Daily Archives: February 3, 2026

Authors: Sanjith Rajesh, Prof Ankit Shrimankar

Abstract: Artificial Intelligence (AI) is quickly changing aerospace, fields traditionally shaped by human creativity and engineering skill. AI helps optimize rocket trajectories and allows for autonomous spacecraft navigation. It has become a crucial part of modern exploration. Its capacity to handle large amounts of data in real time enables engineers to foresee mechanical failures before they happen. It also helps design more efficient propulsion systems and simulate complex missions to distant planets. It can also pre-calculate whether our expectations from an aircraft are met as per design conjectures. As humanity aims to colonize Mars and expand the limits of space travel, AI serves as both a driving force and a protector. It is transforming how we build, launch, and maintain the machines that help us to circumnavigate and go beyond the Earth. The objective of this hybrid review is to find and abstractly define AI’s use in aviation. analyze faults that can occur due to its use from real published fault reports and extrapolate its use in Aeronautics and in some cases Astronautics. All inferences are concluded based on exhaustive review of research by reports published by credible government recognized sources on events occurring from the date of induction of AI in the field of aerospace. Multiple angles were viewed mostly from the consumer, the manufacturer and regular civilians.

Published by: vikaspatanker

Authors: Ms. V. Dhanalakshmi, Sanjuga S K, Sindulaxme J, Soundarya M

Abstract: Carbon dioxide (CO₂) emissions from industrial and organizational operations such as energy consumption, transportation, and operational processes significantly impact environmental sustainability. Accurate carbon footprint prediction is essential for reliable emission analysis and informed reduction planning. However, traditional systems rely on static calculation methods, which fail to capture dynamic operational patterns and complex emission relationships. The proposed system employs a machine learning–based framework to predict carbon footprint in industrial and organizational environments. Activity-based operational data such as electricity consumption, fuel usage, and transportation parameters are first subjected to data preprocessing and feature engineering. The processed data are then utilized in ensemble regression modeling to generate reliable emission predictions. The system predicts total carbon emissions and provides category-wise emission analysis to identify major emission-contributing activities. The proposed solution enables data-driven decision-making for sustainable operational planning and emission reduction, fostering environmentally responsible practices through analytical assessment of carbon emissions.

DOI: https://doi.org/10.5281/zenodo.18468154

 

Published by: vikaspatanker

Authors: Sathya Seelan J, Dharshini S

Abstract: Cloud computing has become a foundational technology for modern organizations, enabling scalable, flexible, and cost-efficient access to computing resources through the internet. Enterprises across sectors increasingly rely on cloud services for data storage, application deployment, business operations, and critical decision-making processes. The flexibility offered by cloud computing allows organizations to dynamically scale resources, reduce operational costs, and rapidly deploy innovative applications. Despite these significant advantages, the widespread adoption of cloud computing has introduced complex cybersecurity challenges that threaten data confidentiality, integrity, and availability, creating an urgent need for robust security frameworks. The shared and distributed nature of cloud environments, coupled with multi-tenancy, virtualization, and third-party service management, expands the attack surface and exposes systems to a variety of sophisticated cyber threats. These threats are further amplified by rapid technological advancements, including the integration of Internet of Things (IoT) devices, edge computing, and artificial intelligence (AI) applications in cloud platforms, which increase connectivity but also add layers of vulnerability. Malicious actors exploit misconfigurations, weak authentication mechanisms, and software vulnerabilities to gain unauthorized access, steal sensitive information, or disrupt services, highlighting the importance of proactive security measures. This research paper provides a comprehensive analysis of major cybersecurity threats associated with cloud computing and evaluates existing and emerging security mechanisms employed to mitigate these risks. Key threats discussed include data breaches, account hijacking, insecure application programming interfaces (APIs), insider threats, denial-of-service (DoS) attacks, ransomware, and compliance-related vulnerabilities. Data breaches remain one of the most critical concerns, as attackers can access sensitive information stored in cloud systems through technical exploits, human errors, or inadequate security policies. Account hijacking, often achieved through phishing attacks, malware injection, or credential theft, allows attackers to manipulate cloud resources, disrupt services, or launch further attacks within an organization’s network. Insecure APIs, which serve as communication gateways between applications and cloud services, pose substantial risks if improperly designed or inadequately secured, enabling unauthorized access, data manipulation, or denial-of-service attacks. Insider threats, whether intentional or accidental, continue to be a persistent challenge due to the trusted access employees or contractors have to cloud resources. The paper also explores the shared responsibility model in cloud computing security, which delineates the division of security obligations between cloud service providers and cloud users. While providers are tasked with securing the underlying infrastructure, including physical hardware, virtualization layers, and platform services, users are responsible for securing data, applications, access credentials, and configurations. Misunderstanding or neglecting these responsibilities can result in security gaps, misconfigurations, and increased exposure to cyberattacks. To address these challenges, the study analyzes a range of mitigation strategies, including advanced encryption techniques for data at rest and in transit, identity and access management (IAM) solutions, multi-factor authentication, continuous monitoring, intrusion detection and prevention systems, and compliance with international security standards such as ISO/IEC 27001, NIST frameworks, and GDPR.

Published by: vikaspatanker

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

Published by: vikaspatanker

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