Daily Archives: November 14, 2026

Authors: Bhor Anwit Rajendra, Prof. M. A. Auti, Prof. M. C. Kolse, Prof. S. K. Said

Abstract: Sudden rain and intense sunlight affect daily commuting, yet people frequently leave home without an umbrella or forget it at offices, buses and shops. Conventional umbrellas offer protection but no intelligence. This paper reviews the design of a smart umbrella reminder system that combines Internet of Things (IoT) sensing, wireless proximity tracking and machine-learning-based weather prediction to remind users at the right moment. The review covers environmental sensing of rainfall, temperature, humidity and light; Bluetooth Low Energy (BLE) links between the umbrella and a smartphone for left-behind alerts based on received signal strength; consumption of weather forecasts and short-term precipitation nowcasts; and context-aware logic that considers the user's schedule and location before issuing a reminder. Low-power hardware design, including duty cycling and energy harvesting, is examined because the device must operate for months on a small battery. A layered architecture covering the umbrella node, the smartphone gateway, a cloud service and the user interface is proposed, followed by a research gap analysis, objectives, comparison of connectivity and prediction options, and a discussion of challenges. The review concludes that a low-cost, context-aware smart umbrella is a practical example of how everyday objects can become helpful IoT companions.

DOI: https://doi.org/10.5281/zenodo.24220115

Published by: vikaspatanker

Authors: Bangar Piyush Rajesh, Prof. S. K. Said, Prof. M. C. Kolse

Abstract: Social media, websites and online portals have become major sources of news, but they also allow false and misleading information to spread rapidly, creating confusion and social harm. Fake news detection aims to identify such content automatically so that users and platforms can respond quickly. This paper reviews machine learning and natural language processing approaches to detecting fake news from the text of articles and headlines. It covers how fake news is defined and why it spreads, benchmark datasets such as LIAR, ISOT and FakeNewsNet, preprocessing steps including cleaning, tokenization and stop-word removal, feature extraction with bag-of-words, n-grams and TF-IDF, and classification with Logistic Regression, Naive Bayes and Support Vector Machines, as well as deep models based on BERT. A practical five-layer pipeline that can be implemented with Python libraries such as Pandas, NumPy, Scikit-learn and NLTK is proposed, together with research gaps, objectives and a comparison of algorithms. Limitations of text-only detection, dataset bias and the need for explainable, regularly updated models are discussed. The review concludes that a well-built text-classification pipeline is an effective first line of defence and a strong foundation for richer detection systems.

DOI: https://doi.org/10.5281/zenodo.24220040

Published by: vikaspatanker

Authors: Chapekar Ved Sudhir, Prof. M. A. Auti, Prof. M. C. Kolse, Prof. S. K. Said

Abstract: Fraud in banking, digital payments, online shopping and insurance causes large financial losses and erodes customer trust. Traditional fraud detection relies on predefined rules and manual verification, which struggle with complex and constantly changing fraud patterns. Artificial Intelligence (AI) enables systems that analyse large volumes of transaction data in real time, recognize unusual behaviour and generate alerts. This paper reviews AI-based fraud detection. It explains the characteristics of fraud data, including extreme class imbalance, concept drift and delayed labels, and surveys statistical approaches, supervised classifiers such as logistic regression, random forests and gradient boosting, unsupervised anomaly detection, sequence models that learn from customers' transaction histories, and graph neural networks that expose fraud rings. Hybrid systems that combine supervised and unsupervised learning, benchmark datasets and evaluation metrics suited to imbalanced problems are discussed. A five-layer real-time fraud detection architecture is proposed, along with research gaps, objectives, a comparison of methods and challenges including false positives, explainability and adversarial adaptation by fraudsters. The review concludes that layered AI systems with human investigators in the loop provide the most effective defence.

DOI: https://doi.org/10.5281/zenodo.24219829

Published by: vikaspatanker

Authors: Chakave Sakshi Revannath, Prof. M. A. Auti, Prof. M. C. Kolse, Prof. S. K. Said

Abstract: The number and sophistication of cyberattacks, including malware, phishing, ransomware and denial-of-service campaigns, continue to grow, and signature-based security tools struggle to recognize new or modified threats. Machine Learning (ML) offers a complementary approach that learns the characteristics of normal and malicious behaviour from data and flags suspicious activity in real time. This paper reviews ML-based threat detection with emphasis on network intrusion detection. It traces the evolution from the classical intrusion-detection model to rule-based systems, and examines supervised classifiers such as Decision Trees, Random Forests and Support Vector Machines, unsupervised anomaly detectors and deep learning models. Public benchmark datasets, feature engineering from network flows, evaluation metrics and the practical difficulties of class imbalance, concept drift and false alarms are discussed. A layered architecture for a real-time detection system is proposed, comprising traffic capture, feature extraction, a hybrid detection engine, alert management and response integration. A research gap analysis, objectives and a comparative analysis of algorithms are presented. The review concludes that ensemble ML models combined with explainable alerts and continuous retraining can significantly improve detection accuracy and reduce false alarms in operational security environments.

DOI: https://doi.org/10.5281/zenodo.24219742

Published by: vikaspatanker

Authors: Bacche Sanket Kailas, Prof. S. K. Said, Prof. M. C. Kolse

Abstract: Managing personal and organizational expenses with paper receipts and spreadsheets is slow, error-prone and offers little insight into spending behaviour. Cloud-based expense trackers address these problems by centralizing financial records, synchronizing them across devices and applying analytics to reveal patterns. This paper reviews the technologies behind such systems. It examines cloud service and deployment models, intelligent capture of receipts through Optical Character Recognition (OCR) and document-understanding models, automatic categorization of transactions, budget forecasting and anomaly detection, and integration with banking data through consented interfaces such as India's Account Aggregator framework. Security is treated as a core requirement: the review discusses encryption in transit and at rest, multi-factor authentication, role based access control for multi-user organizations and common web-application vulnerabilities. A five-layer reference architecture is proposed that separates client applications, an API gateway, microservices, data storage and an analytics engine. A research gap analysis, objectives, comparison of storage and capture options and a discussion of challenges are presented. The review concludes that combining automated data capture with explainable analytics and strong security controls can make expense tracking both effortless and trustworthy for individuals and enterprises.

DOI: https://doi.org/10.5281/zenodo.24219665

Published by: vikaspatanker

Authors: Dhole Om Santosh, Prof. N. G. Rokade, Prof. M. C. Kolse, Prof. S. K. Said

Abstract: Artificial Intelligence is moving from single models that answer one question at a time toward agentic systems that plan, use tools, coordinate subtasks and execute multi-step workflows. A Multi-Agent AI System (MAAS) contains two or more autonomous or semi-autonomous agents that interact to achieve a shared or related goal, where each agent may have a specialized role, memory, tools and decision process. This paper reviews the foundations and recent advances of such systems. It traces the classical theory of multi-agent systems, including agent communication languages and the contract-net protocol, and examines how large language models have revived the field through frameworks for role-playing, conversation-driven collaboration, standardized operating procedures and debate. Coordination topologies such as orchestrator–worker, hierarchical, peer-to-peer and blackboard designs are compared, along with mechanisms for memory, tool access and verification. A layered reference architecture is proposed that separates user interface, orchestration, specialized agents, shared memory and tools, and governance. The paper also analyses failure modes, including error propagation, coordination overhead, prompt injection and cost, and identifies research gaps in evaluation and reliability. It concludes that multi-agent designs are most valuable when tasks decompose naturally and when intermediate outputs are verified.

DOI: https://doi.org/10.5281/zenodo.24219567

Published by: vikaspatanker

Authors: Ms. Sneha Ghule

Abstract: Background: Anxiety can affect psychological well-being and daily functioning. Meditation is used as a complementary relaxation practice. Aim: To assess the effectiveness of concentrative meditation on anxiety levels among people in a selected rehabilitation centre in Pune. Methods: A quantitative quasi-experimental study was conducted using a non-equivalent control-group approach. Sixty people aged 18–60 years were selected through non-probability quota sampling. Anxiety was assessed using the Beck Anxiety Scale. Participants received concentrative meditation using a breathing technique for 14 days, and anxiety was assessed following the intervention. Descriptive statistics, a paired t-test and one-way analysis of variance were used. Results: The mean anxiety score decreased from 32.34 (SD=4.49) before the intervention to 21.25 (SD=1.60) after the intervention. The reported t value was 27.23 (p<0.001). The reported anxiety-category distribution shifted from predominantly moderate and severe anxiety before the intervention to minimal and mild anxiety after the intervention. Gender, occupation, income, marital status and family type were reported to have statistically significant associations with anxiety scores. Conclusion: The findings indicate a reduction in anxiety scores following a 14-day concentrative meditation programme among people in a selected rehabilitation centre in Pune.

DOI: https://doi.org/10.5281/zenodo.24218543

Published by: vikaspatanker

Authors: Dr. S. Mohana Murali

Abstract: Artificial Intelligence (AI) has emerged as one of the most important technologies transforming the banking and financial services industry. The rapid expansion of internet banking, mobile banking, digital wallets, real-time payments and account-based payment systems has generated enormous volumes of financial and transactional data. AI and Machine Learning (ML) provide financial institutions with the ability to analyse this data, identify patterns, automate decisions and deliver personalised services. This research paper examines the role of Artificial Intelligence in digital banking and payment systems, with particular emphasis on fraud detection, cybersecurity, customer service, credit assessment, Know Your Customer (KYC), Anti-Money Laundering (AML), risk management, personalised banking, payment processing and operational efficiency. The paper also examines the Indian context, including the development of UPI and the increasing use of AI in India's banking and payment ecosystem. The study adopts a descriptive and analytical research methodology based on secondary sources, including reports and publications of the Reserve Bank of India (RBI), Bank for International Settlements (BIS), International Monetary Fund (IMF), World Bank and National Payments Corporation of India (NPCI). The study finds that AI can improve the speed, efficiency, security and personalisation of digital banking and payment services. However, AI also introduces challenges relating to data privacy, algorithmic bias, explainability, cybersecurity, model risk, third-party technology dependence and accountability. Therefore, responsible AI governance, human oversight, cybersecurity controls and appropriate regulatory frameworks are essential for sustainable adoption of AI in banking and payment systems.

DOI: https://doi.org/10.5281/zenodo.24218110

Published by: vikaspatanker

Authors: Ketki Satish Pimparkar, Samruddhi Ramesh Rindhe, Assistant Professor Dr. S.D. Shinde

Abstract: Steel is important for buildings, bridges, roads, vehicles and machines. Its production supports development, but it also uses energy and raw materials and may create dust, air pollution, greenhouse gas emissions and solid waste. This paper reviews published research on environmental challenges in the steel industry and explains the main manufacturing stages. It describes the Blast Furnace–Basic Oxygen Furnace route and the Electric Arc Furnace (EAF), including its working principle and advantages. A manufacturing flow chart, a labelled EAF diagram and the supplied photograph of an industrial furnace are included. Ten major steel and metals-sector companies operating in India are also listed. The review suggests that good pollution control, efficient equipment, energy management and recycling can help reduce the environmental impact of steel production. This is a literature-based student paper and does not report new experiments. Steel is a fundamental material for construction, transportation, machinery and infrastructure. This review paper presents an overview of the steel industry, including raw materials, major production routes, steel manufacturing stages, Electric Arc Furnace (EAF) technology, environmental impacts and sustainable practices. It explains the blast furnace–basic oxygen furnace route and the EAF route, followed by secondary refining, continuous casting and rolling. Particular attention is given to EAF components, operation, advantages andlimitations. The paper also discusses greenhouse-gas emissions, air pollution, water use, solid by-products and occupational safety, together with commonly applied control measures. Applications of steel in civil engineering and other sectors are outlined, and examples of major Indian steel producers are listed for context. The review indicates that energy efficiency, responsible scrap recycling, cleaner electricity, pollution control and by-product utilization are important measures for improving the sustainability of steel production. The paper is a descriptive review and does not present plant-specific experimental results.

DOI: https://doi.org/10.5281/zenodo.24217845

Published by: vikaspatanker

Authors: Suyash Dahitule, Ketan Sonawane, Om Ranmode, Prasad Ingole, Dr.Chetana Nemade

Abstract: Color blindness, also known as color vision deficiency (CVD), is a visual condition in which an individual experiences difficulty distinguishing certain colors, particularly red, green, and blue. Early detection of color blindness is important for improving visual health awareness and supporting individuals in their daily activities. This study, titled “Color Blindness Detection Using Ishihara Plates,” focuses on identifying color vision deficiencies through the Ishihara Color Test, a widely used screening method based on specially designed pseudoisochromatic plates containing numbers, patterns, and symbols formed by colored dots. The proposed system aims to provide a simple, cost-effective, and user-friendly approach to preliminary color blindness detection. During the testing process, individuals are asked to identify the numbers or patterns displayed on a series of Ishihara plates. Their responses are analyzed to determine possible difficulties in distinguishing specific color combinations, particularly red-green variations. The system can record responses, evaluate test results, and generate an assessment indicating whether the individual may have normal color vision or a possible color vision deficiency. The approach helps reduce the time and effort required for preliminary screening while increasing accessibility to basic color vision testing. The study highlights the importance of digital screening solutions in promoting early identification and awareness of color vision deficiencies. However, the results are intended for preliminary screening and should be confirmed through a comprehensive examination by a qualified eye-care professional.

DOI: https://doi.org/10.5281/zenodo.24216479

Published by: vikaspatanker