Category Archives: Uncategorized

Authors: Lalmuanawma Chhangte, Remlalsiama, Lalnunpuia

Abstract: Ground level Gamma Radiation and Radon gas concentration at different depth beneath the ground surface of oil exploration areas in Mizoram, India is studied and correlation graph is drawn. The oil exploration areas of Meidum(MD) and Zanlawn(ZL) in Kolasib district, are studied. The main instrument utilized for the study was RnDuo machine devised to survey Radon 222 (222Rn) connected to soil probe of 1mtr long to be baptized at different depth. Background gamma radiation survey at ground level is conducted with Russian base Gamma Survey Meter (PM 1405). The background gamma radiation at ground level varies from 177 nSv/hr at MD-3 to 202 nSv/h at MD-1 location with an average of 186.5 nSv/h. An in-situ measurement of soil gas was carried out at three different spots at four different depths each namely 10cm, 30cm, 50cm and 70cm. The radon gas concentration beneath the soil, within the study area ranges from 0.10 kBq/m3 at MD-3 to 1.31 kBq/m3 at MD-1 location. A correlation graph between ground level gamma radiation and the radon concentration in soil at different dept shows that the correlation coefficient is highest at 10cm with R2=0.466 and lowest at 70cm with R2=0.175. The Radon gas concentration obtained in these areas are below the worldwide average of 35-40 kBq/m3 .(UNSCEAR 2000).

DOI: http://doi.org/10.5281/zenodo.20948672

Published by: vikaspatanker

Authors: Lalnunpuia, Remlalsiama, Lalmuanawma Chhangte

Abstract: Radon gas concentration at different depth beneath the ground surface is studied at different oil exploration areas of Mizoram, India. The oil exploration areas of Thenzawl(TZ) in Serchhip district, is studied. The main instrument utilized for the study was RnDuo machine devised to survey Radon 222 (222Rn). The other instrument is a soil probe of 1mtr long to be baptised at different depth. The study was conducted at four different depths namely 10cm, 30cm, 50cm and 70cm. For each oil exploration areas, an in-situ measurement of soil gas was carried out at three different spots to cover the oil fields. The minimum value of radon gas concentration is observed at TZ-2 spots at 10cm deep; and the maximum concentration is recorded at TZ-2 spot at 70cm deep. The radon gas concentration beneath the soil, within the study area ranges from 0.14 kBq/m3 to 1.37 kBq/m3. The Radon gas concentration obtained in these areas are below the worldwide average of 35-40 kBq/m3.(UNSCEAR 2000).

DOI: http://doi.org/10.5281/zenodo.20948158

Published by: vikaspatanker

Authors: Jayshree Pansare, Karan Singh, Affan Ali Sayyed, Rushikesh Langhi, Prathamesh Dive

Abstract: The rapid expansion of digital communication platforms has significantly increased the need for secure and efficient messaging systems. Modern users rely heavily on chat-based applications for academic collaboration, professional coordination, and personal communication. However, traditional messaging systems often fail to provide an optimal balance between data security and efficient information management. While some platforms emphasize usability, they frequently compromise on privacy, whereas others focus on encryption but lack intelligent tools to manage large volumes of conversational data. This research presents a Secret Chat Room with AI Summarization System, a web-based platform designed to address both security and usability challenges. The system integrates end-to-end encryption using AES and RSA algorithms to ensure confidentiality and protect messages from unauthorized access. Additionally, it employs WebSocket-based real-time communication to enable low-latency and efficient message exchange between users. A key contribution of this work is the integration of an AI-based summarization module that utilizes transformer-based model Gemini Summarization. This module processes chat histories and generates concise summaries, allowing users to quickly understand lengthy discussions without manually reviewing all messages. This feature significantly reduces information overload and enhances productivity. The system follows a modular architecture consisting of authentication, encryption, messaging, and AI components. Experimental observations indicate that the system achieves efficient performance with minimal latency while maintaining strong security standards. The proposed solution is suitable for applications in education, enterprise communication, and collaborative environments.

DOI: http://doi.org/10.5281/zenodo.20937546

Published by: vikaspatanker

Authors: Vidya Deshmukh, Samradnyi Patil, Akshada Veer, Jayada Talharkar, Tahareen begampalli

Abstract: Modern security environments, particularly on the battlefield, demand autonomous systems capable of real-time threat detection and neutralization without relying on human intervention. This paper presents the Autonomous Threat Detec-tion and Elimination System (ATDES), an integrated hardware-software platform designed to detect enemy armored threats — specifically tanks — using a vision-based AI detection pipeline and respond autonomously through a servo-controlled targeting and firing mechanism. The system leverages a Raspberry Pi 3B+ as the central processing unit, integrating a 2- megapixel camera for visual acquisition, an IR transceiver pair for friend-or-foe (IFF) identification, an RF receiver for enemy signal detection, and a servo-mounted firing mechanism for threat neutralization. A lightweight deep learning model is deployed on-device for real-time tank detection from camera frames, achieving sub-50 ms inference latency at a resolution of 480×640 pixels. IR-based IFF communication ensures that allied units are correctly identified and excluded from targeting, minimizing the risk of fratricide. Blynk IoT cloud integration enables remote monitoring and event logging. The system operates off-grid using a battery and solar power combination, enabling continuous 24×7 autonomous surveillance. Simulation results confirm consistent real-time de-tection with high confidence scores, demonstrating the feasibility of deploying edge AI for autonomous military threat response. The proposed system contributes a cost-effective, scalable, and intelligent prototype for next- generation autonomous defense systems.

Published by: vikaspatanker

Authors: Ayush Wankhede, Ajinkya Patil, Partth Thombre, Mohit Patil, Mahesh Korade

Abstract: Online pet adoption platforms face significant chal- lenges with fraudulent listings and attribute misrepresentation, eroding user trust. This paper presents a complete pet adoption system integrating CNN-based image verification to authenti- cate listing attributes before publication. Transfer learning with EfficientNet-B0 is applied to 110,425 images spanning 712 breed classes across dogs, cats, and birds. A two-stage training strategy first trains the classification head with frozen base layers, achiev- ing 84.7% validation accuracy, then fine-tunes the top 40 layers to reach 89.3% validation accuracy. The verification pipeline combines breed confidence, color confidence, and prediction certainty into a normalized trust score (VScore, range 0–100). Server-side scoring with an 85-point threshold prevents client manipulation while achieving 98.0% fraud detection accuracy. A one-way privacy gateway protects adopter identities, and automated digital adoption certificates with unique certification IDs formalize successful adoptions. Experimental validation on 128 verification requests demonstrates an 84.4% acceptance rate, 1.8 s average processing time, and only 1.6% false positive rate.

DOI: http://doi.org/10.5281/zenodo.20930615

Published by: vikaspatanker

Authors: Mayuri Dongre, Arshiya Sahare, Sarang Dumbhare

Abstract: In the age of digitalisation, a lot of transaction information is produced online, and it is significant to understand customer behaviour and market trends. This paper aims at examining the behavioural patterns in transaction data based on a data-driven approach. The information is gathered using web scraping on Flipkart, primarily in the electronic products categories of mobiles, headphones, smart watches, speakers, accessories with the help of Selenium WebDriver and Python. The obtained data is saved in the CSV format and processed with Python libraries, such as Pandas and NumPy, that allow cleaning data, eliminating duplicates, missing values, and categorizing products. Additional analysis is conducted to establish customer preferences, expenditure trends and product demand trends. The end results are presented in visual representations in the form of dashboards and reports to aid in improved business decision-making. This research assists in interpreting the behaviour of transactions and is useful in the data-driven strategies.

DOI: https://doi.org/10.5281/zenodo.20929867

Published by: vikaspatanker

Authors: Deepa Barethiya, Deepak Vinod Chouksey, Ankur Sanjeev Khurpadi

Abstract: Cardiovascular diseases remain the leading cause of mortality worldwide, accounting for approximately 17.9 million deaths annually according to the World Health Organization. Early detection and accurate risk assessment of heart disease are critical for effective clinical intervention and improved patient outcomes. Traditional diagnostic methods often depend heavily on subjective clinical judgment, which can be inconsistent and time-consuming. This research proposes a Machine Learning-based predictive system that leverages clinical data to assess the risk of heart disease with high accuracy. The proposed system employs multiple classification algorithms including Logistic Regression, Random Forest, Support Vector Machine (SVM), and XGBoost, and evaluates their performance on the UCI Cleveland Heart Disease dataset. Feature selection techniques such as correlation analysis and Recursive Feature Elimination (RFE) are used to identify the most significant clinical predictors. The proposed ensemble model achieves an accuracy of 91.8%, sensitivity of 93.2%, and specificity of 90.4%, outperforming individual classifiers. The results demonstrate that machine learning can serve as a reliable and scalable decision-support tool for cardiologists and general physicians in early heart disease diagnosis.

DOI: https://doi.org/10.5281/zenodo.20929505

Published by: vikaspatanker

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