Category Archives: Uncategorized

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.

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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

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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.

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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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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.

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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.

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Authors: Nagul Nisok K S, Nidhish V, Nirmal B, Nivesh S, Sai Sarvesh P G

Abstract: Communication barriers faced by individuals with speech and hearing impairments represent a significant societal challenge. This paper presents an AI-enabled smart glove system designed to translate hand gestures into synthesized voice output in real time. The proposed system integrates an array of flex sensors, an inertial measurement unit (IMU), and surface electromyography (sEMG) electrodes embedded within a lightweight, wearable glove. Raw sensor data are transmitted wirelessly via Bluetooth Low Energy (BLE) to a companion edge-computing module, where a multi-stream convolutional neural network–long short-term memory (CNN-LSTM) architecture performs gesture classification. Classified gestures are subsequently converted to speech using a neural text-to-speech (TTS) engine. Evaluated on a 250-class American Sign Language (ASL) dataset comprising 48,000 gesture samples from 40 subjects, the system achieves a top-1 classification accuracy of 97.4 % and an average end-to-end latency of 68 ms. Power consumption is maintained at 84 mW during continuous operation, enabling up to 11 hours of use on a 1,000 mAh Li-Po cell. Comparative analysis demonstrates that the proposed design outperforms existing glove-based and vision-based translation systems in accuracy, latency, and portability. The findings highlight the potential of the system as an effective assistive device for the deaf and hard-of-hearing community.

DOI: https://doi.org/10.5281/zenodo.19707715

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Authors: Niral Parmar, Hetal Parmar, Krushi Savani, Fakhruddin Kamdar

Abstract: Everybody throws around the term “blockchain” these days, like it’s some secret sauce. But smart contracts are where things actually start to get interesting. Forget endless forms and relying on someone’s handshake; smart contracts handle things automatically. They’re just coded agreements that trigger themselves no middlemen, no second-guessing if someone’s being honest. You know what you’re getting. This review looks at how smart contracts are changing the game in two touchy areas: healthcare and voting, where trust and privacy can’t be taken lightly. Dealing with healthcare is usually a hassle. People lose records, insurance companies bounce you around, and privacy feels flimsy. With smart contracts, you’re in charge of your data, claims happen faster, and private info stays private. Doctors can share what they need to, without breaking the rules. Voting? It’s had trust issues forever people aren’t sure their votes count for anything. Smart contracts clean things up. They make voting more transparent, help stop fraud, and lock down the results. You can check your ballot and know nobody’s changing numbers behind the scenes. Of course, it’s not all smooth sailing blockchain slows down when things get big, laws haven’t caught up, some of the interfaces are confusing, and big organizations don’t like change. This paper covers what works, what needs help, and where things could go next.

DOI: https://doi.org/10.5281/zenodo.19707494

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Authors: Mr. Karthiban R, Dhanushya S, Akash S, Bharath Raj P, Mathan Kumar J

Abstract: This project presents SecureLearn Browser, a secure browser integrated with a Learning Management System (LMS) to ensure examination integrity while supporting flexible learning. It follows a dual-mode architecture with Practice Mode and Exam Mode, each designed for different educational needs. Users access the system through a centralized login interface. After authentication, Practice Mode allows unrestricted access to course materials, including Full Stack Development and HTML modules, enabling self-paced learning without time or security restrictions. In contrast, Exam Mode enforces strict controls such as time limits, password protection, and browser lockdown to prevent navigation, screen capture, and application switching. Based on Safe Exam Browser (SEB) principles, the system creates a secure environment for assessments while maintaining a user-friendly space for learning. The integrated HTML course supports hands-on practice within the same platform. Overall, the system balances security and flexibility by providing a unified platform for both learning and examinations, eliminating the need for separate tools.

DOI: https://doi.org/10.5281/zenodo.19705480

 

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