INTERNATIONAL JOURNAL OF ENGINEERING TRENDS AND TECHNOLOGY RESEARCH (IJETTR)
International Journal of Engineering Trends and Technology Research (IJETTR) is a global open-access journal dedicated to publishing high-quality research in engineering and emerging technologies. IJETTR provides a professional platform for researchers, academicians, and industry experts to share innovative findings, technical advancements, and practical engineering solutions. The journal follows a strict peer-review process, ensuring originality, relevance, and scientific accuracy in all published papers. IJETTR welcomes contributions from all major engineering disciplines, including computer science, electronics, mechanical, civil, electrical, information technology, artificial intelligence, and interdisciplinary fields. As an open-access publication, IJETTR ensures worldwide visibility and unrestricted access to scholarly work, supporting global knowledge exchange and fostering technological progress.
Power-Aware Physical Design Strategies for Nanoscale VLSI Circuits Using Floorplanning, Placement, and Clock Tree Optimization
With the continuous scaling of semiconductor technologies into the nanoscale regime, power consumption has become one of the most critical challenges in Very Large-Scale Integration (VLSI) circuit design. Physical design stages, including floorplanning, placement, and clock tree synthesis, significantly influence the overall power dissipation, timing performance, and reliability of integrated circuits. This paper reviews power-aware physical design strategies developed for reducing dynamic and leakage power in nanoscale VLSI systems. The study examines the impact of optimized floorplanning techniques, congestion-aware placement methodologies, and low-power clock tree optimization approaches on energy-efficient chip design. Furthermore, the paper discusses contemporary research trends, challenges, and future directions in physical design automation for advanced semiconductor technologies. The findings indicate that integrated power-aware optimization across multiple physical design stages can substantially reduce total power consumption while maintaining timing closure and design integrity.
Copyright (c) 2026 Rohit Banwarilal R (Author) https://creativecommons.org/licenses/by-nc/4.0
Perceived Bias and Ethical Concerns in Scopus Journal Delisting: A Critical Analysis
This paper presents a critical examination of perceived bias and ethical concerns surrounding Scopus journal delisting practices. As one of the world's largest abstract and citation databases, Scopus plays an increasingly influential role in shaping global academic recognition, researcher career advancement, and institutional rankings. However, the process by which journals are removed from Scopus indexing has generated significant controversy regarding transparency, procedural fairness, and potential systemic biases. Through analysis of delisting data from 2020 to 2025, examination of specific case studies including Nurture and the International Journal of Innovative Research and Scientific Studies (IJIRSS), and critical evaluation of Scopus's governance structure, this paper argues that current delisting practices exhibit significant procedural deficiencies and perceived biases against journals from developing countries. The findings reveal that between 2020 and 2025, 335 questionable journals were removed by Scopus, with Publication Concerns (59.7%) and Outlier conduct (14.5%) representing the primary grounds for removal. This paper concludes by proposing recommendations for greater transparency, accountability, and equitable treatment in Scopus's journal evaluation and delisting processes.
Content Selection and Advisory Board (CSAB)
Copyright (c) 2026 Prof. Dr. S. Balasubramanian (Author) https://creativecommons.org/licenses/by-nc/4.0
MuleSoft Platform Implementation Strategies for Enterprise Connectivity and API Management
Modern enterprises increasingly rely on interconnected applications, cloud services, legacy systems, and data platforms to support business operations and digital transformation initiatives. Managing these heterogeneous environments requires a robust integration and API management strategy. MuleSoft has emerged as a leading integration platform that enables organizations to connect systems, expose reusable APIs, and streamline data exchange across distributed environments. This paper examines MuleSoft platform implementation strategies for enterprise connectivity and API management, focusing on architecture, deployment approaches, governance frameworks, security considerations, and performance optimization. The study also reviews literature to understand the evolution of enterprise integration and API-led connectivity practices. Findings suggest that a well-structured MuleSoft implementation improves operational efficiency, enhances scalability, reduces integration complexity, and supports agile business innovation. The paper further presents a graphical representation of implementation benefits and a comparative table highlighting key platform capabilities.
Copyright (c) 2026 Karan Singh Chauhan (Author) https://creativecommons.org/licenses/by-nc/4.0
Artificial Intelligence Driven Regulatory Affairs Framework for Accelerating Global Pharmaceutical Product Approvals Through Predictive Compliance Intelligence and Automated Submission Management
Regulatory approval timelines in the pharmaceutical sector remain constrained by fragmented jurisdictional requirements, repetitive documentation cycles, inconsistent interpretation of compliance expectations, and persistent information asymmetries between sponsors and regulatory authorities. Digital transformation initiatives have improved operational efficiency in isolated domains, yet approval pathways continue to exhibit systemic latency caused by manual dossier preparation, reactive compliance assessment, and inadequate anticipation of regulatory objections. Artificial intelligence (AI) presents an alternative paradigm in which regulatory intelligence is transformed from a retrospective administrative activity into a predictive decision architecture capable of forecasting compliance risks before formal submission events occur. The evidence is contradictory at best. While machine learning systems demonstrate substantial gains in document classification, adverse event detection, and knowledge extraction, their integration into regulatory affairs remains methodologically fragmented and strategically immature. A conceptual framework is developed integrating predictive compliance intelligence, automated submission management, regulatory knowledge graphs, natural language processing engines, and adaptive risk-scoring models. The framework is evaluated through simulated regulatory workflows representing multinational pharmaceutical submissions across the United States, European Union, Japan, Canada, and emerging regulatory markets. Results suggest measurable reductions in submission preparation cycles, regulatory query frequency, and compliance deviation rates when predictive regulatory analytics are embedded upstream within development programs. Short gains hide deeper tensions. The analysis reveals that organizational resistance, data governance failures, model opacity, and jurisdictional heterogeneity remain dominant barriers limiting large-scale deployment. An AI-driven regulatory affairs framework appears capable of accelerating global approvals; however, its effectiveness depends less on algorithmic sophistication than on the quality of institutional integration, regulatory trust mechanisms, and continuous model validation structures.
Electronic Common Technical Document (eCTD)
Copyright (c) 2026 Robert Christopher Miller (Author) https://creativecommons.org/licenses/by-nc/4.0
Machine Learning Integrated Cloud Reliability Engineering Approaches for Dynamic Resource Optimization and Fault Tolerant Distributed Systems
Cloud reliability engineering has drifted into a paradoxical condition where infrastructure elasticity increases operational volatility rather than suppressing it, largely because machine learning controllers optimize for local efficiency while distributed systems fail globally through correlated resource exhaustion, cascading retries, and temporal synchronization faults. Static orchestration policies collapsed under hyperscale workloads long before the industry admitted the problem. This paper evaluates machine learning integrated reliability engineering approaches developed, focusing on dynamic resource optimization and fault tolerant distributed systems, while questioning the exaggerated confidence frequently attached to reinforcement learning schedulers, predictive autoscaling frameworks, and anomaly detection pipelines. The evidence is contradictory at best. Systems advertised as “self-healing” often relocate instability rather than eliminate it, creating hidden shadow costs in observability overhead, retraining latency, and policy drift under non-stationary workloads. A hybrid methodology combining historical fault telemetry, predictive orchestration logic, and adaptive redundancy allocation is examined through comparative reliability metrics including mean time to recovery, latency collapse thresholds, and systemic leakage ratios. The reality is simpler. Machine learning does not replace reliability engineering discipline; it merely exposes how fragile distributed assumptions have always been.
Copyright (c) 2026 Marcus Wayne (Author) https://creativecommons.org/licenses/by-nc/4.0
Intelligent Cloud Computing Infrastructure for Financial Services Through Artificial Intelligence Enabled Transaction Analytics and Cybersecurity Aware Data Protection Models
Financial cloud infrastructures have drifted into a contradictory condition where computational elasticity increases transaction throughput while simultaneously amplifying attack surfaces, regulatory opacity, and systemic dependency on algorithmic trust assumptions that remain empirically unstable under adversarial pressure. Banking institutions now process transactional streams exceeding several million events per minute across distributed cloud nodes, yet the prevailing architectural doctrine still treats cybersecurity as a modular overlay rather than an endogenous computational variable embedded within transaction analytics itself. The consequence is visible. Latency inflation emerges not merely from encryption overhead or network congestion, but from recursive verification loops, fragmented identity assertions, redundant compliance orchestration, and cascading false-positive escalation generated by artificial intelligence models trained on temporally inconsistent financial behaviors. One could argue the data has been misinterpreted because many studies report high fraud detection accuracy while excluding infrastructural entropy costs, shadow compute expenditure, analyst fatigue, and decision paralysis caused by explainability bottlenecks. The friction lies in the assumption that more AI automatically implies stronger resilience. Evidence suggests otherwise. This paper develops an intelligent cloud computing framework integrating AI-enabled transaction analytics with cybersecurity-aware data protection models for financial services environments operating under distributed cloud-native conditions. The proposed architecture combines adaptive behavioral anomaly detection, federated encryption governance, dynamic trust recalibration, and transaction-contextual risk scoring within a unified orchestration layer designed to reduce systemic leakage during high-frequency financial operations. Contrary to established norms, the study does not treat scalability as synonymous with reliability. The analysis instead examines breakdown thresholds where distributed synchronization delays, explainability overhead, and recursive security validation begin degrading operational coherence. Experimental evaluation demonstrates that transaction analytics systems exhibit diminishing security returns beyond specific computational density thresholds despite increased resource allocation. The system resists simplicity. Two analytical tables and two critical diagrams expose hidden infrastructural friction often omitted from optimistic cloud-financial literature
Copyright (c) 2026 Isaac Martin Jain (Author) https://creativecommons.org/licenses/by-nc/4.0
Engineering Distributed Data Intelligence in Event DrivenMicroservices for Next Generation High Throughput Applications
Next-generation high-throughput applications (e.g., IoT telemetry, financial trading, real-time recommendation engines) demand sub-second latencies and processing of millions of events per second. Traditional monolithic architectures and batch-oriented data pipelines fail to meet these requirements due to coordination overhead and data movement bottlenecks. This paper proposes a novel engineering framework for embedding distributed data intelligence directly within event-driven microservices (EDMs). By coupling in-memory data grids, stream processing fabrics, and adaptive event routing, the framework enables intelligent data placement, locality-aware computation, and real-time analytics without a centralized data lake. We present a reference architecture, discuss state management patterns, and evaluate throughput-latency trade-offs using a simulated high-frequency trading use case. Results show that intelligent event-data co-location improves throughput by 3.2x compared to conventional microservices with remote databases, while maintaining p99 latency under 10 ms. The paper concludes with engineering guidelines for adopting data-intelligent EDMs in production.
Copyright (c) 2025 Surya Jayachandra Roe (Author) https://creativecommons.org/licenses/by-nc/4.0
Adaptive Zero Trust Security Frameworks for Scalable Cloud Computing Environments with Dynamic Identity and Access Management
Zero Trust is frequently marketed as a deterministic solution to cloud insecurity, yet the empirical evidence is far less stable, revealing structural inconsistencies in identity propagation, policy orchestration, and runtime enforcement across distributed systems. The proposed framework reinterprets Zero Trust not as a static perimeter replacement but as an adaptive, feedback-driven control system where identity signals, contextual risk scoring, and access policies continuously mutate under load, latency, and adversarial pressure. The results suggest that scalability introduces paradoxical fragility; as identity granularity increases, so does the probability of misalignment between authentication confidence and authorization execution. This is not accidental. The evaluation across simulated multi-cloud deployments demonstrates that dynamic identity and access management mechanisms reduce lateral movement risk by 38–52%, yet simultaneously increase policy evaluation latency and operational entropy under peak conditions. The system exhibits a clear failure threshold where excessive trust recalibration leads to decision fatigue within policy engines, degrading response accuracy. The evidence is contradictory at best. Zero Trust, in its current form, behaves less like a security paradigm and more like a probabilistic balancing act between enforcement precision and system coherence
Copyright (c) 2026 Devaraja Reddy (Author) https://creativecommons.org/licenses/by-nc/4.0
Enhancing Scalability and Ethical Decision-Making in Artificial Intelligence Systems: A Hybrid Framework Integrating Machine Learning, Explainable AI, and Real-Time Data Analytics
Artificial Intelligence systems are increasingly deployed in high-stakes domains, necessitating both scalable architectures and robust ethical decision-making mechanisms. However, scalability often conflicts with transparency, while ethical reasoning introduces computational overhead and complexity. This paper proposes a hybrid framework integrating Machine Learning (ML), Explainable Artificial Intelligence (XAI), and Real-Time Data Analytics to address these dual challenges. The framework emphasizes adaptive learning, interpretability, and continuous monitoring to ensure responsible AI deployment at scale. Conceptual models, flow architectures, and evaluation metrics are presented to demonstrate feasibility and effectiveness.
Copyright (c) 2025 Ekaterina Tatiana (Author) https://creativecommons.org/licenses/by-nc/4.0
Optimizing Artificial Intelligence Performance through Data Engineering Architectures and Machine Learning–Driven Analytics
Artificial Intelligence (AI) systems increasingly rely on scalable data infrastructures and advanced analytics to achieve high performance and reliability. The efficiency of AI models is significantly influenced by the design of data engineering architectures and the integration of machine learning–driven analytics pipelines. This research explores how modern data engineering frameworks—such as distributed data processing, scalable storage systems, and automated pipelines—can enhance the accuracy, scalability, and efficiency of AI models. The study also examines how machine learning–driven analytics improves decision-making by optimizing data preparation, feature engineering, and model deployment processes. The paper synthesizes findings from existing literature and proposes an integrated framework that combines robust data engineering architectures with machine learning analytics for performance optimization. The proposed framework highlights how efficient data ingestion, transformation, and management directly impact AI model training and inference performance. Additionally, the study presents diagrams illustrating architectural workflows and comparative tables demonstrating performance improvements across different data engineering approaches
Copyright (c) 2024 Daniel John McCarthy, (Author) https://creativecommons.org/licenses/by-nc/4.0
An Empirical Study on AI-Driven Anomaly Detection for Improving Data Integrity in Real-Time Data Streams
The proliferation of real-time data streams from IoT sensors, financial transactions, and network logs presents significant challenges in maintaining data integrity. Anomalies, whether from faulty sources, malicious attacks, or system errors, can corrupt analytics and decision-making. This paper presents an empirical study evaluating the efficacy of Artificial Intelligence (AI)-driven models for anomaly detection in real-time streaming environments. We implement and compare a hybrid model combining a Long Short-Term Memory (LSTM) autoencoder with a contextual random forest classifier against standalone models. Using a benchmark dataset of IoT sensor readings, our experiments demonstrate that the hybrid approach achieves a superior balance between high detection precision (94.3%) and low latency (under 15 ms). The study further proposes a modular architectural framework for integrating such AI models into streaming pipelines, highlighting practical considerations for deployment. Results confirm that AI-driven methods are pivotal for ensuring data integrity by enabling immediate, accurate identification of deviations in high-velocity data.
Copyright (c) 2025 Martinez Nguyen Sophia (Author) https://creativecommons.org/licenses/by-nc/4.0
A Scalable Architecture for Intelligent Document Processing in Enterprise Environments Using Machine Learning and Natural Language Processing for Data Insight Generation
The exponential growth of unstructured enterprise data, particularly documents in various formats, demands scalable, intelligent solutions for processing and analysis. This paper proposes a robust architecture for Intelligent Document Processing (IDP), integrating scalable machine learning (ML) and natural language processing (NLP) pipelines for effective data insight generation. The architecture supports multiple document types, leverages cloud-native and containerized microservices, and incorporates model lifecycle management for adaptability across enterprise domains. We evaluate existing approaches and position our system as a modular, automated solution capable of reducing manual document processing costs while improving accuracy and decision-making efficiency.
Copyright (c) 2024 Maurice Daudet Giono (Author) https://creativecommons.org/licenses/by-nc/4.0
Managing Digital Infrastructure Through AI-Driven Microservices that Adapt Based on Continuous Machine Learning Model Feedback
This paper explores the integration of artificial intelligence with microservices architecture for the adaptive management of digital infrastructure. Through continuous feedback from machine learning models, infrastructure components can dynamically self-optimize, respond to load variations, and enhance operational resilience. This AI-driven methodology transforms traditional static infrastructure into intelligent systems capable of learning and evolving over time. By examining current trends, systems architecture, and adaptation strategies, the paper demonstrates how organizations can harness machine learning feedback to automate and scale infrastructure efficiently.
Copyright (c) 2020 Liane Marcia Moriarty (Author) https://creativecommons.org/licenses/by-nc/4.0
Architectural Framework for Deploying Modular Microservices Empowered by AI in High-Performance Machine Learning-Driven Digital Infrastructure Systems
The proliferation of cloud computing, the rise of containerization technologies, and the expansion of intelligent applications have driven the evolution of digital infrastructure systems. This paper proposes a modular architectural framework designed to integrate microservices and artificial intelligence (AI) within machine learning (ML)-driven infrastructures. In particular, it explores how modular microservices can optimize high-performance computing environments and enhance scalability, maintainability, and automation in enterprise ecosystems. The paper further presents a layered design model aligned with key AI components including model training, data pipelines, and continuous deployment. Leveraging historical data from pre-2018 architectures and trends, this research outlines a scalable, fault-tolerant framework optimized for modern digital demands.
Copyright (c) 2022 Mario Vargas Juan (Author) https://creativecommons.org/licenses/by-nc/4.0
Validating Machine Learning Outputs in Medical Software through Scalable Test Case Generation and Evaluation
With the growing integration of machine learning (ML) into medical software, ensuring the accuracy, reliability, and safety of algorithmic outputs has become a critical concern. This study presents a scalable framework for test case generation and evaluation to validate ML outputs in clinical decision-support systems. By automating scenario construction and systematically analyzing model predictions, the proposed methodology enhances software robustness without manual overhead. We demonstrate this approach using synthetic and real-world datasets, achieving high coverage of edge cases and critical patient risk profiles. The results support scalable validation as essential to regulatory compliance and clinical trust.
Copyright (c) 2026 Nabil Sawsan, Jamal Fayez (Author) https://creativecommons.org/licenses/by-nc/4.0
Cognitive Load Balancing in AI-Enhanced Microservices for Edge–Cloud Continuum Systems
Purpose: This paper investigates the integration of cognitive load balancing techniques in AI-enhanced microservices within edge–cloud continuum systems, aiming to improve resource utilization, system responsiveness, and resilience. Design/methodology/approach: We propose a framework that combines cognitive AI decision-making with dynamic load balancing to handle fluctuating workloads in distributed edge-cloud environments. The study evaluates key architectural patterns and orchestration mechanisms across the edge-cloud continuum. Findings: Cognitive load balancing improves system adaptability by reducing latency, avoiding resource bottlenecks, and enhancing energy efficiency. AI-driven orchestration enables microservices to migrate and scale based on workload predictions and edge-cloud resource states. Practical implications: The results can guide the design of resilient, self-adaptive microservice systems for smart cities, industrial IoT, and intelligent transportation where latency and scalability are critical. Originality/value: This work uniquely integrates cognitive AI with microservice orchestration in a multi-layer edge–cloud architecture and presents validated optimization strategies for dynamic load distribution.
Copyright (c) 2023 Ian McEwan du Maurier, (Author) https://creativecommons.org/licenses/by-nc/4.0
Secure and Compliant Cloud Migration Strategies for Healthcare Providers Using Risk-Based Data Classification and Encryption Protocols
Healthcare organizations increasingly migrate to cloud environments to improve scalability, interoperability, and cost efficiency. However, such migration introduces significant security, privacy, and regulatory compliance challenges due to the sensitivity of electronic health records (EHRs). This research paper examines secure and compliant cloud migration strategies for healthcare providers, emphasizing risk-based data classification and encryption protocols. Through a focused literature review and conceptual analysis, the paper synthesizes best practices, identifies gaps in existing approaches, and proposes a structured migration framework integrating regulatory compliance, encryption mechanisms, and risk assessment. The findings highlight that aligning data sensitivity with encryption strength and governance controls significantly reduces breach risks while maintaining regulatory compliance.
Copyright (c) 2025 Alice Carolina Benedita, Vicente Joao Lourenco (Author) https://creativecommons.org/licenses/by-nc/4.0
Leveraging Advanced Data Science Pipelines and Explainable AI to Identify Systemic Vulnerabilities in Financial Transactions and Medical Billing Systems
Systemic vulnerabilities in financial and healthcare transaction systems present significant operational and compliance risks, particularly in increasingly digitized and automated environments. This research introduces a unified framework that integrates advanced data science pipelines with explainable artificial intelligence (XAI) to detect anomalies and reveal latent structural weaknesses in both financial and medical billing systems. The study employs machine learning models embedded with interpretability mechanisms to enhance transparency in anomaly detection and fraud analytics. Through case-based analysis and model evaluation, the framework demonstrates improved capability in identifying transaction irregularities and non-compliant patterns, offering both predictive accuracy and interpretive clarity. Findings suggest that the incorporation of XAI significantly improves system auditability and supports regulatory compliance by providing stakeholders with actionable insights into model behavior and underlying risk factors. This approach contributes to the development of more trustworthy and resilient digital infrastructures in high-stakes domains.
Copyright (c) 2024 Fritz Otto Noah (Author) https://creativecommons.org/licenses/by-nc/4.0
Secure and Scalable Engineering Infrastructure Development through Intelligent Computational Methods
The evolving nature of engineering infrastructure requires innovative solutions that can meet growing demands in terms of security, scalability, and computational efficiency. This paper focuses on the role of intelligent computational methods in advancing the development of secure and scalable engineering infrastructures Purpose - The purpose of this paper is to investigate the impact of intelligent computational methods on the development of secure and scalable engineering infrastructures. By analyzing the intersection of modern computational technologies and engineering design principles, the study aims to highlight the critical role of artificial intelligence (AI) and machine learning (ML) in addressing the complex challenges faced by engineers today. The paper also seeks to provide a roadmap for integrating these advanced methods into infrastructure projects to ensure long-term sustainability, adaptability, and enhanced security. Design/Methodology/Approach - This research utilizes a combination of qualitative and quantitative approaches to explore the application of intelligent computational methods in engineering infrastructure development. Through a review of current literature, case studies, and technological frameworks, the study assesses various methodologies, including AI algorithms, predictive analytics, and optimization techniques, to address security and scalability concerns. Furthermore, the paper employs a simulation-based approach to model and analyze potential improvements in infrastructure systems when integrating intelligent computational tools. Findings - The findings indicate that intelligent computational methods significantly improve the efficiency and security of engineering infrastructure. AI-powered algorithms can predict system failures, detect vulnerabilities, and optimize resources, contributing to better risk management. Machine learning models can analyze vast datasets to predict future demands and ensure that infrastructures remain scalable in the long run. The integration of AI-driven security measures is crucial in mitigating cyber threats, particularly in systems such as smart grids and autonomous transportation networks. Practical Implications - The practical implications of this research suggest that infrastructure developers should adopt intelligent computational methods at the design and operational stages of projects. The findings provide actionable insights for engineers and policymakers to enhance the security of critical infrastructures through AI-driven threat detection and risk analysis. Additionally, the scalability of systems can be optimized by incorporating machine learning techniques to adjust to real-time needs, making infrastructures more adaptable and efficient. Originality/Value - This paper contributes to the growing body of knowledge on the application of intelligent computational methods in engineering infrastructure development. It provides a novel perspective by exploring the combined effect of AI and machine learning on improving both security and scalability in complex infrastructure systems. The value of this research lies in its practical insights and frameworks for implementing intelligent technologies in real-world infrastructure projects, offering a pathway toward sustainable and secure infrastructure development.
Copyright (c) 2026 Marcel Sebastian, Christoph Patrick (Author) https://creativecommons.org/licenses/by-nc/4.0
A Robust Methodology for Integrating Artificial Intelligence into Modern Engineering Workflows
The integration of artificial intelligence into engineering workflows has transformed how complex systems are designed, analyzed, and optimized. Modern engineering increasingly relies on intelligent automation to enhance decision-making, reduce development cycles, and improve system reliability. This paper proposes a robust methodology for embedding artificial intelligence across engineering workflows by aligning data pipelines, model governance, human-in-the-loop mechanisms, and lifecycle management strategies. The methodology emphasizes scalability, transparency, and interoperability with existing engineering tools. Through structured workflow mapping, performance evaluation frameworks, and risk-aware deployment strategies, the proposed approach supports sustainable and trustworthy artificial intelligence adoption in engineering environments. The study further consolidates insights from prior research to establish best practices that can be adapted across multidisciplinary engineering domains.
Copyright (c) 2025 James Matthew, Lewis Harry William (Author) https://creativecommons.org/licenses/by-nc/4.0
Future Ready Engineering Optimization through Neuro-Adaptive Computational Frameworks
Engineering optimization is increasingly challenged by uncertainty, scale, and nonlinearity across modern cyber-physical and intelligent systems. Neuro-adaptive computational frameworks offer a unifying approach by integrating learning-based models with adaptive optimization mechanisms to support resilient and efficient engineering decision processes. This paper presents a structured discussion of neuro-adaptive frameworks for future-ready engineering optimization, emphasizing architectural principles, algorithmic components, and application-driven requirements. Conceptual models, comparative tables, and illustrative figures are included to support clarity and practical interpretation. The discussion highlights how such frameworks enable continuous adaptation, robustness, and scalability within complex engineering environments.
Copyright (c) 2025 Parwiz Danesh (Author) https://creativecommons.org/licenses/by-nc/4.0
Analytical Investigation of Hybrid Engineering Architectures Using Machine-Assisted Decision Models
Hybrid engineering architectures, which integrate human-centric design strategies with machine-driven decision systems, are transforming modern engineering workflows. This paper presents an analytical investigation into the design, performance, and decision-making capabilities of hybrid systems augmented by machine-assisted models. The study explores the interplay between traditional engineering heuristics and algorithmic optimization models, focusing on improving structural reliability, process adaptability, and intelligent automation. Key machine-assisted approaches analyzed include rule-based engines, supervised learning classifiers, and probabilistic inference networks. Experimental design simulations and performance evaluations are used to assess accuracy, responsiveness, and interpretability.
Copyright (c) 2022 Gayana Herambe, Stanley Jonathan (Author) https://creativecommons.org/licenses/by-nc/4.0
Performance Evaluation of Data-Driven Engineering Models Under Rapid Technological Transition
The rapid pace of technological innovation significantly impacts the effectiveness and adaptability of data-driven engineering models. This paper investigates the performance stability, generalization capability, and robustness of these models in environments characterized by frequent technological shifts. By integrating historical data and emergent design paradigms, the study assesses model accuracy, resilience to concept drift, and responsiveness to real-time system dynamics. The analysis involves comparative performance evaluation, identifying key metrics that determine a model's sustainability across evolving technological contexts. The results reveal that adaptability, model retraining frequency, and hybrid algorithmic approaches play critical roles in ensuring consistent performance under transition stressors.
Copyright (c) 2020 Aisyah Syafiq, Adriana Zulaikha (Author) https://creativecommons.org/licenses/by-nc/4.0
A Multi-Agent AI System for Dynamic Supply Chain Management: Leveraging SAP, NLP, and Blockchain for Intelligent Coordination
Supply chains are undergoing radical transformation through the adoption of advanced digital technologies. This paper proposes a Multi-Agent AI System (MAAS) architecture for dynamic supply chain management (DSCM), leveraging SAP S/4HANA, Natural Language Processing (NLP), and blockchain to enhance coordination, transparency, and responsiveness. The integration of these technologies creates a real-time, intelligent decision-making ecosystem. This paper discusses system design, core technologies, use-case simulation, and performance metrics, while also reflecting on pre-2016 foundational research that led to this convergence
Copyright (c) 2021 Benson Merry Nite (Author) https://creativecommons.org/licenses/by-nc/4.0
A Big Data Analytics Architecture for Predictive Maintenance and Risk Mitigation in Cyber-Physical Systems
The increasing integration of Cyber-Physical Systems (CPS) into industrial operations has amplified the need for intelligent, data-driven frameworks capable of supporting predictive maintenance and comprehensive risk assessment. Advances in Big Data Analytics (BDA) have enabled the processing of high-volume, real-time data streams generated by CPS, facilitating proactive responses to emerging system faults and operational risks. This paper introduces a multi-layered architecture that fuses BDA technologies with CPS environments to enhance maintenance planning and reduce system vulnerabilities. A thorough literature review outlines the evolution of predictive maintenance and risk analytics, laying the groundwork for the proposed framework. The architecture leverages edge analytics, machine learning algorithms, and cloud-based processing to enable efficient system monitoring and decision support. Visual diagrams and tabular data illustrate the architecture’s structure and applicability within contemporary industrial systems.
Copyright (c) 2024 Kingsly Antony (Author) https://creativecommons.org/licenses/by-nc/4.0
Federated Learning Implementation for Privacy-Preserving AI Model Training Across Distributed Cloud Platforms
In response to increasing privacy concerns and the demand for scalable, decentralized machine learning (ML) models, federated learning (FL) has emerged as a promising paradigm. This paper explores the implementation of federated learning across distributed cloud platforms to enable privacy-preserving AI model training without centralized data storage. We present an architecture integrating secure model aggregation, differential privacy, and platform interoperability. Additionally, we review literature to situate current developments, analyze system performance, and highlight security and scalability trade-offs. Our experimental simulations demonstrate that federated learning offers significant privacy guarantees while maintaining competitive model accuracy compared to centralized training.
Copyright (c) 2024 Zhang Kin Linine (Author) https://creativecommons.org/licenses/by-nc/4.0
Advancing Predictive Analytics through Hybrid Machine Learning Pipelines for Complex Data Ecosystems
The rapid growth of data in various industries has led to the need for more sophisticated methods of predictive analytics. Hybrid machine learning pipelines, which combine different types of algorithms and data processing frameworks, have emerged as a powerful solution for handling complex data ecosystems. These pipelines enable the integration of diverse machine learning models, allowing for more accurate predictions and better performance. This paper explores how hybrid machine learning pipelines can be used to advance predictive analytics, focusing on the combination of traditional models like regression and decision trees with more complex models such as deep learning and ensemble methods. The goal is to show how such hybrid approaches can enhance predictive capabilities, particularly in large-scale, multi-source data environments.
Copyright (c) 2021 Michael Morgan Chloe (Author) https://creativecommons.org/licenses/by-nc/4.0
Sustainable Information Technology Practices and Their Contribution to Green Computing and Environmental Conservation
The rapid proliferation of Information Technology (IT) has heightened concerns about its environmental impact, from energy consumption to electronic waste. Sustainable IT practices and green computing aim to address these challenges by reducing carbon footprints, optimizing energy usage, and promoting environmentally conscious decision-making. This paper explores core practices such as virtualization, green data centers, e-waste recycling, and energy-efficient hardware, linking them to broader environmental and conservation goals. Drawing on prior literature, especially, we examine how these technologies contribute to sustainability frameworks and propose future research directions for IT's role in ecological balance.
Copyright (c) 2023 Ameera Jasmine1, Brezina Josua (Author) https://creativecommons.org/licenses/by-nc/4.0