Authors: Mohammad Sameer Hussain, Jaspreet Kaur, Er. Gundeep Kaur

Abstract: Decision making systems are using a combination of style rules and new style artificial intelligence to help people make good choices. The old style rules are good because they are clear and easy to understand and they make sure people follow the rules. The old style rules have some problems though. They are hard to scale up. They cost a lot to maintain. Decision making systems that use style rules do not adapt well to new situations. On the hand the new style artificial intelligence like the kind that understands human language can find patterns and help with tough decisions. The style artificial intelligence is really good, at helping people make good choices because it can understand what people are saying and find patterns that the old style rules cannot. The style artificial intelligence is a big help to decision making systems because it can do things that the old style rules cannot. Decision making systems that use the style artificial intelligence can make better choices because they have more information and can understand what people are saying. This kind of intelligence has some problems. Artificial intelligence can make things up. It can be hard to understand intelligence. Also when something goes wrong with intelligence systems like these artificial intelligence systems it is not clear who is responsible, for the artificial intelligence. This paper reviews expert perspectives on both approaches and compares them in terms of interpretability, robustness, data dependence, deployment constraints, and evaluation. Evidence across multiple domains suggests that hybrid architectures integrating explicit rules, structured knowledge, and generative components provide a practical path toward trustworthy and adaptive decision- making.

DOI: http://doi.org/10.5281/zenodo.18359722

Published by: vikaspatanker

Authors: Leo. Tanyam. Encho, Abraham Okolo

Abstract: The Gompertz distribution is widely applied in describing human mortality, establishing actuarial tables, and various other fields. Historically, it was originally introduced by Benjamin Gompertz (1825) in connection with human mortality. This study aims to derive and analyze the mathematical and statistical properties of the Gompertz distribution, providing explicit expressions for parameter estimation from both frequentist and Bayesian perspectives. We then apply these estimation methodologies to analyze COVID-19 data in Cameroon. We investigate and compare numerous frequentist approaches for parameter estimation, including maximum likelihood, method of moments, pseudo-moments, modified moments, L-moments, percentile-based, least squares (including weighted), maximum product of spacings, minimum spacing absolute distance, minimum spacing absolute-log distance, Cramér-von-Mises, and Anderson-Darling (including right-tail) estimators. Their performance is evaluated using extensive numerical simulations, and their coverage probabilities are also assessed. Our results indicate that among the frequentist estimators, modified moments and moments estimators generally perform better than their counterparts. For Bayesian estimators, those based on the Mean Squared Error Loss Function (MSELF) and Kullback-Leibler Loss Function (KLF) demonstrate superior performance. The maximum product of spacings estimators also exhibit competitive performance.

DOI: https://doi.org/10.5281/zenodo.18359495

 

Published by: vikaspatanker

Authors: Ravi Teja Yarlagadda

Abstract: The increasing adoption of globally distributed cloud architectures has intensified the need for deployment mechanisms that ensure consistency, reliability, and scalability across multiple geographic regions. Traditional deployment approaches, often reliant on manual coordination or fragmented automation, struggle to meet the operational demands of modern cloud-native systems, leading to configuration drift, delayed releases, elevated failure rates, and prolonged recovery times. In this context, automated Continuous Integration and Continuous Deployment (CI/CD) pipelines integrated with Infrastructure-as-Code (IaC) have emerged as a promising paradigm for managing complex, multi-region cloud deployments; however, their systemic behavior, scalability characteristics, and reliability properties remain insufficiently explored at an empirical and analytical level. This study presents an in-depth evaluation of an automated CI/CD framework tightly coupled with Infrastructure-as-Code for multi-region cloud deployments, analyzed under medium-scale, production-like conditions. The research adopts a design-oriented experimental methodology to examine pipeline execution dynamics, failure semantics, resource utilization patterns, and recovery behavior across geographically distributed cloud regions. Infrastructure and application deployments are treated as deterministic, version-controlled artifacts, enabling systematic analysis of deployment repeatability, configuration convergence, and fault isolation. Comprehensive results demonstrate that the proposed CI/CD–IaC framework significantly enhances deployment performance and operational stability. Stage-wise analysis reveals low temporal variance and predictable execution behavior, while scalability experiments show sub-linear growth in deployment time as the number of target regions increases. Reliability metrics indicate consistently high availability exceeding 99.9%, with low mean time to recovery and strong isolation of regional failures. Failure characterization further confirms that most deployment anomalies are detected early in the pipeline lifecycle, minimizing downstream impact. Resource utilization analysis identifies build and testing stages as the dominant computational cost, validating the efficiency of state-aware infrastructure provisioning. Importantly, repeated deployments exhibit near-zero persistent configuration drift, confirming the system’s convergence toward a stable desired state. Overall, the findings establish that automated CI/CD pipelines integrated with Infrastructure-as-Code transform multi-region cloud deployment from a fragile, human-driven process into a resilient, self-stabilizing distributed system. This work contributes empirical evidence and system-level insights that advance understanding of deployment automation as a controlled and scalable engineering discipline, providing a foundation for future research in autonomous cloud operations, adaptive deployment pipelines, and AI-driven infrastructure management.

DOI: https://doi.org/10.5281/zenodo.18358912

 

Published by: vikaspatanker

Authors: Mr.Santosh Handignoor, Mr.Himanshu Singh, Prof. Vaishali Suryawanshi, Prof. Dipak Kadve

Abstract: Interview readiness is a decisive factor in determining employability and professional advancement; however, a large number of students struggle to perform effectively due to limited practice opportunities, anxiety, and the absence of structured, objective feedback. Recent developments in Artificial Intelligence (AI) have enabled the creation of intelligent systems capable of simulating interview scenarios and evaluating candidates in a consistent and data-driven manner. This research examines an AI-based mock interview framework that utilizes Natural Language Processing for response evaluation, speech analytics for assessing confidence and fluency, and facial expression analysis for understanding non-verbal behavior. By combining these AI techniques, the system delivers personalized feedback that highlights communication gaps, confidence issues, and knowledge deficiencies. Unlike traditional mock interviews, the proposed approach allows repeated practice without dependency on human evaluators, ensuring scalability and fairness. The study is supported by quantitative analysis conducted on a student dataset, revealing notable improvements in interview performance, self-confidence, and communication effectiveness after exposure to AI-driven mock interviews. The results demonstrate that AI-based interview preparation tools can significantly enhance interview readiness and serve as an effective alternative to conventional training methods. This work reinforces the growing role of AI in employability skill development and its potential to transform interview preparation practices in academic and recruitment environments.

DOI: https://doi.org/10.5281/zenodo.18387039

 

Published by: vikaspatanker

Authors: Ekaterina Gubina

Abstract: Digital transformation is reshaping public sector communication, particularly within state and intergovernmental press offices that operate under conditions of high accountability, limited resources, and constant public scrutiny. This paper explores how artificial intelligence (AI)–driven technologies can be leveraged to automate public relations workflows, enhance message consistency, and improve responsiveness to media and citizens. Focusing on tools such as natural language processing, automated content generation, media monitoring, sentiment analysis, and workflow orchestration systems, the study examines both the operational benefits and governance challenges of AI adoption. Through analysis of existing practices and emerging use cases, the paper proposes a framework for responsible automation that balances efficiency, transparency, ethical communication, and human oversight. The findings suggest that AI, when strategically implemented, can strengthen institutional credibility, support evidence-based communication, and enable press offices to better manage the growing complexity of public information ecosystems.

DOI: http://doi.org/10.5281/zenodo.18384412

Published by: vikaspatanker

Authors: Mr. Abhay Pathak

Abstract: The rapid expansion of digital resources and user expectations in academic environments has driven universities and research institutions to adopt cloud-based data storage solutions for their libraries. With the growing volume of sensitive academic content, user records, metadata, and digital archives, the security of academic library data has emerged as one of the most critical concerns for library administrators, IT personnel, and stakeholders. This paper presents a comprehensive comparative analysis of private, public, and hybrid cloud models with a specific focus on data storage security in academic library environments. The study examines the fundamental architecture, security mechanisms, governance controls, performance trade-offs, legal and compliance implications, and cost considerations associated with each cloud model. Private cloud solutions, hosted either on-premises or in secure managed environments, offer strong data control and customizable security policies, but may require substantial operational investment and in-house expertise. Public cloud services, provided by global vendors such as AWS, Microsoft Azure, and Google Cloud Platform, deliver scalable storage, advanced built-in security features, and cost flexibility, but they introduce concerns related to multi-tenant exposure, third-party dependency, and complex regulatory compliance across jurisdictions. Hybrid cloud architecture emerges as a middle ground, combining the on-site control of private clouds with the scalability of public clouds, but also introduces additional complexity in secure integration, data partitioning, and unified policy enforcement. The abstract highlights that despite the rapid adoption of cloud technologies, academic libraries face nuanced security challenges that extend beyond basic encryption or access control. Issues such as secure data migration, key management, identity and access governance, incident response, and threat monitoring differ significantly depending on the chosen cloud model. This study utilizes comparative security metrics such as data confidentiality, integrity assurance, availability guarantees, authentication strength, and compliance readiness to evaluate each cloud paradigm. The research employs both qualitative expert assessment and quantitative performance measurements derived from simulated workloads on representative cloud environments. Results indicate that while public clouds often lead in raw scalability and advanced automated threat detection capabilities, private clouds consistently provide higher levels of administrative control and predictable performance under peak load. Hybrid solutions show promise for balancing security needs, cost, and flexibility, especially in libraries with mixed data classification levels — segregating highly sensitive materials in private segments while maintaining open access resources in public segments. Importantly, this paper also explores the human and governance factors associated with cloud security, including staff training, shared responsibility models, contract nuances with cloud vendors, and audit transparency.

DOI: http://doi.org/10.5281/zenodo.18348933

Published by: vikaspatanker

Authors: Gopal Ghosh

Abstract: The integration of Artificial Intelligence (AI) in agriculture has transcended traditional applications like precision farming and crop monitoring. This paper explores cutting-edge and unconventional uses of AI, such as predictive climate-resilient agriculture, AI-driven bioengineering, autonomous swarms, and ethical AI for sustainable farming practices. It delves into the role of AI in reshaping the agricultural landscape by optimizing not only productivity but also sustainability and resilience against climate change. The paper concludes with a discussion on future possibilities, challenges, and the need for inclusive AI solutions in agriculture.

Published by: vikaspatanker

Authors: Keshav Jangir, Manish Saini, Rupali Tanwar, Hridyansh Pradhan

Abstract: SNIPP is a smart remote interview platform that allows secure technical assessments through real-time coding and AI evaluation. It includes MediaPipe-based proctoring, role-based question generation, fullscreen enforcement, and automated handling of violations. These features help ensure fair and reliable interviews. The platform uses a full-stack setup with Next.js for both the frontend and backend. It uses Convex for real-time data synchronization, Clerk for secure authentication, and Monaco Editor for an interactive coding environment. It supports conflict-free interview scheduling, automatic email notifications, and real-time updates through Convex mutations. The system allows browser-based code execution across multiple programming languages with a responsive and device-optimized interface. Key features include MediaPipe AI proctoring, AI-generated questions, a 6-strike violation policy, automatic interview termination, and enforced fullscreen mode. It provides detailed violation reports after interviews, while role-based access control helps manage sessions securely and maintains data integrity. Thorough testing confirmed the platform's effectiveness and reliability. It achieved a 100% functional test pass rate and can handle up to 50 concurrent users. The average API response time is 1.5 seconds. The platform is fully secure, implementing JWT-based authentication and input validation, and maintained 99.9% uptime during load testing. SNIPP delivers a scalable, robust solution that reduces scheduling conflicts in remote interviews, removes the need for infrastructure setup for coding assessments, and helps recruiters work efficiently.

DOI: http://doi.org/10.5281/zenodo.18337196

Published by: vikaspatanker

Authors: Walid Bebal, Joydeb Nandi, Taha Ghole, Prof.S.E.Gawali

Abstract: This paper presents HealthAI, a mobile-based per- sonal health advisor application designed to improve preventive healthcare awareness. The application integrates multiple public APIs to deliver real-time weather-based health insights, nutrition analysis, drug safety alerts, and health-related news through a unified mobile interface. A modular client–server architecture is adopted to ensure scalability, reliability, and low response latency. Experimental evaluation demonstrates that the system performs efficiently under varying workloads, making it suitable for real- world mobile health applications.

DOI: http://doi.org/10.5281/zenodo.18428124

Published by: vikaspatanker

Authors: Omkar Dhas, Sarthak Kulthe, Akhilesh Barate, Pruthviraj Zinge, Prof. Sonali Navale. S

Abstract: In this paper, the design of a footstep-based power generation system using piezoelectric sensors is presented. The increasing demand for energy due to rapid population growth has led to the depletion of conventional power resources. Therefore, the utilization of renewable and alternative energy sources has become essential. This proposed system focuses on harvesting mechanical energy generated from human footsteps and converting it into electrical energy. The concept is highly suitable for densely populated countries like India and China, where public places such as railway stations, bus stands, and streets experience continuous human movement. By implementing this system, the mechanical energy produced during walking can be efficiently converted into usable electrical energy.

Published by: vikaspatanker