International Journal of Emerging Trends in Computer Science and Information Technology
Cloud-Native Micro services Architecture
The emergence of cloud-native microservices architecture has redefined how modern AI-driven systems are developed, deployed, and maintained. Unlike traditional monolithic systems, microservices enable modular development, independent scaling, and resilience across distributed environments (Dragoni et al., 2017). With the increasing demand for AI applications, integrating machine learning workloads into containerized and orchestrated microservices environments offers enhanced flexibility and scalability (Kalske, Mäkitalo, & Mikkonen, 2018). Cloud-native ecosystems leverage technologies such as Docker and Kubernetes to facilitate continuous integration and deployment (CI/CD), automated scaling, and fault isolation (Pahl & Jamshidi, 2016). Furthermore, adopting microservices in AI pipelines improves system observability and accelerates model iteration cycles through MLOps practices (Karmakar & Saha, 2020). However, challenges remain in managing data consistency, inter-service communication, and runtime monitoring at scale (Fazio et al., 2020). This research investigates the principles, advantages, and architectural considerations of deploying AI workloads in cloud-native microservices environments to enhance scalability, resilience, and operational efficiency. The findings contribute to understanding how enterprises can leverage microservices and container orchestration for intelligent, adaptive, and high-performing AI systems
The Decorator Pattern in Software Engineering: Principles, Design, and Applications
Design patterns provide reusable solutions to recurring software design problems, supporting the development of flexible and maintainable systems. Among these, the Decorator pattern is a structural pattern that enables dynamic extension of object behavior without altering existing code. This paper presents a comprehensive review of the Decorator pattern, examining its theoretical foundations, standard structure, and practical implementation. It highlights how the pattern reinforces key object-oriented principles particularly the Open/Closed Principle and composition over inheritance and demonstrates its application through a Java- based text-formatting example. Comparative discussion with related patterns such as Proxy, Strategy, and Composite clarifies its distinctive role in incremental behavior extension. Real-world applications, including the Java I/O framework and middleware or network-processing systems, further illustrate its practical relevance. The paper concludes by evaluating the pattern’s strengths, limitations, and performance considerations, and by outlining future directions involving functional, aspect-oriented, and AI-assisted approaches
An AWS-Native Pattern for Audited, Just-in-Time SSH Access Using Short-Lived Certificates
Traditional SSH access in multi-account AWS environments relies on long-lived keys, bastion hosts, or static IAM mappings, patterns that are costly, difficult to audit, and misaligned with just-in-time (JIT) and zero-standing-privilege principles. This whitepaper introduces an AWS-native approach using short-lived SSH certificates issued by a central SSH CA stored in AWS Secrets Manager, combined with GitHub Actions OIDC federation, OpenSSH user certificates, hardened EC2 access via AWS Systems Manager, and EventBridge-driven one-time network revocation. It also supports optional ServiceNow validation for production governance. The result is a low-cost, reusable framework delivering auditable, short-lived EC2 SSH access across multiple AWS accounts. The paper also outlines architecture, implementation, operational considerations, and comparisons with commercial JIT and AWS SSM Session Manager [9] solutions
Evaluation of Transformer Models for Summarizing Lengthy Clinical Notes
Digitization of healthcare records are set to grow at a phenomenal rate, particularly unstructured clinical data (such as long clinical notes). These reports usually contain irrelevant and repetitive data and it is difficult to get important information needed by the healthcare providers in a short span of time. Automatic text summarization is an effective way out since it gives the shortened form of these notes but does not lose any important medical data. The paper will analyze the best transformer-based models such as BART, T5 and Longformer on the task of abstractive summarizations of clinical notes. We source our experiments on MIMIC-III data and we test the quality of the model on ROUGE scores and qualitative evaluation of humans. As we can see, more generic transformer models are adequate to work, but more architectures have proven to be better in longer sequences (like Longformer). Our research indicates the advantages and disadvantages of each model and gives future considerations to the future improvement of clinical natural language processing
Secure Cloud Operations: Balancing Compliance, Data Privacy, and Performance in Healthcare Systems
Modern healthcare systems have turned to cloud computing as a cornerstone that provides scalability, agility, cost-efficiency and opportunities to support telemedicine, large scale data analytics and patient centric services. Yet, the safe use of healthcare clouds requires a fine balance between three tendencies, which seem to conflict with each other, including regulation protection (e.g., HIPAA, GDPR), protection of data privacy of electronic protected health information (ePHI), and high performance and availability of the system needed by clinical activities. In this paper, the researcher explores the manner through which healthcare organisations can design and deploy cloud operations to meet all the three dimensions concurrently. We discuss major security, privacy, and performance issues in cloud based healthcare setting based on recent empirical and industry findings [1], [2]. We next suggest a framework with compliance automation, privacy by design implementation, and performance conscious cloud configuration and monitoring. We discuss certain tradeoffs that can be made, like the overheads of encryption versus latency, the depth of auditing/logging versus throughput, multitenant resource isolation versus cost/performance efficiency. The knowledge gained in case studies reveals to change agents how their healthcare facilities can employ adaptive policy engines, data classification levels, and control levels, auto-scaling resources, and real time monitoring in order to ensure compliance, privacy protection, and service level assurance. Lastly, there are implications to governance, vendor management and continuous assurance in healthcare clouds which we discuss. The results indicate that there is no single silver bullet; instead success is based on an integrated model of functioning, cross functional teamwork, and constant balancing between controls and performance. We conclude and make our own recommendations on best practice by healthcare system operators wishing to implement icyes operations in a high performance regulated environment
Remix for Finance: A Full-Stack Framework for Secure, Low-Latency Web Applications
Financial web applications must simultaneously satisfy low-latency user experiences, strict data integrity requirements, and demanding regulatory and security constraints. Traditional single-page application (SPA) architectures often push too much logic to the client, creating complexity around security, state synchronization, and SEO, while classical multi-page applications struggle to deliver highly interactive interfaces. Remix is a full-stack React-based web framework that combines server rendering, nested routing, and an explicit data-loading and mutation model. It emphasizes progressive enhancement with applications function fully without JavaScript and become more interactive when scripts are available [1]–[3].This article examines Remix through the lens of financial use cases, and proposes a reference architecture for portfolio dashboards, trading workflows, and risk/compliance consoles. We show how Remix’s loader/action model, nested routes, streaming, and Backend for Frontend (BFF) deployment pattern can address common challenges in finance: real-time data, secure server-side logic, traceability, and performance [1], [2], [4]–[7]
Credit Risk Management Practices in Microfinance Institutions
Credit risk management is among the most important decisions that influence the long-term sustainability and social mission of microfinance institutions (MFIs), a business that involves unique borrower characteristics, information asymmetries, and contextual vulnerabilities. Traditional banks rely extensively on formal collateral, established credit records, and developed credit bureaus, whereas MFIs operate in contexts of limited borrower documentation, informal income-earning streams, seasonal cash flows, and a high level of socio-economic diversity. These features recreate the credit risk landscape for MFIs who have to develop screening, monitoring, and portfolio controls that are well-suited to their context, where financial sustainability has to be reconciled with outreach concerns. This article aims to provide an empirically based, analytically rigorous, and operationally implementable framework developed with reference to the literature, comprehensively critically assessing credit risk management practices in MFIs. Weaving together the conceptual advances, empirical studies, and methodological contributions on borrower-level screening, group-lending mechanisms, credit-scoring models, loan officer-monitoring systems, and portfolio-level indicators (e.g., PAR), the abstract sums up our knowledge about this long-studied field. The paper also highlights the expanding role of data analytics, including, for example, logistic regression-based scorecards, survival analysis for time-to-default modelling, and cookery book-type machine learning tools. Within MFIs that aim to enhance predictive accuracy in areas where data remain sparse but are slowly improving. Additionally, the abstract emphasizes institutional governance, loan-officer incentives, and transparent reporting - factors that repeatedly surface in literature as firm mediators of portfolio quality. Poor regulation, fast growth in unsupervised credit, and poor incentives have a history of leading to delinquency surges and over-indebtedness crises in several microfinance markets. This research draws from literature, academic studies, reports from CGAP and MIX Market, and practitioner frameworks to develop an end-to-end methodology for the assessment and management of credit risk. The tools are composed of (1) borrower-level standard variables to build logit scorecards, (2) methods to compute and interpret PAR30/PAR90, (3) an econometric model of portfolio for estimating determinants of delinquency at the branch or institution level, and 4 large-scale stress testing, which combines economic crisis, seasonality, and local market saturation. The study also highlights ethical issues related to credit scoring in microfinance, such as privacy, fairness, and inclusiveness, as well as the importance of clear human oversight to avoid discrimination or excessive reliance on automated processes. Aggregated from past empirical literature, results indicate that MFIs that implement multi-level risk management perform significantly better in their portfolio quality. This approach normally involves customer screening, group or social collateral solutions as appropriate, early delinquency signals, staff incentives for better alignment, and credit growth cautiously done. Also, scorecard-based credit appraisal has demonstrated substantial predictive enhancement over heuristic judgmental lending in conjunction with a robust monitoring and MIS framework. The findings also highlight the significance of macroeconomic context by showing that even optimally crafted institutional risk mitigators can be undermined by exogenous disturbances such as economic recession, changes in agricultural prices, or market saturation.
From Breaches to Bank Frauds: Exploring Generative AI and Deep Learning In Modern Cybercrime
The emergence of Generative Artificial Intelligence (AI) and advanced deep learning models has fundamentally altered the dynamics of modern cybercrime. Threat actors now leverage large language models, generative adversarial networks, and reinforcement learning agents to automate reconnaissance, craft adaptive phishing campaigns, produce polymorphic malware, and execute highly personalized financial frauds at unprecedented scale. As a result, traditional rule-based and signature-driven security systems are increasingly ineffective against AI-generated attack variants that continuously evolve in real time. This paper provides a systematic investigation into the role of generative and deep learning technologies in accelerating contemporary cyber threats, with a specific focus on data breaches, banking frauds, synthetic identities, and deepfake-based impersonation attacks. We develop a prototype AI-driven attack generation and fraud simulation framework to empirically demonstrate how these models can be weaponized to bypass modern defenses. Experimental evaluations reveal significant increases in attack success rates, evasion capability, and automation efficiency when compared to conventional cyberattack methods. The findings underscore the urgent need for AI-augmented defense mechanisms, behavioral analytics, risk-aware deception strategies, and regulatory oversight to mitigate this new class of intelligent and autonomous cybercrime
Carbon-Negative Transportation Corridors for the U.S. Interstate System – AI-Optimized Carbon-Negative Logistics Corridors Using Biofuels, Electrification, and CCS for Long-Haul Freight
Freight transportation along the United States Interstate System remains one of the most difficult sectors to decarbonize, and current strategies focus largely on reducing emissions rather than achieving net negative outcomes. This study introduces a new concept known as a carbon-negative logistics corridor, which is designed to support long-haul freight while removing more carbon from the atmosphere than it emits. The research combines three complementary pathways: advanced biofuels, electrified freight systems, and carbon capture and storage integrated into major freight hubs. These elements are evaluated within a corridor planning and optimization framework that identifies where carbon-negative potential is strongest across the national interstate network. The methodology uses freight flow mapping, life cycle carbon accounting, infrastructure analysis, and multi-scenario comparison for selected corridors. The results show that a properly designed corridor can reach sustained net negative carbon performance by 2050, while also improving freight system efficiency and strengthening progress toward the United States Department of Transportation's 2050 climate goals. The findings highlight the importance of coordinated planning across energy systems, freight operations, and carbon removal infrastructure. The study provides a foundation for national-scale deployment of carbon-negative freight corridors and sets a direction for future work on regional integration, investment planning, and long-term system resilience
Development of Mobile-based Health Monitoring and Disease Prediction System for Antenatal Care
This study presents the development and evaluation of mobile-based antenatal care support systems for rural pregnant women to enhance quick registrations, medical diagnosis, and consultation with healthcare professionals. The system was analysed and designed using an object-oriented methodology that ensures that different functionalities and requirements are adequately met. This helps to reduce complexity, identify appropriate design elements, and streamline software design. In addition, the user interface implementation was carried out using Java and Eclipse to enable seamless interaction among pregnant women and healthcare practitioners. The results of the evaluations of the system indicated that the system provides a platform for efficient registration, transmission of vital patient information, and quick decision-making by doctors and nurses. Furthermore, the system demonstrated its ability to remind pregnant women of antenatal days, educate them on healthy living during pregnancy, and monitor the vital signs of pregnant women. This facilitates healthcare staff to provide timely personalized medical services for pregnant women, improve the quality of pregnancy management, and improve delivery outcomes
The Role of AI in Enhancing Talent Acquisition
Artificial Intelligence (AI) is revolutionizing talent acquisition, presenting an effective option to ineffective, biased recruitment systems. AI improves sourcing, screening, and candidate interaction, with advantages such as enhanced efficiency and potential bias elimination. Yet major challenges remain, including threats of algorithmic discrimination, data privacy concerns, and dehumanizing the hiring process. The most effective solution is human-focused, utilizing AI to enhance, not eliminate, human recruiters in a "human-in-the-loop" model. This calls for openness and ongoing auditing to take advantage of AI's opportunities and mitigate its threats
Recursive Transaction Hash Chains for Immutable Audit Trails in Mortgage Platforms
Mortgage platforms face increasing regulatory scrutiny and the need for robust audit mechanisms. Traditional audit systems often suffer from mutability risks and lack cryptographic verifiability. This paper proposes a Recursive Transaction Hash Chain (RTHC) architecture that provides an immutable and tamper-evident audit trail across the lifecycle of mortgage transactions. Each transaction's hash recursively incorporates previous transaction hashes, ensuring integrity and chronological verifiability. The proposed architecture supports secure compliance reporting, enhances system accountability, and integrates well with existing digital infrastructures
The digitalization of the agricultural industry has reached an unprecedented level due to the integration of data analytics, AI, and IoT technologies in the industry. With the increased accessibility of real time data for farming operations, numerous opportunities exist to develop yield forecasts, predictive models and optimize the use of resources through the use of real time data. This study examines how data-driven decision-making (DDDM) frameworks for the agritech sector, which integrate big data analytics with precision farming, will reshape the agritech sector. The study reviews existing research and policy documents that were produced prior to June 2022. Specifically, the study identifies key applications of machine learning, data visualization and predictive analytics within decision-making frameworks for agriculture. The study proposes a conceptual model that links data collection, model creation and actionable insights to create enhanced agricultural results. The study found that DDDM not only enhances agricultural productivity but also contributes to sustainable agricultural practices through improved resource utilization and the ability to address climate-related issues. Further, the study identified that DSS and business analytics tools support farmers and policymakers in making timely and informed decisions related to their farm and agricultural policies. The study concludes that for DDDM to be effectively implemented requires technological integration, collaborative institutions and the development of digital competencies. Ultimately, the study demonstrates the potential of data analytics to transform agriculture into a smart, adaptable and sustainable sector capable of addressing global food security concerns
A Data Governance and Analytics-Enhanced Approach to Mitigating Cyber Threats in NoSQL Database Systems
NoSQL databases have transformed data management for modern applications but introduce unique cybersecurity challenges. This research comprehensively analyzes security threats specific to major NoSQL categories (document, key-value, wide-column, graph) based on technical data up to 2022. We identify critical vulnerabilities including NoSQL injection (NoSQLi), insecure configurations, access control flaws, and emerging risks in serverless and containerized environments. Technical analysis reveals 68% of MongoDB breaches originate from misconfigurations (Shodan, 2021), while NoSQLi incidents increased by 121% between 2019-2022 (OWASP data). We examine exploit mechanics such as JavaScript injection via $where operators and BSON deserialization attacks. The paper proposes a defense-in-depth framework incorporating CIS benchmarks, application-level encryption, and real-time query anomaly detection. Findings indicate that 43% of NoSQL deployments lack transport encryption, and 61% use default credentials in development environments. Mitigation strategies include strict schema validation, client-side field-level encryption, and SIEM integration. The research concludes with future directions including homomorphic encryption and formal query verification
A Federated Learning Approach for Predicting Resource Allocation in Multi-Access Edge Computing
In modern networks, where stronger ultra-low latency and data throughput are needed, Multi-Access Edge Computing (MEC) becomes a necessary architecture for 5G/6G networks that support real-time applications. Nevertheless, a dynamic edge ecosystem, diverse device properties, and privacy preservation needs interfere with MEC resource management. This paper proposes a new Federated Learning (FL) framework to predict resource allocation in MEC that removes such barriers by enabling decentralized model training to be performed directly at the network edge. In contradiction to conventional centralized strategies, our approach significantly reduces communication costs by up to 90% while providing competitive performance due to the efficient use of non-IID data at edge locations. Feeding lightweight CNNs and reducing the whole energy demand is achieved by the balanced computational requirements in the design aggregation through FedOpt aggregation. Based on the results of outcome analysis on MNIST and Fashion-MNIST, we observe accelerated convergence, increased energy savings and performance scalability, where energy consumption per training round is 29% lower than in centralized systems. This approach shows impressive results in processing non-IID data due to reliable performance on different edge devices. Such discoveries show that FL has a high potential to transform MEC resource allocation and thus contribute to more adaptive, protected, and efficient edge computing architecture
AI-Driven Cloud Integration and Orchestration for Next-Generation Enterprise Systems
The combination of AI and cloud is revolutionizing enterprise architecture, empowering businesses to automate processes at an unprecedented scale while benefiting from immense cost savings. This study explores AI-enabled cloud integration and orchestration in the next-generation enterprise systems, considering their effects on resource optimization, security reinforcement and predictive analytics. With systematic analysis of the recent cases in healthcare, finance and manufacturing sectors, this work shows that AI integrated cloud orchestration contributes significant performance gains to deployment efficiency, cost reduction and system reliability. The study design is a mixed-methods approach, integrating quantitative performance data with qualitative analysis of factors affecting implementation. As per data, organizations deploying AI based cloud solutions benefit from a reduction in deployment time by 53-70%, a cut down in infrastructure costs up to 28-42%
Enhancing Reliability in Java Enterprise Systems through Comparative Analysis of Automated Testing Frameworks
In complex software environments, Java enterprise systems require strong testing frameworks that help bring reliability and maintainability. This paper provides a detailed comparative study of java-specific automated detectors in finding defects and improving the quality of developed code and the overall reliability of the systems. The research evaluates the performance metrics, defect detection potency, and integration feasibility of various multiple testing frameworks such as JUnit, TestNG, Selenium, and Calabash. Your main goal is to determine the best approaches for testing, which yield the highest degree of reliability while consuming resources the least when dealing with Java applications on an enterprise level. This study uses a qualitative framework with a mixed-methods approach to quantitative performance analysis and to qualitative framework evaluation, detailing empirical data on real-world implementations through Java projects. The approach includes comparison of frameworks based on time taken to run tests, coverage, defect finding and subsequently maintenance overhead. The results show that the performance of the frameworks varies considerably between testing environments, indicating that some frameworks are better suited to specific circumstances than others. Statistics indicate that integrated testing approaches using multiple frameworks produce a 34.7% better defect detection rate than single-framework implementations. The implications of these findings offer important insights for software engineering teams seeking to optimize their testing strategies and, ultimately, improve the reliability of Java enterprise systems by making informed decisions regarding their framework selection and implementation practices
Enhancing CRM Accuracy Using Large Language Models (LLMs) in Salesforce Einstein GPT
Customer Relationship Management (CRM) systems have emerged as essential software to deal with client interaction, enhance customer satisfaction and boost revenues. Nevertheless, conventional CRM systems tend to have problems like lack of fully filled inputs, communication and automation and thus has hindered accuracy of the system and inefficiencies when performing operations. The advent of Large Language Models (LLMs), especially of the transformer type, such as GPT, allows extending CRM platforms to include intelligent, contextual, and adaptive functions. The present paper unravels the implementation of LLMs in Salesforce Einstein GPT printing higher accuracy of the CRM Devision through a more detailed analysis of customer data, the automatic production of responses, prognosis, and deepened communication processes. We discuss an end-to-end system to adopt LLMs during CRM operations, propose architecture and middle-ware layers and perform empirical analysis of real CRM data. We have found that data completeness, product relevancy and customer satisfaction have improved measurably as a result of applying generative AI in our CRM tank settings, and thus demonstrating the transformational potential of the approach. These results are corroborated by a case study of the telecommunication sector that indicates steep improvements in the customer response period and ticket costing rates. This paper ends with the discussion of the challenges, i.e., hallucination, data privacy, and domain adaption and the best practices of deploying LLMs in enterprise CRM systems. These findings indicate that, under adequate governance and integration design, LLMs are capable of increasing CRM precision to the extent that organizations will no longer have to adopt reactive attitudes toward managing their customers but can instead manage them with a proactive and customized approach
Evaluating the Effectiveness of Prompt Engineering in Salesforce Prompt Studio
The appearance of Large Language Models (LLMs) changed the environment of enterprise applications and allowed natural language to be used to automate processes, communicate to the customer, and analyze the data. In this ecosystem, timely engineering has become one of the most efficient definitions of the output quality, particularly in those platforms incorporating instructions written by users to produce text, such as Salesforce Prompt Studio. The present paper explores the effect of various prompt design approaches, i.e. few-shot prompting, template-based inputs, and contextual primes, on the performance and reliability of AI output on Salesforce Prompt Studio. Several use cases are considered in the study, such as customer service automation, lead scoring and email generation. Creating and comparing the variants of prompts based on such metrics as the factual accuracy, relevance, alignment with the tone, the satisfaction of the users, the paper demonstrates an assessable relationship between the prompt design and the quality of the outcome. The results indicate outstanding practices in timely development and provide suggestions on enterprise-scale optimal prompt engineering. The study is relevant to the emerging market of applied LLMs and supplies prompt optimization into the context of realistic business problems and with the evidence of improved productivity and consistency outcomes in CRM settings
Real-Time AI Integration Architectures for HIPAA-Compliant Healthcare Data Interoperability
In the ever-changing world of healthcare technology, the demand for a smooth, secure, real-time data transfer between Electronic Health Record (EHR) systems has become a clinical as well as a legislative mandate. Healthcare providers are facing increased pressure to modernize their integration infrastructure, enabling sophisticated DSS (Decision Support Systems), patient-centric care models, and population health analytics, while maintaining full compliance with HIPAA (Health Insurance Portability and Accountability Act). Legacy healthcare integration patterns, which often rely on batch processing, stove-piped data stores, and static point-to-point connections, are inadequate for the dynamic requirements of contemporary clinical environments, emphasizing low latency, scalability, and data fidelity. In this paper, we present a comprehensive AI-based integration architecture designed and implemented for HIPAA-compliant solutions, as mandated by the Health Insurance Portability and Accountability Act (HIPAA). The proposed approach aims to address the limitations of current architectures by integrating microservices orchestration, event-driven architectural (EDA) patterns, and intelligent data processing through machine learning (ML) and natural language processing (NLP) technologies. Not limited to a traditional approach, the architecture's design targets real-time clinical decision support, secure data-to-data interoperability, and scalable enterprise applications, applicable in scenarios such as large-scale healthcare networks or multi-regional operations. The reference architecture is categorized into five layers: (1) A Data Ingestion Layer that supports interfacing with diverse health systems, including EHR, medical imaging, LIS, and external HIE domains; (2) An AI Processing Layer that features data intelligence via trained ML models, semantic transformation applied by NLP and predictive modeling to anticipate clinical events; (3) An Integration Orchestration Layer that emulates the microservices design pattern for workflow automation and system-wide events; (4) A Security and Compliance Layer, including HIPAA controls, such as access auditing, AES-256 encryption, TLS 1.3, MFA, and RBAC/ABAC model for role/attribute-based access control; and (5) An API Management Layer that exposes RESTful endpoints compliant with HL7 FHIR standards for cross-system compatibilities and governance.The investigation confirms the proposed architecture through its real-world deployment across several Fortune 500 healthcare entities that collectively handle over 100 million patient records. The findings indicate substantial enhancement in operation and clinical quality indicator scores. Patient data retrieval in a distributed system was up to 50–70 times faster as the data access latency was minimized. This measure led to gains of up to 75% in API response times, resulting in more responsive, front-line, clinical-facing applications. The response time to clinical alerts decreased by 70% to 85%, resulting in more timely interventions and ultimately improving patient safety. System availability consistently exceeded 99.9% at all times, a level typically associated with enterprise-class availability. In addition, integration costs per transaction were reduced by 35–55%, resulting in a substantial economic benefit. These results were reinforced by decreases in overall clinical documentation time, as well as by enhancements in care team coordination and the throughput of concurrent outpatient healthcare transactions.The architecture’s HIPAA compliance. Was 100% aligned with HIPAA across all required categories of safeguards, including audit control and access verification, as well as integrity and transmission security. Daily exception alerts for the organization also addressed customer concerns, which were significantly mitigated by automated monitoring and incident alerts that generated short-term notifications (down to 15 minutes), thereby providing active data governance. No violations were observed across multiple years of the evaluation. The TCO analysis revealed a 25-35% reduction over three years, with a sub-18-month ROI for most healthcare organizations. The contributions of this paper are threefold: it provides a scalable and modular reference model for AI-based maintenance of healthcare data integration solutions, demonstrates potential measurable progress in clinical efficacy and compliance, and outlines strategies for operationalising at scale. It also discusses prospects, including federated learning for privacy-preserving AI training on distributed data sources, as well as international standardization of health data about global health data regulations. With intelligence, security, and compliance built in, this framework lays the foundation for healthcare organizations to responsibly process automated ML and AI on their data, addressing patient needs and providing safe and frictionless care.
Automated QA Testing for AI-Generated Game Content: Using LLMs to Validate NPC Behavior and Narrative Integrity
Procedural content generation (PCG) and generative artificial intelligence (AI) has led to the modern generation of games to provide dynamic conversation, procedural non-player character (NPC) behavior, and story-based choices. Despite providing more depth and replayability, it becomes a major challenge to quality assurance (QA) where more non-deterministic worlds are required to be tested with scripts or rules no longer sufficient. The proposed paper will describe an LLM-based QA system, which is meant to be used to mechanize the process of testing narrative consistency, NPC coherence, and rule compliance in AI-generated game content. It encompasses four significant components, namely, a game simulation environment, an LLM validation engine, a logs extraction layer, and a game developer-specific QA report module, which shows that the game-based approach is more efficient and more accurate than the old-fashioned QA, with a higher narrative coherence (95% vs. 82%), behavior coherence (93% vs. 80%), and consistency of rule enforcement. Such results suggest that semantic reasoning may be applied on the basis of LLM and enables scalable and automated tests of QA, involving more players and enhancing a development pipeline
Designing an Effective Governance Framework for AI Compliance in SMEs under the EU AI Act
The EU AI Act, set for enforcement in 2026, imposes stringent compliance requirements on small and medium-sized enterprises (SMEs) deploying high-risk AI systems, such as HR CV screeners. These obligations, while critical for trust and safety, pose resource challenges for SMEs. This paper proposes a modular, cost-effective governance framework tailored to SMEs, aligning with the Act’s six pillars: risk management, data governance, technical documentation, human oversight, transparency and cybersecurity. Drawing on regulatory texts, cost studies and pilot programs like EU sandboxes and European Digital Innovation Hubs (EDIHs), we outline a step-by-step lifecycle covering system inventory, quality management systems (QMS), risk management and continuous improvement. Our framework, tested via simulations with a prototype HR CV screener, reduces compliance costs by 20% and enhances stakeholder trust. This blueprint empowers SMEs to operationalize compliance-by-design, turning regulatory demands into competitive advantages
Vendor Highlights – IoT, AI, and Process Mining
The rapid advancement of digital technology is transforming their company operations, competition & innovation, with the confluence of the Internet of Things (IoT), Artificial Intelligence (AI) & process mining emerging as a highly disruptive trend in the contemporary business landscape. The Internet of Things (IoT) is providing actual time insights into assets, operations & consumer behaviors, whilst Artificial Intelligence (AI) is enhancing intelligence, automation, and predictive functionalities that are beyond many conventional analytics. Process mining enables firms to acquire a data-driven comprehension of these processes, inefficiencies & improve their possibilities on a huge scale. Collectively, these areas are establishing new pathways for digital transformation by enabling organizations to transition from reactive decision-making to proactive and predictive frameworks. Prominent companies are not only providing their specialized solutions but are also progressively establishing their comprehensive platforms that integrate IoT connection, AI-driven insights & process mining tools to achieve these quantifiable business results. This essay emphasizes how manufacturers are maneuvering through these rapidly evolving sectors, their distinctive strategies for market distinctiveness & the potential for interoperability within these ecosystems. Key results highlight the increasing integration of IoT, AI, and process mining as organizations want these comprehensive solutions that enhance operational efficiency & foster strategic innovation. This article provides an overview of vendor strategies & their alignment with corporate requirements, intending to provide business executives, IT practitioners & these decision-makers with practical insights for assessing solutions and formulating adoption roadmaps
Designing Hybrid ETL Pipelines for Multi-Cloud Integration
The growing popularity of multi-cloud infrastructures has led to the need for scalable and interoperable ways to integrate data that is distributed across several platforms. In this project, we outline the design and simulation of a multi-cloud integration ETL pipeline using Python. We train using generated data from public clouds such as, AWS, Microsoft Azure and GCP, into a single cloud-agnostic data warehouse with open source libraries like pandas, SQLite3 and matplotlib. The ETL system was architecture as a set of modules, including extraction, transformation, loading and performance monitoring. Scalability and resource usage were tested in three runs using graduated data volumes. The results indicate that the processing times were between 0.01 and 0.02 seconds but used a fairly constant memory size, ranging from 90.98 MB with 32 records to 91.41 MB for parsing of up to 864 records. These results validate the efficiency and stability of the pipeline, as well as its ability to account for multi-source data integration in a low computational burden. The work shows that local simulation of hybrid ETL systems is possible and gives a scalable and reproducible basis for deployment in real-world multi-cloud infrastructures to come. This work adds to the efforts on data interoperability, performance optimization and cloud-native ETL architecture design
Explainable AI Models for Clinical Decision Support Systems
The data-driven world has led to the intervention of artificial intelligence and clinical decision support systems technologies in the healthcare sector in the decision-making process. However supportive these advanced systems are, they suffer from an issue of “black-box” or unexplainably of their working process, which hinders their utilisation in the health sector. Explainable Artificial Intelligence can mitigate such challenges by providing automation models that are transparent to healthcare individuals about their working procedures, which promises the clinical trust in healthcare systems and aids them in efficient decision-making. The evaluation of the data and information through statistical analysis has outlined that explainable artificial intelligence is the epitome of clinical data accuracy. In addition to this, the evaluation of the data through graphical representation has also outlined that, specifically, gender-based specialisation, the male patients mostly face problems due to respiratory conditions, along with heart conditions, while most of the female patients have faced diabetes. Explainable artificial intelligence has the capability in enhancing the data accuracy of the patients and developing treatments according to it
Clinical Decision Support Systems (CDSS)
Explainable Artificial Intelligence (XAI)
Self‑Learning Bots & Cloud‑Native Platforms
Self-learning bots along with cloud-native platforms are quickly merging and are changing the operational scale of intelligent systems, which are definitely different from one another. Self-learning bots refer to AI-powered decision-making entities that are able to reprogram themselves every time they have to face an environment that is different from the previous one. These are software environments that have been designed to use the flexibility, scalability, and resilience of the cloud through containerization, microservices, and orchestration tools such as Kubernetes. The combination of these two forces gives us the benefit of the best of both worlds: the continuous developmental nature of self-learning bots is completely dependent on the ever-changing, widely spread, and stable infrastructure that cloud-native systems offer; conversely, cloud-native platforms become more autonomous and can respond quicker with the help of AI. Thus, they become able to explore their hitherto unknown potential, for instance, from the very easy automatic scaling of intelligent customer support systems during periods of great demand up to the predictive maintenance of IoT devices by bots, as well as digital assistants that manage workflows across multi-cloud deployments. As an example, the retail case of self-learning bots integrated with a cloud-native backbone enabled real-time personalization for millions of customers, automatically scaling compute resources during seasonal surges while continuously fine-tuning recommendations based on shopper behavior. However, this trend is not just limited to retail; the healthcare, logistics, and financial sectors are also seeing the benefits of this mix and are utilising it for creating virtual health coaches, streamlining supply chains, and detecting fraud in real time. What is more, we can anticipate a future where ecosystems will be more and more autonomous; the bots will not only learn but at the same time will be able to self-deploy, self-heal, and move smoothly across hybrid and multi-cloud lands; thus, enterprises will find themselves on the brink of a whole new era of intelligent, adaptive, and highly scalable digital operations
Hyperautomation and Cloud Robotic Process Automation (RPA) are the two technologies that have a great impact on the digital operations, process optimisation, and enterprise resilience of an organisation. Hyperautomation, which basically extends traditional automation by overwoven RPA with AI, ML, NLP, and advanced analytics, empowers companies to automate those complicated end-to-end workflows that require human judgement and intervention. In tandem, Cloud RPA magnifies these functions by delivering automation as a scalable, flexible, and less costly service in cloud ecosystems; thus, enterprises are less loaded with heavy infrastructure, and they are assured of a smooth deployment in networks. This document delves into two major enquiries: the first one being how hyperautomation changes the enterprise processes fundamentally by implementing smart, adaptable processes and the second is about Cloud RPA and what scalability it offers given the fact that it is different from on-premises solutions. The method used in this paper is a combination of conceptual analysis and real-world case studies that present theoretical insights followed by practical applications that demonstrate how organizations in different sectors—from finance to healthcare—are using these technologies to make their operations simpler. The results from the study emphasise the impact of the work disappearing on efficiency when the level of manual work is decreased, the optimization of the expenditure made possible by the use of the cloud due to the elasticity of the resources, the strengthening of the capacity to recover from disturbances in that the organization can now react quickly and the improvement in the compliance that is due to the fact that the use of AI-driven process management makes it easier to audit
Integrating Salesforce and UiPath: Cross-System Intelligent Automation
In the current enterprise landscape, enterprises are commonly operating with siloed systems which cause customer data stored in Salesforce to be isolated from repetitive back-office tasks that might be automated by RPA. Such a disconnect results in inefficiencies in the workflow, slow decision-making, and higher operational costs. However, the pairing of Salesforce, a top CRM platform, with UiPath, a leader in RPA, gives a very attractive solution to this problem by allowing seamless intelligent automation across systems. A conceptual study combined with a case study method was employed to investigate how the two platforms make this possible and, consequently, how they may improve the business process, facilitate the work of the business team, and provide a better customer experience. Results reveal that through the use of this integration, enterprises can accomplish the automation of the processes that need data transfers, report generation, and case management directly within Salesforce, thus, leading to the great manifestation of efficiency. Furthermore, the UiPath bots can be programmed to run in the background and do the repetitive, rule-based tasks which frees up the workforce to do more critical thinking tasks. Hence, the labor-intensive activities that are by nature cost-intensive are diminished which creates a win-win situation whereby organizations realize tangible cost savings while customers get the opportunity to engage with intelligent workflows which are tailored dynamically to their needs and are thus less prone to operational faults. Moreover, the synergy between two platforms not only contributes to the operational excellence of organizations, but it also plays a significant role in endorsing the strategic value of companies by facilitating the organization's transition to become more agile, data-driven, and customer-centric in their methodology
SYNAPSE-Fed: A Bio-Inspired Framework for Continual, Secure, and Explainable AI
Artificial intelligence systems deployed in the real world must be capable of continual learning—acquiring new knowledge and skills over time without catastrophically forgetting previously learned information. This challenge is particularly acute in decentralized settings where data is streamed and owned by different entities. Drawing inspiration from the brain’s mech- anisms of neuroplasticity, we introduce SYNAPSE-Fed (Synaptic Network Adaptation for Perpetual Secure Explainable Federated Learning), a novel framework designed for robust continual learning in a federated environment. At its core, SYNAPSE- Fed features a meta-learning algorithm that models synaptic consolidation to mitigate catastrophic forgetting. By identifying and protecting network parameters crucial for past tasks, our algorithm preserves existing knowledge while adapting to new data streams. To ensure the integrity of this learning process across distributed silos, we embed our algorithm within a trust- aware federated learning protocol. A dynamic trust metric evaluates each participant’s contribution based on performance, consistency, and their ability to balance knowledge stability with plasticity, ensuring accountability. Finally, we address the critical need for transparency by introducing a quantitative frame- work to optimize the trade-off between the continual learner’s performance and its explainability. We demonstrate through experiments on continual learning benchmarks that SYNAPSE- Fed significantly outperforms existing methods in preventing catastrophic forgetting and shows high resilience in a federated setting with heterogeneous participants
Graph-based Active Learning for Dynamic Industrial Systems with Temporal Evolution
In the era of Industry 4.0, dynamic industrial systems characterized by temporal evolution present significant challenges for anomaly detection and predictive maintenance. This paper proposes a novel graph-based active learning frame- work that integrates time-evolving node features to enhance model performance in such environments. We introduce a trust metric-based federated learning architecture to ensure integrity and accountability across distributed data silos, addressing privacy and collaboration issues in industrial IoT. Additionally, we develop a quantification and optimization framework for the trade-off between model explainability and performance, enabling practitioners to balance these often conflicting objectives. Through extensive experiments on benchmark datasets and simulated industrial scenarios, we demonstrate the superiority of our approach in terms of accuracy, efficiency, and interpretability. Our results show improvements of up to 15% in anomaly detection precision while maintaining high explainability scores. This work contributes to the advancement of machine learning applications in dynamic industrial systems, providing a comprehensive solution that is both practical and theoretically grounded