Daily Archives: October 1, 2025

Authors: Mr.R. Palanikumar, Ap / It, R.Nithya, M. Dharshika, N. Kanishka

Abstract: Ride Safe is an IoT-based smart helmet designed to improve two-wheeler safety by detecting accidents and sending real-time emergency alerts. It uses an ESP32 microcontroller along with an accelerometer and impact sensor to identify sudden falls or collisions. A GPS module (NEO-6M) captures the rider’s live location, while a GSM module (SIM800L) transmits SMS alerts to family members or emergency services. To ensure reliability, the system activates only when the helmet is worn using a helmet-wear detection switch, and a cancel switch allows the rider to stop alerts in case of false triggers. This low-cost, reliable, and portable solution not only reduces emergency response time but also increases survival chances, enforces helmet usage, and offers applications in personal safety, fleet monitoring, and smart city systems.

DOI: https://doi.org/10.5281/zenodo.17374588

 

Published by: vikaspatanker

Authors: Piyush Kumar

Abstract: This research paper examines the emerging paradigm of sustainable warfare, investigating the technological innovations and strategic approaches being developed to reduce the environmental footprint of military operations. Through a systematic literature review and critical policy analysis, this study explores the inherent tensions between military objectives and environmental sustainability. The research analyzes current initiatives by major military powers, including NATO's Climate Change and Security Action Plan and various "green military" technologies, while also addressing the critical perspectives that challenge the very concept of environmentally sustainable warfare. Findings indicate that while technological advancements in renewable energy integration, biodegradable munitions, and resource efficiency can marginally reduce military environmental impacts, the concept of truly sustainable warfare faces substantive limitations. The study reveals that current sustainability initiatives primarily serve operational effectiveness and strategic advantage rather than representing genuine ecological commitment. Furthermore, the discourse of sustainable warfare risks legitimizing continued militarization through what critics term "green militarism"—the co-option of environmental concerns to justify military expansion. The paper concludes that approaches focusing on conflict prevention and peaceful resolution may offer greater environmental benefits than attempts to green military operations. This research contributes to understanding the complex relationships between security, sustainability, and justice in an era of ecological crisis, suggesting that genuine ecological sustainability requires a fundamental rethinking of security paradigms rather than technological fixes within existing military frameworks.

 

 

Published by: vikaspatanker

Authors: Dr.M.Sravani, Kalyan Kumar Bethu

Abstract: Stock market volatility makes accurate forecasting vital for informed trading decisions and profit maximization. Over the years, various models have been introduced to enhance the reliability of time series predictions. This study applies the ARIMA model to evaluate data stability and forecast movements in the BSE Index. Model selection was guided by statistical measures including SIGMASQ, Adjusted R², AIC, and BIC, with ARIMA (2,1,2) emerging as the most suitable specification. Using monthly data from January 2021 to January 2025 (49 observations), the model generated forecasts for February 2025 through December 2025, yielding 11 projected values. The results highlight ARIMA’s effectiveness as a short-term forecasting tool, offering actionable insights for informed investment decisions.

DOI: https://doi.org/10.5281/zenodo.17240410

 

Published by: vikaspatanker

Authors: Rutuja Gavai, muniba Ali, zarah Ali, Astha Gulhane, Prof. Sanju D. Garle

Abstract: The effective management of visitors has become a critical aspect of institutional security, smart campus initiatives, and organizational operations. Traditional visitor tracking methods, which rely on manual record-keeping or identity cards, are often prone to errors, delays, and inefficiencies. To address these shortcomings, vehicle plate recognition has emerged as a promising technology for developing intelligent visitor tracking systems. By leveraging the uniqueness of license plates as identifiers, organizations can implement automated, contactless, and reliable mechanisms to verify and monitor visitor entries and exits. This review paper presents a comprehensive survey of existing research on visitor tracking systems that integrate vehicle plate recognition. Key enabling technologies such as image preprocessing, Optical Character Recognition (OCR), fuzzy string matching, and cloud-based services (e.g., Microsoft Azure Cognitive Services) are analyzed for their role in improving accuracy and scalability. The study also discusses the integration of data analytics and reporting frameworks, which transform raw recognition results into actionable insights, such as visitor frequency patterns, identification of unknown vehicles, and predictive analytics for enhanced security planning. In synthesizing current literature, this review identifies major challenges, including image quality variations, diverse license plate formats, and real-time adaptability under unconstrained conditions. It also outlines research gaps in the application of deep learning, edge-based processing, and multimodal verification techniques for intelligent visitor management. The findings highlight that the combination of vehicle plate recognition with intelligent data-driven analysis offers a scalable and efficient pathway toward next-generation visitor tracking systems, particularly in academic institutions, corporate environments, and smart city infrastructures.

 

Published by: vikaspatanker