Authors: Priya Sharma

 

Abstract: The modern cyber threat landscape has evolved into a high-velocity adversarial environment where automated botnets, polymorphic malware, and AI-driven exploits outpace human cognitive limits. Traditional reactive security models, which rely on manual intervention and static rule-based thresholds, are increasingly inadequate against multi-stage, stealthy campaigns. This review examines the paradigm shift toward Autonomous Cyber Defense Systems (ACDS) powered by Artificial Intelligence (AI) and Machine Learning (ML). Unlike conventional tools, ACDS are designed to operate within the "OODA loop" (Observe, Orient, Decide, Act) at machine speed, performing real-time threat discovery, risk-weighted decision-making, and automated remediation without human oversight. This article categorizes current ACDS methodologies, including Reinforcement Learning (RL) for dynamic policy optimization, Deep Learning (DL) for behavioral anomaly detection, and Graph Neural Networks (GNNs) for mapping lateral movement. We explore the transition from "Security Orchestration" to "Autonomous Orchestration," where the system self-configures its defensive posture based on shifting environmental variables. Furthermore, the review addresses critical challenges, such as the "Black Box" transparency problem, the risk of "automated cascading failures," and the emerging threat of adversarial machine learning. By synthesizing recent academic breakthroughs and industrial case studies, this paper provides a strategic roadmap for achieving "Self-Healing" infrastructures. The findings suggest that while human-in-the-loop models remain necessary for high-level strategic oversight, the tactical frontline of cyber defense must become fully autonomous to ensure resilience against the next generation of automated adversarial competition.

DOI: https://doi.org/10.5281/zenodo.19427289

 

Published by: vikaspatanker

Authors: Kiran Das

Abstract: The global regulatory landscape is currently undergoing a period of unprecedented volatility, characterized by the introduction of complex frameworks such as GDPR, CCPA, HIPAA, and the evolving EU AI Act. For modern enterprises, manual compliance monitoring—once the standard for risk management—is no longer a viable strategy due to the sheer volume, variety, and velocity of data generated across distributed digital ecosystems. This review examines the paradigm shift toward AI-powered compliance monitoring systems, which leverage Natural Language Processing (NLP), Machine Learning (ML), and Computer Vision to provide real-time, continuous oversight. By automating the ingestion and interpretation of legal texts and cross-referencing them with internal operational telemetry, these systems identify "compliance gaps" before they manifest as legal liabilities. This article categorizes current methodologies, including the use of Large Language Models (LLMs) for semantic policy mapping and Deep Learning for detecting anomalous financial patterns indicative of money laundering or fraud. We explore how AI mitigates "regulatory fatigue" by filtering noise and highlighting high-priority risks, thereby allowing compliance officers to transition from administrative data processors to strategic advisors. Furthermore, the review addresses the critical challenges of algorithmic bias, the "black-box" nature of deep neural networks, and the necessity for Explainable AI (XAI) in regulatory reporting. By synthesizing recent academic research and industrial case studies, this paper provides a strategic roadmap for building "compliance-by-design" architectures. The findings suggest that AI-powered systems not only reduce the cost of adherence but also foster a culture of transparency and proactive ethical governance.

DOI: https://doi.org/10.5281/zenodo.19427276

 

Published by: vikaspatanker

Authors: P.Vijay, S.Manikanth, S.Chaitanya, V.Ramya

Abstract: In an era dominated by digital communication, traditional social games that rely on physical presence and non-verbal interaction face the risk of obsolescence. Games like Pictionary, which thrive on creativity, quick thinking, and shared laughter, are often difficult to replicate in a virtual environment without losing their core essence. To address this, we present Sketch Rush: A Real- Time Digital Pictionary Experience, a web-based multiplayer game that faithfully recreates the excitement and social dynamics of the classic drawing and guessing game. Sketch Rush leverages modern web technologies to provide a seamless, interactive platform where players can connect, create, and compete in real-time. Sketch Rush is designed not merely as a digital adaptation but as an enhanced, accessible version of the original game. It addresses the limitations of physical Pictionary—such as the need for physical drawing tools, proximity of players, and manual scorekeeping—by automating these processes within an intuitive digital interface. The system comprises two primary modules: a real-time drawing canvas with a rich set of tools for the "Artist," and a dynamic chat interface for the "Guessers." The core game logic, powered by a Node.js backend and WebSocket communication, ensures low-latency synchronization of drawings, guesses, and game states across all connected clients. Preliminary user testing with a cohort of 40 participants has shown that Sketch Rush successfully captures the engaging and collaborative spirit of the original game. Feedback highlighted the platform's intuitive interface, the responsiveness of the real-time features, and its effectiveness in fostering social connection, even among geographically dispersed players. Users reported a high degree of satisfaction, with average System Usability Scale (SUS) scores of 85.6, indicating excellent usability. In essence, Sketch Rush reimagines a beloved social game for the digital age. It transcends the limitations of physical location, offering a platform that is not only functional but also fun, engaging, and socially enriching. By combining intuitive design with robust real-time technology, Sketch Rush provides a compelling case for the successful digital transformation of traditional social experiences.

DOI: https://doi.org/10.5281/zenodo.19416157

 

Published by: vikaspatanker

Authors: Dr. Sarika Sharma

Abstract: Atmospheric chemistry plays an important role in the global climate system as greenhouse gases (GHGs) are involved in the Earth's climate system, radiation, and atmosphere. GHGs such as carbon dioxide (CO₂), methane (CH₄), nitrous oxide (N₂O), and halogenated compounds absorb infrared light and emit it in the atmosphere of Earth as greenhouse gases, and this is associated with the greenhouse effect. The heat in the lower atmosphere is retained, and global warming and the surface temperature of the Earth are increasing. As such, the chemistry of greenhouse gases depends on the concentration of atmospheric gases as well as their chemical composition, reactivity, lifetime, and interaction with solar and terrestrial radiation (e.g., photochemical reactions, oxidation processes, gas-aerosol interaction). For example, methane oxidation and nitrogen oxide cycles play an important role in ozone production and secondary radiative forcing, so that the chemistry of atmospheric chemistry and climate are interrelated. Since the 20th century, anthropogenic activities such as combustion of fossil fuels, industrial pollution, deforestation, and agricultural processes have increased the GHG levels in our atmosphere, thus adding to the natural greenhouse effect. However, CO₂ is the most important greenhouse gas present now, but it is not the only one that is responsible for warming, and other gases such as CH₄ and N₂O are essential in the global warming process as well. Atmospheric chemistry reveals that the greenhouse effect is not only dependent on CO₂, but many interacting gases are involved in the climate processes. Recent studies have also shown that changes in the composition of the atmosphere can lead to severe weather events, radiative forcing, and climate feedback loops, and the consequences can be dramatic for global warming. In any system for climate change, the interplay of greenhouse gases, aerosols, and chemical reactions in the atmosphere should be taken into account. From a global perspective, understanding the chemistry and nature of greenhouse gases is necessary to understand what is driving us toward global warming. The chemical properties and interactions of these gases are also useful in understanding how climate change must be countered in the long run and how to identify solutions to this problem for climate policy.

DOI: https://doi.org/10.5281/zenodo.19416017

Published by: vikaspatanker

Authors: Sagar Nadavati, Jeffrey Joe, Janakiraman

Abstract: This study investigates the crashworthiness performance of a FSAE roll cage subjected to frontal impact using explicit dynamic simulation. Two high-strength materials, AISI 4130 chromoly steel and Docol R8 advanced high- strength steel, were evaluated. The roll cage geometry was modelled using SolidWorks and imported into ANSYS Explicit Dynamics for frontal crash simulation at an impact velocity of 8 m/s against a rigid wall boundary condition. Key performance indicators such as total deformation, equivalent von-Mises stress distribution, plastic strain, and energy absorption characteristics were analysed. A comparative study between both materials was conducted to determine structural safety performance and weight optimization potential. Results indicate that Docol R8 provides improved strength-to-weight performance compared to AISI 4130, demonstrating its suitability as an alternative roll cage material for Formula Student vehicles.

DOI: https://doi.org/10.5281/zenodo.19415920

Published by: vikaspatanker

Authors: Sandip Thube, Dr. M. A. Badgujar

Abstract: Several complexes derived from thiosemicarbohydrazide, specifically α-benzilmonoximethiosemicarbohydrazide-m-chlorobenzaldehyde (HBMTSmCB) and its complexes with Fe(II), Ni(II), Cu(II), and Co(II), have been synthesized and meticulously characterized. The characterization employed a range of analytical techniques, including elemental analysis, conductivity measurements, and magnetic susceptibility assessments. Spectroscopic methods such as Proton Magnetic Resonance (PMR), Fourier Transform Infrared (FTIR) spectroscopy, and electronic absorption spectra were also utilized to elucidate the structural and bonding characteristics of these complexes. It was determined that all trivalent metal complexes synthesized exhibit octahedral geometries.

DOI: https://doi.org/10.5281/zenodo.19415792

 

Published by: vikaspatanker

Authors: Sagar Kumar, Harish Dutt Sharma, Ram Bhawan Singh

Abstract: Phishing attacks have emerged as one of the most significant cybersecurity threats, targeting users by creating fraudulent websites that mimic legitimate platforms to steal sensitive information. Traditional rule-based and blacklist-based detection techniques are often ineffective against newly generated phishing websites. This paper proposes a machine learning-based phishing website detection system that utilizes multiple classification algorithms to identify malicious URLs. The system extracts various URL-based and domain-based features such as URL length, presence of special characters, domain age, and HTTPS usage. Machine learning models including Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) are evaluated. Experimental results demonstrate that the proposed approach achieves high accuracy and outperforms traditional detection methods.

DOI: https://doi.org/10.5281/zenodo.19415751

Published by: vikaspatanker

Authors: Suren kumar Selvamani

Abstract: This work presents a hybrid aluminium-assisted hydrogen generation system utilizing waste aluminium feedstock for continuous hydrogen production through a combination of chemical reaction and electrolysis. Aluminium scrap is processed into fine particles and reacted with water in the presence of a catalyst to generate hydrogen. The system integrates a secondary electrolysis unit to extract additional hydrogen from residual water, thereby improving overall efficiency. A catalyst regeneration loop is incorporated to enable repeated use of catalytic material, while aluminium is consumed as an energy carrier and converted into aluminium oxide. The system is designed for decentralized, on-demand hydrogen generation, particularly suited for remote, off-grid, and waste-to-energy applications.

Published by: vikaspatanker

Authors: Tanhaji Walunj, Madhukar Badgujar

Abstract: A new unsymmetrical p-fluorobenzaldehyde derivative of α-benzilmonoximethiosemicarbohydrazide (HBMTSpFB) ligand is prepared via condensation of α-benzilmonoximethiosemicarbohydrazide and p-fluorobenzaldehyde in the 1:1 ratio. Metal complexes of Fe(II), Co(II), Cu(II), Zn(II), Hg(II) and Ni(II) have been prepared. These prepared compounds were characterized by physicochemical study, PMR, FT(IR), electronic absorption, and magnetic moment, and the purity of the HBMTSpFB ligand was analyzed by thin layer chromatography study. All prepared compounds are color-solid, air-stable, and soluble in common organic solvents. On the basis of elemental analysis metal to ligand and stoichiometry is 1:2 ratio for all complexes. Comparison of the FT(IR) spectra of the HBMTSpFB ligand and its trivalent metal complexes confirm that the HBMTSpFB ligand is a monobasic, tridentate ligand towards the central trivalent metal ion with an ONS and sequence.

DOI: https://doi.org/10.5281/zenodo.19415327

Published by: vikaspatanker

Authors: Mayur Patil, Kunal Viroje, Harsh Waingankar, Dr. Vivek Khalane, Dr. Vaibhav Narawade

Abstract: Online examinations have become a common part of modern education, especially with the growth of remote learning platforms. However, maintaining fairness and preventing malpractice in such environments remains a major challenge. In this work, we present an AI-based online proctoring system designed to monitor candidates during examinations using real-time video and audio analysis. The system combines face recognition, gaze tracking, head pose estimation, and audio monitoring to detect suspicious activities such as impersonation, presence of multiple individuals, and abnormal behavior. During our testing across multiple sessions and varying environmental conditions, we observed that the system achieved an overall detection accuracy of approximately 92.6% while maintaining real-time performance of 20–30 frames per second. The proposed system reduces dependency on human invigilators and provides a scalable solution for large-scale online examinations.

Published by: vikaspatanker