Authors: Mr. Danish Ishfaq, Ms. Aasifa Jan

Abstract: Artificial Intelligence (AI) and robotic surgical systems represent transformative technologies in modern healthcare, with profound potential for enhancing surgical precision, reducing operative risk, and improving patient outcomes. In the Indian context, research and clinical practice are increasingly exploring this convergence, encompassing both academic inquiry and real-world deployments. This paper synthesizes recent literature on AI integration with robotic surgery, highlights Indian research efforts, examines clinical case developments, identifies technical and ethical challenges, and discusses future directions for advancing AI-enabled surgical robotics within India’s healthcare ecosystem.

DOI: https://doi.org/10.5281/zenodo.18426331

Published by: vikaspatanker

Authors: Jasmine A, Dr. G. Arul Selvi

Abstract: Social media has become an inseparable part of young people’s everyday life, particularly among college students. This article examines how social media engagement influences value orientation among college students in Tamil Nadu, with specific reference to empathy, morality and civic engagement. Drawing on empirical observations among undergraduate students from rural and urban backgrounds, the study shows that excessive and unregulated social media use is associated with weakened empathy, reduced family bonding and diminished moral responsibility. At the same time, responsible and reflective engagement with social media platforms enhances prosocial values, civic awareness and social sensitivity. The article emphasises the need for value-based digital literacy in higher education to ensure ethical and socially responsible digital citizenship.

Published by: vikaspatanker

Authors: Mohd saquib siddiqui, ashar ahmed

Abstract: Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

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Published by: vikaspatanker

Authors: Mustakim Khan, Ashok Yadav

Abstract: We present a multi-layer email threat detection system that integrates header authentication analysis, URL/attachment reputation checks via threat intelligence, and machine learning classification. The system parses incoming emails, verifies SPF/DKIM/DMARC results, extracts URLs and attachment hashes, and queries VirusTotal for each indicator. It then applies a trained ML model (TF-IDF + Logistic Regression) to classify the email as phishing or benign. Finally, a scoring engine correlates all signals into a composite risk score. In testing, the system successfully identified simulated phishing emails: for example, a malicious email with known bad links and spoofed headers was flagged as Phishing with high confidence, while benign messages were rated low-risk. The GUI (Figures 1–2) displays the analysis report, including header results, VirusTotal findings, ML verdict, and final threat score. Our multi-layer method leverages complementary techniques to improve detection accuracy and reduce false negatives compared to single- method approaches.

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Published by: vikaspatanker

Authors: Sudipkumar Ghanvat, Aditi Shintre, Sohail Hawaldar

Abstract: The study gives a comprehensive bibliometric study of the sentiment analysis research in customer reviews using Scopus as major data source. The analysis, spanning the 2010 to 2024 period, covers trends in publication, key contributors, collaborative networks, thematic hotspots and emerging research directions. The results indicate that the publication of sentiment analysis increased significantly in artificial intelligence, business, e commerce, fields over the last decade. Top institutions including Universiti Sains Malaysia and Vellore Institute of Technology have done most of the work along with leading authors like Hashimoto, K. and Okada, M. In terms of geography, India, China, the United States, Malaysia and Indonesia are the countries that dominate global research contributions. Thematic analysis shows that machine learning, deep learning, natural language processing, and explainable AI are popular subjects, along with practical applications in customer satisfaction and recommendation systems. The work also suggests promising future directions such as real time sentiment dashboards, multilingual and multimodal approaches and integration of ethical and privacy aware practices. This bibliometric review highlights influential authors, institutions, and research themes to enable both researchers and industry practitioners to understand the history and future of sentiment analysis.

DOI: https://doi.org/10.5281/zenodo.18412327

Published by: vikaspatanker

Authors: Thangadurai M, Hareshwar M S, Manonmani R, Manimaran R

Abstract: Food waste has become a major global concern, with tons of edible food discarded daily while millions of people remain hungry. Traditional food donation systems rely on manual coordination and delayed communication, leading to inefficiency and limited outreach. To address this issue, the Smart Food Donation and Waste Reduction System is proposed a web-based application designed to connect restaurants, supermarkets, and NGOs for the efficient redistribution of surplus food. The platform features dedicated donor and receiver interfaces, supported by centralized cloud database for seamless data management. Using real-time location matching powered by the Google Maps API, the system identifies the nearest NGOs for available donations. Instant notifications and automated alerts ensure quick food collection before spoilage occurs. All data related to food type, quantity, and pickup time are processed through RESTful APIs, while integrated analytics dashboards visualize donation trends and track food waste reduction. Ultimately, it provides a reliable, realtime, and sustainable solution that contributes to the Zero Hunger goal.

DOI: https://doi.org/10.5281/zenodo.18410917

Published by: vikaspatanker

Authors: Sanjive R, Arjun A G

Abstract: Cloud computing has emerged as a revolutionary technology that enables on-demand access to shared computing resources such as storage, applications, and processing power through the internet. In recent years, educational institutions have increasingly adopted cloud computing to modernize teaching, learning, and administrative processes. This shift is driven by the growing demand for flexible learning environments, digital collaboration, remote accessibility, and cost-effective infrastructure management. Traditional educational systems rely heavily on physical hardware and locally installed software, which often leads to high maintenance costs, limited scalability, and restricted access to learning resources. Cloud computing overcomes these limitations by offering scalable, reliable, and affordable solutions tailored to academic needs. This paper explores the adoption of cloud computing in educational institutions, focusing on its architecture, service models, and practical applications. Cloud-based platforms such as Learning Management Systems (LMS), virtual classrooms, digital libraries, and online assessment tools have transformed the educational ecosystem by enabling anytime-anywhere learning. The study highlights key benefits of cloud adoption, including reduced operational costs, improved collaboration among students and faculty, enhanced data storage and backup capabilities, and increased institutional efficiency. Additionally, cloud computing supports innovation in education by integrating emerging technologies such as artificial intelligence, big data analytics, and smart learning environments. Despite its advantages, the adoption of cloud computing in education also presents challenges such as data security, privacy concerns, internet dependency, and vendor lock-in. This paper discusses these challenges and emphasizes the importance of implementing strong security policies, data protection mechanisms, and regulatory compliance to ensure safe and effective cloud usage. The study concludes that cloud computing plays a vital role in the digital transformation of educational institutions and has the potential to significantly improve the quality, accessibility, and sustainability of education. With proper planning and governance, cloud computing can serve as a powerful enabler for the future of education. Cloud computing has revolutionized the way computing resources are provisioned, managed, and consumed by enabling on-demand access to scalable infrastructure and services over the internet. At the core of this paradigm lies virtualization, a foundational technology that enables efficient utilization of physical resources by abstracting hardware and allowing multiple isolated computing environments to coexist on a single physical system. Virtualization techniques play a critical role in achieving the essential characteristics of cloud computing, including scalability, elasticity, fault tolerance, resource pooling, and cost efficiency. This paper presents a comprehensive study of virtualization techniques in cloud computing, focusing on their architecture, types, operational mechanisms, and performance implications. The study explores key virtualization approaches such as hardware virtualization, operating system–level virtualization, para-virtualization, full virtualization, and container-based virtualization, highlighting their advantages, limitations, and suitability for different cloud service models (IaaS, PaaS, and SaaS). Hypervisors such as VMware ESXi, Xen, KVM, and Hyper-V are discussed as critical enablers that manage virtual machines and ensure isolation, security, and efficient resource allocation. In addition, modern lightweight virtualization through containers (e.g., Docker and Kubernetes) is examined due to its growing adoption in cloud-native environments. The abstract also emphasizes the role of virtualization in supporting dynamic workload management, live migration, high availability, disaster recovery, and multi-tenancy, which are essential for large-scale cloud data centers. Performance overhead, security vulnerabilities, and resource contention are identified as major challenges associated with virtualization, along with emerging solutions such as hardware-assisted virtualization, AI-driven resource optimization, and secure enclave technologies. Furthermore, the paper highlights the importance of virtualization in enabling emerging trends such as edge computing, serverless architectures, and hybrid cloud environments. Overall, this study demonstrates that virtualization remains a cornerstone of cloud computing, continuously evolving to meet the demands of modern applications. By providing insights into current techniques and future directions, the paper aims to assist researchers, cloud architects, and practitioners in selecting appropriate virtualization strategies to build efficient, secure, and scalable cloud infrastructures.

Published by: vikaspatanker

Authors: Mr. Shantanu

Abstract: Artificial Intelligence (AI) is increasingly integrated into criminal justice systems worldwide, influencing decisions related to policing, bail, sentencing, and parole. While AI-driven tools promise efficiency, consistency, and predictive accuracy, their deployment raises serious ethical concerns. Issues such as algorithmic bias, lack of transparency, accountability gaps, and threats to fundamental rights challenge the legitimacy of AI-based decision-making. This paper critically examines the ethical challenges associated with AI in criminal justice, evaluates their implications for fairness and due process, and emphasizes the need for ethical governance frameworks. The study adopts an analytical and doctrinal approach, drawing on existing literature, case studies, and ethical theories to assess how AI can be aligned with principles of justice, equality, and human dignity.

DOI: http://doi.org/10.5281/zenodo.18401805

Published by: vikaspatanker

Authors: Mary M. Asia, Dr. John Lenon E. Agatep

Abstract: The healthcare industry is a critical sector globally, directly influencing human life. Ensuring the confidentiality, integrity, and authenticity of health data is paramount for protecting individual privacy. While Advanced Encryption Standard (AES) is widely recognized encryption technique, it has inherent vulnerabilities, particularly in secure key sharing. Compromises in these channels can undermine the overall strength of AES encryption. In response to the rising threat of data breaches, numerous cryptographic algorithms have been developed to protect digital health records and communication. These include symmetric algorithms like the Advanced Encryption Standard (AES) and Data Encryption Standard (DES), and asymmetric algorithms like RSA and Elliptic Curve Cryptography (ECC). This study presents an enhanced AES algorithm integrated with Elliptic Curve Diffie-Hellman (ECDH), which strengthens key management by offering secure key generation and additional cryptographic layers. The research employed an experimental design, utilizing PyCryptodome for implementation, alongside tools such as NumPy, psutil, and Matplotlib for performance testing and analysis. Comparative evaluations between the enhanced AES-ECDH and standard AES algorithm were conducted in terms of execution time, CPU usage, memory consumption, and security analysis. To uphold ethical standards, dummy datasets were used, ensuring no sensitive information was compromised during testing. The findings revealed that while the enhanced AES-ECDH algorithm significantly improves security—offering features like forward secrecy and heightened resistance to various attacks—it comes at the expense of increased resource consumption. Despite this trade-off, enhanced algorithm is highly suitable for scenarios that prioritize data protection over system performance, especially in healthcare environments.

Published by: vikaspatanker

Authors: Sangeeta Rani, Vikram Singh, Tanisha Mittal

Abstract: Customer churn poses a significant challenge to businesses, necessitating robust predictive solutions. We propose a novel hybrid stacking framework that integrates four diverse base classifiers—logistic regression (LR), random forest (RF), artificial neural network (ANN), and XGBoost—with a meta-learner to enhance churn prediction performance. In the first stage (Level 0), the base models independently learn from preprocessed customer behaviour and demographic features, capturing both linear and non-linear patterns. Their predicted class probabilities subsequently serve as input features to a deep feedforward neural network at Level 1, which functions as the meta-learner. This architecture is trained using categorical cross-entropy loss with the Adam optimiser, incorporating dropout to mitigate overfitting. The stacking ensemble leverages the complementary strengths of the base models (e.g., interpretability from LR, decision-boundary flexibility from RF, complex pattern recognition from ANN, and from XGBoost to achieve superior predictive accuracy and generalisation compared to any individual classifier. Experimental results on a real-world churn dataset demonstrate that the hybrid model consistently outperforms traditional baselines, achieving statistically significant improvements in AUC and F1-score. The findings suggest that stacking heterogeneous learners with a deep meta-model provides a powerful methodology for addressing the complexities of churn prediction.

DOI: http://doi.org/10.5281/zenodo.18385968

Published by: vikaspatanker