Authors: Riya Srivastava, Dr. Shilpi Aggarwal

Abstract: This study delves into the diverse perceptions of mental health, with a particular focus onSchizophrenia, across different generational cohorts. By examining how perceptions have evolved over decades, from the "Gen Z" cohort to older generations, this research aims to broaden our understanding of the disorder and its impact on individuals and society. The study encompasses an extensive analysis of Schizophrenia, covering its historical evolution, contemporary awareness, and societal attitudes. Through a comparative lens, it investigates how perceptions of Schizophrenia and the resulting social distance vary among individuals of diverse ages. Employing a mixed-methods approach, primarily utilizing an online survey, this research captures a comprehensive picture of mental health literacy and stigma across these generations. This work contributes valuable, actionable data to the field of mental health advocacy and education. Ultimately, it advocates for a more inclusive and supportive society, where mental health is understood, accepted, and supported across all generations.

DOI: https://doi.org/10.5281/zenodo.19728938

Published by: vikaspatanker

Authors: Yaminideavi A, Elakkiya N S

Abstract: The rapid expansion of urban populations has significantly intensified waste generation, straining the efficiency of traditional collection methods that rely on static, fixed schedules. Such conventional systems often result in overflowing bins, inefficient collection routes, and escalated operational costs. This paper proposes a comprehensive Long Range Wide Area Network (LoRaWAN) infrastructure designed to modernize Smart City waste management. Unlike existing single-task architectures, the proposed framework integrates a multi-tiered hierarchy of LoRaWAN device classes to manage services of varying complexity. At the foundational level, smart bins utilize ultrasonic sensors and low-power microcontrollers to monitor fill levels and environmental conditions. Higher-level smart drop-off containers facilitate user interaction and support asynchronous downlink queries for real-time data exchange. Data is transmitted via LoRa gateways to a centralized cloud-based dashboard, enabling municipal authorities to monitor bin status and dynamically optimize collection routes. Experimental results suggest that this scalable, energy-efficient IoT paradigm not only prevents bin overflow through automated threshold alerts but also reduces fuel consumption and environmental impact. The integration of diverse LoRaWAN node classes provides a robust, cost-effective solution for real-time urban process control within the Smart City ecosystem.

Published by: vikaspatanker

Authors: Dr. Dhanalakshmi S, B. Sasi Prabha

Abstract: In an era where financial transactions are increasingly digital, the threat of cyber fraud has become a growing concern for both institutions and individuals. With every swipe, click, or transfer, there's a risk that sensitive data could be exploited by attackers using sophisticated techniques. As fraudsters become smarter, our defenses must evolve too. This chapter presents a practical approach to fraud detection using MATLAB, focusing on a simple, transparent, and explainable rule-based system. Rather than relying on complex machine learning models that can act as "black boxes," this method uses intuitive rules based on transaction amount, time, and location to flag potentially fraudulent activity. The system is built with ease of implementation in mind, making it ideal for financial institutions looking for an interpretable starting point or a lightweight solution for early warning detection. The model is demonstrated on simulated transaction data, and its results are visualized clearly to show the difference between normal and suspicious behavior. By the end of this chapter, readers will not only understand how to build a basic fraud detection system in MATLAB, but also appreciate the importance of balancing technical rigor with real-world usability in cybersecurity efforts.

DOI: https://doi.org/10.5281/zenodo.19718898

Published by: vikaspatanker

Authors: Kriti.R. Shukla

Abstract: As of 2026, the biotechnology sector has undergone a fundamental paradigm shift from a traditional "wet-lab first" experimental model to an "in silico first" computational framework. This evolution is driven by the maturation of generative artificial intelligence (AI), geometric deep learning, and multi-modal foundational models. This article explores the current state of AI in biotechnology, focusing on protein engineering, generative chemistry, genomic interpretation, and bioprocess optimization. We examine how the integration of Large Language Models (LLMs) and diffusion-based generative models has accelerated the drug discovery pipeline, reduced R&D costs, and enabled the design of de novo biological systems with unprecedented precision.

Published by: vikaspatanker

Authors: Ms.S.Hari Priya, Akalya M, Anupriya S, Bala.G, Dhanusurya.S

Abstract: With the rapid growth of software development, platforms like GitHub have become essential for code sharing and collaboration. However, many developers, especially students and beginners, often upload code without proper security checks, leading to vulnerabilities such as hardcoded credentials, exposed API keys, and insecure coding practices. This project presents an AI-Based GitHub Security Scanner designed to automatically analyze repositories and identify potential security risks. The system integrates with GitHub to scan source code using a combination of static code analysis and AI-driven techniques. It detects common vulnerabilities, misconfigurations, and sensitive data exposure in real time. The AI component enhances detection accuracy by learning patterns from known security issues and suggesting improvements to developers. Additionally, the tool provides detailed reports and recommendations, helping users understand and fix vulnerabilities effectively. By automating security analysis, this project aims to improve coding practices, reduce risks, and promote secure software development. Overall, the proposed system offers a scalable and intelligent solution for early detection of security flaws in GitHub repositories, making it especially useful for students, developers, and organizations.

DOI: https://doi.org/10.5281/zenodo.19755384

Published by: vikaspatanker

Authors: Faisal Chaudhary, Ms. Ayushi Sanjiv Desai

Abstract: The Wallet App is an innovative digital financial system designed to provide users with a credit-based mining system. Users earn credit at a predefined mining speed, which increases with continuous usage and referrals. The app implements a unique referral tree structure, encouraging engagement and organic user growth. The mined credit can be utilized within the app ecosystem to purchase essential goods and services, including medical expenses, through affiliated service providers. The wallet does not support external transactions, ensuring that all financial activities remain within the ecosystem. Users progress through different stages, unlocking benefits and higher mining speeds. Additionally, the system rewards active users by transferring 1/10th of their annual credited amount as a bonus. Key features include app-to-app transfers, daily credit mining, referral-based growth, transaction verification by admins, and stage-based progression. The app is designed to function as a closed-loop financial service, reducing dependency on traditional banking while promoting financial inclusion. With an intuitive UI and robust backend, the wallet app provides a secure, engaging, and rewarding financial experience for users.

DOI:

Published by: vikaspatanker

Authors: Fatima Ibrahim Baiwa, Amina Ibrahim Baiwa

Abstract: The environmental and health hazards associated with conventional organic solvents have intensified the global shift toward sustainable chemical processes. This review critically examines the role of green solvents in modern organic synthesis, with emphasis on their physicochemical properties, reaction performance, environmental impact, and industrial applicability. A systematic review methodology was adopted, involving the analysis of peer-reviewed literature, industrial reports, and green chemistry databases. Studies were selected using defined inclusion criteria based on reaction efficiency, toxicity, recyclability, energy consumption, and economic feasibility. Comparative evaluation was performed across six major solvent classes: water, supercritical carbon dioxide, ionic liquids, deep eutectic solvents, bio-based solvents, and solvent-free systems. The analysis reveals that green solvents consistently demonstrate improved reaction yields (typically 85–99%), enhanced selectivity, reduced volatile organic compound emissions, and significantly lower energy requirements compared to traditional solvents. Water-mediated and solvent-free reactions showed the highest sustainability performance, while deep eutectic solvents and bio-based solvents emerged as the most promising scalable alternatives due to their low cost, biodegradability, and high recyclability. Industrial case studies further indicate substantial reductions in hazardous waste generation and regulatory burden following adoption of green solvent technologies. This review contributes a comprehensive comparative framework for evaluating green solvent performance and identifies key research gaps, including the need for standardized sustainability metrics and long-term toxicity assessment of emerging solvent systems. The findings reinforce the critical role of green solvents in advancing sustainable organic synthesis and highlight future opportunities in AI-assisted solvent design, switchable solvent systems, and circular solvent economies.

DOI: https://doi.org/10.5281/zenodo.19711912

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Published by: vikaspatanker

Authors: Fatima Ibrahim Baiwa, Amina Ibrahim Baiwa

Abstract: The environmental and health hazards associated with conventional organic solvents have intensified the global shift toward sustainable chemical processes. This review critically examines the role of green solvents in modern organic synthesis, with emphasis on their physicochemical properties, reaction performance, environmental impact, and industrial applicability. A systematic review methodology was adopted, involving the analysis of peer-reviewed literature, industrial reports, and green chemistry databases. Studies were selected using defined inclusion criteria based on reaction efficiency, toxicity, recyclability, energy consumption, and economic feasibility. Comparative evaluation was performed across six major solvent classes: water, supercritical carbon dioxide, ionic liquids, deep eutectic solvents, bio-based solvents, and solvent-free systems. The analysis reveals that green solvents consistently demonstrate improved reaction yields (typically 85–99%), enhanced selectivity, reduced volatile organic compound emissions, and significantly lower energy requirements compared to traditional solvents. Water-mediated and solvent-free reactions showed the highest sustainability performance, while deep eutectic solvents and bio-based solvents emerged as the most promising scalable alternatives due to their low cost, biodegradability, and high recyclability. Industrial case studies further indicate substantial reductions in hazardous waste generation and regulatory burden following adoption of green solvent technologies. This review contributes a comprehensive comparative framework for evaluating green solvent performance and identifies key research gaps, including the need for standardized sustainability metrics and long-term toxicity assessment of emerging solvent systems. The findings reinforce the critical role of green solvents in advancing sustainable organic synthesis and highlight future opportunities in AI-assisted solvent design, switchable solvent systems, and circular solvent economies.

DOI:

Published by: vikaspatanker

Authors: Mrinalinee Singh

Abstract: Cancer remains one of the leading causes of mortality worldwide, necessitating early and accurate detection mechanisms to improve patient survival rates. Traditional diagnostic methods, while effective, often face challenges regarding time efficiency, inter- observer variability, and sensitivity. In recent years, Machine Learning (ML) and Deep Learning (DL) have emerged as pivotal tools in oncology, offering automated, high-precision diagnostic capabilities. This paper reviews the strengths of various ML paradigms—including Support Vector Machines (SVM), Random Forests (RF), and Convolutional Neural Networks (CNN)—in the detection of malignancies. We critically analyze the performance of these algorithms across different cancer modalities, such as breast, lung, and skin cancer. Furthermore, the review highlights the transition from feature-based classical ML to automated feature extraction via Deep Learning, discusses current challenges such as data heterogeneity and model interpretability, and proposes future directions for integrating AI into clinical workflows.

DOI: https://doi.org/10.5281/zenodo.19708441

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Authors: Veena Vanamane, Vimala V, Pallavi C, M.Bharathi, Yashawini.C.K

Abstract: Achieving efficient and stable temperature regulation remains a challenge for both industrial and domestic applications, especially where conventional PID control methods require precise modelling and struggle with nonlinear or uncertain systems. This paper presents a fuzzy logic–based temperature control system that improves performance by mimicking human decision-making. By using temperature error and change in error as input variables and processing them with linguistic rules, the proposed controller effectively manages uncertainties to achieve smoother, more reliable control. Simulation and experimental data confirm that this fuzzy controller reduces overshoot, provides faster responses, and enhances stability compared to traditional methods. Its design shows clear potential for use in industrial heating, smart homes, and thermal management.

Published by: vikaspatanker