Authors: Suhirdham K G, Abinaya S , Induja M K, Kanimozhi S

Abstract: Pre-eclampsia is one of the most severe pregnancy-related disorders and continues to be a major contributor to maternal and infant morbidity globally. The early detection of this disorder is difficult owing to the intricate relationship between clinical, demographic, and pregnancy- related variables. Traditional screening methods are highly dependent on manual analysis and are often ineffective in identifying high-risk cases at an early stage. This paper proposes an automated, non-IoT, machine learning-based clinical decision support system for the early detection of pre-eclampsia using routine antenatal data. Patients are classified into low, moderate, and high-risk categories to help clinicians take early action. To improve interpretability and reliability, artificial intelligence methods are integrated to identify prominent risk factors contributing to each prediction. Experimental results show that the proposed system enhances the accuracy of early risk detection while maintaining clinical interpretability, there by bridging the gap between artificial intelligence research and maternal healthcare practice.

DOI: http://doi.org/10.5281/zenodo.18467300

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Authors: Abubakar Sadiq Yusha’u, Aminu Aliyu Abdullahi

Abstract: Fairness metrics have become central instruments for identifying, quantifying, and mitigating bias in machine learning (ML) systems deployed in high-stakes decision-making contexts such as credit scoring, employment screening, welfare allocation, and criminal risk assessment. However, the rapid proliferation of fairness definitions has introduced substantial ambiguity regarding how algorithmic bias should be measured, interpreted, and governed in practice. This paper presents a comprehensive conceptual and empirical analysis of six widely adopted fairness metrics: Statistical Parity, Disparate Impact, Equalized Odds, Predictive Parity, Calibration, and Individual Fairness. Using a supervised classification task on a benchmark dataset, we empirically evaluate how fairness assessments vary across metrics under identical modeling conditions and decision thresholds. Our findings reveal substantial divergence among fairness metrics, with models satisfying one fairness criterion frequently violating others. These results demonstrate that algorithmic fairness is inherently multidimensional and context-dependent. We conclude that responsible AI governance requires multi-metric auditing, transparent metric selection, and domain-specific interpretation rather than reliance on any single fairness definition.

DOI: https://doi.org/10.5281/zenodo.18466710

Published by: vikaspatanker

Authors: Vineet Hemendra Mehta

Abstract: Python has emerged as a foundational technology in modern software development despite the emergence of numerous specialized programming languages. This paper examines Python’s sustained adoption across critical domains such as artificial intelligence, data science, web development, automation, and edge computing. The study analyzes Python’s design philosophy, ecosystem maturity, and recent toolchain innovations. A comparative analysis with other popular programming languages is presented to highlight Python’s strengths in productivity and ecosystem support. The paper concludes that Python’s adaptability and community-driven evolution ensure its continued relevance in both academic research and industrial applications.

DOI: http://doi.org/10.5281/zenodo.18466137

Published by: vikaspatanker

Authors: Dr.B Purna chandra rao, Dr. K. Subbarao, Dr. S. Kondala Rao, B. V. Rama Rao

Abstract: This review is about the synthesis of different types of carbon nanostructures by Radio Frequency Plasma Enhanced Chemical vapor deposition (RF-PECVD) and its feasibility to grow variety of carbon nanostructures and their features. A variety of carbon nanostructures like carbon nanosheets, carbon nanoparticles, carbon nanotubes, nanoellipse like structures, nanorods and other islands like carbon nanostructures were grown at possibly low synthesis temperatures was reported at various international and national level journals is a part of my own research work. With the mission of make benefit for the easy understanding of the graduate students, scholars, academicians and researchers, it is presenting as a review report. In this report, first section contains a review on different types of synthesis techniques and their failure in the growth of pure, individual and aligned carbon nanostructures at low synthesis temperatures and the feasibility of RF-PECD in the growth of carbon nanostructures for full filing the above-mentioned requirements is discussed. Second section deals about the RF-PECVD technic and it’s inbuilt facilities for the growth of carbon nanostructures compared to the other techniques. Third section presents about the different types of grown carbon nanostructures during the period of my own research work using RF-PECVD. Fourth section presents about the applications of these carbon nanostructures in various fields.

DOI: https://doi.org/10.5281/zenodo.18466013

Published by: vikaspatanker

Authors: Vinod Kumar Jangala

Abstract: Design patterns play a pivotal role in addressing recurring design challenges in modern Java Enterprise applications by providing reusable, proven solutions that enhance maintainability, scalability, and architectural consistency. As enterprise systems evolve toward distributed, cloud-native, and microservices-based architectures, the effective application of design patterns has become increasingly critical for managing system complexity, supporting modular development, and ensuring long-term adaptability. This paper presents a comprehensive review of design patterns in modern Java Enterprise environments, examining their relevance, practical applications, and limitations within contemporary development frameworks such as Spring, Jakarta EE, and MicroProfile. The study systematically categorizes patterns into creational, structural, behavioral, and enterprise integration patterns, analyzing how each category addresses specific challenges related to object creation, component composition, interaction management, and inter-service communication. Particular emphasis is placed on the integration of classical Gang of Four (GoF) patterns with enterprise-specific and cloud-native patterns, including Dependency Injection, Facade, Observer, Strategy, and Enterprise Integration Patterns, within microservices, reactive systems, and containerized deployments. The paper further evaluates framework-level support for pattern implementation, highlighting how inversion of control, aspect-oriented programming, messaging frameworks, and service orchestration platforms simplify pattern adoption while introducing considerations related to performance, abstraction overhead, and vendor dependency. Performance implications, scalability concerns, and common pitfalls such as overengineering and improper pattern selection are critically discussed. Additionally, emerging trends, including cloud-native design patterns, event-driven architectures, and AI-assisted architectural optimization, are explored as future directions for pattern-driven enterprise design. By synthesizing existing literature and practical insights, this review provides a holistic reference for developers, architects, and researchers seeking to apply design patterns effectively in modern Java Enterprise applications, ensuring robust, scalable, and maintainable software systems in rapidly evolving technological landscapes.

DOI: https://doi.org/10.5281/zenodo.18465049

 

Published by: vikaspatanker

Authors: Vinod Kumar Jangala

Abstract: Continuous Integration (CI) and Continuous Deployment (CD) have become essential practices in enterprise software engineering, enabling organizations to deliver high-quality software at an accelerated pace while maintaining reliability and scalability. CI focuses on the frequent integration of code changes into shared repositories with automated builds and testing, whereas CD extends this process by automating application deployment across environments, including production. Together, CI/CD pipelines support DevOps principles by fostering collaboration among development, operations, and quality assurance teams, reducing manual intervention, and enabling rapid feedback loops. This paper presents a comprehensive review of CI/CD tools and enterprise practices, examining how organizations adopt and operationalize these technologies to address the growing complexity of modern software systems. It analyzes widely used CI tools such as Jenkins, GitLab CI, TeamCity, Bamboo, and Travis CI, alongside CD and delivery platforms including Spinnaker, Argo CD, Harness, and GitOps-based frameworks. The review highlights key enterprise adoption practices, performance metrics, and comparative tool capabilities, with particular attention to scalability, security, compliance, and integration with cloud-native technologies such as containers, Kubernetes, and infrastructure-as-code. Challenges related to heterogeneous toolchains, cultural transformation, pipeline performance, and regulatory requirements are critically discussed. Furthermore, the paper explores emerging trends shaping the future of CI/CD, including AI-driven pipeline optimization, DevSecOps, GitOps, multi-cloud orchestration, and edge deployments. By synthesizing existing literature and industry practices, this work provides actionable insights for software engineers, DevOps practitioners, and IT managers, while identifying research gaps and future directions to advance reliable, efficient, and secure enterprise-scale CI/CD implementations.

DOI: https://doi.org/10.5281/zenodo.18464806

 

Published by: vikaspatanker

Authors: Vishmitha. E, Madhumitha. M

Abstract: Serverless computing is an emerging paradigm in cloud computing that abstracts infrastructure management from developers and enables fully event-driven execution of applications. Unlike traditional cloud models that rely on continuously running virtual machines, serverless platforms dynamically allocate resources and execute functions only in response to events, thereby improving scalability and resource utilization. This paper presents a comprehensive analysis of serverless computing, focusing on its architectural design, performance characteristics, advantages, and inherent challenges. The core components of serverless architecture, namely Function-as-a-Service (FaaS) and Backend- as-a-Service (BaaS), are examined in detail to illustrate how they support stateless execution, automatic scaling, and rapid application development. A comparative study between serverless computing and traditional virtual machine-based cloud models is conducted with respect to scalability, latency, cost efficiency, and operational complexity. Performance factors such as cold start latency, execution overhead, and throughput under varying workloads are analyzed to highlight the trade-offs involved in adopting serverless systems. Furthermore, this paper discusses critical challenges including security concerns arising from multi-tenancy, vendor lock-in due to provider- specific services, limitations in observability and debugging, and complexities in state management. Finally, the paper outlines future research directions aimed at reducing latency, improving portability, enhancing security mechanisms, and integrating serverless computing with edge and hybrid cloud environments to support next-generation distributed applications.

Published by: vikaspatanker

Authors: Samruddhi Pramod Bangar, Prasad Sakharam Khose,, Nikita Sandeep Ubhe, Sonali Dongare

Abstract: Communication barriers persist for hearing and speech-impaired individuals due to the lack of real-time, offline, bidirectional Indian Sign Language (ISL) translation tools. This paper presents SignBridge, an offline bidirectional system supporting Sign-to-Text/Speech and Text-to-Sign translation. MediaPipe is used for extracting hand and pose landmarks, while a TensorFlow-based hybrid CNN– Transformer model performs dynamic gesture recognition. A full-stack implementation using React.js and Flask ensures real-time interaction, and an avatar-based rendering module generates visual sign outputs. The system is designed for low- resource environments with improved privacy and reduced dependency on internet connectivity.

Published by: vikaspatanker

Authors: Jaydonn Arvin H. Santillana, Jay Laurence L. Quimque, John M. Pagaran, Benjo R. Saraka, Joseph Ramos, Jaine D. Luz

Abstract: This study describes Amihan’s Breath, an Internet-of-Things–based air quality management system using Arduino R4 Wi-Fi that monitors and regulates indoor air quality (IAQ) in Davao City. The system measures 12 air quality and thermal parameters, including PM1.0, PM2.5, PM10, NOx, CO, O₃, NH₃, VOCs, temperature, and humidity, and provides real-time monitoring, alerts, and air-filtering functions. Sensor accuracy ranged from 97–100% before and after filtration. Initial IAQ analysis indicated moderate pollution levels, with PM10 averaging 85.64 µg/m³ and NOx averaging 96.89 µg/m³. After filtration, pollutant levels significantly decreased, including a 29% reduction in NOx and substantial reductions in particulate matter. Overall, Amihan’s Breath is an effective and cost-efficient IAQ management system recommended for high-risk environments such as schools and healthcare facilities.

DOI: https://doi.org/10.5281/zenodo.18457385

Published by: vikaspatanker

Authors: Vignesh P, Sribharath K

Abstract: The rapid growth of Artificial Intelligence (AI) and Machine Learning (ML) has significantly increased the demand for high computational power, massive data storage, and efficient model deployment. Traditional on-premise infrastructures often fail to meet these requirements due to high cost, limited scalability, and maintenance complexity. Cloud computing provides a flexible, scalable, and cost-effective platform that supports the complete lifecycle of AI and ML systems. By offering powerful computing resources such as GPUs, TPUs, distributed storage, and pre-built AI services, cloud computing enables faster innovation and real-time intelligent applications. This paper presents an in-depth study of cloud computing and its role in AI and ML, covering architecture, service models, platforms, applications, benefits, challenges, security concerns, and future scope.

Published by: vikaspatanker