International Journal of Advanced Research in Computer Science & Technology(IJARCST)
Agentic AI-Driven Smart Supply Chain Orchestration for Paint Manufacturing and Retail Ecosystems
In modern supply chains, emerging technologies create unprecedented opportunities for organizations to operate smarter by better orchestrating end-to-end processes across their ecosystems Intelligent decision-making that spans across multiple functions such as fulfillment, manufacturing, sourcing, and inventory management can help achieve complex business objectives not possible at a function level. Fulfilling such objectives calls for traditional supply chain technologies to improve operational efficiency while overcoming limitations related to factors such as end-consumer demand, transit times, product demand patterns, and possible production non-compliance. This scholarly work presents a comprehensive architectural framework to enable agentic orchestration of paint industry supply chain ecosystems. An agentic architecture orchestrates, in an optimized manner, processes that extend beyond the borders of the organization, with intelligent decision-making enabled through smart sourcing, supplier collaboration, quality and compliance management, and inventory optimization capabilities. While providing specific capabilities and technology requirements for paint manufacturing and retail ecosystems, the research contributes to the wider supply chain domain by defining agentic capabilities as smart decision-making that fulfills user-defined objectives based on constantly changing inputs and constraints
Cloud-Native Big Data Architecture for Real-Time Tax Analytics, Fraud Detection, and Fiscal Governance
Tax Analytics, fraud detection within tax systems, and governance are three areas where data is increasingly processed, thanks to the growing volume of tax records, payment transactions, company trades, and digital services and applications. Real-time processing engines allow continuous ingestion, detection of useful insights while data flows, and preparation of data-ready-to-query in analytical repositories. A novel modular architecture that fits the cloud-native paradigm and employs a mix of batch, micro-batch, and stream processing paradigms allows for a scalable and flexible solution. A monitoring framework allows the detection of problems in the flow and content of the data. The architecture addresses Governance, Compliance, Security, and Auditability aspects without letting these concerns become a bottleneck. The proposed architecture paves the way for a full-fledged implementation, as a clear roadmap emerges. The Cloud-Native principles and the Elasticity, Loose Coupling, Service Orientation, and Microservices properties define how the solution must evolve. Modular and easily interchangeable components are favoured over monolithic architectures, offering flexibility and maintaining the costs for less frequently used modules in the investment background. The reference architecture, its components, the direction of the data flow and the Governance areas of concern are presented, together with the high-level design criteria and relative evaluation metrics
Generative AI for Intelligent Medical Coding and Healthcare Analytics
Generative AI is emerging as a powerful enabler for intelligent and efficient medical coding to facilitate health data analytics. For predictive analytics, generative models are used alongside conventional classification approaches to predict the onset of diseases, while also serving as an internal validation tool for classifier performance. For risk stratification, generative models enhance the unsupervised stratification of patient populations. Adaptive risk-scoring systems are proposed to identify patients likely to require surgery within the next year, with analysis of state transition paths also possible. Considerations related to the privacy and security of health information in using generative models, and for computer-aided healthcare decision and workflow–support systems in general, are presented. Automated model validation frameworks for supporting ICD and CPT coding systems, along with change management in general, are outlined
Generative AI for Autonomous Accounting and Tax Filing: A Cloud-Driven Framework for Smart Financial Services
Generative AI for Autonomous Accounting and Tax Filing: A Cloud-Driven Framework for Smart Financial Services presents an objective, evidence-based analysis of cloud-enabled autonomous financial operations. The general research question is whether autonomous accounting for self-employed individuals using Generative Artificial Intelligence (GAI) and other native AI services can improve the level of automation and value offered in comparison with current software-as-a-service solutions. Other questions pertain to governance, data ingestion, storage, and booking service model design, SLA level determination, and suitability of the overall architecture for autonomous tax filing. Overall, an end-to-end cloud-native, fully automated bookkeeping process is described. Artificial Intelligence (AI) techniques allow the design of smart cloud-based systems able to provide high-level autonomous financial service operations. The architecture covers autonomous accounting for self-employed individuals. Bookkeeping services leverage data generated from connected payment wallets and AI-based services detect anomalies, suggest corrective actions, and generate reports in near real time. Tax computation incorporates Generative AI services to optimize deductions and determine credits eligibility. An outcomes-based governance framework addresses the specific autonomous and Generative AI risk elements present. The approach satisfies the guidelines characterized by the highest level of autonomy in the AI assessment classification. By minimizing manual bookkeeping tasks, the solution delivers greater value compared to current-market cloud solutions.
Explainable Agentic AI Framework for Automated Insurance Fraud Detection and Predictive Risk Intelligence
Automated insurance fraud detection and risk prediction are inherently difficult but critical responsibilities for the insurance service industry. The fraud detection process must rely on explainable artificial intelligence (XAI), especially deep learning methods, to satisfy important requirements for high-stakes decision-making in the domain. An explainable agentic AI framework is therefore proposed to address the challenges and needs in these application areas. Component methods and components of agentic explainable AI are specifically tailored to the task of insurance fraud detection, while a decoupled and independently engineered complementary risk-prediction component develops predictive risk-intelligence measures. An insurance fraud-detection component builds on a convolutional neural network trained on engineered features of insurance fraud—such as insured party, loss date, occupied region, and other features derived from claim descriptions. Automated bidding by fraudsters and the high costs of actuating fraud motivate explainable-response analytics for the detection process. Consequently, XAI methods such as LIME (Locally Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive explanations) are deployed to enhance transparency in multiple detection dimensions and satisfy the explainability requirement. Performance across a range of detection capabilities presents an overall satisfactory solution.
Explainable Agentic AI for Secure and Adaptive Supply Chain Decision Intelligence in Food Service and Financial Systems
Cognition-based theories for agentic, explainable AI inform secure and adaptive methods for food service and financial decision technologies. However, the use of cognition-based theories has largely been limited to studying and modelling human vs. AI agentic decision-making behaviour without a clear application scope or objectives based on practical applications and associated needs. Indeed, no clear strategy or way has yet been proposed or implemented for applying an agentic-based explanation approach in decision intelligence supporting agentic actor repeat operations across diverse application domains. A recently devised framework for AI explanation addressed only a subset of potentially critical domain-specific needs, and it has not yet been tested or applied in a different field, nor any specific area, nor adapted for enhanced security and the inclusion of agentic patterns from cognition. Suitability of forensically annotated, structured deep and predictive decision processes for domain context explanation has not yet been established, nor has an inline explain-by-do-don’t concern-expression capability. Addressing these two related gaps requires examining particular domain food service decision clusters and flows from a specific competence pattern perspective. Food service cognition-based operational support systems with graphical process overlay have become widespread. A database for securing repeat operational actions over other less autonomy-rich decision domains has also recently been established, capturing executive management and board-level financial decision succession and securing decisions from important event signals for high-fidelity decision-intelligence applications within predictive operations and agentic explanation patterns.
Autonomous Insurance Analytics Using Agentic AI for Personalized Coverage and Predictive Risk Intelligence
Autonomous insurance analytics through agentic AI enable personalized premium models, proactive coverage recommendations, risk-averse product feature enhancement, and predictive claims intelligence. Agentic AI exhibits decision-making autonomy, pursues user-centric goals, and interacts with external environments, serving insurance analytics where historical patterns alone remain insufficient. Empirical methods, including data collection, feature engineering, predictive modeling, personalization pipelines, and customer segmentation, operationalize the theoretical foundations. Data ecosystems and ethical considerations encompass sources, quality assessment, bias mitigation, and stakeholder responsibility. System architecture describes components, workflows, and integration within existing analytical platforms. Operating in highly data-driven sectors characterized by large volumes of historical-time series data—spanning product features and pricing, demand-supply interactions, resource and capacity deployment, and claims, loss, fraud, and warranty patterns—traditional analytical methods can produce high-fidelity predictive models. However, such environments can also require proactive actions and decision-making for customers and companies. In insurance underwriting, pricing, and claims domains, developing such proactive actions—one of the principal tenets of agentic AI—constitutes a significant challenge because of the catastrophic and highly risk-averse nature of the sector. The available historical-time-series data for specific products, geographies, and time periods are usually limited by the infrequent occurrence of catastrophic events such as floods and earthquakes. Consequently, merely relying on historical patterns to predict future behavior is rarely adequate for standard insurance companies, especially the ones implementing conservative risk-management strategies or seeking consolidation in the market.
AI-Powered Predictive Analytics for Climate-Aware Flood and Mobile Home Insurance Risk Management
Fulfilling a growing need for climate-aware flood risk management of mobile-home insurance portfolios, this research develops AI-powered predictive analytics that quantify risk and associated capital requirements over time. A comprehensive data analysis prepares a rich database integrating historical claims, exposure characteristics, data records of climate drivers, and additional geospatial, socioeconomic, and business-climate indicators. Using proven methodologies of statistical modeling and machine learning, climate-risk patterns across flood frequency, intensity, and inundation depth are interpreted. Predictive outputs are successively integrated into risk pricing and capital models, stimulating innovations in dynamic risk appetite adaptation, product design, geographical concentration, and operational decision-making. Role players are equipped to monitor evolving tail-risk exposures and arrive at well-grounded, real-time choices. Market forces are pressuring insurers to better serve and support selected vulnerable customer segments, particularly mobile-home communities that routinely live under very high concentrations of climate Risk for flood peril, especially in view of changing climatic trajectories. Climate risk landscapes are being reshaped by factors such as increasing precipitation, longer monsoon seasons, and eventually increasing draughts, all of which differ by region and country. Consequently, demand government and social attention on the part of both regulators and portfolio holders. These growing anxiety are leading role-players to call for comprehensive approaches that enable proactive real-time pricing and service decisions adapted to the fortuitous and/or stress-test-data flow.
Darkshield: Mobile Intrusion Detection using Post-Authentication Failure Analysis and Android Security APIS
Smartphones store sensitive personal, financial, and confidential data, making them frequent targets for theft and unauthorized access. Traditional security methods such as PINs, passwords, and biometrics mainly prevent entry but offer limited protection after repeated intrusion attempts. This paper presents DarkShield, an Android-based intrusion detection and anti-theft system that proactively detects suspicious unlock attempts and alerts the rightful owner. When multiple incorrect PIN or password entries are detected, the system captures the intruder’s image, retrieves the current GPS location, uploads the evidence to cloud storage, and sends alerts through SMS and email. SMS notifications include the location and image link, while emails contain location details and the captured image as an attachment. The system also supports offline SMS alerts, online email notifications, and SIM change detection for unauthorized SIM replacement. To reduce false alerts, a machine learning-based face recognition module verifies whether the captured face matches the authorized user. By integrating evidence capture, location tracking, dual alert mechanisms, SIM monitoring, and intelligent face verification, DarkShield provides an effective solution for smartphone theft detection and unauthorized access prevention.
M. Parvinraj, J. Dinesh, M. Venkatramana, P. Suryaprakash Raj
Intelligent AI-Driven Cloud Frameworks for Cancer Detection and Secure Healthcare Data Transformation
The integration of artificial intelligence (AI) and cloud computing has significantly transformed healthcare systems, particularly in cancer detection and data management. This study proposes an intelligent AI-driven cloud framework designed to enhance early cancer detection while ensuring secure and efficient healthcare data transformation. The framework leverages machine learning and deep learning algorithms for accurate diagnosis using medical imaging and patient data, combined with scalable cloud infrastructure for real-time processing and storage. Security is addressed through advanced encryption techniques, access control mechanisms, and blockchain-based auditing to ensure data integrity and privacy compliance. The proposed system enables seamless data sharing among healthcare providers while maintaining strict confidentiality standards. Furthermore, the framework supports interoperability across different healthcare systems, facilitating collaborative diagnostics and treatment planning. Experimental evaluations demonstrate improved detection accuracy, reduced latency, and enhanced data security compared to traditional systems. This research highlights the potential of AI-powered cloud solutions in revolutionizing cancer diagnostics and secure healthcare data management, paving the way for more efficient, accessible, and reliable medical services.
The AI-Powered Knowledge Sharing Web Portal is designed to provide an intelligent platform for effective knowledge exchange and query resolution. Traditional knowledge-sharing systems often lack efficiency, accuracy, and real-time interaction. To overcome these limitations, the proposed system integrates Artificial Intelligence to enhance the overall user experience and improve the quality of responses. The system allows users to register, log in, ask questions, and share answers in a collaborative environment. The Question & Answer Management module handles the storage and retrieval of user queries and responses. The AI Evaluation module plays a key role by analyzing multiple answers and selecting the most relevant and accurate response based on parameters such as relevance, clarity, and correctness. Additionally, the Admin and Analytics module enables system monitoring, user management, and content moderation, ensuring data integrity and system reliability. The platform is designed with a scalable architecture that supports real-time interaction and secure data handling. Overall, the proposed system provides an efficient, reliable, and user-friendly solution for knowledge sharing by combining AI-based automation, intelligent evaluation, and collaborative learning features. It is suitable for educational and professional environments where quick and accurate information exchange is essential.
Enhancing Customer Churn Prediction and Retention for E-Commerce
Customer churn is a major challenge in e-commerce, as losing customers directly impacts revenue and business growth. This project presents an intelligent customer churn prediction and retention system that utilizes machine learning techniques to identify customers who are likely to discontinue their engagement. The system is designed as a complete data pipeline, incorporating stages such as data collection, preprocessing, feature engineering, model training, and prediction. Multiple machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting, are implemented to analyze customer behavior and improve prediction accuracy. The system evaluates and compares model performance using metrics such as accuracy, precision, recall, and F1-score to ensure optimal results. Based on the prediction outcomes, a retention strategy module is integrated to recommend business actions such as personalized offers, loyalty rewards, and targeted marketing campaigns to retain customers. Furthermore, the system includes visualization and reporting features through an interactive dashboard, enabling users to easily interpret insights and make data-driven decisions. This project demonstrates the effective application of machine learning in real-world e-commerce scenarios to enhance customer retention, optimize business strategies, and improve overall operational efficiency.
A Hybrid Biometric and Geo – Fencing Based Smart Attendance System
The Smart Classroom Attendance System using Biometric and Location-Based Automation is designed to improve the accuracy and efficiency of attendance management in educational institutions. Traditional attendance methods, such as manual roll calls and paper registers, are time-consuming and prone to errors or proxy attendance. This system uses biometric technology, such as fingerprint or face recognition, to uniquely identify students and ensure that only authorized individuals can mark their attendance. In addition to biometric authentication, the system incorporates location-based verification using technologies like GPS or Wi-Fi to confirm that the student is physically present inside the classroom. The system automatically checks the student's location and compares it with the predefined classroom location. If both biometric and location verification are successful, the attendance is recorded in the database along with the date and time, ensuring reliable and tamper-proof records
B. Namitha, J. Divya Dharshini, K. Livetha
Next-Generation Enterprise Cloud-Oriented Intelligent Workflow Automation through AI-Driven Natural Language Processing Models
The rapid digital transformation of enterprises has accelerated the demand for intelligent workflow automation systems capable of improving efficiency, reducing operational costs, and enhancing decision-making accuracy. Traditional workflow automation tools rely heavily on rule-based engines and structured inputs, which limit their adaptability in dynamic enterprise environments. With the emergence of cloud computing and artificial intelligence, particularly natural language processing (NLP), organizations now have the opportunity to build next-generation intelligent workflow systems that can interpret, process, and execute tasks based on unstructured human language inputs. This research proposes a cloud-oriented intelligent workflow automation framework powered by AI-driven NLP models. The system leverages advanced deep learning architectures such as transformers to understand enterprise-level textual data including emails, documents, tickets, and chat-based communications. By integrating NLP with cloud-native microservices, the framework enables automated task classification, prioritization, routing, and execution across distributed enterprise systems. The study further explores how AI-based workflow intelligence enhances scalability, reduces human intervention, and improves operational agility. It also incorporates security, governance, and compliance mechanisms to ensure enterprise-grade reliability. The proposed approach demonstrates how organizations can transition from traditional automation to adaptive, context-aware, and self-learning workflow systems, enabling smarter enterprise operations in highly dynamic digital ecosystems
Enhanced Prediction of PCOS and PCOD using Deep Learning for Early Diagnosis and Clinical Risk Stratification
Polycystic Ovary Syndrome and Polycystic Ovarian Disease are common endocrine disorders affecting women of reproductive age, often causing infertility, hormonal imbalance, and metabolic issues. Early diagnosis is difficult due to overlapping symptoms and limitations of traditional methods. This study proposes a machine learning–based predictive framework that integrates conventional algorithms with deep learning models such as CNNs and Vision Transformers (ViTs) for accurate early detection and risk stratification. The system identifies hidden patterns and disease indicators that are often missed in manual diagnosis. It also incorporates explainable AI techniques to improve transparency and support clinical decision-making. The framework is deployed as a scalable web-based application using Django, enabling real-time prediction and seamless healthcare integration. Overall, the proposed model improves diagnostic accuracy, supports personalized treatment, and enhances women’s healthcare outcomes through efficient and non-invasive analysis.
Explainable Artificial Intelligence (XAI)
Nivedha S, Harisha V, Derik Mol R, Janaranjini J R
Intelligent AI Systems and Secure Cloud Architectures for Next Generation Digital Transformation
The rapid acceleration of digital transformation across industries has been driven by the convergence of intelligent artificial intelligence (AI) systems and secure cloud computing architectures. Organizations are increasingly dependent on cloud-native infrastructures to process massive volumes of data while leveraging AI for automation, predictive analytics, and decision intelligence. However, this dependency introduces critical challenges related to cybersecurity, data privacy, system scalability, and operational resilience. This paper explores the integration of intelligent AI systems with secure cloud architectures to enable next-generation digital transformation. The proposed framework emphasizes adaptive intelligence, zero-trust security models, encryption-first design, and AI-driven cloud resource optimization. Intelligent AI systems enhance decision-making by analyzing real-time data streams, automating workflows, and enabling predictive insights across sectors such as healthcare, finance, manufacturing, and governance. Secure cloud architectures ensure that these capabilities operate within a robust security perimeter supported by identity-based access control, continuous monitoring, and automated threat detection. The convergence of these technologies enables organizations to achieve scalability, agility, and resilience while minimizing security risks. This study also examines federated learning, edge-cloud integration, and AI-powered cybersecurity mechanisms that strengthen cloud ecosystems. The findings highlight that the fusion of intelligent AI and secure cloud infrastructure is essential for building adaptive, self-healing, and autonomous digital enterprises capable of sustaining long-term innovation and trust in a highly interconnected digital economy.
Generative AI Powered Cloud Native Architecture for Intelligent Healthcare Governance and Autonomous Clinical Intelligence
The integration of Generative Artificial Intelligence (GenAI) into healthcare systems presents transformative opportunities for intelligent data governance and autonomous clinical decision-making. However, the increasing volume, sensitivity, and fragmentation of healthcare data pose significant challenges related to privacy, interoperability, and scalability. This paper proposes a Generative AI-powered cloud-native architecture designed to enable secure, scalable, and intelligent healthcare data governance while supporting autonomous clinical decision intelligence. The proposed framework leverages large language models and generative models to synthesize insights, automate clinical workflows, and enhance decision support systems. Cloud-native technologies such as containerization, microservices, and orchestration ensure flexibility, resilience, and efficient resource utilization. The architecture incorporates privacy-preserving mechanisms, including data anonymization, access control policies, and federated data handling, to ensure compliance with regulatory standards such as HIPAA and GDPR. Additionally, explainable AI components are integrated to improve transparency and trust in automated clinical decisions. Experimental evaluations demonstrate improvements in decision accuracy, system scalability, and operational efficiency. This research highlights the potential of combining Generative AI and cloud-native platforms to build next-generation healthcare systems that are secure, intelligent, and capable of autonomous clinical reasoning
Intelligent Cloud Security Architectures Using Artificial Intelligence and Federated Learning Techniques
The rapid adoption of cloud computing has transformed modern digital infrastructure, enabling scalable, flexible, and cost-efficient services. However, this transformation has also introduced complex security challenges, including data breaches, insider threats, and sophisticated cyberattacks. Traditional security mechanisms often fail to address these evolving threats due to their static and reactive nature. This paper explores intelligent cloud security architectures that leverage Artificial Intelligence (AI) and Federated Learning (FL) techniques to enhance threat detection, privacy preservation, and system resilience. AI-driven models enable real-time anomaly detection, predictive threat analysis, and automated response mechanisms, significantly improving security efficiency. Meanwhile, Federated Learning facilitates collaborative model training across distributed cloud environments without sharing sensitive data, thereby preserving privacy and ensuring regulatory compliance. The integration of AI and FL creates a decentralized, adaptive, and robust security framework capable of addressing emerging cyber risks. This study examines the design principles, architecture components, and operational workflows of such systems. Additionally, it evaluates their effectiveness compared to traditional approaches and identifies implementation challenges. The findings suggest that intelligent cloud security architectures can significantly improve both security posture and data privacy, making them a promising solution for next-generation cloud environments.
AI Driven Secure Intelligent Framework for Fraud Detection Cybersecurity and Cloud Based Enterprise Systems
The rapid evolution of digital technologies and the widespread adoption of cloud-based enterprise systems have significantly increased the risk of cyber fraud and security breaches. Traditional fraud detection mechanisms often fail to address sophisticated, real-time threats due to their static and rule-based nature. This research proposes an AI-driven secure intelligent framework designed to enhance fraud detection capabilities within cybersecurity and cloud environments. The framework integrates machine learning, deep learning, and anomaly detection techniques to identify suspicious activities in real time. It leverages big data analytics and behavioral analysis to improve detection accuracy while minimizing false positives. Additionally, the model incorporates secure data encryption, identity management, and adaptive authentication to strengthen overall system security. The proposed system is scalable, making it suitable for enterprise-level cloud infrastructures, and is capable of continuous learning to adapt to emerging threats. Experimental analysis demonstrates improved detection rates, reduced response time, and enhanced resilience against evolving cyberattacks. This study contributes to the advancement of intelligent cybersecurity solutions by combining artificial intelligence with robust cloud security strategies, providing a proactive approach to fraud prevention in modern enterprise ecosystems.
Architecting Intelligent Enterprise Systems by Integrating AI and Cloud Computing with Data Governance for Scalable Digital Transformation
The rapid evolution of digital technologies has compelled enterprises to adopt intelligent systems that enhance efficiency, scalability, and decision-making. Integrating Artificial Intelligence (AI) with cloud computing provides a powerful foundation for building such systems, enabling organizations to process large volumes of data, automate workflows, and derive actionable insights in real time. However, the effectiveness of these systems heavily depends on robust data governance frameworks that ensure data quality, security, compliance, and ethical usage. This paper explores the architectural principles required to design intelligent enterprise systems by combining AI capabilities with cloud infrastructure while embedding strong data governance practices. It highlights how scalable cloud platforms facilitate the deployment of AI models, while governance mechanisms maintain data integrity and trustworthiness across distributed environments. The study also examines challenges such as data silos, regulatory constraints, and system interoperability, proposing structured methodologies to address them. By aligning AI, cloud computing, and governance strategies, organizations can achieve sustainable digital transformation. The paper concludes that a holistic architecture not only improves operational agility but also supports long-term innovation, risk management, and competitive advantage in an increasingly data-driven economy.
ARCHITECTING SCALABLE DATA INTEGRATION FRAMEWORKS FOR HYBRID ENTERPRISE PLATFORMS WITH STRONG DATA GOVERNANCE
Modern enterprises increasingly operate within hybrid technology environments that combine on-premises infrastructure, multiple public cloud platforms, Software-as­ a-Service (SaaS) applications, and distributed data ecosystems. While this hybrid model provides flexibility and scalability, it also introduces significant challenges in integrating data across heterogeneous systems while maintaining consistent governance, security, and operational reliability. Traditional data integration approaches based on tightly coupled middleware architectures and centralized batch pipelines are often insufficient for handling the scale, velocity, and diversity of modern enterprise dataflows. This paper presents an architectural approach for designing scalable data integration frameworks tailored for hybrid enterprise platforms while embedding strong data governance mechanisms throughout the data lifecycle. The proposed framework emphasizes modular and layered integration architectures that combine API-driven connectivity, event-driven data streaming, distributed data processing pipelines, and metadata-driven governance controls. These architectural components enable organizations to efficiently integrate data from diverse enterprise systems including transactional platforms, cloud services, analytics environments, and external partner systems. The study further examines governance strategies such as metadata management, data lineage tracking, policy-based access control, and automated compliance monitoring that ensure transparency, security, and reliability in enterprise data flows. By integrating governance capabilities directly within the integration architecture, organizations can maintain consistent data quality and regulatory compliance while enabling scalable data exchange across distributed systems. Through conceptual architecture models, integration workjlow designs, and governance frameworks, this research outlines practical design principles for building resilient enterprise data integration ecosystems. The proposed approach supports modern enterprise requirements including real-time analytics, AI-driven applications, and cross-platform interoperability, providing a foundation for scalable and governed data integration in hybrid enterprise environments.
Secure and Resilient AI-Driven Architectures for Next-Generation Cyber-Physical and Cloud Enterprise Systems
The rapid convergence of artificial intelligence (AI), cloud computing, and cyber-physical systems (CPS) is transforming modern enterprise infrastructures. These systems, which integrate computational intelligence with physical processes, are increasingly deployed in critical domains such as smart manufacturing, healthcare, transportation, and energy. However, their growing complexity and interconnectivity introduce significant security vulnerabilities and resilience challenges. This paper explores the design and implementation of secure and resilient AI-driven architectures tailored for next-generation cyber-physical and cloud enterprise systems. It emphasizes the integration of adaptive AI models, zero-trust security frameworks, distributed cloud-edge infrastructures, and real-time threat detection mechanisms. The proposed architecture leverages machine learning for anomaly detection, blockchain for data integrity, and federated learning for privacy preservation. Furthermore, resilience is enhanced through self-healing systems, redundancy strategies, and fault-tolerant design principles. The study also examines existing limitations and emerging threats, including adversarial AI attacks and data poisoning. By synthesizing current research and proposing a robust architectural framework, this work aims to guide enterprises in building secure, scalable, and resilient systems capable of operating in dynamic and hostile environments. The findings contribute to advancing trustworthy AI-enabled cyber-physical ecosystems.
Next Generation AI Enabled Cognitive Platform for Secure Cloud Network Intelligence Self Healing Enterprise Systems and Data Driven Optimization
The increasing complexity of cloud-based infrastructures and enterprise systems demands intelligent, adaptive, and autonomous solutions to ensure security, efficiency, and resilience. This paper presents a next-generation AI-enabled cognitive platform designed to enhance secure cloud network intelligence, enable self-healing enterprise systems, and support data-driven optimization. The proposed platform integrates artificial intelligence, machine learning, cognitive analytics, and automation into a unified framework capable of real-time monitoring, threat detection, and autonomous decision-making. By leveraging advanced data analytics and predictive modeling, the system can identify anomalies, anticipate failures, and implement corrective actions without human intervention. The concept of self-healing is central to the platform, allowing systems to diagnose and recover from faults dynamically. Additionally, the platform utilizes data-driven optimization techniques to improve resource allocation, performance efficiency, and operational agility. The architecture incorporates multi-layered security mechanisms and adaptive infrastructure components that evolve with changing environmental conditions and threat landscapes. While the benefits are significant, challenges such as data privacy, computational complexity, and integration issues remain. This research provides a comprehensive framework for building intelligent, secure, and resilient enterprise systems in modern cloud environments.
Scalable Cloud-Native AI Systems for Cryptocurrency Markets: Integrating Fraud Detection and Predictive Volatility Modeling
The rapid growth of cryptocurrency markets has introduced significant challenges, including increasing fraudulent activities and extreme price volatility. Traditional analytical systems are often unable to scale effectively or adapt to the dynamic and distributed nature of blockchain ecosystems. This research proposes a scalable cloud-native artificial intelligence framework that integrates fraud detection and predictive volatility modeling for cryptocurrency markets. The system leverages cloud computing technologies, containerization, and microservices to enable real-time data processing and model deployment. Advanced machine learning and deep learning models are employed to analyze both on-chain and off-chain data sources. Fraud detection is achieved through anomaly detection techniques that identify irregular transaction patterns and suspicious wallet behaviors. Predictive volatility modeling uses time series forecasting models, including recurrent neural networks and transformer architectures, to capture complex market dynamics. The cloud-native design ensures scalability, fault tolerance, and high availability, allowing the system to handle large volumes of streaming data. Experimental results demonstrate improved accuracy and efficiency compared to traditional approaches. The proposed framework provides valuable insights for investors, regulators, and financial institutions, enhancing risk management and decision-making in cryptocurrency markets.
Intelligent Enterprise Transformation through Artificial Intelligence and Cloud Powered Data Engineering
Intelligent enterprise transformation represents a strategic shift in how organizations leverage advanced technologies to enhance operational efficiency, innovation, and decision-making. Artificial Intelligence (AI) and cloud-powered data engineering have emerged as critical enablers of this transformation, allowing enterprises to harness vast amounts of data and convert it into actionable insights. This study explores how the integration of AI with cloud-based data platforms supports the development of intelligent enterprises capable of real-time analytics, automation, and adaptive decision-making Cloud-powered data engineering provides scalable, flexible, and cost-efficient infrastructure for managing complex data pipelines, while AI introduces capabilities such as predictive analytics, natural language processing, and machine learning. Together, they enable organizations to move from reactive to proactive and autonomous operations. The research also addresses key challenges, including data governance, system integration, and security concerns, particularly in highly regulated industries By synthesizing existing literature and proposing a structured methodology, this paper outlines a comprehensive framework for implementing intelligent enterprise systems. The findings highlight that organizations adopting AI-driven, cloud-enabled data engineering practices achieve improved agility, enhanced customer experiences, and sustained competitive advantage in an increasingly data-driven economy
Architecting Financially Compliant Enterprise Point-of-Sale Systems: Data Integrity and Revenue Recognition at Scale
Enterprise Point-of-Sale (POS) systems have evolved from simple transaction recording tools into complex digital platforms that support large-scale retail ecosystems, omnichannel commerce, and real-time financial reporting. As organizations scale globally, ensuring financial compliance, transaction accuracy, and reliable revenue recognition becomes a critical architectural challenge. Modern POS infrastructures must process millions of transactions daily while maintaining strict financial controls, auditability, and regulatory compliance with standards such as IFRS 15, ASC 606, and regional tax regulations. This article presents a generalized architectural framework for building financially compliant enterprise POS systems that prioritize data integrity, high availability, and scalable revenue recognition mechanisms. The discussion examines architectural layers, distributed transaction management, reconciliation pipelines, and financial data governance models. Additionally, the paper explores event-driven architectures, real-time financial processing, and automated reconciliation workflows designed to reduce discrepancies between operational sales systems and enterprise financial ledgers. Through the integration of distributed databases, financial compliance engines, and analytics pipelines, modern POS architectures can support scalable transaction throughput while ensuring traceability and regulatory transparency. The study further highlights best practices for maintaining transactional consistency, implementing financial audit trails, and managing revenue recognition in large enterprise environments. The findings provide a practical architectural roadmap for enterprises designing resilient POS infrastructures capable of supporting global retail operations while meeting stringent financial governance requirements
Advanced AI Driven Cloud Systems for Secure Scalable and Intelligent Enterprise Operations with Autonomous Decision Making
The rapid advancement of artificial intelligence (AI) and cloud computing has transformed enterprise operations by enabling intelligent, scalable, and secure digital ecosystems. This research focuses on advanced AI-driven cloud systems that support autonomous decision-making capabilities to enhance enterprise efficiency and resilience. By integrating machine learning, deep learning, and cloud-native technologies, modern enterprises can process vast amounts of data, predict system behavior, and optimize performance in real time. Autonomous decision-making systems leverage AI algorithms to analyze patterns, detect anomalies, and respond to dynamic changes without human intervention. This significantly reduces operational latency and enhances system reliability. Furthermore, cybersecurity mechanisms integrated with AI ensure proactive threat detection and adaptive defense strategies against sophisticated cyberattacks. The proposed framework emphasizes intelligent resource allocation, real-time monitoring, and self-healing system architectures that continuously evolve with changing enterprise requirements. These systems enable organizations to achieve higher productivity, reduced costs, and improved service delivery. The study highlights the importance of combining AI intelligence with cloud scalability to build robust enterprise systems capable of operating in complex and uncertain environments. The findings demonstrate that AI-driven cloud systems play a critical role in shaping the future of secure and intelligent enterprise operations.
A Contemporary Agricultural Approach Utilizing Fog-Cloud Computing and the Internet of Things
Agriculture is the major source for the large population in Bangladesh to earn money and carry out their livelihood. Precision agriculture is an approach to farm management that uses to measure agri-related information like temperature, humidity, soil PH, soil nutrition levels, water level etc. It is already adopted in alternative countries, however, we tend to still got to involve IoT and cloud computing technologies for the higher production of crops. Using IoT and cloud computing technologies can control the cost, maintenance and monitoring performance. Here the architecture for the smart agriculture was presented based upon the Concept of the cloud computing and IOT. Fog computing basically extends cloud computing and services to the edge of the network and reduce the amount of data transported to the cloud for processing, analysis and storage. Together they are capable to provide the required information with a significant reduction in cost. In this paper, we propose an IoT and fog computing based modern agricultural system model, which reduces the transmission cost, latency, delay etc. The proposed architecture also enhances the data transmission efficiency that is impossible to achieve by fog or cloud computing alone.
Building Secure Enterprise Intelligence using AI and Cloud Computing for Scalable Digital Systems
The rapid evolution of digital technologies has transformed how enterprises manage, process, and utilize data for decision-making. Enterprise Intelligence (EI), powered by Artificial Intelligence (AI) and Cloud Computing, enables organizations to derive actionable insights, enhance operational efficiency, and achieve scalability. However, with increased data reliance comes heightened concerns around security, privacy, and system resilience. This paper explores the integration of AI-driven analytics with cloud-based infrastructures to build secure and scalable enterprise intelligence systems. It emphasizes the role of advanced machine learning algorithms, distributed computing, and secure cloud architectures in enabling real-time decision-making. The study also highlights key security challenges such as data breaches, unauthorized access, and compliance issues, proposing mitigation strategies including encryption, zero-trust architecture, and AI-based threat detection. Furthermore, it examines how scalable cloud platforms facilitate elasticity and cost efficiency while supporting large-scale data processing. By combining security frameworks with intelligent automation, enterprises can create robust digital ecosystems capable of adapting to dynamic business environments. This research provides insights into designing secure, scalable EI systems and outlines best practices for organizations seeking to leverage AI and cloud technologies effectively.
Psychological Wellbeing Assessment Platform
Mental health has become a critical concern for students, employees, and the general public, as stress, anxiety, depression, and emotional imbalance continue to rise. However, many individuals hesitate to seek professional help due to social stigma, lack of access to counselors, or limited awareness. This project proposes a Psychological Wellbeing Assessment Platform, an intelligent digital solution designed to evaluate a user's mental health condition using standardized psychological questionnaires, AI-based scoring models, and personalized recommendations. The system assesses wellbeing in multiple dimensions—stress level, emotional state, cognitive behavior, lifestyle factors, and social wellbeing. It provides instant insights, mood tracking, mental wellness reports, and suggestions such as breathing exercises, meditation, and professional contact options. By integrating psychology with technology, the platform promotes early identification of mental health issues, encourages self-awareness, and facilitates timely support, making mental wellbeing more accessible and stigma-free.