International Journal of Research and Applied Innovations
Welcome to IJEETR The International Journal of Engineering & Extended Technologies Research (IJEETR) is a prestigious, peer-reviewed, open-access international journal committed to fostering innovation, collaboration, and excellence in research across the broad spectrum of engineering and technological sciences. In a rapidly evolving world where technology is reshaping every facet of human life, IJEETR strives to be more than just a journal – it is a knowledge hub where scholars, academicians, researchers, and industry practitioners come together to exchange pioneering ideas, transformative research, and ground breaking solutions to global challenges. We believe in bridging the gap between theory and practice, encouraging interdisciplinary collaboration, and ensuring that innovative discoveries reach a worldwide audience with maximum impact. Why Choose IJEETR? Publishing with IJEETR offers you: Rigorous Peer Review Global Reach Timely Publication Indexing & Abstracting Interdisciplinary Collaboration Impact-Oriented Dissemination IJEETR is currently accepting manuscripts for its upcoming issue. We encourage submissions that present: Novel methodologies and experimental results. Innovative frameworks and technological applications. Critical reviews of contemporary research trends. Submit your manuscript to editor@ijeetr.com and be a part of a global network of innovators shaping the future of engineering and technology. Call for Papers Volume 7, Issue 4, July-August 2025 Deadline: 31st August 2025 Notification: 3 to 4 Days Publication: 24 to 36 Hours E-Certificates: 6 Hours Contact Us For manuscript submission, queries, or collaboration opportunities: Email: editor@ijeetr.com Website: www.ijeetr.com Support: +91-9710583466
Telemetry-to-Ticket-to-Fix — A Data-Contracted, Agentic Closed Loop Linking Product Telemetry, CloudOps, and Customer Support
The current paper provides a quantitative analysis of Telemetry-to-Ticket-to-Fix (T2T2F) framework that is a form of an agentic closed loop involving telemetry and CloudOps, in addition to the customer support, linked by means of data contracts. Operational performance in the study will be measured by new metrics namely; Defect-to-Containment Latency, Preemptive Ticket Avoidance Rate and Actionability Score. Findings indicate that the system leads to an enormous cut in the time of defect resolution, high level of automation and customer satisfaction. The results affirm the fact that agentic AI provides the opportunity to safely automate the most important support and engineering work. The present work shows a implementing scaling a direction toward the integration of data based autonomous incident management in complicated enterprise contexts
Explainable AI Frameworks for Transparent Healthcare Reimbursement and Policy Compliance Systems
The integration of Artificial Intelligence (AI) into healthcare reimbursement systems has significantly enhanced efficiency, accuracy, and fraud detection capabilities. However, the growing complexity of machine learning models particularly deep learning has introduced critical challenges related to transparency, interpretability, and regulatory compliance. In highly sensitive domains such as healthcare finance, opaque "black-box" decision-making can lead to disputes, policy violations, and reduced stakeholder trust. This paper presents a comprehensive exploration of Explainable AI (XAI) frameworks tailored for transparent healthcare reimbursement and policy compliance systems. It highlights the need for interpretability in automated claims processing, medical coding validation, and fraud detection workflows. The study examines various XAI techniques including model- agnostic approaches, rule-based systems, and interpretable machine learning models and evaluates their applicability within healthcare reimbursement architectures. Furthermore, the paper proposes a layered XAI framework that integrates explainability modules into existing healthcare IT ecosystems, enabling real-time decision tracing, auditability, and compliance with regulatory standards such as HIPAA and emerging AI governance guidelines. Through architectural diagrams, comparative analysis, and use-case scenarios, the research demonstrates how explainable AI can bridge the gap between automation and accountability. The findings suggest that adopting XAI-driven reimbursement systems not only improves operational transparency but also enhances trust among healthcare providers, insurers, and patients, while reducing compliance risks and financial discrepancies. This work contributes to the growing body of knowledge on responsible AI by aligning technical innovation with ethical and regulatory imperatives in healthcare systems.
Explainable Artificial Intelligence (XAI)
Unified Identity Fabric for Hybrid Financial Systems: A Scalable Cloud Architecture for Centralized Access Governance
Financial institutions are the foundations of fragmented identity environments such as legacy systems, cloud environments and third-party services that have created inconsistent access control and audit gaps. This work introduces a novel architectural paradigm—Unified Identity Fabric—that abstracts identity governance across heterogeneous systems into a single, policy-driven control layer. The proposed Unified Identity Fabric is a centralized identity governance across hybrid systems proposed in this paper. The framework goes a long way in enhancing identity and security performance. The findings indicate that there was a decrease in identity duplication from 27.8% to 4.2%, and policy consistency from 71.3% to 97.6% and an improvement in the audit completeness from 68.4% to 98.9%. The time latency was reduced from 320 ms to 245 ms. The model also decreased the compliance reporting time from 14.2 hours to 2.3 hours demonstrating good scalability, security and governance efficiency
AI-Driven Cloud-Native Solar Energy Intelligence Platform for Smart Educational IT Ecosystems
Energy management is becoming increasingly important in the implementation of university IT governance. This need is driven by rising costs, the increasing magnitude of a campus's IT energy footprint, and the requirement posed by Campus 2030. Current energy intelligence platforms are not sufficient for intelligent energy management of campus IT assets because they do not primarily serve educational goals, do not enable IT energy optimization, and do not allow the security and privacy of learners' data to be assured in AI applications. Solar energy can be considered as a source of clean energy. In addition, cloud-native applications are becoming increasingly important across the IT ecosystem. Within this context, a cloud-native Solar Energy Intelligence Platform (SEIP) for Education is proposed. A SEIP monitors, forecasts, and manages the solar generation of a campus within a whole-of-campus approach. It also allows the intelligent management of the campus IT energy footprint with intelligent demand forecasting capabilities for classrooms and laboratories, while supporting curriculum and pedagogy with specific features. Finally, a curriculum-informing SEIP-Datastore is proposed for seamless integration of all platform features offered by the ecosystem.
competencies in sustainable technologies
A Review on Paracetamol: Pharmacology, Uses and Toxicity
One of the most widely used analgesic and antipyretic medications used in the management of pain and fever worldwide is Paracetamol (acetaminophen). Its efficacy, low cost, broad availability and relatively good safety profile when used within its recommended dosage makes it a first-line therapeutic option since its introduction into clinical practice. Contrary to non-steroidal anti-inflammatory drugs (NSAIDs), paracetamol is an effective agent of mild to moderate pain relief, fever reduction, and none of the drugs has significant side effects on the gastrointestinal tract, platelets, or cardiovascular systems. This is particularly helpful when used with children, elderly patients, pregnant women due to medical supervision, and patients in whom NSAIDs are contraindicated. Pharmacologically, paracetamol acts primarily in the central nervous system by inhibiting the synthesis of prostaglandins hence giving it analgesic and antipyretic properties. It has little anti-inflammatory effect due to its low peripheral cyclooxygenase inhibition. Paracetamol is readily absorbed after oral administration, widely distributed and mainly metabolised in the liver by glucuronidation and sulfation pathways. Cytochrome P450 enzymes are able to convert a small proportion to a toxic metabolite, N-acetyl-p-benzoquinone imine (NAPQI), which would otherwise be detoxified by glutathione. But when glutathione stores are depleted (either in overdose or in prolonged excessive use) glutathione accumulates in the body, leading to severe hepatocellular injury. One of the most common causes of acute liver failure in world and may occur as either an intentional overdose of paracetamol, a repeated high-dose of paracetamol or as an accidental ingestion of multiple combination products containing paracetamol. Serum drug level and treatment with N-acetylcysteine (NAC) is the key to preventing serious complications. This review discusses the pharmacology, mechanism of action, therapeutic uses, dosage, administration, toxicity, and management of paracetamol. It also notes major advantages of it like safety, accessibility, and tolerability, and major limitations of it such as hepatotoxicity and important lack of anti-inflammatory effects. Rational prescribing, patient education, and close monitoring are all fundamental in maximizing therapeutic benefits, and minimizing the risks associated with the use of this widespread drug.
Gursimran Singh, Sachi Rajpoot, Md Moidul Islam
Prediction Stock Movement by using Gradient Boosting
This project presents a comprehensive study on the predictive analytics of Tesla and Google stock prices using advanced machine learning frameworks. Financial markets are highly dynamic and influenced by numerous factors, including historical performance, market sentiment, and external economic indicators. To address the complexity of stock price forecasting, this work utilizes data-driven modeling approaches capable of identifying hidden patterns and temporal trends within large volumes of historical data. The system collects and processes stock market data, followed by extensive preprocessing to ensure accuracy and consistency. Key financial indicators are extracted to enhance the quality of predictions. These features are then used to train robust machine learning models that learn from past behavior to provide future stock value estimates. A web-based interface, built using the Django framework, facilitates user interaction with the predictive system. Users can input parameters, view historical data, compare trends, and visualize predicted values through interactive graphs and charts. This interface bridges the gap between complex data science and practical financial analysis, making the system accessible to users with varying levels of technical expertise.
R Syed Imran Hussian, S Tamilvanan, R Raghul
Reliability Engineering for Zero-Downtime Integration of HPC Systems into Regulated Environments
The paper provides a reliability engineering strategy of zero-downtime integration of high-performance computing (HPC) systems in controlled environments (healthcare, energy, and government) to support regulated settings. The paper dwells upon such critical approaches such as site-specific configuration validation, firmware alignment, secure uplinks, firewall hardening, rollback strategies, and regression testing. The outcomes indicate evident enhancements in the system-performance and safety. The failure rate dropped to 0.004 failures per hour from 0.021 and the system reliability increased to 99.6% from 97.2%. The network availability also increased from 98.1% to 99.8% and mean time to repair (MTTR) reduced from 35 minutes to 8 minutes. Deployment time went down to zero from 30 minutes and recovery was lowered to 15 seconds from 120 seconds. The general risk mitigation was approximately 72%. All these findings support the idea that the given methodology guarantees the uninterrupted operation in the service. The paper also focuses on the benefits that these enhancements have on the safety of the population and ensuring regulation adoptability. The article demonstrates that a combination of technical and well-advanced leadership can accomplish a dependable, as well as secure, integration of HPC.
Automated Bug Detection and Auto Fix Generation by using Ml Model
This project is a developing an Intelligent Machine Learning based system for automated bug detection and auto fix generation in software applications. Debugging is one of the most time-consuming and challenging tasks in software development, especially for beginners. In this system, the user provides source code or error messages as input. The system analyzes the code using Machine Leaming and Natural Language Processing (NLP) techniques trained on large datasets of buggy and corrected code. Based on learned patterns, the model identifies the type of bug and generates appropriate fix suggestions. The system works similar to a chatbot, providing interactive and user-friendly debugging assistance. This intelligent approach reduces human effort. improves coding efficiency, and helps developers learn better programming practices. The proposed system is cost-effective, scalable, and suitable for real-time software development environments. It provides real-time feedback, enabling immediate correction of mistakes during development. The machine learning model continuously updates its knowledge base as new types of bugs are encountered. The proposed system accepts source code or error messages as input from the user. It processes this input using advanced ML models trained on large datasets containing buggy code samples and their corresponding corrected versions. By learning patterns, structures, and common error types from historical data, the model is capable of identifying syntax errors, logical bugs, runtime exceptions, and semantic inconsistencies in the code. The integration of NLP techniques enables the system to interpret error messages and understand programming context, making it more adaptable across different programming languages and development environments.
Machine Learning. Automated Bug Detection
Cybersecurity Enhancement in Electric Vehicle Systems using Principal Component Analysis (PCA)
The increasing deployment of Electric Vehicle Supply Equipment (EVSE) introduces significant cybersecurity vulnerabilities in EV charging networks. Traditional Intrusion Detection System (IDS) is computationally intensive and cannot be used in resource-limited EVSE environments. The paper suggests a lightweight, PCA based, cybersecurity framework to detect and monitor real-time cyber-attack in EVSE systems. The dataset is CICEVSE2024 (464,165 samples, 74 features of two real EVSE charging stations) on which four dimensionality reduction strategies are tested: Baseline (74 features), Standard PCA (3 components), Randomized PCA (2 components), and Mini-Batch Sparse PCA (4 components). Three machine learning classifiers, namely, Random Forest, Bagging, and ANN (MLP) are also trained on the reduced features sets. The framework proposed has F1-scores of 87.7-92.1% with AUC-ROC values of 0.943-0.978, and inference time that is 3-5x lower than those of baseline models. Random Forest using standard PCA provides the best accuracy-efficiency trade-off (F1=89.9% inference 0.235 s). An implementation is the Flask-based Security Operations Centre (SOC) dashboard which supports HMAC-SHA256 authenticated multi-station support, automated email/SMS alerting, and GPS location-aware reporting, which is validated as a deployable prototype.
Priyadharshini R, Vaishnavi P S, Shalini M
Self-Adaptive Predictive Analytics Frameworks using Reinforcement Learning and Federated Cloud Intelligence
Self-adaptive predictive analytics frameworks built on reinforcement learning (RL) and federated cloud intelligence represent a next-generation paradigm in intelligent computing systems designed for healthcare, cybersecurity, finance, and large-scale industrial environments. These frameworks aim to dynamically optimize decision-making processes while preserving data privacy, ensuring distributed intelligence, and enabling continuous learning across heterogeneous cloud ecosystems. Unlike traditional centralized machine learning systems, which rely on static datasets and offline training pipelines, self-adaptive frameworks continuously evolve based on environmental feedback, reward mechanisms, and decentralized knowledge sharing.Reinforcement learning plays a central role in these systems by enabling agents to learn optimal policies through interaction with dynamic environments. In healthcare predictive analytics, RL models can optimize treatment recommendations, disease progression forecasting, and resource allocation in real time. The learning process is driven by reward signals that reflect clinical outcomes, system performance, or cybersecurity risk reduction. However, the effectiveness of RL in real-world applications depends heavily on data availability and diversity, which introduces challenges when dealing with sensitive healthcare data.To address privacy concerns, federated learning (FL) is integrated into the framework. Federated cloud intelligence allows multiple distributed nodes—such as hospitals, research institutions, and edge devices—to collaboratively train machine learning models without sharing raw data. Instead, only model updates or gradients are exchanged, significantly reducing the risk of data leakage and ensuring compliance with regulations such as HIPAA and GDPR. This decentralized learning approach also enhances scalability and enables cross-institutional collaboration. The combination of reinforcement learning and federated learning creates a powerful hybrid architecture capable of self-adaptation. The system continuously adjusts model parameters based on feedback loops, evolving healthcare trends, and emerging cybersecurity threats. Cloud intelligence further enhances this framework by providing elastic computational resources, distributed storage, and AI orchestration capabilities across multiple cloud providers Despite its advantages, the framework introduces several challenges. These include communication overhead in federated networks, convergence instability in reinforcement learning models, heterogeneous data distribution across nodes, and security vulnerabilities in model aggregation processes. Additionally, interpretability remains a major concern, as complex RL-driven decision-making systems often lack transparency, making clinical validation difficult.This paper explores the design, architecture, and operational mechanisms of self-adaptive predictive analytics frameworks using reinforcement learning and federated cloud intelligence. It evaluates their applications in healthcare and cybersecurity, identifies key limitations, and discusses potential optimization strategies. The study emphasizes the importance of hybrid AI-cloud ecosystems in achieving scalable, secure, and intelligent predictive analytics systems capable of real-world deployment in critical domains
Plastitrack - A Community – Driven Plastic Waste Collection and Redemption Platform
This project is a Java-based web application that promotes plastic recycling through Public, Recycle Center, and Admin modules. Users can upload plastic waste details, locate nearby recycling centers, track submissions, and earn reward points redeemable for eco-friendly products. Recycling centers manage waste requests, schedule collections, and update statuses after admin approval. Admins oversee registrations, monitor activities, manage products, and ensure transparency. The system includes a community dashboard showing total waste collected and location-based ratings. By combining technology with incentives, the platform encourages public participation and builds a sustainable, collaborative ecosystem for effective plastic waste management and environmental awareness.
A Java-based web application for plastic waste management that enables users to upload waste details, connect with nearby recycling centers, track submissions, and earn rewards. It promotes sustainability through gamification, community participation, transparent monitoring, and efficient recycling processes
R. Thamaraikannan, S. Sunil Kumar, B.Siva
Socialflow AI: Voice to Social Media Scheduler
The Smart Voice‑Based Content Posting System for Social Media Platform Using Artificial Intelligence is a comprehensive solution designed to transform the way users create, optimize, and publish content on social media by leveraging voice interaction and intelligent automation. In today’s digital era, social media platforms play a vital role in communication, marketing, and personal branding; however, users often face challenges related to manual content creation, typing‑intensive caption writing, selecting effective hashtags, and maintaining a consistent posting schedule. To address these issues, this project proposes an innovative system that enables users to generate high‑quality social media posts simply by speaking their ideas, thereby eliminating the need for manual typing and reducing effort. The system captures the user’s voice input and accurately converts it into text using advanced speech‑to‑text technology. The raw text is then processed using Natural Language Processing (NLP) techniques to analyse context, enhance clarity, correct grammar, and refine sentence structure for better readability and engagement. Based on this processed text, the system intelligently generates relevant captions, suitable hashtags, and appropriate emojis using AI models trained to recognize trends and semantic relevance, which in turn increases post visibility and engagement. In addition, users can schedule posts to be published at specific dates and times, allowing for consistent posting and improved audience reach. The scheduled content is stored securely and automatically posted to selected social media platforms without further user intervention, ensuring both convenience and efficiency. Integration with social media APIs and secure authentication mechanisms allows cross‑platform posting, reducing repetitive tasks while preserving account security. The system’s voice‑based interface enhances accessibility, making social media content creation more inclusive for users with limited typing skills or physical challenges. By minimizing manual typing and automating repetitive tasks, the system improves productivity and accessibility, particularly for users with limited writing abilities or physical constraints. Overall, the Smart Voice-Based Content Posting System demonstrates how AI-powered voice interfaces can modernize digital communication, making social media engagement more efficient, user-friendly, and accessible. Overall, this project demonstrates a practical application of AI, speech recognition, and automation technologies to simplify and improve the process of social media content management, making it faster, smarter, and more user‑centric.
Automated Skin Disease Analysis and Detection using AI Powered Mobile Application
Skin diseases are one of the most common health problems affecting people worldwide, and early identification is essential for effective treatment and prevention. However, due to lack of awareness, limited access to dermatologists, and time constraints, many individuals fail to seek timely medical attention. To overcome these challenges, this project presents an AI-based system for multi skin disease detection using Flutter and Firebase, aimed at providing a preliminary and accessible skin disease identification solution. The proposed system utilizes image processing and traditional machine learning techniques to analyze skin images and detect multiple skin diseases without using deep learning models. The user captures or uploads a skin image through a mobile application. The image undergoes preprocessing steps such as resizing, noise removal, and color normalization to improve image quality. Feature extraction techniques are then applied to obtain important visual characteristics related to skin color, texture, and shape. These extracted features are classified using machine learning algorithms to identify possible skin diseases. The mobile application is developed using Flutter, which enables cross-platform compatibility and a user friendly interface. Firebase is used as the backend service to handle user authentication, cloud storage, and secure data management. The detected skin disease, along with relevant confidence information, is displayed to the user, and the results are securely stored in Firebase for future reference and analysis.
Intelligent Cloud-Oriented Smart Connect Frameworks for Seamless Multi-Device Experiences
The rapid proliferation of smart devices and the increasing demand for uninterrupted digital experiences have necessitated the development of intelligent cloud-oriented smart connect frameworks. These frameworks aim to enable seamless interaction and synchronization across multiple devices, including smartphones, wearables, laptops, IoT systems, and smart home appliances. By leveraging cloud computing, edge intelligence, and AI-driven orchestration, such frameworks ensure real-time data sharing, adaptive resource allocation, and context-aware service delivery. This paper explores the architecture, design principles, and implementation strategies of intelligent cloud-based connectivity frameworks that support multi-device ecosystems. It highlights the role of distributed cloud infrastructures, interoperability standards, and intelligent middleware in achieving seamless integration. Furthermore, the study examines challenges such as latency, security, privacy, and device heterogeneity, proposing solutions through hybrid cloud-edge models and machine learning techniques. The research emphasizes how these frameworks enhance user experience by providing continuity, personalization, and automation across devices. Ultimately, intelligent smart connect frameworks represent a foundational component of next-generation digital ecosystems, enabling efficient, scalable, and user-centric computing environments in both consumer and enterprise domains.
Flutter Based SOS Alert and Location Tracking Application with Volunteer Assist and Rescue
Timely emergency response is crucial for saving lives during accidents, medical emergencies, and natural disasters. Traditional emergency systems often face challenges such as delayed assistance, limited resource availability, and insufficient real-time location tracking. This paper proposes a Flutter based SOS alert and real-time location tracking application that facilitates efficient emergency management by integrating hospitals, ambulances, and community volunteers. The system enables users to send SOS alerts with a single tap, automatically transmitting accurate GPS coordinates and emergency details. Registered Assist Rescue volunteers and nearby medical responders are notified instantly through cloud-based messaging. Key features include volunteer coordination for immediate first-aid support, real-time responder tracking, status updates, and secure role-based access control. Firebase backend services provide authentication, real-time data management, and cloud messaging, while an admin dashboard allows monitoring of active emergencies, response times, and resource utilization
Smart Enterprise Transformation through Explainable AI Adaptive Cloud Platforms and Predictive Analytics
Smart enterprise transformation is increasingly driven by the integration of Explainable Artificial Intelligence (XAI), adaptive cloud platforms, and predictive analytics. As organizations digitize operations, the need for transparency, scalability, and data-driven foresight becomes critical. Explainable AI ensures that complex machine learning models remain interpretable, fostering trust, regulatory compliance, and better decision-making. Adaptive cloud platforms provide the flexibility and scalability required to manage dynamic workloads, enabling enterprises to rapidly deploy and evolve intelligent applications. Predictive analytics leverages historical and real-time data to anticipate trends, optimize processes, and enhance customer experiences. This study explores how the convergence of these technologies enables enterprises to transition from reactive to proactive operational models. It examines their roles in improving efficiency, reducing risk, and enabling strategic innovation. Furthermore, the paper highlights the challenges associated with implementation, including data privacy concerns, integration complexity, and the need for skilled personnel. Through a comprehensive review and methodological framework, this research demonstrates that a synergistic approach to these technologies can significantly enhance organizational agility and competitiveness. Ultimately, smart enterprise transformation is not merely technological adoption but a holistic shift in how organizations operate and create value.
Intelligent Data Engineering and AI Integration for Modern Secure and Scalable Enterprise Systems
The rapid evolution of enterprise systems has necessitated the integration of intelligent data engineering practices with advanced artificial intelligence (AI) technologies to ensure scalability, security, and efficiency. Modern enterprises generate vast volumes of structured and unstructured data, requiring robust architectures capable of real-time processing, adaptive learning, and secure handling of sensitive information. Intelligent data engineering encompasses automated data pipelines, data quality management, and scalable storage solutions, while AI integration enables predictive analytics, anomaly detection, and decision automation. This study explores the synergy between data engineering and AI in building secure and scalable enterprise systems. It highlights architectural patterns such as data lakes, data meshes, and hybrid cloud infrastructures, alongside AI-driven optimization techniques. Security considerations, including data privacy, encryption, and compliance frameworks, are also examined in the context of AI-enabled systems. Furthermore, the paper evaluates challenges such as data governance, model bias, and infrastructure complexity. The research concludes that the integration of intelligent data engineering with AI significantly enhances operational efficiency, supports real-time decision-making, and strengthens system resilience. However, careful design, governance, and ethical considerations are critical to ensuring long-term sustainability and trustworthiness in enterprise environments.
Zero Trust AI Driven Cloud Native Framework for Secure Healthcare Data Governance and Autonomous Intelligence
The rapid digitization of healthcare systems has introduced unprecedented opportunities for improving patient outcomes, operational efficiency, and clinical decision-making. However, it has also exposed sensitive healthcare data to complex cybersecurity threats and governance challenges. This paper proposes a Zero Trust, AI-driven, cloud-native framework designed to enhance secure healthcare data governance while enabling autonomous clinical and enterprise intelligence. The framework integrates Zero Trust Architecture (ZTA) principles—continuous verification, least privilege access, and micro-segmentation—with artificial intelligence techniques such as machine learning, anomaly detection, and predictive analytics. Leveraging cloud-native technologies, including containerization, microservices, and orchestration platforms, the framework ensures scalability, resilience, and real-time data processing. It supports secure interoperability across healthcare ecosystems while maintaining compliance with regulatory standards. Furthermore, the system enables intelligent automation in clinical workflows, diagnostics, and enterprise resource management. The proposed architecture addresses challenges related to data silos, insider threats, and dynamic risk environments. By combining security, governance, and intelligence into a unified model, this framework offers a robust solution for modern healthcare systems seeking to balance innovation with data protection and trustworthiness.
Self Adaptive AI Framework for Cloud Centric Cybersecurity and Intelligent Threat Response Systems
The increasing dependence on cloud-centric infrastructures has transformed modern enterprise operations while simultaneously exposing them to advanced cybersecurity threats. Traditional security mechanisms often fail to address dynamic and evolving attack patterns due to their static and rule-based architectures. This research proposes a self-adaptive artificial intelligence (AI) framework designed to enhance cybersecurity in cloud environments through intelligent threat detection and automated response mechanisms. The framework integrates machine learning, deep learning, and reinforcement learning techniques to continuously learn from data, adapt to new threats, and improve detection accuracy over time. It employs behavioral analytics and anomaly detection to identify deviations from normal system activities in real time. Additionally, the framework incorporates secure authentication, encryption, and access control strategies to ensure data integrity and confidentiality. The self-adaptive nature of the system enables it to respond autonomously to threats, minimizing human intervention and reducing response time. Experimental evaluation demonstrates improved performance in terms of detection rate, scalability, and resilience compared to conventional systems. This research contributes to the development of next-generation cybersecurity solutions by combining adaptive intelligence with cloud-based infrastructures for proactive and efficient threat management.
Preventing Data Inconsistency in Distributed Architectures Using AI-Driven Synchronization and Governance Models
Distributed architectures have become the backbone of modern digital systems, enabling scalability, resilience, and global accessibility. However, maintaining data consistency across distributed nodes remains a critical challenge due to latency, partial failures, and concurrent updates. Traditional consistency models, such as eventual consistency and strong consistency, often involve trade-offs between performance and reliability. This research explores the role of artificial intelligence (AI) in enhancing synchronization and governance mechanisms to mitigate data inconsistency. AI-driven synchronization models leverage machine learning algorithms to predict conflicts, optimize replication strategies, and dynamically adjust consistency levels based on workload patterns. Additionally, governance frameworks powered by AI can enforce data policies, monitor anomalies, and ensure compliance across distributed environments. The study proposes an integrated model combining predictive analytics, intelligent conflict resolution, and adaptive governance to maintain data integrity. Through conceptual analysis and methodological design, this research highlights how AI can significantly reduce inconsistency risks while preserving system performance. The findings suggest that AI-driven approaches offer a promising direction for next-generation distributed systems, enabling more autonomous, efficient, and reliable data management across complex infrastructures.
Trusting ServiceNow AI to Deliver Business Value
The growing complexity of corporate operations needs the use of innovative technology to improve efficiency and value. ServiceNow, a major digital workflow platform, uses artificial intelligence (AI) to improve business processes across a variety of industries. This thesis looks into the potential of ServiceNow AI to provide concrete business value by automating workflows, improving decision-making and increasing customer experiences. ServiceNow's platform integrates AI capabilities, allowing enterprises to streamline operations, cut costs, and drive innovation. The findings indicate that trusting ServiceNow AI not only optimizes existing processes but also enables businesses to react to changing market needs, resulting in enhanced performance and competitive advantage.
Data Encryption and Policies Via Digital Transformationsand Services
In an era marked by fast digital transformation, data encryption and the development of strong regulations to protect sensitive information have never been more important. This article investigates the complex interaction between data encryption, digital transformation, and the changing regulatory environment that oversees these processes. It emphasizes the critical significance of encryption in safeguarding personal data from unwanted access and cyber threats while also simplifying compliance with demanding standards such as GDPR and HIPAA. The article delves deeper into the issues of digital transformation, such as increased vulnerabilities and data management complexity, and underlines the importance of enterprises implementing complete encryption policies. It also examines upcoming technologies, specifically artificial intelligence (AI) and cloud-based solutions, which are changing dataencryption tactics. By addressing these components, this study hopes to shed light on how businesses can effectively traverse the difficulties of data security in a digitally transformed environment, assuring both protection and trust in their operations.
Digital Transfor mation and artificial intelligence (AI)
Economic Impact of Service Now Among Financial Institutions
This opinion examines the transformative economic impact of ServiceNow's digital workflow platform on financial institutions, analyzing cost optimization, operational efficiency, and return on investment across banking, insurance, and investment sectors.
Agentic AI Frameworks in Cloud Environments for Autonomous and Intelligent Cybersecurity Defense
The rapid evolution of cloud computing has introduced unprecedented flexibility and scalability for modern enterprises, but it has simultaneously expanded the attack surface for cyber threats. Traditional rule-based and reactive cybersecurity approaches are increasingly inadequate in addressing sophisticated, dynamic, and large-scale attacks. This paper explores the role of agentic Artificial Intelligence (AI) frameworks in enabling autonomous and intelligent cybersecurity defense within cloud environments. Agentic AI refers to systems capable of independent decision-making, adaptive learning, and goal-directed behavior without continuous human intervention. By integrating machine learning, reinforcement learning, and multi-agent systems, these frameworks can detect, analyze, and respond to threats in real time. The study examines architectural models, operational mechanisms, and deployment strategies of agentic AI in cloud infrastructures. It further evaluates their effectiveness in mitigating advanced persistent threats, zero-day vulnerabilities, and insider attacks. Challenges such as scalability, interpretability, ethical concerns, and adversarial manipulation are also discussed. The findings highlight that agentic AI frameworks significantly enhance proactive defense capabilities while reducing response time and operational overhead. However, careful design, governance, and continuous monitoring are essential to ensure reliability and trustworthiness in autonomous cybersecurity systems.
MODERNIZING LEGACY DATA CENTERS THROUGH VIRTUALIZATION AND SOFTWARE-DEFINED INFRASTRUCTURE
The rapid evolution of digital transformation initiatives has placed unprecedented demands on traditional data center infrastructures. Legacy data centers, often characterized by rigid architectures, underutilized resources, and high operational costs, struggle to meet the scalability, agility, and efficiency requirements of modern enterprises. This paper explores the strategic modernization of legacy data centers through the adoption of virtualization technologies and software-defined infrastructure (SDI). Virtualization enables the abstraction of physical resources, allowing multiple virtual machines to run on a single hardware platform, thereby improving resource utilization and reducing capital expenditure. Complementing this, software-defined infrastructure introduces programmability and automation across compute, storage, and networking layers, enhancing flexibility and operational efficiency. The study presents a comprehensive overview of key modernization approaches, including server virtualization, storage virtualization, network virtualization, and the integration of software-defined data center (SDDC) architectures. It further examines the benefits of transitioning to a virtualized and software-defined environment, such as improved scalability, enhanced disaster recovery capabilities, reduced downtime, and optimized energy consumption. Additionally, the paper addresses challenges associated with migration, including legacy system compatibility, security concerns, and organizational change management. Through a generalized and vendor-neutral perspective, this research highlights best practices and architectural considerations for enterprises seeking to modernize their data centers. The findings underscore the critical role of virtualization and software- defined technologies in enabling agile, resilient, and cost-effective IT infrastructures aligned with contemporary business needs.
AI-DRIVEN CHILD SUPPORT OPTIMIZATION SYSTEMS USING PREDICTIVE ELIGIBILITY MODELING AND CASE PRIORITIZATION
Child support systems play a critical role in ensuring the financial well-being of children in separated or economically vulnerable families. However, traditional child support enforcement and eligibility determination processes are often reactive, fragmented, and resource-intensive, leading to delays, inefficiencies, and inequitable outcomes. This paper proposes an AI-driven framework for optimizing child support systems through predictive eligibility modeling and intelligent case prioritization. The approach leverages machine learning techniques to analyze historical case data, socio-economic indicators, payment patterns, and behavioral signals to proactively assess eligibility, predict payment risks, and identify high-impact intervention opportunities. The proposed system integrates predictive analytics with rule-based policy engines to enhance decision accuracy while maintaining regulatory compliance and transparency. A multi-layered architecture is introduced, comprising data ingestion pipelines, feature engineering modules, predictive modeling components, and decision orchestration layers. The framework emphasizes fairness, explainability, and data privacy, addressing key ethical considerations associated with AI deployment in social welfare systems. In addition, the paper explores prioritization strategies that enable caseworkers to focus on high-risk or high-need cases, improving operational efficiency and maximizing child support outcomes. Simulation-based evaluations demonstrate improvements in processing time, collection rates, and resource allocation efficiency compared to traditional approaches. The study also discusses implementation challenges, including data quality, system integration, and governance, and provides a roadmap for scalable adoption in government and enterprise environments. This research contributes to the growing field of AI-enabled public service transformation by presenting a practical and scalable solution for modernizing child support systems, ultimately enhancing service delivery, equity, and financial stability for affected families.
Design of an Intelligent Clinical Decision Support System Using Machine Learning Techniques
Clinical Decision Support Systems (CDSS) play a crucial role in improving healthcare outcomes by assisting clinicians in diagnosis and treatment planning. This paper presents the design and implementation of an intelligent CDSS using machine learning techniques to enhance clinical decision-making. The proposed system integrates patient data, predictive modeling, and real-time analytics to support early disease detection and risk assessment. Various machine learning algorithms, including logistic regression, decision trees, and neural networks, are evaluated for performance. The system architecture, data preprocessing methods, and evaluation metrics are discussed in detail. Experimental results demonstrate improved accuracy and efficiency compared to traditional rule-based systems. The study highlights the potential of AI-driven CDSS in modern healthcare environments.
Integrating Decision Intelligence and Business Rules Management for Enterprise Applications
Decision-making is a critical component of enterprise applications, influencing operational efficiency, business performance, and strategic outcomes. With the increasing complexity of business environments and the growth of data-driven processes, traditional decision-making approaches are no longer sufficient. Decision Intelligence (DI) has emerged as an advanced paradigm that combines data analytics, artificial intelligence, and decision modeling to support intelligent and automated decision-making. At the same time, Business Rules Management Systems (BRMS) provide a structured approach to defining, managing, and executing business rules within enterprise systems. This paper presents an integrated framework that combines Decision Intelligence and BRMS to enhance enterprise decision-making capabilities. The proposed system leverages data analytics, machine learning techniques, and rule-based logic to provide accurate, consistent, and scalable decision support. The architecture, methodology, and implementation aspects of the integrated system are discussed in detail. Experimental evaluation demonstrates improved decision accuracy, flexibility, and efficiency compared to standalone systems. The study highlights the potential of combining DI and BRMS to enable intelligent, adaptive, and automated enterprise applications [1], [3], [5].
A Framework-Driven Approach to Data Validation and Reconciliation for Operational Accuracy
In modern enterprise environments, operational decisions depend heavily on the accuracy, consistency, and reliability of data flowing across heterogeneous systems. However, organizations frequently encounter discrepancies due to data integration complexities, distributed architectures, and asynchronous processing pipelines. These inconsistencies can lead to reporting errors, financial misstatements, and operational inefficiencies if not detected and corrected in a timely manner. This paper proposes a framework-driven approach to data validation and reconciliation designed to ensure operational accuracy across large-scale data ecosystems. The proposed framework introduces structured validation checkpoints, automated reconciliation mechanisms, and standardized metadata-driven validation rules that operate across data ingestion, transformation, and storage layers. By integrating rule-based validation, statistical anomaly detection, and reconciliation workflows, the framework enables early detection of inconsistencies while maintaining system performance and scalability. The study further discusses architectural considerations for implementing such frameworks in modern data platforms, including cloud-based data warehouses, distributed processing systems, and microservices-based data pipelines. Through systematic validation layers and automated reconciliation strategies, the framework improves data trustworthiness and operational reliability. The findings demonstrate that adopting a framework-driven validation strategy significantly reduces reconciliation delays, improves data integrity, and enhances decision-making accuracy within enterprise operational environments.
ENTERPRISE RECONCILIATION ARCHITECTURES FOR FINANCIALLY CRITICAL PLATFORM TRANSITIONS: A FRAMEWORK FOR ACCURACY AND CONTROL DURING SYSTEM REPLACEMENT
Enterprise platform modernization initiatives frequently require the replacement of legacy financial systems with modern, scalable platforms. While such transitions offer improvements in performance, automation, and data accessibility, they introduce significant risks related to financial accuracy, data integrity, and regulatory compliance. One of the most critical challenges during these transitions is ensuring that financial transactions processed in the new system reconcile accurately with historical records and parallel operational systems. Without robust reconciliation mechanisms, organizations may face financial discrepancies, reporting errors, and audit failures. This paper presents a lifecycle-aligned enterprise reconciliation architecture that treats reconciliation as a first-class architectural control, rather than a downstream operational activity. Unlike manual, tool-centric, or post-migration reconciliation approaches, the proposed framework embeds automated validation layers directly within enterprise integration architectures, operating continuously across migration, coexistence, and stabilization phases. The framework introduces a modular reference architecture encompassing data ingestion mechanisms, staging repositories, reconciliation engines, rule-based validation modules, exception management workflows, and monitoring dashboards. It further provides a structured reconciliation methodology covering transaction-level matching, balance-level comparison, rule-driven validation, and threshold-based variance management. A governance and control model supporting audit readiness, accountability, and regulatory compliance is also defined. The paper validates the framework through a scenario-based risk-to-control mapping that demonstrates applicability under realistic enterprise transition conditions. The proposed architecture enables organizations to reduce operational risk, maintain financial transparency, and preserve the integrity of financial reporting throughout complex system replacement programs.