International Journal of Emerging Trends in Computer Science and Information Technology
Anomaly Detection and Fault Prediction using ML in Telecom Operations
Telecommunication networks generate massive volumes of heterogeneous data, making timely fault detection and proactive failure prevention increasingly challenging. Traditional rule-based monitoring systems lack scalability and adaptability to the dynamic behaviors of modern networks. Recent advancements in machine learning (ML) have demonstrated strong potential for automating anomaly detection and predicting network faults before service degradation occurs. This study investigates ML-driven methods, including Isolation Forest, autoencoder-based deep learning models, and gradient-boosting algorithms, for identifying anomalous Key Performance Indicator (KPI) patterns and forecasting node-level faults. Using operational data collected from multi-vendor telecom environments, the proposed framework achieves improved detection accuracy and earlier fault prediction compared to statistical baselines, aligning with industry trends toward autonomous networks and self-organizing capabilities (Zhang et al., 2020; Chiaraviglio et al., 2021). Results show that deep learning approaches, particularly LSTM and autoencoders, outperform traditional models in capturing temporal dependencies and subtle fault signatures (Kim & Park, 2022). The findings highlight ML's effectiveness in reducing false alarms, minimizing network downtime, and enhancing operational efficiency. This research contributes to ongoing efforts to incorporate intelligent automation into telecom operations, supporting the evolution toward predictive maintenance and resilient 5G/6G networks (Li et al., 2023)
Proactive AI Systems: Engineering Intelligent Platforms that Sense, Predict, and Act
Proactive Artificial Intelligence (AI) systems are paradigm shifts in the old models of computations based on traditional reactive and rule-driven computational frameworks to intelligent platforms that can sense continuously, reason predictively and act autonomously. The traditional AI applications will normally react on explicit user inputs or preset events and this limits the effectiveness of such applications in very dynamic, unpredictable and complex environments. Conversely, proactive AI systems are designed to infer the future, detect new risks and opportunities and apply context specific interventions without having to be prompted by humans. This is becoming more important in areas like smart cities, health care, cybersecurity, industrial automation, financial systems and intelligent governance where the timeliness of response may have serious economic, social or safety impacts. In this paper, I will provide an in-depth engineering analysis and analytical examination of proactive AI systems with the perspective of understanding the architectural basis, algorithmic underpinning, and system-level design principles to facilitate machine sensing of environment signals, forecast the future, and make autonomous decisions. The paper integrates multi-artificial knowledge in machine learning, data engineering, control theory, cognitive computing, and distributed systems to establish a cohesive approach to proactive intelligence. This paper suggests a multi-layer system architecture which combines perception layers that obtain real-time data, predictive intelligence layers that predict and evaluate risks, and action layers that make autonomous decisions and adaptive changes. An extensive literature review shows how proactive intelligence has developed over the years since the first rudimentary expert systems and models of control to the current state of deep learning-powered anticipative systems. The methodology section makes the proactive AI lifecycle official, presents the mathematical models of the predictive decision-making, and describes the mechanisms of learning that facilitate self-improvement and optimization on the basis of feedback. The experimental outcomes and discussions reveal proactive AI systems to have the benefits of fast latency of responding, good predictive power, functional resilience, and scalability as compared to reactive AI systems. Lastly, the paper discusses the ethical and governance and deployment issues and recommends future research directions in the area of trustworthy, explainable, and human-compatible proactive AI systems
Enhancing Data Quality and Consistency in Large-Scale Analytical Systems through AI-Driven Engineering Workflows
Large-scale analytical systems integrate heterogeneous, fast-evolving data from operational databases, event streams, and third-party sources conditions that routinely introduce schema drift, missing values, semantic inconsistencies, and latency spikes. Introduce an AI-based workflow of engineering that advances the hygienic quality and consistency of data to a proactive and quantifiable field of discipline. The framework is associated with the declarative data contracts and active metadata coupled with learning-based observability to identify the freshness, volume, schema, and distributional anomalies along the batch and streaming routes. Policy-sensitive remediation module is a deduplication, imputation fixes and type harmonization controlled by the context of anomalies, lineage and downstream blast radius. Schema management and consistency enforcement re-check erroneous output with canonical definitions; versioned governance makes all changes auditable, reversible and scope-based in their impact. Training detectors, recalibration of thresholds, and re-training of contracts are done by a feedback and constant learning loop that is driven by incident outcomes and consumer feedback. On a reference lakehouse stack (CDC + streaming ingestion, Spark transformations, contract checks, lineage capture, and MLflow-managed models), it is shown that there are better results in error rates and recovery time, better timeliness and validity, and an increase in report alignment across analytical layers. Collectively, these results indicate that embedding AI within robust DataOps/MLOps practices can deliver durable reliability, faster incident resolution, and consistent semantics at scale, without sacrificing governance or cost control
Dinesh Babu Govindarajulunaidu Sambath Narayanan
User Experience Patterns for Front-End Integration of Retrieval-Augmented Generation in Enterprise Platforms
The concept of Retrieval-Augmented Generation (RAG) has become a baseline architecture paradigm of enterprise-level artificial intelligence systems that allow large language models (LLMs) to generate accurate, context-aware and verifiable responses by basing generation on external sources of knowledge. Although there are studies dedicated to the issues of back-end architecture, retrieval optimization, and model performance, limited attention is dedicated to user experience (UX) concerns regarding the implementation of RAG systems as a part of enterprise front-end systems. This breach is decisive, with system accuracy and transparency, trust, usability, and alignment of workflow being paramount factors in the adoption of the enterprise. The current paper is the detailed analysis of user experience patterns of front-end integration of Retrieval-Augmented Generation to enterprise platforms. It logically examines model of interaction, interface design, feedback schemes and explanability approaches that affect user confidence and effectiveness. The study integrates human-computer interaction (HRI), explainable artificial intelligence (XAI), and enterprise software design to suggest a structured UX pattern taxonomy to be used in systems with RAG.The methodology it uses is mixed, which incorporates research writing design science, some UX heuristic evaluation, and empirical usability testing involving several enterprise applications such as knowledge management, customer support, and decision support systems. Task completion time, perceived usefulness and trust calibration are measured in quantitative metrics as well as qualitative feedback on the users.The findings prove that properly developed UX patterns (source attribution panels, retrieval confidence indicators, iterative query refinement interfaces) can help the user gain great trust, cognitive, and decision-making accuracy. The paper ends with operational design considerations and future inquiries that innovate UX as an upper-class element in the adoption of a successful implementation of Retrieval-Augmented Generation in business settings
Identity Threat Detection: Techniques for Preventing Credential Abuse in Cloud Systems
Identity-related threats have quickly become a common way for hackers to get into these modern cloud systems. This is because people are reusing their credentials a lot, phishing is common, access restrictions are very weak as well as businesses are leaving a bigger digital footprint. As cloud workloads grow & access gets more spread out, attackers are using stolen or hacked credentials more and more to pretend to be actual users, which makes traditional security measures very less effective. This study looks at how credential-based assaults are becoming more common, such as account takeovers, privilege escalation, session hijacking, API key exploitation as well as lateral movement across cloud identities. It also stresses the need for adaptive, context-aware detection. We give a brief overview of the most advanced methods, such as behavioral analytics, continuous authentication, anomaly-based access monitoring, identity threat detection and response (ITDR), machine learning models for user behavior profiling & role-based privilege baselining. Our research introduces a unified framework that amalgamates signal correlation, identity-focused risk evaluation along with cloud-native telemetry analysis to detect these credential misuse with enhanced accuracy and promptness. This case study shows how the proposed method can find small behavioral changes, like unusual login locations or strange API invocation patterns, well before major breaches happen. It does this by simulating a credential compromise in a multi-cloud context. The paper ends with suggestions that businesses may use, such as making sure that their identity context is part of cloud security operations, constantly improving detection models with real-time data, and making sure that these identity governance follows zero-trust principles. We see identity threat detection not just as a way to protect ourselves, but as a key way to keep cloud systems safe from the next generation of attacks that use credentials
Detecting and Resolving Bias in Healthcare AI
Artificial intelligence (AI) is changing healthcare in a huge way by making it easier to get many quick diagnoses, personalized treatment & predictive analytics. However, it also has the huge problem of bias. Healthcare AI can become biased through datasets that aren't adequately represented, inaccurate their information labeling & systemic disparities in the healthcare systems itself. This can lead to disproportionate recommendations, inadequate diagnoses, or not a sufficient representation of these specific groups. These biases make clinical accuracy very less reliable & make health disparities worse, particularly for those who are poor or disadvantaged. Recognizing & correcting this kind of prejudice is essential to ensuring that these AI systems lead to fair healthcare outcomes. This paper investigates several bias detection approaches, including fairness audits, model interpretability analysis & statistical parity evaluations, as well as mitigation measures such as data rebalancing, adversarial de-biasing & continuous model retraining using these diverse datasets. It reiterates the need for clear conceptual governance, ethical evaluation protocols & clinical involvement during their AI validation. Research indicates that a comprehensive approach integrating technological improvements with ethical & governmental oversight may significantly reduce their algorithmic prejudice and enhance confidence regarding healthcare AI systems. In the end, fighting prejudice is not only a technological problem; it is also a moral one. AI should improve the decision-making process in a manner that is equitable, reliable & transparent to all types of patients as well
Describes a data‑ingestion and caching mechanism using graph path cache structures to support low‑latency real‑time fraud or analytics pipelines
In today's world of rapid fraud detection and analytics, systems need to be able to process and respond to large amounts of data in milliseconds. However, traditional data intake and caching pipelines don't meet these ultra-low-latency needs because they read data in order, do the same computations twice, and don't store data in the best place. These limitations make decision-critical systems, such as payment monitoring or behavioral analytics, have unnecessary bottlenecks. Even a delay of a millisecond might impair accuracy or risk reduction. To fix this problem, we provide a mechanism to store and use data that uses a graph route cache structure. This structure shows information & cache links as traversable graph routes instead of flat or hierarchical caches. This method treats each piece of information as a node that is linked to many other nodes by context-aware connections. This makes predictive & dependency-based caching possible. The ingestion system skillfully matches incoming data streams to this graph, which makes it easy to look things up & keeps the cache up to date without having to recalculate. When the latest data point comes in, the system finds the right graph paths & only gets and updates the nodes that are affected, instead of reloading complete datasets. Graph-based caching in the intake layer makes it easier to get their information that changes depending on the context & makes sure that the system stays in sync with changing actual time inputs. Tests show that this the latest cache design cuts query latency by 40% & doubles performance compared to previous cache setups. This technology combines fast intake with smart caching to create their scalable, low-latency data pipelines that may be used for more continuous fraud detection, actual time analytics & adaptive machine-learning processes
AI-Driven Fail Operational Safety in Wire Control Systems
In the current paper, we discuss the topic of artificial intelligence (AI) implementation to improve fault-operational safety in wire control systems, which are widely used in aerospace and industrial automation, as well as robotics. Control systems with wires are essential in matters of precision tasks, and a breakdown may have disastrous impacts. Failure alerts can be tracked, identified, and addressed in real time by using AI methods, especially the machine learning ones, sensor fusion, and predictive maintenance systems. The present study explores the current obstacles of wire control systems, the analysis of AI-implemented techniques of predictive faults, and offers a scheme of AI application to the functioning safety standards. To enhance the reliability of systems and higher fault tolerance, the proposed approach is the combination of sensor data analytics, anomaly detection algorithms, and redundancy management. The results of the experiment show that there is a large decrease in the downtime of systems and an increase in the metrics of operational safety. The research results indicate the potential transformational role of AI in real-time safety surveillance and fail operational insurance in wire-controlled procedures
Data-Governed Autonomous Decisioning: AI Models for Real-Time Optimization of Enterprise Financial Journeys
The business financial processes are under mounting pressure to be timely, regulated, and data-driven in nature. The existing financial decisioning processes are however limited by the aspect of fragmented data sources, delays in the pipeline of analytic processes, manual approvals and lack of single order of governance controls. Such restrictions are a detrimental factor in terms of timely risk management, optimal resource distribution, and active response to quickly evolving financial circumstances. To overcome such difficulties, the paper is going to suggest a data-governed and autonomous decisioning model that combines metadata-based governance with machine learning (ML) and end-of-real-time decision engines to optimize records of enterprise financial trips.The system proposed is a set of policy-conscious data management, predictive and optimization model, and closed-loop autonomous action engine to assist in automating decisions at high throughput and with compliance. Major elements are controlled data ingestion, sensitivity constrained feature pipelines, reinforcement learning-based optimization of financial events and explainable AI modules, which guarantee auditability and trust. Practical testing on real-world enterprise financial data shows that there are massive gains in decision latency, decision accuracy, compliance obedience, and financial performance relative to conventional rule-based systems. The findings demonstrate that data-controlled artificial intelligence decision models can provide considerable operational responsiveness and regulatory effectiveness to provide scalable, next-generation autonomous enterprise financial systems
Enterprise Risk Intelligence: Machine Learning Models for Predicting Compliance, Fraud, and Operational Failures
Companies are growing more vulnerable to interdependent and intricate risks of regulatory non-compliance, financial fraud, and operation breakdowns. Traditional rule-based monitoring and periodic audits struggle to cope with the scale, velocity, and heterogeneity of modern enterprise data, leading to delayed detection and residual risk. This paper suggests a unified Enterprise Risk Intelligence framework which uses machine learning (ML) models to forecast compliance violations, fraud cases, and business outages with one data and feature engineering pipeline. Heterogeneous data sources such as compliance logs, financial transactions and events of the operational system are merged in an enterprise records layer and converted to risk indicators of event frequency, severity, and cross-domain dependencies. Above this base, ensemble models like Gradient Boosting and Random Forests, and then augmented with time-series architectures, like BiLSTM-FCN, provide high predictive accuracy on both types of risk, and their performance is assessed by AUC, precision, recall, F1 score and AUPRC. The transparency of a specific model and the need to extract rules and liabilities makes the implementation of a focused model interpretability layer through SHAP, LIME, and rule extraction an essential requirement of extremely regulated settings. The framework is deployed in Enterprise Risk Management (ERM) systems in a scalable, secure manner through APIs, alerting systems, and dashboards, taking into consideration the data governance, privacy, and regulatory limitations. The data quality, imbalance, and concept drift identified in error analysis and segment-level diagnostics are the main challenges that encourage the continuation of future research in continual learning, graph-based risk modeling, and fairness-aware risk analytics.
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.