Category Archives: Uncategorized

Innovations In Sustainability: Green Airports Integrating Renewable Energy And Smart Waste Systems

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Authors: Prachi Kishan Varu

Abstract: The aviation industry is among the most energy-intensive, producing about 2.5% of total CO₂ emissions worldwide. As demand for air travel continues to grow and airports as major energy consumers and infrastructure hubs continue to develop, the role of green airports is increasingly evident. Green airports are transforming aviation infrastructure through renewable energy systems, sustainable technology, and intelligent and rational waste management systems. This paper investigates the innovations in sustainability that have the potential to contemporize airport operations, including the application of renewable energy deployment systems, smart waste systems, and digital technologies that reduce environmental footprint. This paper employs global and India examples to highlight best practices, policy enablers, and challenges as traditional airports transition to sustainable airports that are ready for the future.

DOI: https://doi.org/10.5281/zenodo.18492944

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A Comprehensive Analysis Of The OSI Model In Modern Networking

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Authors: Sachin Kumar

Abstract: Smart Tourist Safety refers to the integration of digital technologies such as the Internet of Things (IoT), mobile applications, artificial intelligence, and real-time data analytics to enhance the safety and security of tourists. With the rapid growth of global tourism, ensuring tourist safety has become a critical concern for destinations and governments. Smart safety systems enable real-time monitoring, emergency response, location tracking, and risk prediction, helping tourists navigate unfamiliar environments securely. This study explores the concept of Smart Tourist Safety, examines key technologies involved, and discusses their role in improving emergency management, crime prevention, and overall tourist confidence. The findings highlight that smart safety solutions not only reduce risks but also enhance destination attractiveness and sustainability

DOI: https://doi.org/10.5281/zenodo.18492762

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Architecting Secure and Compliant Hybrid Cloud Database Systems: Frameworks, Cryptography, and Big Data Platforms

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Authors: Madhava Rao Thota

Abstract: The adoption of hybrid cloud architectures has accelerated across enterprises seeking to balance scalability, cost efficiency, and regulatory compliance, particularly as data intensive applications increasingly span on premises infrastructure and public cloud services, creating tightly coupled yet operationally fragmented execution environments. Databases and Big Data platforms operating across these heterogeneous domains introduce compounded security, governance, and compliance challenges that extend beyond traditional perimeter models, including fractured trust boundaries, non-uniform identity propagation, divergent encryption postures, complex data residency and sovereignty constraints, and reduced end to end auditability across distributed storage and processing layers. This article synthesizes established security frameworks, regulatory standards, and foundational academic research to articulate a structured, end to end security posture for hybrid cloud database environments, integrating architectural guidance from the NIST Cloud Computing Reference Architecture with cryptographic enforcement models derived from encrypted query processing systems such as CryptDB and operational best practices observed in production grade distributed databases including MongoDB, Apache Cassandra, and DataStax Enterprise. The proposed layered security and compliance framework aligns data plane protections, control plane governance, and operational monitoring through coordinated application of field level and transport encryption, federated identity and policy based access control, continuous telemetry driven auditing, and formalized control mapping to regulatory requirements, demonstrating how enterprises can preserve confidentiality, enforce compliance, and sustain fault tolerant, high throughput Big Data operations across cloud boundaries without compromising scalability or performance.

DOI: https://doi.org/10.5281/zenodo.18479002

 

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Policy-Driven Automation for Scalable Governance in Enterprise Big Data Platforms

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Authors: Madhava Rao Thota

Abstract: As enterprise data platforms continue to expand in scale, diversity, and operational criticality, the combination of high data velocity, exponential data growth, and increasingly stringent regulatory requirements renders manual governance and ad hoc operational controls both inefficient and error-prone. In response to these pressures, policy-driven automation has emerged as a foundational paradigm for managing end-to-end data lifecycles, fine-grained access control, regulatory compliance, and repeatable operational workflows across heterogeneous, distributed environments. This article synthesizes prior academic research and industry practices published between 2000 and 2018 to examine how declarative, machine-interpretable policies can be systematically translated into automated enforcement actions within modern enterprise data platforms. Drawing on established policy frameworks, rule-oriented and distributed data management systems, and workflow orchestration engines, we present an integrated architectural perspective that spans policy definition, policy decision evaluation, and policy execution. The discussion is grounded in practical, widely adopted database and Big Data technologies including MongoDB, Apache Cassandra, and DataStax Enterprise illustrating how policy-driven automation enables scalable governance, operational resilience, and auditable compliance while preserving the flexibility and performance required by contemporary enterprise data ecosystems.

DOI: https://doi.org/10.5281/zenodo.18478880

 

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A Low-Code CRM Architecture for Fuel Booking and Inventory Control

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Authors: Akhilash Pennam

Abstract: This paper proposes a cloud-based CRM solution developed on the Salesforce platform to modernize gas station operations by automating fuel booking, inventory management, supplier coordination, and customer interactions. The system uses custom objects, role-based security, low-code automation through Flows, and Apex triggers to enforce business rules and reduce manual effort. Real-time dashboards and analytical reports provide insights into fuel consumption, inventory status, and revenue trends. Testing and validation results indicate improved operational efficiency, data accuracy, and service responsiveness, confirming that the proposed CRM solution is scalable, secure, and suitable for multi-branch deployment and future enhancements.

DOI: https://doi.org/10.5281/zenodo.18477450

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Artificial Intelligence In Aviation And Aerospace

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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.

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An Intelligent System For Carbon Footprint Prediction Using Ensemble Regression

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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

 

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Cybersecurity Threats In The Age Of Cloud Computing

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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.

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An Automated Framework For Early Identification Of Pre-Eclampsia

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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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Six Approaches To Measuring Algorithmic Bias: An Empirical Evaluation Of Fairness Metrics In Machine Learning

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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

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