International Journal of Emerging Research in Engineering and Technology
Predictive Supplier Risk Modelling in Wholesale Distribution: An SPSS-Based Quantitative Study
Wholesale distribution is subject to operating interruptions when suppliers fail to perform adequately. Stock-outs, unexpected delays, premeditated quality defects, etc. affect profits, reputations and adherence to agreed service-levels. The classical “scorecard” means of evaluating suppliers rely heavily on memory, subjective responses or “star” rating systems that cannot forecast evolving risk levels nor blend well into an increasingly “multi-tier” distribution channel. This paper attempts to investigate the predictive modelling of supplier risk based on quantitative analytic methods using SPSS_. Utilising a real-world case-study taken from a wholesale distributor, the research delves into the development of an experimental model using actual responses to survey-scales, supplier indicators of performance and constructs of risk. Using other risk models as a base, a statistically validated predictive risk model is developed in the context of a supply chain using reliability assessment, factor reduction, correlation analysis and multiple regression modelling procedures. The consequent findings show that supplier risk is predicted from delivery consistency, defect rates, responsiveness, financial stability and communication efficiency. The research develops a “refined” predictive model that explains a significant portion of variance in the overall risk scoring of wholesaler suppliers, used as a basis for supplier “segmentation” and for “pro-active” risk mitigation. Such research can be easily replicated on behalf of wholesale distributors who wish to develop user-defined downside-risk modelling against an up-to-date notion of resilience
Routing and Switching in Gigabit Network Deployment for Enterprise Infrastructure
Enterprise networks today demand high-speed, reliable, and scalable data connectivity to support mission-critical applications, industrial automation, and inter-departmental communication. This paper presents the deployment and performance evaluation of a Gigabit network implementation carried out across a large-scale manufacturing environment, integrating Layer-2/Layer-3 switches, structured cabling, network monitoring systems, and security components. The work involves the design and rollout of a structured optical-fiber backbone, switching infrastructure, IP addressing and VLAN segmentation, and the integration of tools such as HP OpenView Network Node Manager, Gauntlet Firewall, Microsoft Proxy Server, and DNS/DHCP services. Performance measurements indicate sustained throughput in the 900+ Mbps range, forwarding rates above one million packets per second, end-to-end latency below 3 ms, and packet-loss levels under 0.001%. These results demonstrate that a well-architected Gigabit infrastructure can significantly improve operational efficiency, network visibility, and security posture in industrial environments. The paper also discusses deployment methodology, challenges encountered, monitoring strategy, and recommendations useful for similar enterprise-scale network rollouts
Machine Learning–Driven Behavioral Analysis of High-Volume Network Traffic for Advanced Persistent Threat Detection
The proliferation of high-volume network traffic in modern enterprises poses significant challenges for detecting Advanced Persistent Threats (APTs), which often evade traditional signature-based security mechanisms. This study presents a machine learning–driven framework for behavioral analysis of network traffic aimed at identifying APTs in real time. By leveraging both supervised and unsupervised learning models, the proposed approach constructs behavioral profiles of normal network activity and identifies deviations indicative of malicious actions. Extensive experiments were conducted on benchmark and simulated enterprise datasets, evaluating model performance in terms of detection accuracy, false positive rate, and computational efficiency. Results demonstrate that hybrid modeling, combining anomaly detection with pattern recognition, achieves superior detection of stealthy APT campaigns compared to conventional methods. Additionally, the framework addresses scalability and real-time deployment considerations, enabling its integration within high-throughput network environments. The findings highlight the potential of machine learning for proactive cybersecurity and provide actionable insights for enhancing enterprise threat monitoring systems. The study contributes a comprehensive methodology, experimental validation, and a reference architecture for ML-based behavioral analysis in high-volume networks
Machine Learning, Network Traffic Analysis
Adaptive Machine Learning Driven Compliance Scoring Models for Automated Risk Detection, Quality Validation of AI-Generated Content in Regulated Industries
Controlled industries are using generative AI to write customer messages, summarize cases, and generate operational documents with increasing use, yet all of these may bring compliance risk by hallucinating facts, omitting required disclosures, breaching privacy, using language biased to a purpose, and lack of auditability. The proposed paper suggests an adaptive machine learning-based compliance scoring model which will automatically detect risks and verify the quality of AI written content in the context of finance, healthcare and pharmaceutical. The method is a mixture of deterministic regulatory mechanisms (policy rules, forbidden terms, obligatory templates of the disclaimer, and mapping of jurisdiction) and monitored learning and anomaly detection on semantic and structural characteristics. Each artifact is assigned (i) a continuous compliance index and (ii) a discrete risk class (approve/escalate/block), accompanied by explainable rationales that link flagged spans to relevant controls and evidence sources. To maintain performance in the shifting regulations and evolving patterns of language, the framework has drift monitoring, active learning based on reviewer feedback, and versioned updates of the rules and models based on change-control governance. Experiments are also planned to achieve across various content types and channels, high-risk recall, false-positive reduction, calibration, and auditability indicators, reproducible scoring and traceable hits on rules. Findings encourage a combined rule-ML solution as a viable way to scaleable, defensible AI-content governance to ensure organizations can speed up content creation and operational risk and enhance consistency of reviews
From Intelligent Automation to Agentic AI: Engineering the Next Generation of Enterprise Systems
Businesses are shifting away, however, and realizing agentic AI systems of autonomous, goal-oriented, software agents that are able to perceive, reason, make actions, and learn end-to-end systems. In this paper, a conceptual and engineering blueprint of such transition is proposed. We define agentic enterprise systems initially on a set of directions of autonomy, adaptivity, coordination, and governance, and propose an Observe-Reason-Act-Learn cognitive loop as the fundamental behavioral pattern. Based on this, we showcase a high-level reference architecture connecting an enterprise knowledge layer (data lake, vector search, knowledge graphs) with an autonomous agent layer with a reasoning and planning layer based on the use of LLM-based paradigms and multi-agent coordination engine to coordinate cross-domain workflows. It describes in the methodology section how to model agent roles and skills, break down business processes into tasks that can be performed by agents, establish protocols of cooperation, and deploy memory and tool-use systems that are based on enterprise APIs and robots-pilots. These patterns are illustrated by an implementation sketch that illustrates how these patterns can be achieved with the use of contemporary agent frameworks in hybrid cloud/on-premise settings. Quantitative evidence that has been synthesized so far on the basis of emerging deployments shows significant cost, productivity, and cycle-time improvements compared to conventional IA, as well as new failure modes in the areas of hallucinations, governance gaps, and safety. The paper ends with the overview of major research directions in the field of reliability, privacy-preserving knowledge access specially in rare disease and alignment of autonomous agents and claims that a powerful AgentOps and governance are the key to the full potential of agentic enterprise systems
Autonomous Threat Detection and Response in Cloud Security: A Comprehensive Survey of AI-Driven Strategies
Cloud computing has changed online communities in three dimensions, which are scalability, adaptability and reduced overhead. But there are serious security concerns which are brought about by its distributed and multi-tenant characteristics. The old methods of detecting and reacting to threats which are mostly reliant on fixed signatures, predefined rules and human operators are becoming less and less effective even in the advanced stages of cyberattacks of cloud infrastructures. The recent trend in the field of addressing these limitations is the creation of technologies of artificial intelligence (AI). The strategies allow independent protection, anomaly detection, and real-time analysis with references to using deep learning, machine learning, and reinforcement learning. Through imbuing AI with a constantly-learning feature, it enables the intrusion detection system to be more accurate and generate a lesser number of false positives and it also enables the possibility of adaptive and predictive security. The fusion of large-scale language models with efficient orchestration platforms contributes to reacting to the arising threats with a quicker and more precise response. This allows automatic control over incidences, self-healing network, and defense mechanisms on a policy basis. Considering the current detection and response methods, this discussion assesses their strengths and weaknesses and outlines key issues such as data privacy, adversarial machine learning and integration complexity in the context of AI-based cloud security. These results suggest the future application of AI to support autonomous, scalable and active cloud security operations
Integrating Explainable AI in Financial Fraud Detection Systems for Enhanced Decision Transparency
The high rate of development of digital financial services has increased the vulnerability of financial systems to fraud. Despite the effect of traditional machine learning models on fraud detection, it is often not flexible and transparent to the evolving financial landscape and regulatory requirements. This research paper is a description of a new way of detecting financial fraud and is more effective than the previous one. It uses the Kaggle credit card data that comprises over 284,000 transactions yet only 492 frauds. The data set was preprocessed in different ways, like label encoding, Min–Max normalization, PCA-based feature selection, SMOTE balancing, and other methods, and then the models were trained. Two significant models, Decision Tree and Multilayer Perceptron, were developed and compared with the existing models (GBM, ANN, LR, NB) on the measurements of accuracy (ACC), precision (PRE), recall (REC), and F1-score (F1). MLP performed optimally with the highest accuracy of 99.52% and the DT model performed optimally in recall and F1-score since it could effectively detect fraudulent cases. SHAP and LIME were the keys to explaining the determinants of the model processing by the use of explanatory AI technologies. Overall, the results revealed that the accuracy of predictions and reproducibility are the cornerstones of building effective fraud detection systems in the finance sector, where the real-life context is vital
AI-Driven Identity Threat Detection and Response Systems for Modern Cloud Security Operations Centers
Identity is the new perimeter in cloud-first enterprises, where adversaries increasingly weaponize stolen credentials, malicious OAuth consents, token replay, and over-permissioned roles to traverse control planes and SaaS estates with minimal endpoint noise. In this paper, a telemetry-based Identity Threat Detection and Response (ITDR) architecture integrating identity provider (SSO/OAuth/OIDC), cloud API (AWS/Azure/GCP), endpoint, and SaaS audit log telemetry into a privacy-conscious feature fabric is proposed using AI. Our combination of sequential modelling of session dynamics, graph learning of privilege, relationship abuse, and self-supervised representation learning reveals low-and-slow compromise patterns. An anomaly strength is combined with contextual role criticality of data, device trust, and just-in-time elevation, session isolation, token revocation, and step-up authentication executed by a policy-as-code and SOAR playbooks-based risk layer that is calibrated. Details Implementation summary Streaming feature stores (training/serving parity) Feedback loops (with analyst adjudications) Governance (mapped to ISO 27001, NIST SP 800-207, and GDPR) In hybrid real-world and synthetic tests, the method does better recall at constant low false-positive rates, as well as fivefold trimming alert volume by deduplication and model calibration and with less time-to-detect and time to respond with real-time inference and automated containment. Describe stability to adversarial log poisoning and concept drift, and explainability methods that do not compromise auditability. The findings reveal that AI-based ITDR has the potential to support SOC effectiveness in a material manner without going against the principles of Zero Trust nor regulatory requirements
Identity Threat Detection and Response (ITDR)
The Future of Software Development and the Expanding Role of ML Models
ML models were transforming software development through data driven learning and intelligent decision making. Software engineering had begun shifting away from fully deterministic approaches toward adaptive systems capable of handling uncertainty and complex patterns. This study examines how ML influences the future of software development, the software lifecycle, organizational workflows, industry innovation, and long term engineering practice. The paper presents a thorough exploration supported by contemporary research [1] [2] [4] and identifies emerging challenges related to ethics, security, quality, and sustainability. The analysis shows that ML is not an add on feature but an essential component that defines the next generation of intelligent software systems
Data Engineering for Responsible AI: Architecting Ethical and Transparent Analytical Pipelines
Responsible AI succeeds or fails on the strength of its data foundations. This paper presents a practical, end-to-end architecture that embeds ethics, transparency, and compliance directly in the analytical pipeline itself: turning principles into verifiable, automatable behaviors. Introduce governance-by-design patterns that start at ingestion with consent- and license-aware data contracts, continue through privacy-preserving preprocessing by tokenization, k-anonymity, and differential privacy, and culminate in versioned feature stores, model registries with gated promotion, and lineage-aware observability. A signed provenance graph connects sources, transformations, features, models, and decisions for reproducibility, contestability, and audit readiness. Bias mitigation is a multi-stage discipline: representative sampling, proxy-feature audits, continuous fairness monitoring, and human-in-the-loop overrides for high-risk use cases. There are also interpretability services that generate attribution and counterfactual evidence for both batch and real-time decisions. Compliance is operationalized via policy-as-code evaluated at pipeline gates of ingest, transform, publish, and deploy, with immutable logs and evidence binders in support of regulatory obligations and incident forensics. In a case study of a credit-risk workload, Measure significant gains: predictive quality improves, selection, and error-rate gaps are reduced; documentation completeness is high; strong recall of PII detection; and drift remediation is rapid via lineage-driven root-cause analysis. The result is a reference stack that aligns technical performance with legal and societal expectations: showing how responsible behavior can emerge as a routine reliability property of modern data and ML operations
Dinesh Babu Govindarajulunaidu Sambath Narayanan
Frontend-Driven Metadata Governance: A Full-Stack Architecture for High-Quality Analytics and Privacy Assurance
To maintain superior quality of analytics and protect the privacy of individuals, then metadata must be exact, comprehensive and regulated in all levels of the data life cycle. Conventional metadata governance systems are based on mostly backend enforcement, and therefore they have delayed validation, did not capture metadata in a consistent manner and had quality gaps every time a new data is generated. This paper presents a metadata governance paradigm that is frontend-based, and that implements metadata capture, validation and privacy enforcement directly into user-facing applications. The architecture provides real time lineage tagging with schema based signature-driven UI elements, policy constrained form generators and a minimum number of user friction. Resources included in the model are a lightweight frontend governance SDK, which links to one middle tier comprised of a policy orchestrator and a backend metadata repository that supports versioning, lineage tracking, and compliance auditors. A committed privacy assurance engine implements controlling principles such as verification of consent, restriction of purpose and personalization of the data in accordance with GDPR, CPRA, and further developing global laws. Experimental findings indicate that this approach of a frontend-based methodology raises completeness of metadata, consistency by 38 and 32 percent, and accuracy of privacy compliance by 41 percent compared to using backend-based strategies only. The large retail analytics ecosystem Architecture has been validated as a production deployment with sub-50 ms end-to-end validation latency and throughput making millions of metadata events per day. On the whole, this work makes the following contribution: (1) a new paradigm of governing the origin of data creation, (2) a single full- stack architecture combining metadata lineage, privacy assurance, and analytical preparedness, and (3) experimental validation of significant quality and compliance improvement
The adoption of digital twin (DT) technologies in infrastructure systems is rapidly transforming how built assets are designed, monitored, and maintained. A digital twin is a dynamic virtual representation of a physical asset or system that integrates real-time data, simulation, and predictive analytics to support decision-making (Wang et al., 2023). In the context of infrastructure including transportation networks, utilities, and civil assets these technologies offer significant potential to enhance resilience, optimise lifecycle performance, and enable proactive maintenance. However, the integration of artificial intelligence (AI) and Internet of Things (IoT) with infrastructure digital twins remains an evolving research frontier, with persistent challenges around data interoperability, cybersecurity, and scalable deployment (Attaran, 2023; Qiu et al., 2023). This paper presents a conceptual framework for AI‐driven digital twins in infrastructure management, grounded in current literature and supported by case-study analysis. It examines how advanced analytics, sensor networks, and simulation models converge to form a closed-loop infrastructure digital twin workflow, spanning design, operation, and decommissioning phases. The findings suggest that infrastructure owners and practitioners can achieve improved performance metrics such as reduced downtime, lower maintenance costs, and enhanced situational awareness through DT-enabled systems. Nonetheless, significant barriers remain, including standardisation of data models, secure connectivity for large-scale asset networks, and the cultural shift required for operational adoption. The paper concludes by outlining research and implementation pathways that address these gaps and advance infrastructure digital twins toward smarter, more adaptive systems
Foreign Currency Accounting: Remeasurement and Translation Explained
Finance is one of the most diverse and complex areas in international trade. Each country has their own rules, regulations and statutory requirements for managing financial transactions in their territories whereas business partners from other geographies or foreign investors may not be well versed with local laws and regulations, hence accounting standards such as IFRS (International Financial Reporting Standards) and GAAP (Generally Accepted Accounting Principles) have been established over a period of time to bring uniformity in financial practices and reporting so that it will be easier for anyone to understand the financials of any organization following these standards. Foreign Currency Remeasurement and Translation are two terms that are used quite interchangeably due to lack of knowledge or clear understanding. Both Foreign Currency Remeasurement and Translation are quite different from each other and have their specific definitions and treatment to be followed as per accounting standards such as IFRS, GAAP etc. This article discusses Foreign Currency Remeasurement and translation, their uses and accounting standards established around these two concepts
Realized and Unrealized Foreign Exchange Gains or Losses
International Financial Reporting Standards (IFRS)
US GAAP (Generally Accepted Accounting Principles)
Honeypots in the Age of Generative AI: A Framework for Risk-Aware Threat Detection and Cyber Deception
The rapid advancement of generative artificial intelligence (AI) has introduced both novel opportunities and significant challenges in cybersecurity. Traditional honeypots, long employed to detect, analyze, and deceive malicious actors, face limitations in addressing highly adaptive, AI-driven threats. This paper presents a risk-aware framework for AI-augmented honeypots, designed to enhance threat detection while minimizing operational, legal, and ethical risks. The framework integrates generative AI techniques to create dynamic, interactive, and realistic decoy environments, enabling improved engagement and intelligence collection from sophisticated adversaries. We propose a risk scoring model to evaluate potential hazards associated with AI-driven deception, and illustrate the framework’s implementation through a prototype leveraging synthetic environments, automated response engines, and adaptive interaction strategies. Experimental results demonstrate increased detection efficacy, prolonged attacker engagement, and actionable intelligence extraction compared to conventional honeypots. Finally, we provide operational guidelines and ethical considerations to inform safe deployment in enterprise and cloud environments. This study offers a systematic approach to modernize honeypot design in the age of generative AI, supporting proactive cyber defense and strategic deception
A Review of AI-Enhanced Document Intake Systems Using OCR for Auto and Property Insurance Applications
The algorithms of AI and the use of Optical Character Recognition (OCR) are redefining the automation of auto and property insurance, meaning that the unstructured and rather complex data can be shuffled around. The article cogitates on the processes of document classification, data extraction, and claims management using AI technologies, i.e., Machine Learning (ML), Natural Language Processing (NLP), and Robotic Process Automation (RPA). These systems improve the accuracy of operations, risk assessment, and fraud identification, and reduce the time spent on manual interaction and paper processing by automating volume and processing analysis. Moreover, AI-assisted OCR models are used to facilitate intelligent decisions, workflow, and insurance process compliance. The other aspect that is discussed in the research is a literature review of comparative OCR and its use in multilingual and handwriting text processing, which has led to the development of document intelligence in insurance. By default, AI-enabled OCR systems represent a significant advancement in automation of smart, data-driven and scalable document management in the insurance industry
The Role of Artificial Intelligence in Software Engineering: A Review of Frameworks, and Impact on the Software Development Life Cycle
Computer programming Artificial intelligence is now the key that makes it possible to redesign, develop, and maintain software systems. The automation of software development life cycle (SDLC), predictive modelling, and intelligent decision assistance are all products of AI implementation in the SDLC, which has led to improved productivity and quality of products, in turn. The paper is a comprehensive evaluation of AI models and the reasons why they are relevant to the development of software engineering practice. General-purpose frameworks such as TensorFlow, PyTorch and Keras are very helpful in the creation of larger models, domain-specific models such as Code BERT, GPT-based tools and Auto ML platforms can perform particularly significant tasks, such as code generation, defect detection and automated testing. The paper explores how the different AI methodologies have been incorporated in the SDLC considering aspects like requirements engineering, system design, implementation, testing, deployment, and maintenance. Furthermore, the trend shifts to explainable AI, intelligent maintenance, and self-managed systems have new tendencies, and it is a broader shift towards sustainable and responsible usage of AI in software engineering. Overall, the given paper sums up the most significant progress and achievements, and it can be of great value to the researchers, practitioners, and other interested parties who want to apply AI to create a new layer in the software
AI-Assisted Query Optimization Techniques for Cloud Databases Supporting Hybrid SQL and NoSQL Workloads
Data management in the modern era has changed with the introduction of the paying database solutions which include Database as a Service (DBaaS) with the use of cloud computing. Nevertheless, query optimization in distributed and hybrid systems is highly challenging because of the dynamic workloads, heterogeneity of resources and cross platform data integration. By virtue of being based on standard rules and costs, traditional approaches to rule-based and cost-based optimization do not scale, and cannot easily adjust to the complexity and volume requirements of cloud-based and hybrid SQL-NoSQL databases. The present paper is based on the idea of the use of Artificial Intelligence (AI) and Machine Learning (ML) to optimize queries in a data-driven, adaptive, and autonomous manner. Supervised and unsupervised learning AIs that are used as optimizers are applied to learn cost models, predict optimal execution plans, and dynamically respond to changes in the workload, whereas workload forecasting and resource allocation is possible using deep learning (DL) models such as LSTMs and Transformers. Comparative study of SQLs and NoSQLs system shows that consistency and scalability have a complementary relationship, which makes it crucial to have hybrid system in the cloud ecosystem. The research finds that AI-based query optimization not only enhances efficiency, performance, and adaptability but also preconditions future autonomous and self-tuning database systems, which can work perfectly well in multi-cloud environments
Copyright (c) 2025 International Journal of Emerging Research in Engineering and Technology
Effective model of building Tip Pooling Functionality in Workday Payroll
This article focusses on how we can effectively design the pool tips calculation in Workday Payroll by integrating data from Point of Sales System (POS) using PRISM and Workday payroll. This research outlines the data integration for various tips categories across location and tips calculation for the employees based on the tips collected in the pool for the various categories. This article proves the success of this design with a successful case study by implementing this design in the Retail industry with over forty thousand employees and concludes with the implementation strategy
Battery Degradation Forecasting with FMU and Optimization Framework Using Synthetic Generated Data
For successful operation and life-cycle management of electric vehicles (EVs), accurately predicting battery degradation is paramount. Long-term experimental aging data are typically scarce, costly, and proprietary, which complicates model calibration and validation. This paper presents a hybrid co-simulation and optimization framework that predicts lithium-ion battery degradation, utilizing synthetic data generated from a Functional Mock-up Units (FMUs) representing coupled thermal and electrical-based system dynamics. Using two FMUs—a Thevenin-based electrical model and lumped-parameter thermal model—we established a Python co-simulation environment, following the FMI 3.0 standard. By varying ambient temperature, C-rate, and duty-cycle inputs, we generated synthetic operating profiles to represent realistic conditions for an EV. Embedded in the loop was an Arrhenius-type degradation law that was temperature-dependent and whose parameters were determined using sequential least-squares and particle-swarm-optimization algorithms. This paper demonstrated that the proposed approach could reproduce known thermal–electrical interactions responsible for capacity fade and closely mirrored open experimental data from the NASA Ames Li-ion Battery Aging Repository [21]. Quantitatively, we benchmarked R² = 0.94; RMSE = 0.032; a degradation-rate ratio of 0.92 when compared to experimental trajectories, confirming both physical plausibility as well as statistical support. The results indicate that synthetic data generated from FMU can replace expensive experimental laboratory validation of battery degradation studies, allowing for controlled virtual degradation experiments and often reproducible digital-twin calibration for predictive battery health management in future electric vehicles and plug-in hybrid vehicles
Advancing Sustainable Agriculture through Engineering Innovations
The growing concerns with respect to the sustainability of agriculture globally, as well as the need for increased food security and the preservation of rural economies, are driving the search for new ways to develop sustainable agriculture practices. The need for sustainable agricultural practices will require significant technological innovation to maintain agricultural productivity in the face of climate change and to protect natural resources such as water and soil. Three emerging trends in the area of sustainable agriculture include: (1) Precision irrigation systems; (2) Automation and precision agriculture through the use of sensors and big data; and (3) Community-based watershed management. The objective of this paper is to examine the potential for innovative technologies to provide solutions to the major problems facing the development of sustainable agriculture globally. This paper reviews current literature and academic research related to innovative technologies for sustainable agriculture. A key theme in much of the literature reviewed in this paper is that the effective development of sustainable agriculture will depend upon the adoption of integrated, holistic approaches to agriculture that incorporate both technology and social aspects of the practice. This paper provides a broad overview of current thinking on the role of technology in promoting sustainable agriculture. Specifically, this paper discusses the role of precision irrigation, automation, and big data in providing solutions to the major problems facing the practice of sustainable agriculture. Finally, this paper discusses the importance of the development of community-based watershed management initiatives to provide additional support to sustainable agricultural practices
Scalable Architecture for Next-Gen Behavioral Health EMRs
There is a growing demand among behavioral health providers to have digital infrastructure, which promotes the elements of integrated care, data interoperability, and scalable service provision. Conventional Electronic Medical Record (EMR) systems were developed to address acute and episodic medical events instead of longitudinal, in-depth behavioral health processes, which would encompass psychotherapy, psychiatry, case management, telehealth, pharmacotherapy, as well as Integrated Social Services. With the continued increase in behavioral health conditions like depression, anxiety and substance use disorder and related trauma disorders across the world, the care models are expanding, and the number of service requirements is so great that the long-standing EMR unable to keep pace. The current EMRs are limited in scale and data architecture, interoperability, vendor lock-in and lack proper real-time communication and clinical decision support. This dissertation contemplates why scalable next-generation behavioral health EMR systems are necessary and suggests a current cloud-native architecture offered by microservices, API-first design, FHIR standards, real-time data streaming, and AI-based enhanced analytics. The analysis states that scalable and modular architectures can give a better performance, interoperability, security, clinician usability, and patient engagement coupled with the ability to offer tele-behavioral patient care, cross-provider interaction, and lasting treatment management. The study ends with the findings of gaps in the existing EMR solutions and the prospective avenues of work in the future to enhance scalable EMR solutions in behavioral medical practice during the digital age
Building ROI-Driven Bots: From Insights Dashboards to Outcome Tracking
In an era where data is in abundance, the usage of dashboards has been elevated to a new level, making them the standard way of presenting data. Despite their success in visualising data, they generally fail to let decision-makers touch the actual business impact. The term "decision-makers" here evokes images of people looking at metrics without any directions on what to do next. The lack of clarity in action-taking has led to the development of a new group of tools: ROI-driven bots. These smart agents, unlike the static dashboards, don't stop at only the reporting of performance—they actually take the users through the process, keep track of the value of activities, and keep on improving the flow between data and decisions. ROI-driven bots are the next step in the progression from insights dashboards to actionable outcome tracking. Instead of asking the leaders to comprehend the numbers and manually convert them into initiatives, these bots take over the function of intermediaries that not only present but also explain the given insights, thus allowing the timely interventions to be initiated and at the same time measuring the downstream effects of these interventions. Such as a bot that, instead of visualizing customer churn rates data, would go a step ahead and devise retention plans, engage with customers on the companies' behalf and then calculate the revenue saved from that. This move redefines analytics from serving as the basis for business decisions to actually prescribing the solutions and leading to results in business. ROI-driven bots are developed to point at performance issues and, at the same time, to incorporate the concepts of accountability and value measurement into the daily work processes
Data-Driven Cloud Workload Optimization Using Machine Learning Modeling for Proactive Resource Management
Cloud computing has reinvented delivery of services due to its provision of scalability, elasticity and cost effectiveness. But increased workloads have aggravated management of resources, which has resulted in energy waste, SLA break, and high cost of operation. The study presents an active data-driven optimization model based on machine learning to predict workload dynamics. Google Cluster Trace dataset is processed using logarithmic scaling, Savitzky-Golay filtering and Min-max normalization in order to improve stability and quality. An LSTM model is used to find longitudinal dependencies and forecast workload, CPU, and RAM usage. Measures of evaluation are R2, Mean Squared error (MSE) and Root mean Squared logarithmic error (RMSLE). The findings indicate that LSTM attains almost perfect accuracy, R2 of 0.99%, MSE of 13,934.54 (workload), 128.89 (CPU) and 131.29 (RAM), and RMSLE of 0.15, 0.16 and 0.14. Compared to existing models such as VAMBig, SVM, and SATCN, the LSTM framework significantly outperforms them in prediction accuracy and error minimization. These findings confirm that proactive LSTM-based workload optimization reduces energy usage, enhances SLA compliance, and strengthens scalability in dynamic cloud computing environments. Future work can extend this framework to multi-cloud and federated environments for broader applicability
Building a Resilient Automation Ecosystem: Architecture, Governance, and Teamwork
In a world where businesses are changing rapidly, automation is no longer just a tool for saving time, but it has become the core of the whole process that runs the company—the backbone of modern operations. Nevertheless, as they develop their automation systems, organisations usually come across a problem that is considered one of the most important: resilience. A system of automation that is resilient not only does the work faster but also is capable of adapting to any kind of interruptions, maintaining governance without hampering creativity, and managing through the combined efforts of business and IT. This article is an in-depth examination of the features and functions of building such a system, as it shows how the architect, governor, and collaborator are the three pillars that lead to the greatest success in the long run. On the side of architecture, we look at structures that allow businesses to plan systems that have the characteristics of being modular, flexible, and scalable, from which they can recover from both technical and business crises. From the perspective of governance, we unravel schemes that can manage both control and agility, making it easier for organisations to synchronise automation with compliance requirements while, at the same time, promoting innovation. Nevertheless, it is the points of people and their culture that form the core of resilience, and we show how cross-functional teamwork and shared ownership can change the face of automation from a siloed initiative into a collective enterprise capability. To make these concepts more practical, we are including a real-world case study that illustrates how a company managed to put together a resilient automation ecosystem successfully by blending smart architecture, sound governance practices, and a culture of collaboration. The above-mentioned revelations provide not only strategic guidance but also hands-on advice to the leaders and the professionals who want to get past their period of fame and instead plant the automation foundations that will last in the future
Data-Driven Loan Default Prediction: Enhancing Business Process Workflows with Machine Learning
Financial institutions perform a major activity, loan default prediction, which directly affects risk management, loan approval and profitability. The study presents a powerful method of loan default forecasting on the Lending Club data, comprising 2,260,701 individual loans records between 2007 and 2018. The presented methodology comprises data preprocessing, such as cleaning, missing values, numerical values scaling, one-hot encoding categorical variables, and correlation and importance-based feature selection. Synthetic Minority Oversampling Technique (SMOTE) is implemented to ensure that the instances of default and non-default are represented in a manner that is class-balanced, thereby mitigating the explicit class imbalance. With this data split into training and test sets, and run ensemble machine learning classifiers, including XGBoost and Light Gradient Boosting Machine (LGBM), to make predictions. Accuracy (ACC), precision (PRE), recall (REC), F1-score (F1), and ROC-AUC scores are the metrics utilized to assess performance. It has been experimentally demonstrated that the developed models possess the excellent performance of prediction, with LGBM reaching a record of 99.99% on all measures and XGBoost coming close with 99.96%- 99.98%. The improved efficacy and generalization capability of the ensemble-based developed approach is proved by comparison with more standard models, including Neural Networks, Support Vector Machines, and Convolutional Neural Networks. The findings indicate that both LGBM and XGBoost offer a highly reliable and interpretable solution to assess financial risk that can identify the possible loan defaults with the best level of accuracy and can contribute to the process of lending choices
Compliance-First Automation in the Public Sector
Public sector organizations have to comply with some of the most stringent requirements. These requirements are primarily driven by laws, regulations, and the need for public accountability. Everything from data privacy and financial reporting to procurement and service delivery is being monitored closely. These different layers of oversight, although necessary, often slow down the organizations' processes and can lead to inefficiencies and even the possibility of human error. The heads of government departments have a big problem to solve: that is, they have to ensure on the one hand that all rules and regulations are followed and on the other that they manage to deliver services to citizens in a proper way. The solution to these problems has been found in the use of automation. Automation, beyond the point of merely diminishing manual workloads, provides uniformity, error elimination, and the creation of transparent traces of accounts that enhance accountability. When compliance requirements are embedded into automated processes at the design stage, public sector organisations can move from reactive oversight to proactive governance. In this way, the risk of regulatory violations is lowered significantly and, on top of this, public trust is built through transparency and reliability. Compliance-first automation helps governance in that it ensures processes are in line with the organisation's established policies. It also helps in risk management, as it flags the anomalies in real time and goes a long way to ensuring accountability through detailed logs and reporting
Building Analytics-Driven Bots: RPA Meets Business Intelligence
In the present day's digital economy, businesses are under constant pressure to improve their operations, make better decisions & provide their value quickly. This has led to the combination of Robotic Process Automation (RPA) with Business Intelligence (BI). Traditional automation focuses on their speed, whereas analytics-driven RPA uses BI tools like actual time reporting, predictive analytics & data visualization to provide the context & flexibility. This link lets bots do more than just their jobs; they can also take on many other tasks that help with decision-making, managing their exceptions & improving processes over time. The paper explains how RPA works with different BI systems, looks at actual world examples from fields like banking, healthcare & government, and shows measurable results like lower operational expenses, better compliance & happier customers. This convergence marks a shift from automation serving a supporting function to acting as a strategic enabler, positioning RPA & BI not as separate initiatives but as complementary forces driving intelligent organizational transformation
GenAI-Powered Test Case Generation for Microservices in CI/CD Pipelines
The adoption of microservice architectures has significantly accelerated software development velocity, yet it has introduced profound challenges in software testing. The distributed and independently deployable nature of microser- vices complicates the generation of comprehensive unit and integration test cases, often creating a bottleneck within Con- tinuous Integration/Continuous Deployment (CI/CD) pipelines. This paper introduces a novel framework, ”TestGenius,” that leverages Generative Artificial Intelligence (GenAI) to auto- mate and optimize test case generation for microservices. Our approach utilizes a multi-modal generative model that learns from API contracts (e.g., OpenAPI specifications) and runtime operational logs to produce semantically rich and contextually relevant test cases. To facilitate cross-organizational learning without compromising sensitive data, we propose a federated learning framework governed by a novel trust metric, ensuring the integrity and accountability of model contributions from different silos. Furthermore, we address the critical need for transparency in AI-driven testing by developing a framework to quantify and optimize the trade-off between the performance (i.e., test coverage, bug detection rate) and explainability of the generated tests. Our experimental design and hypothetical results demonstrate that TestGenius can significantly increase test coverage and fault detection rates compared to traditional test generation methods, paving the way for more resilient, efficient, and intelligent testing paradigms in modern software engineering
Transitioning from AUTOSAR Classic to Adaptive for Service-Based Architectures
The growing complexity of advanced automotive system, consisting of connected, autonomous, and software-defined vehicles, has increased the pace of the transformation of the AUTOSAR framework out of its Classic Platform (CP)-optimised to solutions supporting dynamic, service-oriented architectures (SOA). Although AUTOSAR Classic has been shown to be strong in real-time control systems, it is struggling with the polluted communication, computational scalability and over-the-air (OTA) updates needs of future-generation vehicles. In this paper, a systematic transition framework to facilitate a methodical move between AUTOSAR Classic and AUTOSAR Adaptive will be presented. It has suggested that the legacy Classic components can be mapped to Adaptive service abstractions by the use of standardized middleware interfaces like SOME/IP and DDS, ensuring interoperability and downward compatibility. An execution model is devised that shows the presence of coexistence between Classic ECUs and Adaptive nodes in the form of a single service management layer. Experimental validation using a mixed ECU testbed indicates the following improvements: communication efficiency can improve up to 38% and integration time can reduce by 27% and increase scalability in distributed service deployment. The results point out that the switch to the AUTOSAR Adaptive does not only enhance the flexibility of the system but also preconditions the further automotive innovations, i.e. the use of edge intelligence, cloud integration, and autonomous control systems