International Journal of Advanced Research in Computer Science & Technology(IJARCST)
A Blockchain-Based Application for Authenticating Social Profiles and Preventing Fraud
In this blockchain-based application designed to combat fake social profiles, the integration of the Naive Bayes algorithm adds a sophisticated layer of fraud detection. Leveraging blockchain's decentralized nature, user profiles are stored securely, with each profile represented as a block in the chain. Prior to inclusion in the blockchain, profiles undergo a rigorous verification process, employing various authentication methods like government-issued IDs or trusted social media accounts. Once submitted, the Naive Bayes algorithm steps in, extracting pertinent profile features such as images, usernames, and activity histories. Trained on labeled data encompassing both genuine and fake profiles, the algorithm assigns probability scores to new profiles, indicating the likelihood of authenticity. A predefined threshold determines whether a profile is deemed genuine or flagged as suspicious. The blockchain's consensus mechanism validates additions to the chain, ensuring only verified profiles enter the network. Additionally, users play a vital role through community reporting, aiding in the identification of suspicious profiles. Continuous monitoring and periodic algorithm updates uphold the system's effectiveness against evolving fraudulent tactics. Transparency and accountability remain core tenets, with verification criteria and activities recorded transparently on the blockchain, fostering trust among users. Through this amalgamation of blockchain and machine learning, the application offers a robust solution to comb at the prolife ration of fake social profiles while upholding user privacy and decentralization.
E.Abinayagi, Prabha Sasikumar, R.Vaneesha
Hybrid Deep Learning based Adaptive Model for Medium-Term Stock Market Forecasting
An adaptive hybrid deep learning model for medium-term stock market forecasting is presented in this work. The suggested approach combines Convolutional Neural Networks (CNN), Transformer models, Long Short- Term Memory (LSTM), and Reinforcement Learning (RL) to increase the forecast accuracy for key stock indexes such as the Dow Jones, DAX, and NASDAQ-100 over a trading horizon of 70 days. Sentiment analysis and technical indicators are examples of advanced feature engineering techniques that improve forecast reliability. By constantly adjusting to market swings, the suggested approach maximizes investment plans. Comparing experimental results to current methods, accuracy has improved by 85–90%.
M.Mailsamy, S.Kalaivani, L.Devadharshini, S.Manibharathi, R.Jinoth Ahmed
Automated Pest Classification and Pesticide Suggestion using Deep Learning Algorithm
The correct and real-time identification of crop pests in agricultural environments is vital for precision farming and effective pest management. This paper introduces an Attention-Based Compound Convolutional Neural Network (ABCCNN) model to improve the recognition of pests in complex field conditions. The proposed model integrates a Parallel Attention Mechanism (PAM) module with Residual Blocks (RBs) to improve feature extraction, localization, and classification. The PAM module focuses on the relevant pest features while suppressing the background noise so that the system can recognize the objects reliably in cluttered environments. The residual blocks facilitate better gradient propagation and convergence, thus making the network deeper and efficient. The proposed ABCCNN framework achieves accuracy and real-time performance better than state-of-the-art deep models with an optimum trade-off in computational efficiency as well as accuracy. Extensive experiments on pest datasets from reality demonstrate that classification accuracy, inference speed, as well as environmental variation robustness, such as lighting, occlusion, or complex backgrounds for conventional CNNs, are always superior to other existing models. Further, with the proposed model, the generalization of CNN-based pest recognition frameworks boosts up and enhances the possibility of large-scale deployment in smart agricultural systems. ABCCNN also contributes to early pest intervention strategies by providing fast, accurate pest detection, avoiding higher crop losses, and achieving sustainable agricultural practices. The model is planned to be further integrated into the edge AI devices in real-time on-field pest detection and decision support systems.
Agricultural Monitoring and Precision Farming
Flora Recognizer and Botanical Traits using Machine Learning Techniques
Machine learning (ML) and deep learning models have significantly advanced plant identification and classification, addressing the limitations of traditional manual methods. Existing approaches primarily focus on counting plants from 3D scans, limiting detailed botanical analysis. These methods lack the ability to extract intricate leaf structures, making accurate classification challenging. This paper introduces Florasense, an advanced plant recognition system that utilizes Convolutional Neural Networks (CNNs) and Xception features to classify medicinal plants based on leaf morphology. Unlike conventional techniques, Florasense focuses on both identification and medicinal trait analysis, making it a comprehensive tool for researchers and practitioners.The system automates plant identification, significantly improving accuracy, efficiency, and usability across various fields, including medicine, forestry, and agriculture. By leveraging deep learning and leaf vein segmentation, Florasense enhances recognition precision, even in complex environments. The model achieves an impressive classification accuracy of 94.1%, surpassing traditional machine learning methods Additionally, it integrates a medicinal knowledge base, providing users with detailed insights into plant properties, uses, and dosage recommendations.
M Parvathi, P Dhanushya, S Harine, D Kaviya
Deep Neural Network Based Intelligent Scheduling and Optimization Framework for Smart Logistics and Supply Chain Systems
The rapid expansion of global supply chains and increasing demand for efficient logistics operations have created complex challenges in scheduling, routing, inventory management, and real-time decision-making. Traditional optimization methods often fail to handle the high-dimensional, dynamic, and stochastic nature of modern logistics networks. Deep Neural Networks (DNNs) provide an advanced computational approach capable of learning intricate patterns, predicting outcomes, and optimizing multi-variable systems in real time. This research proposes a Deep Neural Network-based Intelligent Scheduling and Optimization Framework for smart logistics and supply chain systems. The framework leverages DNN models to forecast demand, optimize vehicle routing, allocate resources efficiently, and dynamically adjust schedules based on real-time data streams from IoT-enabled logistics platforms. By integrating predictive analytics with optimization algorithms, the system supports end-to-end supply chain visibility, operational efficiency, and proactive decision-making. The research methodology includes system architecture design, simulation of logistics scenarios, training of deep neural networks with historical and real-time data, and evaluation of framework performance against conventional scheduling methods. Results demonstrate that the proposed framework improves delivery efficiency, reduces operational costs, enhances resource utilization, and mitigates delays. This approach provides a scalable, intelligent, and adaptive solution for modern smart logistics and supply chain management, enabling enterprises to optimize operations in complex, dynamic environments.
Cybersecurity and Compliance Automation Framework for Cloud-Based Enterprise Systems Powered by AI
The rapid adoption of cloud computing technologies has transformed enterprise IT infrastructures, enabling scalable, flexible, and cost-efficient business operations. However, the migration of enterprise systems to cloud environments has also introduced significant cybersecurity risks and regulatory compliance challenges. Organizations must protect sensitive data, maintain system integrity, and ensure compliance with evolving regulatory standards while managing complex cloud infrastructures. Artificial Intelligence (AI) has emerged as a powerful tool for enhancing cybersecurity capabilities and automating compliance management in cloud-based enterprise systems. This research proposes an AI-powered cybersecurity and compliance automation framework designed to secure cloud-based enterprise environments and streamline regulatory compliance processes. The framework integrates machine learning algorithms, automated monitoring systems, threat intelligence analytics, and policy-driven compliance mechanisms to detect security threats, predict potential vulnerabilities, and enforce compliance requirements. By leveraging AI-driven anomaly detection and automated policy enforcement, the framework enables real-time identification of cyber threats and ensures continuous compliance with security standards. The research methodology involves architectural modeling, comparative analysis of existing cybersecurity frameworks, and simulation-based evaluation to assess system performance, threat detection accuracy, and compliance monitoring efficiency. The findings demonstrate that AI-powered cybersecurity frameworks can significantly enhance enterprise security posture, reduce operational risks, and automate complex compliance processes, enabling organizations to maintain secure and resilient cloud-based enterprise systems.
AI Powered Secure Hybrid Cloud Architecture for SAP Enterprise Systems: Intelligent Analytics and Autonomous Digital Transformation
The rapid growth of digital technologies has significantly transformed enterprise information systems, compelling organizations to modernize their IT infrastructures to remain competitive. Hybrid cloud architecture has emerged as a strategic solution that enables enterprises to combine on-premise infrastructure with public and private cloud environments. When integrated with Artificial Intelligence (AI), hybrid cloud systems can enhance security, optimize operational efficiency, and support intelligent analytics for enterprise platforms such as SAP. This research explores the design and implementation of AI-powered secure hybrid cloud architectures for SAP enterprise systems to enable autonomous digital transformation. The proposed architecture integrates advanced technologies including AI-based analytics, automated security monitoring, intelligent data integration frameworks, and scalable cloud infrastructure. By leveraging hybrid cloud environments, enterprises can maintain control over sensitive data while benefiting from the scalability and flexibility of cloud platforms. AI technologies further enable predictive analytics, anomaly detection, and automated resource management, improving system resilience and decision-making capabilities. The study also examines architectural frameworks, data governance strategies, and implementation methodologies that support secure and scalable SAP deployments across hybrid cloud environments. The findings indicate that AI-driven hybrid cloud architectures significantly enhance enterprise agility, security, and analytical capabilities, enabling organizations to achieve sustainable digital transformation and improved business performance.
Intelligent Multi Agent Artificial Intelligence Architecture for Secure Cloud Native Digital Transformation and Infrastructure Automation
Digital transformation across enterprises has accelerated the adoption of cloud native architectures artificial intelligence driven automation and secure infrastructure platforms. However managing complex distributed environments requires intelligent autonomous systems capable of monitoring optimizing and protecting digital ecosystems. This paper proposes an Intelligent Multi Agent Artificial Intelligence Architecture designed to support secure cloud native digital transformation and automated infrastructure management. The architecture integrates AI agents machine learning analytics cybersecurity intelligence and cloud orchestration frameworks to enable autonomous decision making predictive operations and resilient enterprise systems. The proposed framework utilizes distributed intelligent agents responsible for workload orchestration resource optimization threat detection compliance governance and operational automation. Each agent collaborates through a coordinated decision layer supported by data driven analytics and real time monitoring. The system is designed to integrate with container orchestration platforms microservices infrastructure and enterprise cloud platforms such as AWS Azure and hybrid cloud environments. The research highlights how multi agent AI architectures enhance scalability operational efficiency cybersecurity resilience and intelligent infrastructure management in modern enterprises. Experimental evaluation and conceptual modeling demonstrate that the proposed architecture significantly improves infrastructure reliability reduces operational overhead and strengthens cyber defense mechanisms. The findings contribute to the development of autonomous enterprise platforms capable of supporting next generation digital transformation initiatives across finance healthcare manufacturing and government sectors.
AI-Integrated IoT and Digital Twin Architecture for Smart Healthcare Industrial Automation and Real-Time Compliance Monitoring
The integration of Artificial Intelligence (AI), Internet of Things (IoT), and Digital Twin technologies is transforming modern industries by enabling intelligent monitoring, predictive analytics, and automated decision-making. In sectors such as healthcare and industrial automation, real-time data collection and analysis are essential for improving operational efficiency, ensuring regulatory compliance, and enhancing system reliability. IoT devices generate large volumes of sensor data, while digital twin models provide virtual representations of physical systems, allowing organizations to simulate, analyze, and optimize real-world processes. When combined with AI technologies, these systems can deliver predictive insights and automated responses that support smart and adaptive operations. This research proposes an AI-integrated IoT and digital twin architecture designed for smart healthcare systems, industrial automation environments, and real-time compliance monitoring frameworks. The architecture leverages IoT sensors for continuous data acquisition, digital twin models for system simulation and performance monitoring, and machine learning algorithms for predictive analysis and intelligent decision-making. The proposed framework enables proactive fault detection, predictive maintenance, patient health monitoring, and automated regulatory compliance verification. The study emphasizes the role of scalable cloud platforms, edge computing, and secure data management in supporting large-scale intelligent systems. The findings highlight the potential benefits of AI-driven digital twin ecosystems in improving operational transparency, safety, and efficiency while also addressing challenges related to system integration, data security, and infrastructure complexity.
Federated Explainable AI Framework for Secure Healthcare Systems and Financial Enterprise Cloud Data Analytics
The increasing adoption of artificial intelligence and cloud computing in healthcare and financial sectors has created significant opportunities for advanced data analytics and intelligent decision-making. However, these systems often rely on centralized data processing and complex machine learning models that raise concerns regarding data privacy, transparency, and regulatory compliance. Sensitive information such as patient health records and financial transactions must be protected while still enabling collaborative analytics across organizations. This research proposes a Federated Explainable Artificial Intelligence (FEAI) framework designed to support secure and transparent data analytics for healthcare systems and financial enterprise cloud environments. The framework integrates federated learning with explainable AI techniques to enable distributed model training without sharing raw data while maintaining interpretability of model predictions. By combining privacy-preserving distributed learning with transparent decision-making mechanisms, the proposed system ensures both data confidentiality and model accountability. The research methodology involves the design of a federated cloud architecture, implementation of machine learning models with explainability modules, and evaluation using simulated healthcare and financial datasets. Experimental analysis demonstrates that the framework provides strong privacy protection, accurate predictive analytics, and improved interpretability for decision support systems. The proposed approach contributes to the development of trustworthy AI-driven analytics platforms for sensitive enterprise environments.
Explainable Artificial Intelligence (XAI)
Secure and Composable Digital Transformation Architecture for Banking Healthcare Manufacturing and Smart Infrastructure Using Advanced Machine Learning
Digital transformation across banking, healthcare, manufacturing, and smart infrastructure demands secure, scalable, and adaptable architectures capable of handling complex workflows, high data volumes, and strict regulatory requirements. Traditional monolithic enterprise systems lack flexibility and resilience against modern cyber threats and operational disruptions. This paper proposes a Secure and Composable Digital Transformation Architecture that integrates advanced machine learning, modular microservices, zero-trust security principles, and hybrid cloud infrastructure. The framework emphasizes composability through reusable digital services, API-driven ecosystems, container orchestration, and policy-based governance. Advanced machine learning models enable predictive analytics, anomaly detection, fraud prevention, medical diagnostics support, predictive maintenance, and infrastructure optimization. Security mechanisms include identity-centric access control, encryption, behavioral analytics, and automated DevSecOps governance. The architecture supports cross-domain interoperability while maintaining compliance with industry regulations. Experimental modeling demonstrates improvements in operational agility, security posture, predictive accuracy, and system scalability. The proposed framework provides a unified digital transformation roadmap enabling sector-specific customization while preserving architectural consistency. This research contributes a comprehensive model for building secure, intelligent, and composable enterprise ecosystems in highly regulated and data-intensive industries.
Cloud Optimized Enterprise Healthcare Analytics Using AI Genetic Algorithms Blockchain and Apache Infrastructure
The rapid evolution of enterprise healthcare systems has led to the generation of massive volumes of heterogeneous data, including electronic health records, medical imaging, insurance claims, genomic data, and real-time patient monitoring streams. Efficient processing, secure sharing, and intelligent analysis of such data require scalable, optimized, and secure computational frameworks. This research proposes a cloud-optimized enterprise healthcare analytics architecture integrating Artificial Intelligence (AI), Genetic Algorithms (GA), Blockchain technology, and Apache-based distributed infrastructure. The framework leverages Apache Hadoop and Apache Spark for large-scale distributed data processing, while AI-driven machine learning models perform predictive analytics and clinical decision support. Genetic Algorithms enhance model performance through feature selection, hyperparameter optimization, and intelligent resource allocation. Blockchain platforms such as Hyperledger Fabric ensure secure, tamper-proof data exchange and decentralized trust among healthcare stakeholders. The integrated approach improves scalability, security, interoperability, and computational efficiency across enterprise healthcare networks. Experimental evaluation demonstrates improved predictive accuracy, reduced latency, enhanced data integrity, and optimized cloud resource utilization. The proposed system supports enterprise-level healthcare analytics, secure patient data sharing, and intelligent hospital management in modern cloud ecosystems.
A Systematic Analysis and Adaptive Hybrid Machine Learning Framework for Online Shopping Behavior Prediction
The rapid growth of e-commerce platforms has generated vast amounts of consumer data, making online shopping behavior prediction an important research area for improving customer experience, personalized recommendations, and business decision-making. However, traditional statistical models and standalone machine learning techniques often face limitations in handling complex data characteristics such as high dimensionality, data imbalance, dynamic user behavior, and heterogeneous feature types. These challenges reduce the overall effectiveness and reliability of predictive models in real-world e-commerce environments. This research presents a systematic analysis of existing statistical and machine learning approaches used for online shopping behavior prediction, highlighting their strengths, limitations, and performance gaps. The study further investigates the influence of key data characteristics—such as feature diversity, data sparsity, temporal patterns, and class imbalance—on model performance. To address these issues, a structured evaluation framework is proposed that assesses predictive models using comprehensive performance metrics beyond conventional accuracy, including precision, recall, F1-score, robustness, and model adaptability. Building upon these insights, the research designs and implements an adaptive hybrid machine learning framework that integrates multiple learning techniques to improve prediction accuracy, stability, and generalization capability. The proposed framework dynamically selects and combines suitable algorithms based on data characteristics and performance feedback. Experimental validation using real-world e-commerce datasets demonstrates that the adaptive hybrid framework significantly outperforms traditional and single-model approaches in predicting online shopping behavior. The findings contribute to the development of more intelligent, reliable, and scalable predictive systems for modern e-commerce applications.
Secure Service Mesh and Container Orchestration Strategies for Telecom FinTech and SAP Integrated Digital Ecosystems
The rapid convergence of telecommunications, financial technology (FinTech), and enterprise resource planning (ERP) platforms such as SAP has led to the emergence of highly interconnected digital ecosystems. These ecosystems demand scalable, secure, and resilient infrastructure capable of handling high transaction volumes, strict regulatory requirements, and real-time service delivery. Secure service mesh architectures and container orchestration platforms have become foundational technologies in enabling such digital transformations. This paper explores secure service mesh and container orchestration strategies tailored for telecom-FinTech environments integrated with SAP systems. Telecom operators increasingly provide mobile payments, digital wallets, and embedded financial services, thereby operating in highly regulated environments similar to banks. Simultaneously, enterprises rely on SAP systems for core business processes including billing, finance, supply chain, and customer management. Integrating these systems through microservices architectures introduces complexity in service-to-service communication, identity management, data protection, and operational governance. Service meshes such as Istio provide encrypted communication, zero-trust networking, traffic management, and observability across distributed workloads. Container orchestration platforms like Kubernetes enable automated deployment, scaling, resilience, and lifecycle management of microservices-based applications. This study proposes a comprehensive architectural framework that integrates Kubernetes-based orchestration, service mesh security controls, SAP integration patterns, and telecom-grade network functions virtualization (NFV). It evaluates strategies for enforcing mutual TLS (mTLS), policy-driven access control, runtime security, compliance auditing, and multi-cluster federation. Furthermore, it analyzes DevSecOps practices, CI/CD security pipelines, secrets management, and identity federation across hybrid cloud environments. The research concludes that a layered zero-trust architecture combining secure service mesh, hardened container orchestration, SAP API governance, and telecom-grade reliability significantly enhances security posture, scalability, and regulatory compliance. The proposed methodology supports high-availability FinTech workloads, real-time telecom billing systems, and mission-critical SAP applications within unified digital ecosystems.
Threat Detection Using Long-Range Sensors and Directional Feedback Mechanisms
In today’s modern world, warfare has become more dangerous and unpredictable. Soldiers not only face enemies directly but also face hidden threats like long-range gunfire, underground bombs, and drones carrying explosives. Many times, soldiers do not get enough time to understand where the danger is coming from, which leads to serious injuries or even death. A normal helmet only protects the head from physical damage. It does not give any warning about nearby danger. To solve this problem, this project introduces a Smart Helmet with a vibration band that can warn soldiers in advance. The helmet uses different sensors to monitor the surroundings continuously. When it detects a possible threat, the system sends a wireless signal to a vibration band worn on the soldier’s hand or arm. The band vibrates on the side where the danger is present, such as left, right, front, or back. This helps the soldier understand the direction of the threat without looking at any screen. By using microcontrollers, wireless modules, and IoT technology, this system improves battlefield awareness and increases the chances of survival.
T. Pon kiruthika, J. Rinsha Rose, R. S. Pavithra
Designing Secure and Scalable Customer and Order Automation Frameworks in AI-Enabled Cloud Ecosystems
The rapid growth of cloud computing and artificial intelligence (AI) technologies has transformed enterprise operations, enabling automated customer interactions and order processing. Optimizing customer and order automation is essential for enhancing operational efficiency, reducing human error, and delivering personalized services in modern enterprise systems. This study explores the integration of AI-driven automation with cloud-enabled enterprise architectures, focusing on secure software engineering practices to ensure system reliability, data integrity, and regulatory compliance. AI models, including machine learning algorithms for customer behavior prediction and natural language processing for automated customer support, are implemented in scalable cloud environments to streamline order management, inventory tracking, and customer engagement. The research evaluates architectural frameworks, workflow automation strategies, and security protocols to minimize latency, maximize throughput, and safeguard sensitive information. A mixed-methods approach, combining simulation, prototype implementation, and security analysis, is employed to validate system performance. Results indicate that integrating AI and cloud solutions with secure software engineering practices enhances operational efficiency, improves customer satisfaction, and reduces operational costs. The study also highlights challenges, including data privacy, model interpretability, and system scalability, providing actionable insights for enterprises aiming to modernize and secure automated business processes.
Cloud Based Secure Enterprise Healthcare Software with AI Centered Risk Governance
Cloud-based secure enterprise healthcare software has emerged as a transformative approach to managing clinical, administrative, and financial operations in modern healthcare systems. With the rapid digitization of medical records and integration of advanced analytics, healthcare organizations increasingly rely on cloud infrastructures to deliver scalable, interoperable, and cost-efficient services. At the same time, the proliferation of sensitive patient data and regulatory requirements necessitates robust security, privacy protection, and risk governance frameworks. Artificial Intelligence (AI) plays a critical role in enhancing cybersecurity, predictive analytics, clinical decision support, fraud detection, and compliance monitoring. This paper explores the architecture, governance structures, and risk management strategies associated with AI-centered cloud healthcare platforms. It analyzes the integration of machine learning models into enterprise systems such as electronic health records, telemedicine platforms, and population health management tools. Furthermore, it evaluates regulatory compliance frameworks including HIPAA and global data protection standards. The study proposes a comprehensive research methodology for designing and implementing secure AI-driven healthcare enterprise systems. Finally, advantages and limitations are discussed to provide a balanced understanding of technological, ethical, operational, and financial implications in contemporary healthcare ecosystems.
Zero-Trust Security Models for AI-Powered Healthcare Systems in Multi-Cloud Architectures
The rapid integration of artificial intelligence (AI) into healthcare systems has transformed diagnostics, predictive analytics, personalized medicine, and operational management. These AI-powered healthcare applications increasingly operate in multi-cloud environments, leveraging distributed infrastructures offered by providers such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform. While multi-cloud architectures enhance scalability, redundancy, and flexibility, they introduce complex security challenges, including identity sprawl, inconsistent policy enforcement, lateral movement risks, and expanded attack surfaces. Traditional perimeter-based security models are insufficient in such decentralized ecosystems. Zero-Trust Security Models (ZTSMs) offer a robust alternative by enforcing continuous verification, least-privilege access, and micro-segmentation across distributed systems. This research proposes a comprehensive zero-trust framework tailored for AI-powered healthcare systems deployed in multi-cloud environments. The framework integrates AI-driven identity analytics, adaptive risk scoring, secure API gateways, encrypted data pipelines, policy orchestration, and continuous compliance monitoring. The study outlines architectural components, implementation methodology, performance evaluation metrics, and regulatory considerations. Findings indicate that zero-trust models significantly enhance data confidentiality, integrity, and availability while supporting AI scalability and interoperability. The proposed approach strengthens healthcare resilience against evolving cyber threats in complex cloud ecosystems.
Monitoring and Observability Solutions for Cloud-Native Applications
Cloud-native applications have revolutionized how enterprises deploy and manage applications, offering unparalleled scalability, flexibility, and resilience. However, these benefits come with complexities in monitoring and observability. This thesis explores various monitoring and observability solutions tailored for cloud-native applications. It evaluates tools, methodologies, and best practices that help in maintaining the health, performance, and reliability of these applications. Through extensive literature review and practical use cases, this thesis aims to provide a comprehensive guide for practitioners seeking to implement effective monitoring and observability strategies in cloud-native environments
Application Performance Management (APM)
Zero-Trust–Based Enterprise Cloud AI Architecture for Secure and Privacy-Preserving Healthcare CNN Systems
The rapid adoption of Convolutional Neural Networks (CNNs) in healthcare has significantly improved diagnostic accuracy, medical image analysis, and clinical decision support. However, deploying CNN-based healthcare AI systems in cloud environments introduces substantial security and privacy risks due to the sensitive nature of patient data and the complexity of distributed infrastructures. Traditional perimeter-based security models are insufficient to address modern cyber threats, insider risks, and regulatory requirements. This paper proposes a zero-trust–based enterprise cloud AI architecture designed to ensure secure and privacy-preserving deployment of healthcare CNN systems. The proposed architecture integrates zero-trust security principles across business, application, data, and infrastructure layers, enforcing continuous authentication, least-privilege access, and real-time monitoring. It incorporates secure data ingestion, cloud-native MLOps pipelines, encrypted model lifecycle management, and compliance-aware governance mechanisms aligned with healthcare regulations such as HIPAA and GDPR. By embedding zero-trust concepts into enterprise architecture, the proposed framework mitigates attack surfaces while maintaining scalability and operational efficiency. This work provides a holistic blueprint for healthcare organizations seeking to operationalize CNN-based AI systems in cloud environments without compromising data confidentiality, integrity, or availability
Composable Commerce Architectures: Building Agile Retail Systems
With the rapidly changing environment of online shopping, the old monolith systems are not sufficient to serve the speed requirement and scale and individuality need at a high motivated rate. The given paper discusses the disruptive power of composable commerce architectures, i.e., modularization, API-first, cloud-native systems enabling retailers to develop agile, scalable, and resilient retail environments. Based on empirical evidence, industry case studies and architecture review we are going to scrutinise how composable systems excel over legacy systems in the deployment speed, operational efficiency, customer satisfaction and organizational agility. Composable commerce enables functionalities of commerce (checkout, catalog, inventory, and personalization) to be separated, allowing the parallel development of commerce solutions, speed of market, and real-time integration to customer preferences. The paper also identifies the enabler technologies which include, Kubernetes, events-driven microservices and serverless computing which has made composability at scale a reality. Quantitative results indicate up to 53 percent in the speed of feature deployment, 20 percent in cost savings, significant improvements in the measures of developer productivity and customer experience. Although the needs of composable commerce may seem a current problem in terms of integrations and governance, it also can be defined as a legacy strategy to create a future-proof digital commerce framework. Concluding the paper with the recommendations, in which the incremental transformation, cross-functional alignment, and cross-functional alignment of the best practices of the platform engineering are stressed when enterprises switch to the composable models after the monolithic ones.
Cost-Aware Cloud Resource Optimization Models for Enterprise Applications
Cost-aware cloud resource optimization models aim to intelligently allocate, scale, and schedule computing resources for enterprise applications by balancing performance, availability, and Quality of Service (QoS) requirements with operational cost constraints, using techniques such as predictive analytics, machine learning, and optimization algorithms to minimize cloud expenditure while ensuring efficient utilization and business continuity.
Secure Healthcare Intelligence using AI-Driven Predictive Systems Integrating Fraud Risk Analytics Cybersecurity and MMS with Cloud Computing and Data Warehousing
The healthcare sector is increasingly dependent on digital intelligence to enhance clinical decision-making, optimize operational efficiency, and ensure data security. However, the rapid adoption of electronic health records, cloud platforms, and mobile medical systems (MMS) has exposed healthcare infrastructures to sophisticated cyber threats and financial fraud risks. This study proposes a secure healthcare intelligence framework that integrates AI-driven predictive systems with fraud risk analytics, cybersecurity mechanisms, cloud computing, and data warehousing. The framework leverages machine learning algorithms to predict fraudulent activities, detect anomalies, and support proactive risk management while ensuring compliance with healthcare data protection standards. Cloud-based architectures provide scalable storage and computational resources, while centralized data warehouses enable efficient data integration and real-time analytics. Advanced cybersecurity layers, including encryption, access control, and intrusion detection systems, safeguard sensitive medical and financial data. The proposed approach enhances trust, accuracy, and resilience in healthcare intelligence systems, enabling healthcare organizations to deliver secure, data-driven services. The research highlights the potential of combining artificial intelligence and secure cloud technologies to address emerging challenges in healthcare fraud detection, predictive analytics, and information security
AI- and Machine Learning-Enabled Secure Scalable Cloud Architectures for Network Reliability Privacy Preservation and Energy Optimization
The rapid adoption of cloud computing has intensified the need for secure, reliable, and energy-efficient architectures capable of supporting large-scale intelligent applications. AI- and machine learning-enabled cloud architectures provide advanced capabilities for adaptive network management, proactive threat detection, and optimized resource utilization. This paper presents a secure and scalable cloud architecture that integrates machine learning models for network reliability enhancement, privacy-preserving mechanisms for sensitive data protection, and intelligent energy optimization techniques. The proposed framework employs predictive analytics to anticipate network failures, anomaly detection models to mitigate cyber threats, and privacy-aware learning approaches to ensure data confidentiality across distributed environments. In addition, energy-efficient scheduling and workload optimization strategies are incorporated to reduce operational costs and environmental impact. The architecture is designed to support heterogeneous workloads and dynamic traffic conditions while maintaining high availability and compliance with security standards. Experimental analysis and architectural evaluation demonstrate improved network resilience, enhanced privacy preservation, and significant gains in energy efficiency, making the proposed solution suitable for next-generation cloud and edge computing ecosystems.
A Governance-Centric AI Framework for Cloud-Native DevOps Automation in Healthcare Applications with Secure Mobile Systems and Network-Enabled Compliance
The rapid adoption of cloud-native architectures, mobile enterprise systems, and AI-driven automation has introduced unprecedented challenges in governance, security, compliance, and operational transparency. Traditional governance models are fragmented, reactive, and insufficient to manage the dynamic, distributed, and autonomous nature of modern enterprise environments. This paper proposes a Unified Governance-Centric Artificial Intelligence (UG-AI) Framework that integrates governance, risk management, compliance (GRC), security, and operational intelligence into a single AI-orchestrated control layer. The framework leverages machine learning, policy-as-code, continuous compliance monitoring, and adaptive risk assessment to ensure secure automation across cloud-native platforms, mobile ecosystems, and network-enabled infrastructures. By embedding governance principles directly into AI decision-making processes, the framework enables proactive compliance enforcement, real-time threat mitigation, and auditable automation workflows. The proposed approach addresses regulatory complexity, data sovereignty, identity management, and cross-platform interoperability while supporting scalability and resilience. This research contributes a conceptual architecture, governance workflow model, and implementation strategy aimed at enhancing enterprise trust, regulatory alignment, and operational efficiency in next-generation digital ecosystems.
Zero Downtime Enterprise Modernization of SAP Infrastructure with Intelligent Automation, Encryption, Cloud Integration, and AI-Enabled Predictive Systems for Supply Chain Resilience
Enterprise modernization is critical for organizations relying on SAP infrastructure to maintain operational continuity, optimize decision-making, and enhance supply chain resilience. This study explores a zero-downtime approach to modernizing SAP systems through the integration of intelligent automation, robust encryption protocols, and cloud-based solutions. Additionally, AI-enabled predictive systems are implemented to anticipate supply chain disruptions, improve responsiveness, and optimize resource allocation. The proposed framework demonstrates that combining automation, security, cloud integration, and predictive analytics can reduce downtime, strengthen operational security, and increase supply chain efficiency. This integrated approach provides a scalable model for enterprises seeking to modernize SAP infrastructure while maintaining high availability and operational resilience.
Generative AI–Powered Cloud and Machine Learning Architectures for Digital Privacy and Risk Management in Banking and Trade Systems over 5G Networks
The proliferation of digital banking and global trade systems has dramatically increased the volume and velocity of financial data processed daily. At the same time, this growth has intensified concerns over digital privacy, cybersecurity, and real-time risk management. The integration of Generative Artificial Intelligence (AI) and Machine Learning (ML) within scalable cloud computing architectures presents a transformative opportunity to address these challenges, especially when leveraged over 5G network infrastructures that support high-speed, low-latency communication. This research proposes a comprehensive cloud-native framework that harnesses generative AI, advanced ML models, and multi-tenant cloud platforms to deliver robust risk-aware analytics, privacy-preserving computation, and adaptive security mechanisms for banking and trade systems. The framework supports continuous ingestion of transactional and trade data, applies generative modeling for anomaly detection, and uses machine learning for proactive risk scoring. Using a simulated dataset of banking operations and trade transactions, we demonstrate the framework’s performance in detecting fraudulent activities while preserving user privacy through differential privacy techniques and federated learning. Findings show high accuracy in risk prediction and significant improvements in privacy protection without compromising system responsiveness, illustrating the potential of generative AI–powered cloud architectures in modern financial ecosystems.
A Secure 5G-Enabled Cloud and AI Computing Model for Financial Risk Prediction and Healthcare Intelligence Using Deep Learning
The rapid adoption of 5G networks, cloud computing, and artificial intelligence has enabled the development of intelligent and data-driven applications across financial and healthcare domains. However, the integration of these technologies introduces significant challenges related to security, scalability, latency, and risk management. This paper proposes a secure 5G-enabled cloud and AI computing model that leverages deep learning techniques for financial risk prediction and healthcare intelligence. The proposed architecture integrates cloud-based data processing, 5G-enabled low-latency communication, and deep learning models to analyze large-scale heterogeneous data in real time. Advanced security mechanisms, including encryption, access control, and secure data transmission, are incorporated to protect sensitive financial and medical information. The model enables accurate risk assessment, anomaly detection, and predictive analytics while ensuring data privacy and regulatory compliance. Experimental evaluation demonstrates improved prediction accuracy, reduced response time, and enhanced system reliability compared to traditional cloud-based approaches. The proposed framework offers a scalable and secure solution for next-generation intelligent financial and healthcare systems.
Multiparty Privacy-Preserving AI with SAP-Based Cyber Defense for Healthcare Business Processes in Cloud and 5G
The rapid adoption of cloud computing, 5G networks, and data-driven automation in healthcare has intensified the need for secure, privacy-preserving, and compliant artificial intelligence (AI) frameworks. This paper proposes a multiparty privacy-preserving AI architecture integrated with SAP-enabled risk-based cyber defense to support secure healthcare cloud and 5G-enabled MLOps environments. The framework leverages federated learning, secure aggregation, and differential privacy to enable collaborative model training across distributed healthcare stakeholders while ensuring sensitive patient data remains protected. SAP security and risk management capabilities are incorporated to provide continuous threat intelligence, policy enforcement, and real-time risk assessment across cloud, network, and application layers. The proposed architecture addresses key challenges related to data confidentiality, regulatory compliance, adversarial attacks, and operational resilience in healthcare AI pipelines. By aligning privacy-preserving AI techniques with enterprise-grade SAP security analytics, the solution enhances trust, scalability, and auditability in multiparty healthcare ecosystems. The model demonstrates how secure MLOps can be achieved across heterogeneous cloud and 5G infrastructures while maintaining robust cyber defense and governance.
Integrating Fiber Broadband with SAP and AI for Secure Cloud-Based Digital Advertising and Financial Systems
The rapid expansion of fiber broadband networks has enabled high-performance cloud infrastructures that support data-intensive enterprise applications. At the same time, organizations increasingly rely on SAP platforms and artificial intelligence to manage digital advertising and financial operations. Despite these advancements, ensuring data security, system integrity, and intelligent decision-making across cloud environments remains a significant challenge. This paper presents an integrated framework that combines fiber broadband connectivity with SAP systems and AI-driven analytics to support secure, cloud-based digital advertising and financial enterprise systems. The proposed approach leverages high-bandwidth network capabilities to enable real-time data processing, predictive intelligence, and continuous security monitoring. AI models analyze transactional, behavioral, and operational data to detect anomalies and optimize business performance. The framework incorporates robust security controls to protect sensitive financial and advertising data while maintaining scalability and regulatory compliance. Experimental evaluation demonstrates improved system responsiveness, enhanced threat detection accuracy, and more informed business insights compared to conventional cloud architectures. The findings highlight the value of integrating broadband networks, SAP platforms, and AI technologies for building secure and intelligent enterprise cloud solutions.
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