American International Journal of Computer Science and Technology
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Machine Learning-Driven Threat Analysis for Enhancing Security in Data Center Networking
Cyber threats constitute ill-purposed practices aimed at hacking into computer systems and stealing classified data with individuals and with more frequency, a broad spectrum of organizations as possible targets. This study offers a solution by exploring various machine learning algorithms for predictively analyzing and evaluating cyber threats. This research presents a cutting-edge approach to threat analysis in data center networking using the UNSW-NB15 data, with the goal of improving security. Data cleaning, normalization, feature selection, and balancing were carried out to ensure the model achieved its maximum potential. There is a single binary output feature and forty-three input features in the data. The suggested Deep Neural Network (DNN), along with several DL and ML models like BKP, C-Support Vector Machine (C-SVM), and K-Nearest Neighbors (KNN), were tested. The DNN one is head and shoulders above the competition when it comes to threat recognition and categorization, boasting an amazing ACC of 97.93, PRE of 97, REC of 97, and F1-score (F1) of 97.
Comparative Performance of Machine Learning Techniques for Sentiment and Insight Analysis in Amazon Product Reviews
The sentiment analysis of online product review has been a pressing concern in perception and support of sound decision-making using information provided by e-commerce portals. Through a random forest (RF) classifier and a sophisticated preprocessor pipeline, this paper proposes a useful sentiment classification model of Amazon product reviews. The data collection is made in the beginning of the research process with Amazon Reviews (2018). It is pre-processed in several stages, such as special character removal, stop-word removal, tokenization and part-of-speech (POS) tagging to normalize and standardize the textual data. TF-IDF is an extraction tool which converts text into meaningful numerical processes. RF model is then trained to predict customer sentiment and its ensemble structure is employed to overfitting and maximize predictive stability. The measures of performance in the model include accuracy, precision, recall, F1-score and loss. RF classifier is an excellent accuracy (ACC) rate of 98.83, a high precision (PRE) rate of 97.70, a high recall (REC) rate of 98.64, and a balanced F1-score (F1) rate of 98.17 compared to other traditional and deep learning classifiers such as the Logistic Regression, SVM, NB-SVM, and CNN-RNN. Overall, the RF-based sentiment analysis model is a highly trustworthy, scalable and efficient way of obtaining insights using a huge volume of e-commerce reviews data.
A Review of Frameworks, and Impact on the Software Development Life Cycle with Artificial Intelligence in Software Engineering
Advances in intelligent software engineering (ISE) have unfolded in the last years. The development of intelligence software engineering as a new field that exemplifies collaboration between artificial intelligence (AI) and software engineering (SE) is one of these advancements. The integration of AI capabilities into the Software Development Life Cycle (SDLC) across all phases through machine learning applications and frameworks, adaptive learning, and data-driven decision-making techniques is a recent trend in contemporary software engineering. With reference to the fundamental frameworks of machine learning, agent-based, and knowledge-based systems that enable efficient software planning, development, testing, and maintenance, the article delivers a thorough overview of application of AI in SE. This paper addresses way AI improves requirement analysis, shortens design and coding time, boosts defect prediction and reinforces testing and maintenance processes with AI tools and automated processes. Also, the paper discusses challenges in implementing AI-based solutions, including data quality concerns, integration risks, model explainability, ethical risks, and high computational costs. A deep analysis of existing literature also shows the growing synergy of AI studies and software engineering practice. Combining structures, effects, and constraints, this paper provides insightful critique on the changing relationship between AI and SE and on the way forward in forthcoming developments.
AI Driven Data Quality and Governance Framework for Sap BW/4hana and Sap Business Objects in Multi Cloud AWS Environments
Contemporary businesses within multi-cloud environments involving the use of SAP BW/4HANA and SAP Business Objects experience ongoing inconsistent data quality, fragmented governance, and underdeveloped real-time exceptions. These are due to the inability of classic rule-based validation and manual stewardship model to scale due to an increase in data volumes and the integration of hybrid clouds, which lead to reporting errors, compliance threats and slack decision-making. The current governance frameworks are mostly reactive even with the improvements in the AI-driven analytics and are metadata-isolated and based on the working SAP flows of data. The proposed area of research lacks scientific understanding of anomaly detection via machine learning, predictive quality scoring, and automated policy enforcement that could be integrated in SAP BW/4HANA architectures applied in a multi-cloud setup. The following paper will present a proposal of data quality and governance framework based on AI, which integrates predictive validation engines, policy orchestration that is aware of metadata, and automatically supported root cause into SAP data pipelines. Its approach is a mixture of data profiling, supervised and unsupervised learning models, and governance rule mining, which is deployed on scalable cloud-native approaches on AWS. The experimental analysis indicates a 37 percent decrease in data errors, 42 percent accuracy improvement of anomaly detection, and 55 percent less work on manual governance than on traditional rule-based systems. The proposed architecture delivers (i) smart data quality judging model, (ii) metadata-driven governance control plane, and (iii) scalable multi-cloud implementation map of enterprise SAP environment.
Conversational Analytics Using LLMs: Transforming Enterprise Data Consumption through Natural Language Interfaces
Enterprise data ecosystems are expanding rapidly, creating new opportunities for organizations to derive insights for strategic decision-making. However, many business users struggle to interact with complex data infrastructures because traditional analytics tools require technical expertise such as SQL knowledge and the ability to interpret complex dashboards. This limitation restricts effective data democratization within organizations. Conversational analytics has emerged as a solution by enabling users to interact with enterprise data through natural language interfaces powered by large language models (LLMs). These systems translate user queries into structured database operations, allowing non-technical stakeholders to easily access and analyze data. By supporting interactive exploration, visualization requests, and contextual insights through dialogue, conversational analytics enhances data accessibility, collaboration, and decision-making agility. Despite its advantages, challenges such as query accuracy, model hallucination, data security, and system scalability must be addressed. This study explores the architecture and implementation of LLM-based conversational analytics systems for enterprise data consumption and evaluates their performance using metrics such as query accuracy, response latency, and user engagement. The findings suggest that conversational analytics significantly improves data accessibility and usability compared to traditional dashboard-based systems, making it a promising approach for future enterprise intelligence platforms.
Multi-Cloud and Hybrid Cloud Security Frameworks
The rapid adoption of multi-cloud and hybrid-cloud architectures introduces significant security and governance complexities for organizations seeking agility and resilience. These deployments combine on-premises, private-cloud and multiple public-cloud services, creating a dispersed environment that challenges traditional perimeter-based security models. Research indicates that in multi-cloud environments, fragmentation of control, inconsistent policy enforcement, and visibility gaps increase vulnerability to misconfiguration, unauthorized access, and data breaches [9, 10]. Further, hybrid-cloud settings amplify these issues as enterprises must manage both legacy systems and diverse cloud platforms under unified governance. To address these evolving threats, this study evaluates existing security frameworks such as the Cloud Security Alliance’s Cloud Controls Matrix and the National Institute of Standards and Technology Cybersecurity Framework in the context of multi-provider, hybrid environments, assesses their strengths and gaps, and proposes an integrated AI-driven security framework tailored to hybrid/multi-cloud systems. The proposed model emphasizes unified identity and access management, encryption and key lifecycle across providers, continuous monitoring via machine-learning anomaly detection, and dynamic policy orchestration. Key implications suggest that enterprises adopting multi-cloud/hybrid strategies should prioritize interoperability and automation to sustain strong security postures while maintaining business agility. Directions for future research include empirical validation of the model via case studies and exploring the impact of emerging threat vectors (e.g., AI‐enabled attacks) on cloud-native frameworks.
Cloud Compliance and Regulatory Standards
Cloud Security Posture Management (CSPM)
Incident Response in Multi-Cloud Environments
Neuro-Symbolic AI: Combining Learning and Reasoning
The field of artificial intelligence has long been divided between two paradigms: data-driven, connectionist approaches such as deep learning, and knowledge-driven, symbolic reasoning systems. While deep learning has achieved remarkable success in perception tasks like image recognition, natural language understanding, and speech processing, it often struggles with abstract reasoning, logical inference, and systematic generalization. Symbolic AI, in contrast, excels at explicit reasoning, knowledge representation, and interpretability but lacks the ability to learn effectively from unstructured, high-dimensional data. Neuro-symbolic AI represents an emerging paradigm that seeks to integrate the strengths of both approaches, combining the pattern recognition capabilities of neural networks with the logical reasoning power of symbolic systems. By unifying learning and reasoning, neuro-symbolic AI aims to create AI systems capable of robust decision-making, explainable inference, and generalization across diverse domains. This article presents a comprehensive exploration of neuro-symbolic AI, covering its theoretical foundations, architectural frameworks, learning methodologies, applications in natural language understanding, robotics, and knowledge-based reasoning, as well as challenges, ethical considerations, and future research directions. The fusion of symbolic reasoning with neural computation promises to advance the development of general-purpose intelligent systems that are both data-efficient and capable of human-like reasoning.
Self-Supervised Learning for Scalable AI Systems
The rapid expansion of artificial intelligence across industries has been fueled largely by advances in data-driven machine learning. However, traditional supervised learning approaches rely heavily on large volumes of labeled data, which are expensive, time-consuming, and often impractical to obtain at scale. As AI systems are increasingly deployed in diverse and data-rich environments, the limitations of annotation-dependent learning have become more evident. Self-supervised learning has emerged as a transformative paradigm that addresses these challenges by enabling models to learn meaningful representations from unlabeled data. By leveraging inherent structures and patterns within raw data, self-supervised methods generate supervisory signals automatically, eliminating the need for manual labeling while preserving scalability and adaptability. This article explores the theoretical foundations, methodological innovations, architectural considerations, and real-world applications of self-supervised learning in the development of scalable AI systems. It examines how this paradigm enhances generalization, supports multimodal intelligence, reduces computational and labeling costs, and enables AI to operate effectively in dynamic and low-resource environments. Furthermore, it discusses the challenges, ethical considerations, and future research directions shaping the evolution of self-supervised learning as a cornerstone of next-generation artificial intelligence.
Federated AI and Big Data Architectures for Global Healthcare Collaboration
Advances in Big Data architectures and AI techniques based on Federated Learning improve the opportunities for collaboration in healthcare for use cases such as pandemic surveillance and precision medicine. Big Data infrastructures have recently evolved from classical data warehouses to include data lakes and data hubs. Fleets of data lakes and data hubs form a cloud of trust, enabling federated computing across organization boundaries. These concepts provide the foundations for a Big Data architecture for global healthcare based on semantically compliant integration of data hubs and lakes using open data exchange and description standards created by the international community. Recent developments in open-source software libraries for Federated Learning support data protection and data sovereignty with minimal data exposure to other organizations. Proposed solutions combine the benefits of Federated Learning with the elastic, self-service, and pay-as-you-go aspects of the public cloud for AI in healthcare.Future developments target global health, with specific applications to pandemic surveillance and response, and to precision medicine and genomics. The evolution of architectures for AI in health, based on Federated Learning and Federated Reinforcement Learning, is guided by the cross-functional collaboration model FR-FA-CFCM. The model formalizes the rules, roles, and mechanisms for co-creating software solutions among different stakeholder roles across organization boundaries, on-premise and in the cloud.
Cross-Organizational Federated Computing
Elastic Self-Service Data Infrastructure
Distributed Healthcare Intelligence Systems
Distributed Data Engineering Models for Real-Time Fraud Monitoring in FinTech Systems
Fraud in FinTech systems, namely in-banking and insurances represents a complex and multifactorial phenomenon that has been further amplified by social distancing and confinement measures to cope with the COVID-19 crisis. According to selected reports, fraud in Europe alone is estimated at 2.2 billion Euros for 20224 and is expected to continue its otherwise upward trend in different markets. To mitigate risks and minimize losses, private and public organizations invest continuously on fraud monitoring systems. However, fraud crime continuously adapts its detection methods and hence, systems are still required to evolve in the fraud investigation and detection area. To facilitate benchmark and comparison of different approaches, the application of an open-source distributed and scalable data engineering model is applied to create fraud monitoring systems in a FinTech environment. Fraud monitoring systems in distributed environments require different and specific execution models in an integrated and open-source environment. Data engineering platforms allow integration of different data sources and types with the possibility of executing distributed data engineering tasks in batch, streaming, micro-batch and real-time modes. An integrated analytical environment is used to support fraud monitoring in enterprises outside the banking sector. GDPR and data anonymization measures are used to guarantee the data privacy with a clear impact of the climate change index in a fraud model. Detection of anti-money laundering events related to COVID-19 is also presented and discussed. Finally, a case study in the European Banking Federation area is further analyzed.
AWS Cloud Architecture for Scalable Healthcare Contact Centers
Many healthcare organizations are using contact center infrastructure technology to address patient needs, including administration of visits, clinical triage, insurance eligibility and response to emergencies. Customary, on-premise PBX offerings are not designed for the high volume of calls that a healthcare environment generates, the capital expenditure required, or variations in demand. This article explores our managed cloud services for scalable, resilient, and HIPAA-compliant patient communication platforms for cloud-native contact centers, which include the architecture for a cloud-native contact center that utilizes cloud-based telephony and routing, a cloud-native backend orchestration platform, and conversational AI for automated patient engagement and clinical NLP for structured information extraction. Security architecture features include encryption, role-based access control, role-based network isolation and auditing for compliance. Resilience engineering features include deploying in multiple availability zones, failover to multiple regions, and replication of data to guarantee that a system is always available to users. The article also mentions that cloud-native architectures enable healthcare organizations to elastically scale to demand, remain compliant with regulations with integrated security frameworks, and transform their operations by moving away from maintaining infrastructure to improving patient experience and clinical workflows.
Healthcare Cloud Contact Center Architecture
Healthcare Disaster Recovery Cloud Computing's Conversational AI Patient Engagement
Intelligent Workforce Management: A Predictive Analytics Approach
Customary methods of managing the workforce are often inefficient, relying on simple forecasting methods, manual scheduling, and reactive actions to manage the expected workload․ Overstaffing, understaffing, and inefficient resource allocation are common issues․ This article explores the opportunities created by artificial intelligence and machine learning for workforce optimization in service-oriented industries. The three technical sub-areas are predictive demand forecasting approaches, clever skill-based scheduling, and the related performance, service quality and employee well-being analysis. Demand forecasting includes the modeling of seasonality using time series methods such as Long Short-Term Memory networks (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models as well as the prediction of surges when unexpected demand spikes occur․ The skill-based scheduling problem gives an overview of multi-dimensional human competency management frameworks and mixed integer linear programming optimization approaches that focus on productivity, compliance and ergonomics. The recent peer-reviewed literature supports that the optimization of the workforce in a sustainable manner depends on a combination of technological capabilities and organizational culture, leadership commitment, and digital preparedness. For organizations to maximize productivity and ensure the health of their employees, human-centered design must be at the crux of AI predictions. The article concludes that AI-driven workforce optimization represents a model shift from administrative labor management to a performance-oriented labor management capability․
RAN-AI Architectures Supporting Personalized Customer Interaction and Virtual Assistance in Banking Services
The integration of Radio Access Network Artificial Intelligence (RAN-AI) into digital banking transforms wireless financial services by enabling low-latency, secure, and highly personalized customer interactions at the network edge. This paper proposes a multi-layer RAN-AI architecture comprising Edge AI Controllers, Contextual Recommendation Engines, QoS-aware Resource Orchestrators, Federated Learning Modules, and Regulatory Compliance Units. The framework combines federated learning, deep reinforcement learning, and QoE-aware scheduling to optimize latency, throughput, and personalization accuracy under regulatory constraints. System-level simulations across dynamic banking scenarios—such as real-time advisory, chatbot transactions, fraud alerts, and omnichannel services—demonstrate up to 28% higher personalization accuracy, 35% lower latency, 22% improved QoE, and 31% reduced computational overhead compared to centralized AI models. The proposed architecture ensures privacy-preserving learning, regulatory compliance, scalability, and efficient wireless resource allocation, positioning RAN-AI as a key enabler of secure, intelligent, and QoE-driven digital banking ecosystems.
Contextual Recommendation and Assistance
AI-Enabled Data-Driven Decision Frameworks for Enterprise Platforms and Tactical Defense Wireless Networks
The use of Artificial Intelligence (AI) has become an enabler of transformation when it comes to making data-driven decisions on the enterprise platforms and the tactical networks of defense wireless networks. The growing complexity, magnitude and heterogeneity of the contemporary data ecosystems require smart systems that could encompass real-time analytics, uncertainty modeling and adaptive decision making. The paper includes a detailed AI-powered data-driven decision framework incorporating cloud-native data structures, real-time decision platforms, learning-assisted intelligence, and explainable AI technologies to enable the mission-critical enterprise and defense missions. The high-dimensional data processing, multi-mode data fusion, dynamic network conditions, and uncertainty in adversarial environments are some of the issues that are tackled in the proposed framework. With the use of deep learning models, Bayesian inference, and reinforcement learning, the framework is capable of predictive, prescriptive, and autonomous decisions and retains interpretability and trust. Special priority is given to tactical defense wireless networks, as latency requirements, disconnected connectivity, and security threats impose resilient and adaptive AI solutions. It is a research study that gives a descriptive architecture design, methodological establishment and analytical assessment of the AI-based decision systems. The outcomes show the essential increases in the accuracy of decisions, the decrease in the latency, and the system robustness in contrast to the traditional rule-based and non-adaptable analytics solutions. The results provide the basis of implementing smart, scaled, and interpretable decision schemes in the enterprise and defense settings.
Bayesian Inference And Uncertainty Modeling
Dynamic State Estimation of Power Systems Using Deep Learning and PMU Data
Dynamic State Estimation (DSE) is vital to the real-time and robust operation of the contemporary power systems especially during dynamic operating conditions due to the incorporation of renewable energy sources, variability of load, and network disruptions. The conventional methods of DSE, which are mainly founded on the Kalman filtering and observer-based methods, are highly dependent on proper mathematical models of the development of the system. These models are however, not always easy to get or sustain because of the system nonlinearities, lack of parameters, and changing topologies. Massive application of Phasor Measurement Units (PMUs) has made it possible to make high-resolution time-synchronized measurements, leading to a move away towards data-driven estimation methods. The recent years have witnessed deep learning (DL) as a potent alternative to dynamic state estimation, which provides good nonlinear modeling ability and resistance to uncertainties without explicit models of the system. The paper gives a detailed review of the dynamic state estimation of power systems with emphasis on the deep learning methods inferred on the PMU data. The development of the traditional Kalman filter-based approaches to the modern deep learning based systems is discussed in a systematic way. Different deep learning networks, such as recycling neural network, long short-term memory networks, convolutional neural networks, graph neural networks, and transformer based networks are discussed and compared. The essential issues concerning the PMU data quality, scalability, usability, and real-time implementation are addressed. Lastly, the actual research problems and future outlook are determined as an indicator of further developing deep learning-based dynamic state estimation.
Seismic Fault Detection Using Convolutional and Transformer-Based Models
Seismic fault detection is a critical component of subsurface characterization, reservoir evaluation, and geohazard analysis. Traditional approaches including manual interpretation, edge detection filters, and seismic attributes have contributed significantly to structural mapping but remain limited by subjectivity, sensitivity to noise, and challenges in handling complex 3D seismic volumes. Recent advances in deep learning have introduced powerful alternatives, particularly convolutional neural networks (CNNs) and transformer-based architectures, which can learn hierarchical and long-range structural patterns directly from seismic data. This review provides a comprehensive synthesis of studies employing CNNs, multi-scale networks, U-Net variants, attention modules, and early transformer models for automated fault detection. We examine their principles, architectures, datasets, evaluation metrics, strengths, and limitations in comparison to conventional methods. Furthermore, we analyze the role of hybrid CNN–Transformer frameworks and discuss challenges such as limited labeled data, computational constraints, and generalization across geological settings. Finally, we outline future research directions, including semi-supervised learning, domain-informed modeling, and scalable architectures suited for high-resolution 3D seismic interpretation.
Vertex AI Agent Builder for Regulated Environments
Healthcare, government, and financial sectors have tried to use A.I. their efforts met with various difficulties in compliance, data governance, and explainability, among other issues. The company does not only deliver a powerful and expandable infrastructural framework for building A.I. systems that are regulation-compliant through the use of Google's Vertex AI Agent Builder but also retains its agility. Transparency, auditability, and policy compliance are the features that organizations can build into AI agents with the Vertex AI environment. The environment supports model management, explainable AI (XAI), and data lineage. The architecture given describes onboard data processing, differential privacy, and human-in-the-loop governance in modular pipelines. These pipelines managed by Agent Builder, and Cloud Audit Logs are there for traceability by providing support. The system's architecture, therefore, becomes the main driver for compliance effectiveness, accountability, and trust, thus enabling companies to be in a position to accelerate the responsible AI deployment without compromising their compliance obligations. The paper, therefore, suggests further investigation of interoperability, continuous risk monitoring, and policy harmonization across different regulatory frameworks. It points out the significance of Vertex AI Agent Builder as an enabler for the scaling of compliant AI.
Progressive Delivery for Models with Quality KPIs
Progressive delivery, a technique that has been in use for a long time in software engineering, gradually gets essential applications in ML and AI model deployment scenarios, where the risks of performance drift, ethical lapses, or bias amplification require cautious and measurable release strategies. This article discusses the use of gradual delivery concepts in ML models, which are mainly controlled, incremental rollouts regulated by well-defined Quality Key Performance Indicators (KPIs). Progressive delivery allows for staged exposure thus, a model is not immediately deployed to full production, but it is exposed only to limited user segments or traffic percentages from the very beginning and the real-time validation metrics such as precision, latency, fairness, and stability are used to make the decisions on promotion or rollback. Before they may get a chance to interact with the model, progressive delivery principles ensure that ML-based changes are tested, verified, and monitored for quality on a smaller scale and with the possibility of rapid rollback. Accordingly, the paper offers a well-thought-out approach that mixes CI/CD principles with ML observability frameworks to build a smart, feedback-driven delivery pipeline. The paper presents a case study that shows how companies can put this framework into practice, managing the trade-off between innovation speed and performance assurance. The experimental evidence conveys that the suggested model deployment pipeline not only encourages implementation trust but also leads to the performance anomalies' early and quick recovery, eventually raising model robustness and business impact to higher levels. Ultimately, the paper conceptualizes progressive delivery as a core capability for the deployment of responsible AI through this synthesis of engineering rigor and statistical governance, thus enabling a smooth transition from experimentation to production at scale.
Hermetic ML Environments using Conda-Lock and Docker
The rising machine workflow complexities have been responsible for the environment managing to put, among the biggest challenges, the data scientists and engineers, who encounter problems such as mismatched package versions, dependency conflicts, and non-reproducible builds that can even derail well-conceived projects. The need to ensure hermetic environments, i.e. fully sealed, deterministic, and reproducible setups, has become a must for organizations that want to deploy ML pipelines at a large scale and do it with safety. One of the works presented is a method that can effectively construct such environments by merging the tool Conda-Lock, which generates platform-specific, fixed-dependency lockfiles, with Docker, which gives containerized isolation and portability across systems. Indeed, these two together allow the user to have a consistent and repeatable method, not to be bothered by “it works on my machine” issues, to be protected from dependency drift influence, and to encourage teamwork with more speed. By turning the exact dependencies at the Conda level into a lock and wrapping them in the lightweight Docker images, the team managed to create a flow of development, testing, and production environments that are the same overall without losing flexibility or performance. It describes the achieved results as follows: quicker introduction of new team members, environmental debugging time that was halved, and CI/CD integration that was more stable, leading to iteration cycles being shortened along with deployment stability increasing. This methodology, besides the case study, gives the understanding of how careful dealing with the environment can mellow the ML operations to become more reliable, predictable, scalable, and, last but not least, safe. In doing so, it shows that investing in hermetic environments is not only a matter of technological neatness but it is also a strategic enabler for building trustworthy and maintainable machine learning machines.
Breaking or Reinforcing the Cycle? Longitudinal Impacts of Bias-Correction Techniques on Feedback Loops and Sustained Financial Inclusion in Machine Learning Credit Scoring
In machine learning–driven credit scoring, fairness interventions can produce unintended long-term effects because models reshape borrower behavior and data over time. Building on Juan C. Perdomo et al.’s performative prediction framework and Pagan et al.’s classification of feedback loops (sampling, feature, outcome, and model loops), this study examines how bias-correction techniques interact with dynamic lending environments. Using 1.2 million U.S. loan applications (2018–2024) plus synthetic emerging-market simulations, we modeled multi-cycle credit systems where decisions feed back into future borrower data. We evaluated adversarial debiasing, pre-processing reweighting, causal proxy mitigation, and threshold adjustments across metrics such as demographic parity, AUC-ROC, Brier scores, credit score progression, and inclusion indices. Results show stark trade-offs. Simple threshold adjustments increased initial approvals for Black and Hispanic applicants by 12–15%, but by later cycles, feedback effects widened disparities by 22% due to proxy discrimination and degraded alternative data. In contrast, dynamic resampling aligned with feedback-aware modeling sustained an 18% equity uplift with less than 3% rise in default rates, even under downturn simulations. Overall, static fairness fixes can backfire. Longitudinal, system-level design—combined with multi-horizon stress testing and richer data-sharing—is essential to achieve durable financial inclusion rather than short-term fairness gains.
Retrieval-Augmented Generation: Enhancing Reliability in Large Language Models
Large Language Models (LLMs) have achieved remarkable success in diverse natural language processing tasks, yet a critical challenge remains: the tendency to hallucinate or generate plausible but factually incorrect information. Retrieval-Augmented Generation (RAG) offers a promising approach by combining LLMs with external retrieval systems to ground generation in factual evidence. This paper reviews key developments in RAG architectures, analyzes empirical results demonstrating improved factual accuracy, and discusses technical challenges related to retrieval quality, latency, and adversarial robustness. We further identify open research areas such as scalable retrieval, multi-hop reasoning, and trustworthy human-AI collaboration. The survey synthesizes foundational research and practical insights, providing a comprehensive understanding of RAG’s role in advancing reliable AI-generated knowledge.
The Future of Cloud Solutions Architecture: Leveraging Databricks Gen AI Tools for Scalable, Secure, and Efficient Systems
The selection of cloud computing plans by organizations becomes necessary due to modern technology requirements for secure flexible automated products which manage their business operations. Cloud solutions architecture reaches maximized potential using Databricks (Gen AI) tools which provide faster automated workflows and improved security features and cheaper costs. Research of Databricks Gen AI solutions for cloud infrastructure in financial institutions and healthcare providers and retail sector relied on expert perspectives along with qualitative-quantitative data analysis. The system obtained better reliability and speeded up processes with reduced operational expenses and resource usage based on analytical results. The implementation of Databricks AI features gives organizations automatic demand response management features combined with resource control and processing speed improvement as well as GDPR/HIPAA security protection for data protection. Databricks solution provides organizations with essential operational advantages that enable them to create adaptable computer systems by using intellectual process management techniques. The Databricks Gen AI tools help build sophisticated cloud systems that feature automatic operational maintenance features and security features to improve business performance according to the document.
Cyber-Resilience of Oracle Cloud Financial Systems: Secure Design for Financial System Resilience
This has caused financial institutions to rely more on cloud-based infrastructures to carry out their mission-critical activities due to the fast digitalization of financial institutions. Oracle Cloud Infrastructure (OCI) has become one of the platforms that have worked in favor of the financial systems as as far as enhanced security and performance features are concerned. Nevertheless, the increasing complexity of cyber threats will dictate an integrated framework of cyber-resilience that will guarantee continuity, integrity, and availability of financial services. The current paper researches the cyber-resilience of Oracle financial systems based on cloud and offers a secure architecture model to build operational resilience. The research incorporates concepts of cybersecurity, risk management, fault tolerance, as well as business continuity to come up with an all-inclusive resilience architecture. A multi-level security model involves managing the identities, encryption, redundancy, two-fourths watch and automated recovery is examined. Moreover, this study measures currently used resilience mechanisms in OCI and contrasts them to the industry standards and regulatory provisions. To prove the proposed framework, a mixed-method approach of qualitative analysis and quantitative simulation is used. Empirical findings confirm that there are enhanced system availability, decreased recovery time purposes (RTO), and a high level of protection against cyber incidents. The results can help to build confidence in the cloud-based financial systems and give useful advice to the system architectures and policymakers. The paper provides a roadmap to resilient finance-based systems, which can sustain cyber disruption and regulatory compliance and efficiency.
Change Management and Organizational Alignment in Oracle Cloud ERP Implementation
Enterprise Resource Planning (ERP) systems are today a tactical pillar of any company that intends to integrate its operations with real-time decision-making and automation. Oracle Corporation Cloud ERP is one of the most recent ERP systems that are cloud-native (in other words, based in the cloud) and covers financial, procurement, project management, supply chain, and human capital management. Though technical abilities of Oracle cloud ERP are well documented empirical evidence continues to show that ERP failures have been largely blamed not on technical incompetency, but rather on to poor change management and mismatch of structures, processes, and culture between the organizations. This paper explores how change management and alignment of an organization is crucial towards the successful implementation of Oracle Cloud ERP systems. It highlights the fact that ERP implementation is not a simple software implementation project, but a radical rebranding of an organization shifting the way business works, positions, system of governance and hierarchies of decision making. These factors are likely to sabotage the ERP initiatives with resistance to change, no executive sponsorship, poor communication and mis-aligned organizational objectives leading to cost overruns, schedule overruns, and overly expectant gains. The paper reviews literature on ERP change management that was published and combines the classical theory of change and implementation frameworks of ERP systems. An integrated approach is suggested, which involves the use of stakeholder analysis, preparedness analysis, communication planning, training plans, and performance measurement. The study provides emphasis on the role of organizational alignment in strategy, structure, processes, and people as a moderating factor between the ERP adoption and the achieved business value. Findings presented in this paper support the above claim by showing that organisations that embrace change management activities through formal mechanisms have considerably high rates of ERP adoption, better user satisfaction and improved operational performance. Comparative percentage-based analysis shows an actual tangible effect of structured change management on the implementation success. The paper will conclude on presenting managerial implications and best practices of aligning organizational capabilities with Oracle Cloud ERP initiatives to enhance the chances of achieving sustainable transformation, and with timeless value realizations.
Deep Learning Architectures Deployed on Cloud Platforms for Dynamic Financial Risk Evaluation and Market Prediction
The financial services industry requires accurate, real-time risk assessments and market predictions amid increasingly volatile global markets. This research investigates deep learning model deployment within cloud computing environments to enable scalable and timely financial risk analysis and market forecasting. Traditional forecasting methods prove inadequate for processing massive volumes of heterogeneous financial data. We examine cloud-based deep learning frameworks leveraging distributed computing and advanced neural network architectures including RNNs, LSTMs, and transformers for time-series analysis, credit risk assessment, and volatility prediction. The study evaluates real-time market data processing incorporating historical prices, trading volumes, and sentiment analysis while addressing challenges including data security, latency requirements, and regulatory compliance. Empirical evaluation demonstrates that cloud-based deep learning systems achieve superior prediction accuracy, reduced latency, and enhanced adaptability to market changes, providing financial institutions with powerful tools for proactive risk management and competitive advantage.
Structured And Unstructured Financial Data Ingestion
High-Net-Worth Individual Decision Support
Probabilistic Forecasting And Uncertainty Quantification
From Ethical Principles to Technical Safeguards: A Unified Framework for Safe and Human-Centered Artificial Intelligence
The growing use of artificial intelligence in healthcare, employment, and business has raised significant concerns regarding safety, ethics, and societal impact. While prior work offers ethical guidelines and technical solutions independently, a gap remains between ethical principles and practical system design. This study presents a unified framework that integrates ethical values, technical safeguards, and governance mechanisms to support safe and human-centered artificial intelligence. The framework maps principles such as fairness, accountability, transparency, and non-maleficence to engineering practices including safe reward design, explainable models, robustness testing, and privacy-preserving techniques. Governance and regulatory alignment are incorporated as continuous components of the AI lifecycle. Use-case analyses demonstrate how ethical objectives can be operationalized across real-world domains. The results emphasize that AI safety must be treated as a socio-technical process combining technical alignment with institutional oversight. This work contributes a practical approach for translating ethical commitments into trustworthy and resilient AI systems.
Predictive Reliability Engineering for IoT Devices Using Deep Learning-Driven Telemetry Analytics and Observability-First SRE Methodologies
The emerging need to build infrastructure networks that are highly reliable and self-monitoring and resistant to failure has enhanced the development of Internet of Things (IoT) systems and devices. Conventional reliability engineering techniques, mainly rule based, reactive, and threshold driven, have difficulty in handling the scale, heterogeneity and real time variability of the contemporary IoT telemetry streams. In order to overcome the barriers, this paper suggests a predictive reliability engineering system that combines deep learning-based telemetry analytics with an observability-first Site Reliability Engineering (SRE) approach. It sends high-velocity device operating metrics, system logs, and sensor measurements through the framework and uses state of the art neural networks such as LSTM and Transformer based sequence models to predict anomalies, forecast failures and estimate device health scores. A layer of observability-first SRE has standardized SLIs/SLOs, is driven by automatic error-budget policy, and coordinates self-healing executive functions via event-driven remediation. Experimental results show that early-failure detectors based on experimentation do markedly better than classical statistical baselines in accuracy of early-failure detection, anomaly recall and prediction lead time. Deep learning structures and SRE governance with Mean Time to Recovery (MTTR) and uptime improvements, and operational resilience across large-scale internet of things applications means faster recovery and better operational availability. The suggested methodology proposes a non-dwarfed, data-driven roadmap towards the attainment of predictive, autonomous, and reliability-focused operations of IoXT.
Resilient IoT Infrastructure Engineering: A Data-Intensive and SRE-Aligned Approach for Reliability-Centric Device Management, Monitoring, and Security Automation
The blistering development of Internet of Things (IoT) ecosystems has added pressure to the requirement of resilient, secure and continuously observable device infrastructures through scale. Old IoT platforms are known to have siloed monitoring systems, manual fault management and non-dynamic security settings that, in addition to high downtime, predictability of reliability and higher vulnerability of cyber threat. The paper has offered a data-intensive and Site Reliability Engineering (SRE)-congruent framework to design resilient internet of things infrastructures with reliability-focused device management, integrated observability and automated security compliance. The suggested architecture incorporates four central pillars, in the form of: (1) a high-throughput telemetry pipeline with support of distributed data ingestion and real-time analytics; (2) an SRE driven with a reliability model featuring service-level objective (SLOs), service-level indicator (SLIs), and adaptive error budgets to device fleets; (3) an intelligence powered monitoring plane that integrates anomaly detection, drift tracking and predictive failure modeling; and (4) an automated security control plane enforcing never relenting compliance, vulnerability scoring, and access. We confirm the framework as experimentally tested with an emulated edge-cloud IoT environment, which shows that the framework improves on mean-time-to-detect (MTTD), mean-time-to-recover (MTTR), uptime compliance with the set SLOs, and security incident propagation is reduced. Findings indicate that the solution under consideration dramatically increases the resilience of operations and decreases the number of human interventions as well as offers the scalable, self-healing, and security-aware management of devices provided needed in large-scale IoT projects. This writing sets a single design of the reliability-focused IoT infrastructure design comprising new SRE values alongside information-driven automation.
Sector-Specific Digital Adoption in Retail, Manufacturing, and Service SMEs
This study examines sector-specific patterns of digital adoption among small and medium-sized enterprises (SMEs) in the retail, manufacturing, and service sectors. The purpose of the study is to analyze how digital technologies are adopted differently across sectors and to assess their impacts on operational efficiency, customer engagement, and business performance. A qualitative and descriptive methodology was employed, drawing on existing literature, industry reports, and selected case examples to compare digital tools, adoption drivers, and challenges across the three sectors. The findings reveal that retail and service SMEs demonstrate relatively high levels of digital adoption, driven by the need for customer interaction, market expansion, and service efficiency, while manufacturing SMEs adopt digital technologies more gradually due to higher costs, technical complexity, and infrastructure constraints. Across all sectors, digital adoption contributes positively to productivity, competitiveness, and scalability, though barriers such as limited digital skills, cybersecurity concerns, and financial constraints persist. The study concludes that digital transformation in SMEs is highly sector-dependent, and targeted policies, capacity-building initiatives, and sector-specific digital strategies are essential to maximize the benefits of digital adoption.
Small and Medium-Sized Enterprises (Smes)
Apache Spark with Java: Architecture, Performance, and Use Cases
Apache Spark has emerged as a widely adopted framework for large-scale data processing due to its in-memory computation model and scalability. This study investigates the architecture, performance characteristics, and practical applicability of Apache Spark applications implemented using the Java programming language. The primary purpose of this research is to evaluate how different workload types perform under varying data sizes and to assess the suitability of Java for enterprise-scale Spark deployments. The methodology involves executing batch processing, SQL-based analytics, and iterative workloads on a distributed Spark cluster while measuring execution time, memory utilization, and performance variability. Each experiment was conducted multiple times to ensure consistency, and statistical analysis was applied to assess performance stability across runs. The results indicate that execution time increases proportionally with dataset size across all workloads. SQL-based workloads demonstrate lower execution times compared to batch and iterative workloads, while iterative workloads exhibit higher memory consumption. Performance variability across repeated executions remains low, indicating stable and predictable system behavior. In conclusion, the findings confirm that Apache Spark with Java provides scalable and reliable performance for large-scale data processing tasks. The study highlights the effectiveness of Spark SQL for analytical workloads and underscores the importance of workload-aware resource tuning, particularly for computation-intensive applications. These results support the continued use of Java-based Spark solutions in enterprise environments.