures, policy coherence, and the role of non-state actors, this review identifies both the opportunities and constraints in Nigeria’s soft power strategy. Ultimately, the study argues for a more coordinated and data-driven soft power policy to strengthen Nigeria’s regional leadership and global influence.
Review of Lung Cancer Detection and Classification Image Processing Techniques
The aim of this review is to present an overview of Lung Cancer Detection and Classification Using Digital Image Processing computer aided design systems. The discovery of cellular breakdown in the lungs through picture handling was a significant apparatus for the analysis. Several techniques were presented with the goal of having a thorough understanding of the techniques that are used during lung cancer detection. The techniques analysed in this review includes, CT scan examination, thresholding technique, nodule segmentation techniques, pre-processing and segmentation techniques and classification techniques. Models used during classification were also analysed with the goal of presenting a comprehensive analysis of these techniques as they are used during lung cancer detection. CAD was found to be the most popular technique being used.
Autonomous Intelligence : A Technical Analysis of Self-Directing AI Systems and Their Enterprise Implementation
This article explores the emerging landscape of autonomous AI agents and personalized assistants, examining how technologies like AutoGPT and BabyAGI are revolutionizing AI system independence through multi-step task execution capabilities. The article delves into the technical foundations enabling these systems to operate with increased autonomy while maintaining relevance to user needs. A comprehensive article on personalized AI assistants, including OpenAI's Custom GPTs and Meta's AI assistants, reveals their advanced context retention abilities and recommendation systems. The discussion encompasses implementation strategies for enterprise adoption, technical integration considerations, and these technologies' strategic implications for business transformation. The article concludes with insights into the evolving relationship between humans and increasingly autonomous AI systems, highlighting both challenges and opportunities in this rapidly advancing field.
AI-Based Crop Disease Detection using Convolutional Neural Networks (CNN)
Agricultural productivity faces significant threats from diseases that affect plant health and reduce crop yields substantially across global farming systems. The conventional approach to identifying crop diseases relies heavily on manual inspection by trained agricultural professionals, a process that is inherently time-consuming, lacks consistency, and cannot scale to meet the requirements of modern large-scale farming operations. This study introduces and evaluates an artificial intelligence framework leveraging Convolutional Neural Networks to automate the detection and classification of crop diseases from digital images of affected plant leaves. Employing the extensively used PlantVillage benchmark dataset encompassing over 50,000 annotated images across multiple crop varieties and corresponding disease categories, the developed system demonstrates classification performance of approximately 88-90% accuracy with inference time below three seconds per image. The approach integrates image acquisition, sophisticated preprocessing techniques, deep neural network classification, and automated recommendation generation to support farming communities. The system design prioritizes accessibility for resource-limited farmers through simplified interfaces and mobile-first deployment strategies. Results indicate that machine learning-based automated detection offers significant potential for advancing agricultural sustainability, reducing reliance on broad-spectrum chemical treatments, and supporting informed decision-making by farming communities worldwide.
A Survey on Wireless Charging of Electric Vehicles
Wireless charging enables efficient power transfer through electromagnetic induction between transmitter and receiver coils, eliminating the need for physical connectors and enhancing safety and convenience. In this proposed system, solar panels serve as the primary energy source, converting sunlight into electrical energy via the photoelectric effect in silicon cells and using it to wirelessly charge batteries through a clean, contactless process. The integration of Wireless Power Transfer (WPT) with renewable solar energy supports the development of eco-friendly charging infrastructure, particularly for electric vehicles (EVs), reducing dependency on fossil fuels and minimizing environmental pollution. The system incorporates Internet of Things (IoT) technology to enable real-time monitoring of charging parameters, energy flow, and automation of billing and control processes. Recent advancements in WPT coil design, compensation networks, and power electronics have improved energy efficiency and alignment tolerance, making wireless EV charging more practical for dynamic and stationary applications. Overall, this work presents a smart, solar-powered, IoT- enabled wireless charging solution aimed at achieving sustainable, contactless, and autonomous electric vehicle charging, contributing to the vision of cleaner mobility and future smart transportation systems.
Hybrid Model for Fake News Detection with Blockchain and Bidirectional Transformer
The consequences of the spread of fake news, particularly in today’s digital world, are serious. Today’s fake news rapid cycle affects public opinion and creates a misinformation effect. Traditional fact-checking methodologies are slow, inaccurate, and manipulatable, limiting our control over the rapidly generated false information cycle. This project proposes an intelligent and safe solution to addressing fake news through the leverage of Blockchain technology with the use of Natural Language Processing (NLP). At the center of BIO is BERT (Bidirectional Encoder Representations from Transformers), a cutting-edge model of deep learning that allows for an understanding of both the context and meaning behind words. This allows BERT to identify subtle nuances of misinformation that older processes have trouble identifying. To record verified news articles in the Blockchain produces a secure, tamperproof, decentralized model with integrity so that news articles cannot be changed post publication. This is a two-pronged effect that will also build confidence by verifying the credibility of the news sources of information. By marrying cutting-edge artificial intelligence and secure data storage, this project is positioned to provide a scalable, reliable and minimal data input method to combat fake news and misinformation while promoting a trustworthy information ecosystem to consumers by offering better source credibility.
A Comprehensive Survey of Machine Learning and Deep Learning Methods for Cardiovascular Disease Classification
As one of the main causes of disability and death worldwide, cardiovascular diseases (CVDs) need precise and efficient classification methods to support early diagnosis and effective clinical decision-determination. A detailed review of the categorization is given in this article of traditional statistical methods, rule -based systems, and data based modelling techniques.It highlights the types of clinical and non-clinical data, including physical signals, patient health records and medical imaging, as well as the exhibit metrics generated to evaluate classification models. The report discusses the primary issues, including feature selection, data imbalance, interpretation, and deployment constraints.In addition, it identifies novel trends and areas for further study to improving accuracy, strength and scalability in the classification of cardiovascular disease. This review serves as a means of understanding the current development for researchers and healthcare professionals and identifying the opportunities for the classification of cardiovascular disease research and advancing applications.
Intelligent Personalized Learning Guide Using Generative AI
The evolution of education from static, one-size-fits-all systems to adaptive, data-driven paradigms marks a profound shift in how knowledge is delivered and consumed. Traditional pedagogical models fail to accommodate individual differences in learning pace, style, and cognitive ability. This paper presents an Intelligent Personalised Learning Guide (IPLG) that leverages Generative Artificial Intelligence (AI) to create customized learning experiences. By combining Large Language Models (LLMs) with dynamic learner profiling, the system generates context-aware study materials, adaptive quizzes, and intelligent feedback mechanisms. The proposed framework not only bridges the gap between learners and educational resources but also enhances comprehension and engagement by continuously adapting to learner performance and behavioral data.The proposed system integrates a Generative AI Engine, a Data Collection and Profiling Layer, and an Adaptive Content Generation Module to deliver interactive, feedback-rich, and personalized learning paths. The use of Generative AI ensures that each learner receives unique content aligned with their current knowledge level, while performance analytics provide actionable insights for continuous improvement. Experiments and literature synthesis suggest that such agentic systems have the potential to revolutionize digital learning environments by transforming static educational resources into intelligent, self-evolving learning companions. This research contributes to the growing field of AI in Education (AIED) by proposing an adaptive framework that enhances personalization, scalability, and learner autonomy.
Generative Artificial Intelligence (Generative AI)
Personalized Learning; Adaptive Learning Systems
AI Surveillance against Counterfeit Notes
The project “AI surveillance against counterfeit notes” aims to develop an intelligent system capable of detecting and identifying currency denominations from both static images and live webcam input. The system utilizes machine learning and computer vision techniques to recog nize different currency notes efficiently and accurately. A React-based frontend enables users to upload images or use a live webcam for real-time detection. The backend (optional) or in browser TensorFlow.js model performs inference, providing denomination labels, confidence scores, and bounding boxes on the detected notes. This application is particularly useful for assisting visually impaired individuals, automated teller machines, and cash-sorting systems, offering a fast, reliable, and user-friendly interface. The model is trained on a dataset of cur rency images with varied lighting, orientation, and background conditions to enhance accuracy and robustness. With a prototype accuracy target above 90, this project demonstrates the po tential of lightweight machine learning models in real-time visual currency recognition.
Air Canvas Application using OpenCV and NumPy
For a very long time, written communication has been a vital tool for sharing knowledge and ideas. Both handwriting and typing are still common ways to record information in the current digital era. However, developments in wearable technology and computer vision have enabled new possibilities, such as generating text in the air with hand or finger gestures. This study presents an Air Canvas system that uses fingertip tracking to convert hand movements into real-time writing. The system uses computer vision techniques to process motion data and generate visible drawings or text. Digital messaging, education, and accessibility tools for people with physical or hearing impairments are just a few of the many potential uses for it.
Brain Tumor Detection and Classification Using ShuffleNet : A Lightweight Deep Learning Approach
Brain tumor detection is an important task in medical imaging and requires accurate and efficient analysis to support early diagnosis and treatment planning. This study presents a deep learning-based system using the Shuffenet V2 algorithm. It is known for its optical architecture and computational efficiency. The proposed system processes MRI scans to recognize and classify brain tumors. That is, the ability to compensate and speed to compensate and speed is harmonious. This is suitable for use in resource-related environments. The workflow includes data preprocessing, model training with marked MRI datasets, and real-time tumor classification. The unique point-to-point group survivors and channel shuffling mechanism of mesh lights allow the model to reduce compensation complexity while simultaneously maintaining robust performance. The integration of segmentation-specific decoders improves the ability of the model to perform the classification of tumor regions within pixels. This approach provides an inexpensive and scalable solution for medical image analysis, offering considerable potential for distant care and undercare environments. This study highlights the effectiveness of shufflenets in coping with medical imaging.
Interval Valued Intuitionistic Fuzzy Set Theoretic Approach for Decision Making Problem using a New Score Function
The interval valued intuitionistic fuzzy set (IVIFS) serves as a powerful and effective framework for representing the genuine preferences of decision makers (DMs) and managing imprecise information. The objective of this study is twofold. First, a novel score function is developed to defuzzify interval valued intuitionistic fuzzy numbers (IVIFNs), and its fundamental properties are examined. Second, two methods are proposed to address multiple attribute decision making (MADM) problems based on the IVIFS framework, where the performance ratings of alternatives with respect to various attributes are expressed in terms of IVIFNs. The proposed methods assign different degrees of importance to each attribute and incorporate a demand function that reflects the satisfaction requirements of the DM. Using this demand function, the DM can determine the most suitable alternative or establish a ranking among the available alternatives. Finally, three test-based validity criteria and a numerical example are presented to demonstrate the rationality and effectiveness of the proposed methods.
Interval valued intuitionistic fuzzy number
REALCODEAI : An AI-Powered Real-Time Code Collaboration and Interview Platform
REALCODEAI is an AI-powered real-time code collaboration and interview platform designed to transform the way technical assessments and collaborative programming sessions are conducted. Unlike traditional coding editors, this system allows multiple users to simultaneously edit and execute code in real time while integrating artificial intelligence for evaluation and feedback. The platform leverages technologies such as Liveblocks and Monaco Editor to enable smooth, synchronized collaboration and employs the OpenAI API to provide intelligent code suggestions, bug detection, and natural language-based assistance. It features an integrated Interview Mode powered by the Judge0 API, allowing interviewers to assign coding challenges, monitor execution, and automatically evaluate candidate per-formance. Developed using the MERN stack (MongoDB, Ex-press.js, React.js, and Node.js), the platform ensures scalabil-ity, cloud deployment readiness, and strong data security. The inclusion of AI-based plagiarism detection, Natural Language Processing (NLP) for communication analysis, and real-time monitoring further enhances fairness and eﬀiciency in the inter-view process. The proposed system aims to reduce interviewer workload, ensure unbiased assessment, and provide an engaging and intelligent coding environment suitable for recruitment, education, and collaborative development.
Automated Vulnerability Assessment and Penetration Testing Tool for CCTV Cameras and DVR
Each CCTV camera and DVR is an ideal target for cyber attacks due to a growing concern regarding insecure configuration, firmware, and network services offered by the deployment of IP-based surveillance systems. Manual VAPT is time-consuming and requires specialized skills. This paper describes the development of an automated VAPT framework that can identify, analyze, and report security vulnerabilities in CCTV and DVR systems. The automated framework integrates network scanning, service fingerprinting, and vulnerability mapping with exploit test validation via automated modules using Python scripting and open source security engines such as Nmap, Nikto, or Metasploit. The system has the ability to perform end-toend security testing, credential testing, open port enumeration, default password tests, and examine firmware for vulnerabilities. Real-time test results can be visualized in a central web-based dashboard indicating severity and remediation recommendations. The framework was experimentally validated across multiple CCTV brands reporting an 87% efficacy rate identifying critical risks, and a 65% reduction in manual testing time. This enhances operational efficiency, scalability, and reliability of VAPT practices; thus providing an effective security solution to public and private surveillance infrastructures threatened by modern cyber attacks.
AI-Enabled Smart Education System is an intelligent academic management platform designed to enhance institutional efficiency through automation, predictive analytics, and artificial intelli-gence. The system unifies students, faculty, and administrators into a single adaptive digital ecosys-tem, streamlining academic and administrative workflows. The platform incorporates an AI-powered chatbot for academic and administrative assistance, predictive analytics for performance tracking, and automated dashboards for institutional reporting and monitoring. By leveraging cloud computing, data analytics, and machine learning, the system ensures reliability, scalability, and continuous availability for users. Developed using React.js, Node.js, Dialogflow, and AWS Cloud, AI-Enabled Smart Education System integrates MySQL and MongoDB databases for secure and efficient data management. Ex-perimental analysis indicates a 40% reduction in administrative workload, 92% chatbot accuracy, and over 90% prediction precision, confirming the systems performance and reliability. This project demonstrates how artificial intelligence and cloud technology can revolutionize traditional education systems into intelligent, adaptive, and data-driven environments that foster collaboration, engage-ment, and innovation in learning and management.
A Review of Machine Learning Approaches for User Engagement Profiling in Online Retail: Integrating Google Analytics for Predictive Insights
The digital marketplace has evolved from a transactional platform to an experiential ecosystem, making user engagement a paramount metric for success. This review paper synthesizes current knowledge and presents a novel framework for integrating Machine Learning (ML) with Google Analytics (GA) data to transition from descriptive analytics to predictive user engagement profiling in online retail. We begin by delineating the critical imperative of understanding user engagement in the modern e-commerce landscape, highlighting the limitations of traditional, descriptive analytics platforms like GA, which, despite providing rich behavioral data, lack inherent predictive capabilities. The paper then systematically reviews and compares prominent machine learning classification algorithms—specifically Decision Trees (DTs), Naïve Bayes (NB), and k-Nearest Neighbors (k-NN)—for their applicability in modeling and predicting user behavior. Drawing on a synthesized empirical study, we demonstrate that Decision Trees can achieve superior accuracy (97.98%) and, more importantly, offer high interpretability through actionable decision rules derived from features like event count and bounce rate. Furthermore, this review elaborates on the architectural considerations for building a robust ML-GA integration pipeline, encompassing data ingestion, feature engineering, model deployment, and continuous monitoring. Finally, we discuss practical applications, identify prevailing research gaps—such as handling class imbalance and the need for real-time, cross-platform profiling—and outline future research directions, including the incorporation of Explainable AI (XAI) and Generative AI. This paper concludes that the synergistic integration of ML and GA data represents the next frontier for enabling proactive, personalized, and data-driven digital marketing strategies.
Development of a Method to Detect Humans and Their Motion Based On a Self-Supervised Learning Method: A Comprehensive Review
This comprehensive review paper examines the recent advancements in human detection and motion analysis using self-supervised learning methods. Traditional approaches for human motion understanding have largely relied on supervised learning techniques that require extensive labeled datasets, which are costly and time-consuming to acquire. The emergence of self-supervised learning paradigms has revolutionized this field by enabling models to learn meaningful representations from unlabeled data. This review systematically analyzes the evolution from traditional optical flow methods and pose-based approaches to modern self-supervised frameworks, with particular focus on the groundbreaking H-MoRe (Human Motion Representation) method. We explore the technical foundations, architectural innovations, and performance benchmarks across multiple applications including gait recognition, action recognition, and video generation. The comprehensive analysis covers performance metrics, comparative evaluations, and practical implementations across diverse datasets including CASIA-B, Diving48, and UTD-MHAD. The paper also discusses current challenges, future research directions, and the potential impact of these methods on various real-world applications in healthcare, surveillance, sports analytics, and human-computer interaction.