Category Archives: Uncategorized

Authors: Deepa Barethiya, Ankita Vairagade, Harshal Kathalkar

Abstract: The role that Artificial Intelligence plays in changing the way people work around the world is really big. Artificial Intelligence makes things more efficient. Creates new jobs but it also makes people worry about losing their jobs and having to be more flexible at work. This paper looks at how Artificial Intelligence's affecting people’s jobs and it uses surveys and reviews of what other people have written to do this. The results show that there is a difference between how worried people are about losing their jobs and how much they are doing to learn new things because things, like money and time get in the way. Artificial Intelligence is making it really important for people to learn skills it is not just something people can do if they want to it is something people have to do.

DOI: https://doi.org/10.5281/zenodo.20928264

Published by: vikaspatanker

Authors: Deepa Barethiya, Himani Shirpurkar, Drushti Dharmik

Abstract: Artificial Intelligence (AI) is increasingly integrated into clinical decision-making, particularly in disease-based treatment selection. AI systems promise efficiency, predictive accuracy, and personalized care by analyzing large datasets and recommending tailored therapies. However, these benefits are accompanied by ethical challenges that must be addressed before widespread adoption. Issues of transparency, bias, accountability, privacy, and patient autonomy are consistently reported in recent literature [1][5]. This paper synthesizes findings from 20 peer-reviewed studies published between 2023 and 2026, offering a systematic review of ethical concerns and governance strategies. By combining thematic analysis with case studies in oncology, cardiology, infectious disease, and neurology, we propose a framework for ethically responsible AI deployment in healthcare.

DOI: https://doi.org/10.5281/zenodo.20927818

Published by: vikaspatanker

Authors: Amit C. Kamble, Himmat T. Jadhav

Abstract: Energy security and tariff volatility are growing concerns for Indian higher-education institutions (HEIs) due to rising digital infrastructure, cooling loads, and escalating electricity prices. This paper presents a multi-year, bill-validated assessment of a 180.18 kWp grid-connected rooftop PV system in- stalled at Shivaji University, Kolhapur (SUK). Beyond reporting measured performance (average generation ≈283,824 kWh/yr; CUF ≈18%), the study introduces a Performance Stability Index (PSI) and a Tariff Resilience Index (TRI) to quantify interannual energy stability and financial robustness under adverse tariff scenarios. A 25-year discounted-cash-flow model, incorporating real tariff evolution, yields an IRR of 18.4%, NPV of about INR 517 lakh, and payback of ∼5.3 years. Annual CO2 avoidance is estimated at ∼233 tCO2/yr using the CEA grid factor. A benchmarking framework situates the system against Indian HEI PV case studies, and a replication pathway is outlined for campus-scale deployment. The results demonstrate that rooftop PV can significantly enhance HEI energy security while support- ing national solar and NEP-2020 sustainability goals.

DOI: http://doi.org/10.5281/zenodo.20927527

Published by: vikaspatanker

Authors: Deepa Barethiya, Dhanashri Pannase, Gangasagar Kashyap

Abstract: Ensemble machine learning has pushed crop recommendation accuracy past 99% on standard soil-weather benchmarks — yet this milestone conceals a troubling gap. Systems built around Random Forest, XGBoost, and gradient boosting produce ranked crop labels while leaving the economic viability of each suggestion entirely unexamined. A farmer told "grow rice with 99% confidence" still does not know whether that choice will leave a positive margin after seed, fertiliser, and irrigation costs. This paper proposes a decision-oriented framework that moves beyond the accuracy plateau by coupling a soft-voting ensemble with per-crop yield regressors and a configurable economic layer that estimates expected profit. Where conventional pipelines terminate at a suitability label, the proposed architecture extends the output to a Risk-Adjusted Expected Profit, mathematically formulated as E[Π_c ]_(risk-adjusted)=P_ensemble (c│X) Π_c, where P_ensemble (c│X)is the Ensemble Suitability Probability and Π_c=((Y_c ) ̂(X)×P_(market,c)×1000)-Total Cost_cis the Nominal Net Profit. This coupling mathematically discounts the apparent value of high-risk crops by their probability of soil-weather failure — a correction absent from every reviewed system. To illustrate the theoretical decision dynamics of this framework, we construct a conceptual walkthrough across 200 hypothetical soil-weather scenarios derived from standard agricultural benchmarks. This analysis suggests that the agronomically top-ranked crop and the economically top-ranked crop diverge in roughly 46% of cases — a finding that, if borne out in empirical deployment, would have direct implications for farm-level income planning. A conceptual Streamlit dashboard design is also proposed, embedding real-time what-if sliders and SHAP-based feature attributions to make the system transparent to extension workers and farming cooperatives. The central argument of this paper is simple: a classifier that ignores profit is only half a tool. This framework proposes the other half.

DOI: https://doi.org/10.5281/zenodo.20927114

Published by: vikaspatanker

Authors: Assistant Professor Deepa Barethiya, Sakshi Jibhkate, Samiksha Daronde

Abstract: This paper compares Java-based frameworks and ASP.NET Core for web applications used by companies. It looks at how they work and how well they handle a large number of users. The study checks things like how long it takes for the application to respond, how much work it can handle, how much of the computer’s brain it uses and how much memory it needs when a lot of people are using it at the same time. They ran tests to see what would happen if many people used the application. The results show that ASP.NET Core is really good at responding and using resources wisely. Java-based frameworks are good at handling a lot of users and working with computers at the same time. This study tells us what is good and what is not so good about Java-based frameworks and ASP.NET Core. It helps people choose the tools to build big web applications for companies.

DOI: https://doi.org/10.5281/zenodo.20926193

Published by: vikaspatanker

Authors: Deepa Barethiya, Kshitij Moon, Dipak Meshram

Abstract: Accurate Remaining Useful Life (RUL) prediction for turbofan engines is critical for implementing effective condition-based maintenance strategies, enhancing operational safety, and reducing maintenance costs. Traditional predictive maintenance approaches often struggle with the non-linear, time-dependent characteristics of engine degradation. This paper presents a data-driven prognostic model utilizing a Long Short-Term Memory (LSTM) neural network to predict the RUL of turbofan engines based on sensor-derived operational data. The model is trained and validated on the NASA C-MAPSS dataset, which contains run-to-failure data for multiple turbofan engines. The proposed methodology involves preprocessing raw sensor data, creating sequential inputs using a sliding window approach, and training a two-layer LSTM architecture designed to learn complex temporal degradation patterns. Model performance is evaluated using standard regression metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R² score. The resulting model demonstrates robust predictive capabilities and is deployed in a Flask-based web application, offering a practical tool for real-world CBM systems and highlighting the efficacy of deep learning for industrial prognostics.

DOI: https://doi.org/10.5281/zenodo.20925860

Published by: vikaspatanker

Authors: Deepa Barethiya, Himanshu Praveen Dethekar, Bhavesh Tembhurkar

Abstract: The dark web constitutes a stratified, operationally sophisticated cybercrime ecosystem whose threat dynamics are shaped by layered anonymity infrastructure, AI-augmented criminal tooling, and resilient financial obfuscation mechanisms. While existing literature provides valuable but fragmented analysis of individual components, few studies integrate these elements within a unified analytical framework. This paper addresses that gap through a hybrid analytical survey approach, advancing four primary contributions: (1) a six-dimension taxonomic model differentiating surface web, deep web, and dark web environments; (2) a Five-Layer Dark Web Threat Ecosystem Model characterising the functional architecture of criminal infrastructure; (3) a structured capability taxonomy of AI-augmented criminal tools (Dark LLMs); and (4) a proposed Cyber Threat Intelligence (CTI) extraction pipeline for dark web environments. Drawing on peer-reviewed literature spanning 2020–2025, operational intelligence from Europol IOCTA, Chainalysis Crypto Crime Reports, and FBI IC3 data, and documented threat actor behaviour, the paper analyses ransomware-as-a-service dynamics, cryptocurrency financial obfuscation, law enforcement response limitations, and post-Tor architectural evolution. Persistent research gaps in multilingual CTI extraction, post-Tor forensic methodology, and AI-threat detection are identified, with a structured research agenda proposed.

DOI: https://doi.org/10.5281/zenodo.20925387

Published by: vikaspatanker

Authors: Anuradha Muttamwar, Esha Dorkhande, Vaibhavi Meshram

Abstract: The exponential proliferation of digital media in the modern era has created an environment where mis- and disinformation as well as "fake news" can spread uncontrollably, leading to challenges to public discourse, political trust and integrity. In this paper we present a detailed research approach toward fake news detection through efficient feature engineering and the use of supervised machine learning. We use a dataset composed of 5,000 current news articles (2,537 real, 2,463 fake news) and conduct an in-depth research regarding the performance of TF-IDF with n-grams. We build and train a Multinomial Naive Bayes model and attain excellent classification accuracy. Furthermore, we investigate the importance of text preprocessing such as stop word removal, stemming and lemmatization. Our model achieves a final accuracy of 93.6%, while also achieving scores for precision, recall and F1 greater than 0.92. When comparing with baseline models, the presented method with enhanced feature engineering shows excellent results. We then developed a web based system with the help of Flask that allows real time fake news detection and confidence. It will establish a reusable, light and scalable pipeline to automate fake news detection in real world applications.

DOI: https://doi.org/10.5281/zenodo.20924573

Published by: vikaspatanker

Authors: Anuradha Muttamwar, Bhumika Balpande, Shantanu Gawai

Abstract: Skin diseases are a major health concern worldwide, but getting an appointment with a dermatologist can be tough, especially in rural areas. That's why we've created a web-based system that uses artificial intelligence to help diagnose skin conditions. Our system is built using the Node.js framework and combines a powerful image classification model with a user-friendly website. Here's how it works: users upload pictures of their skin through a simple interface, and our system uses a special kind of neural network called a Convolutional Neural Network (CNN) to analyze the image and make a prediction. We've trained our model using a technique called transfer learning, which allows it to learn from existing knowledge and apply it to new situations. Our model can accurately diagnose five common skin conditions: eczema, acne, psoriasis, dermatophytosis, and benign nevi. We've designed our system to be fast and efficient, even when lots of people are using it at the same time. Our tests show that it can handle up to 100 users simultaneously without slowing down, and it can give results in under a second. We're excited about the potential of our system to provide a low-cost, accessible way for people to get a preliminary diagnosis and take the first step towards getting treatment. Our system is made up of three main parts: a website that users interact with, a backend server that handles the image analysis, and a database that stores all the information.

DOI: https://doi.org/10.5281/zenodo.20924037

Published by: vikaspatanker

Authors: Mayuri Dongre, Harsh Upase, Krushnakant Shinde

Abstract: Artificial Intelligence (AI) has emerged as a transformative technology in modern agriculture, enabling sustainable and data-driven farming practices through precision agriculture techniques. This research paper presents a comprehensive study of AI-based technologies and their applications in sustainable precision agriculture. The study explores the integration of Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT), computer vision, robotics, drones, and sensor-based systems for improving agricultural productivity, resource optimization, and environmental sustainability. AI techniques are increasingly used for crop prediction, disease detection, soil analysis, irrigation management, yield forecasting, weed identification, and climate monitoring, helping farmers make accurate and timely decisions. The paper also highlights how precision agriculture minimizes the excessive use of water, fertilizers, and pesticides while enhancing crop quality and reducing environmental impact. Furthermore, the study examines recent advancements, real-world applications, challenges, and limitations. AI adoption in agriculture, including high implementation costs, lack of technical knowledge, data availability issues, and infrastructure constraints in rural areas. Precision agriculture harnesses data-driven techniques to optimize crop production, resource use, and sustainability. However, low-income countries like Bangladesh face a short- age of localized, high-quality datasets that reflect regional agroclimatic conditions and cropping practices.

DOI: https://doi.org/10.5281/zenodo.20923886

Published by: vikaspatanker