Jharkhand Rai University

Abstract: The rapid advancement of automation and Internet of Things (IoT) technologies has significantly transformed modern retail systems, aiming to enhance customer experience and operational efficiency. Traditional shopping methods in supermarkets and malls often involve manual product scanning and long checkout queues, leading to increased waiting time and customer dissatisfaction. To address these limitations, this paper presents the design and development of an IoT-enabled Smart Shopping Cart system that automates product identification, real-time billing, and payment processes. The proposed system integrates embedded hardware components such as a microcontroller, RFID reader and/or barcode scanner, LCD display, wireless communication module, and optional weight sensor to ensure accurate and secure product handling. Each product is identified using RFID tags or barcodes, and the corresponding information is retrieved from a centralized database. The system dynamically updates the total bill as items are added or removed, allowing users to monitor their expenses in real time. Additionally, the integration of wireless communication technologies such as Wi-Fi enables seamless synchronization with a cloud-based server for inventory management, pricing updates, and transaction recording. To improve system reliability and prevent fraudulent activities, a weight validation mechanism is incorporated,which compares the measured weight of products with predefined values stored in the database. This enhances billing accuracy and reduces the risk of unscanned items. The system also supports optional digital payment integration, enabling customers to complete transactions directly from the cart, thereby eliminating the need for traditional checkout counters. Experimental evaluation of the proposed system demonstrates a significant reduction in billing time compared to conventional methods, along with high accuracy in product identification and transaction processing. The results indicate that the smart cart system can reduce checkout time by more than 50%, improve user convenience, and optimize retail operations. Furthermore, the modular design ensures scalability and adaptability for future enhancements such as artificial intelligence-based recommendations and customer behavior analytics. Overall, the proposed Smart Shopping Cart system offers a cost-effective, efficient, and user-friendly solution for modern retail environments. It not only enhances customer satisfaction by minimizing waiting time but also provides retailers with valuable insights for inventory control and decision-making. The system has strong potential for real-world implementation and can serve as a foundation for next-generation automated retail systems. Keywords: Smart Shopping Cart, Internet of Things (IoT), RFID Technology, Real-Time Billing, Retail Automation

Abstract Because of the language’s simplicity, flexibility and massive libraries supporting artificial intelligence, web development and data science python has become one of the widely used programming language. But this speedy growth has led to software security and vulnerability concerns. This paper examines the security provisions available in python programming and their role in developing secure applications. Herein the analysis is done based on security mechanisms such as validation of inputs, authentication and authorization, libraries available for cryptography, management of dependency tools, static code analysis tools and secure web frameworks. The paper highlights the common security challenges faced in python programming and emphasis on the importance of secure coding practices and comparison of vulnerability detection tools implemented in the past research works. The finding proclaims that python provides several built-in tools and mechanisms which when properly implemented can notably improve reliability and security of software systems. Keywords: Python Security, Secure Coding Practices, Vulnerability Detection, Machine Learning Based Security Analysis, Static Code Analysis, Cryptography in Python, Software Security

The rapid growth of digital platforms and data-driven business environments has significantly transformed the analysis of consumer behavior. Traditional statistical models, while theoretically grounded, often fail to capture the complex, nonlinear, and high-dimensional patterns present in modern consumer data. This study investigates the effectiveness of machine learning algorithms for predictive modeling of consumer behavior using structured data derived from digital interactions. A comprehensive framework is developed encompassing data preprocessing, feature engineering, model training, and performance evaluation. Multiple supervised and ensemble learning algorithms—including Logistic Regression, Support Vector Machines, Random Forests, Gradient Boosting, and XGBoost—are comparatively evaluated using standard performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The results demonstrate that ensemble and nonlinear models outperform traditional linear approaches in predictive accuracy, particularly in capturing dynamic behavioral patterns. Feature importance analysis reveals that behavioral and temporal variables play a more significant role than static demographic factors in predicting consumer actions. In addition to predictive performance, the study emphasizes model interpretability and ethical considerations, highlighting the importance of transparency, fairness, and data privacy in consumer analytics. The findings provide valuable insights for both academic research and practical decision-making, offering a balanced approach that integrates accuracy, interpretability, and responsible use of machine learning in predicting consumer behavior.

ABSTRACT Prosopis juliflora (mesquite) is a widespread invasive species in India that threatens native biodiversity, rangelands, and agro‐pastoral livelihoods by forming dense thickets, exhausting water resources, and suppressing native flora. The aim of this study is to develop and evaluate sustainable, biomass‐derived adsorbents by converting the invasive plant Prosopis juliflora into activated carbon and further enhancing its adsorption performance through metal oxide functionalization (CuO, Fe 2 O 3 , and ZnO). Specifically, to investigate the comparative efficiency, adsorption behavior, and removal mechanism of these metal oxide–Prosopis juliflora activated carbon nanocomposites for the removal of hexavalent chromium [Cr(VI)] from aqueous solutions under varying experimental conditions. In this study, activated carbon derived from Prosopis juliflora was prepared through calcination at 600°C and subsequently used to synthesize metallic oxide nanocomposites via the co‐precipitation method. These nanocomposites were applied as adsorbents to get rid of hexavalent chromium (Cr 6+ ) ions from aqueous solution. The materials were characterized by IR, BET, SEM, and XRD analyses. Among the composites, PJAC@CuO exhibited the highest Cr(VI) removal efficiency, followed by PJAC@Fe 2 O 3 and PJAC@ZnO. The maximum removal of Cr 6+ ions at 100 mg L −1 was attained at pH 2.3, 1.5, and 3.3 and a sorbent dose of 2.75 gL −1 for PJAC@CuO, PJAC@Fe 2 O 3 , and PJAC@ZnO, respectively. The ideal contact times of 120 min for PJAC@ZnO, 100 min for PJAC@CuO, and PJAC@Fe 2 O 3 were observed. The isotherm studies reveal adsorption following the Freundlich model (R 2 = 0.856–0.950), indicating heterogeneous and favorable adsorption at low Cr(VI) concentrations. The Dubinin–Radushkevich model confirms a physical adsorption mechanism, with adsorption energies ranging from 9.71 to 13 kJ/mol. Overall, PJAC@CuO showed the best adsorption performance, suggesting that Prosopis juliflora ‐based nanocomposites are effective, sustainable materials for treating Cr(VI)‐contaminated wastewater.

The rapid digital transformation of educational institutions has increased reliance on Internet of Things (IoT) technologies for smart classrooms, campus management, attendance monitoring, and administrative processes. While IoT enhances efficiency, it also introduces security and privacy risks due to centralised architectures, weak authentication, and exposure to cyber threats. Blockchain technology offers a promising solution with its decentralised, immutable, and transparent features. This study proposes a Blockchain and IoT-based security framework for educational institutions and evaluates its effectiveness using a conceptual approach supported by a literature review and simulated statistical analysis. Security parameters, including data integrity, access control, privacy protection, authentication, and auditability, were assessed before and after Blockchain integration. Descriptive statistics, paired sample t-test, and one-way ANOVA were applied to analyse the results. Findings reveal a significant improvement in all security parameters post-Blockchain integration, confirming that the framework enhances trust, transparency, and system resilience while mitigating vulnerabilities of traditional IoT systems. The study concludes that Blockchain–IoT integration provides a robust and sustainable approach to securing smart educational environments, offering valuable guidance for policymakers, administrators, and researchers driving secure digital transformation in education.

The ecological consequences of industrial effluents are mediated by river hydrology and sediment dynamics. Indian cities have expanded around rivers that historically sustained drinking water supply, fisheries, floodplain agriculture, cultural practices, and high freshwater biodiversity. Yet, many urban rivers now function as conduits for industrial effluents, municipal sewage, and contaminated stormwater, producing chronic chemical stress, habitat degradation, and biological impoverishment. This review synthesizes published evidence on the Subarnarekha River with emphasis on the Jamshedpur industrial corridor, and situates this case within the wider crisis of Indian urban rivers. The Subarnarekha exemplifies a coupled “industrial–urban river syndrome” in which metal-rich and oxygen-demanding wastes interact with altered flow regimes and sediment processes to create persistent exposure pathways for aquatic biota. The literature consistently indicates (i) elevated potentially toxic elements in water and bed sediments, (ii) strong seasonality linked to monsoonal dilution and dry-season concentration, (iii) sediment acting as both sink and secondary source of contaminants, and (iv) bioaccumulation in lower trophic levels and fish, with implications for ecological integrity and human dietary risk. Evidence from other Indian urban rivers demonstrates similar patterns, including contaminant mixtures (metals, antimicrobials, plastics), microbial community shifts, and degradation of sensitive macroinvertebrate and fish assemblages. The review highlights mechanistic pathways from effluent discharge to biodiversity collapse: toxicity, food-web disruption, eutrophication–hypoxia cycles, and selection for pollution-tolerant taxa. It also evaluates monitoring approaches and management options—source control, improved treatment, sediment risk management, environmental flows, and biomonitoring—relevant to Subarnarekha and comparable rivers. The paper concludes with a synthesis of actionable research priorities and governance needs to reverse biodiversity loss in industrial–urban river systems. Comparative evidence from other Indian urban rivers shows that Subarnarekha is not an isolated case but part of a broader pattern in which contaminant mixtures, oxygen stress, and habitat degradation restructure biological communities.