International Journal of Research and Applied Innovations
Welcome to IJEETR The International Journal of Engineering & Extended Technologies Research (IJEETR) is a prestigious, peer-reviewed, open-access international journal committed to fostering innovation, collaboration, and excellence in research across the broad spectrum of engineering and technological sciences. In a rapidly evolving world where technology is reshaping every facet of human life, IJEETR strives to be more than just a journal – it is a knowledge hub where scholars, academicians, researchers, and industry practitioners come together to exchange pioneering ideas, transformative research, and ground breaking solutions to global challenges. We believe in bridging the gap between theory and practice, encouraging interdisciplinary collaboration, and ensuring that innovative discoveries reach a worldwide audience with maximum impact. Why Choose IJEETR? Publishing with IJEETR offers you: Rigorous Peer Review Global Reach Timely Publication Indexing & Abstracting Interdisciplinary Collaboration Impact-Oriented Dissemination IJEETR is currently accepting manuscripts for its upcoming issue. We encourage submissions that present: Novel methodologies and experimental results. Innovative frameworks and technological applications. Critical reviews of contemporary research trends. Submit your manuscript to editor@ijeetr.com and be a part of a global network of innovators shaping the future of engineering and technology. Call for Papers Volume 7, Issue 4, July-August 2025 Deadline: 31st August 2025 Notification: 3 to 4 Days Publication: 24 to 36 Hours E-Certificates: 6 Hours Contact Us For manuscript submission, queries, or collaboration opportunities: Email: editor@ijeetr.com Website: www.ijeetr.com Support: +91-9710583466
A Cloud-Native Test Automation Framework For Secure Ott Content Delivery Systems
The current paper outlines a cloud-native, automated testing framework dedicated to the purpose of supporting safe distribution of OTT (Over-the Top) media content with the help of Digital Rights Management (DRM) systems. The demand of streaming services has been burning intensely and the content protection schemes and mechanisms have been becoming too sophisticated to handle, but at the same time, ensuring a secure playback across a heterogeneous device type has become an important and a difficult task. The architecture that was proposed combines real-device, UI, API and continuous integration and Continuous Delivery (CI/CD) pipelines built on Kubernetes and containerized microservices. The framework encompasses DRM compliance, content playback integrity and platform compatibility by virtue of setting up the DRM method through automated orchestration and parallel testing, and embedded security validation all at scale. Tests and trials reveal that the system has the capability of carrying out more than 1000 simultaneous DRM tests with success rates that are overwhelming and minimal latency. The CI/CD lifecycle integration of security audit evidenced over 85 percent decrease in the number of exposures in vulnerability upon adoption of the framework. Moreover, cross-platform compatibility of mobile, web, and smart TVs, using real-device playback verification, was also initiated because of the low ratio of buffer rate and crash. The findings prove the efficiency of the framework as the scalable, secure and performance-efficient mode of the contemporary OTT content delivery systems. The method is very scalable and can be used in edge-based streaming and AI based content protection in future.
AI-Driven Virtual Triage for Behavioral Health: A Technical Review
Behavioral health systems face compounding pressures from rising mental health demand, clinician workforce shortages, and triage infrastructures ill-equipped to detect crisis signals in real time. Traditional intake processes use a set series of questions that don't change based on a person's emotional state, which means that people in serious distress may not get the attention they need while less urgent cases take up valuable This article looks at how to create and use an AI-driven virtual triage platform that understands emotions, uses specialized language processing for behavioral health, and assesses risk in real time to change crisis response from being reactive to proactive. The platform looks at voice patterns, the meaning of what is said, and the caller's behavior over time during conversations, creating a flexible risk assessment that automatically prompts further help and directs Application domains that span crisis hotlines, emergency departments, telehealth providers, and managed care organizations. The technical architecture has four layers that depend on each other: The architecture includes emotion recognition, NLP-based semantic risk detection, a multi-factor scoring engine, and a FHIR-compliant integration framework. Each layer has its design trade-offs. Ethical governance issues, such as fairness in algorithms, clarity in processes, the involvement of humans in decision-making, and the ability A phased implementation helps bridge the difference between testing the system and using it on a large scale, while also evaluating its benefits in areas like healthcare, operations, finances, and social effects.
Speech Emotion Recognition In Mental Health
Natural Language Processing For Crisis Detection
Predictive Risk Stratification In Psychiatry
Adaptive Drift Defense: A Unified Framework for Data, Task, And User-Intent Drift in LLM Apps
The deployment of Large Language Models (LLMs) to production systems where user expectations, data source and task requirements are constantly changing is increasing. This change brings in the element of drift-- changes to input distributions, tool-call patterns or user intent--that impairs performance given enough time and is passed unnoticed. Current approaches to drift management tend to be either excessively specific to data-level drift monitoring or directly retrain the model which represents an unacceptable resource-intensive task; thus, much ground remains to be lost with regards to real-time response to drift and resource requirement. In this paper we introduce coherent framework Adaptive Drift Defense, which can integrate three orthogonal layers of detection, like retrieval distribution monitoring, tool-call graph analysis or estimation of output variance, to detect data, task and user-intent drift in a concurrent way. A computing bandit mitigation policy actively chooses timely refinements, retrieval adaptations or tool-routing policies, driving performance to equilibrium in terms of requiring retraining of the model. Experiments on major customer care helpers (~1.2M interactions) and business analytics copilots (~800k queries) show that the system achieves an 88 percent accuracy and 86 percent recall in detecting drift, with 20-35 percent savings in the cost of manual rework with insignificant latency overhead (<50ms). When compared to non-incremental pipelines, task success rates were increased by 15-20 percent, bridging most of the performance-gap to full retraining with only 12-percent extra compute cost. These results imply the conceptual feasibility of conventional, collaborative drift monitoring of LLM system. Analytical paper also furnishes reference dashboards and operational playbooks which assists deployment teams. These results help us understand that adaptive methods with mitigation-first approaches will enable high-quality service quality in highly dynamic settings and that it scales well to situations of frequent model retraining.
Optimizing Cost and Performance in Serverless Databases: A Practical Framework for DynamoDB IA Mode Migration
The paper examines the ways of minimising cost and maintaining good performance in the Amazon DynamoDB when the Infrequent Access (IA) mode is used. Most firms end up paying a great deal in serverless databases since data are being stored even when it is not frequently utilized. In response to this, the paper develops a simple framework that would be used to transfer such data into IA mode. In the study, the quantitative approach is used and two setups are compared, namely standard mode and IA mode, at the same workloads. The logs of latency, throughput and cost are sampled in seven days time. It is demonstrated that with IA mode, it is possible to reduce the cost of storage and operation by approximately 40 percent without increasing the average latency more than 10 milliseconds. The framework involves well defined steps such as the analysis of access pattern, capacity planning and latency testing. The results confirm that this way of migration is able to cost-effectively save performance without damaging it. The study can assist developers, cloud architects, and DevOps, to design intelligent cost-reduction in serverless database platforms.
Dynamic Intelligent Framework Using AI for Data Mining Federated Learning Financial Intelligence and Smart Healthcare Systems
The rapid proliferation of data across industries has necessitated the development of unified frameworks that leverage Artificial Intelligence (AI) to extract actionable insights efficiently and securely. This study proposes a Unified Artificial Intelligence Framework (UAIF) integrating data mining, federated learning, financial intelligence, and smart healthcare applications. The framework employs advanced machine learning algorithms for data preprocessing, pattern recognition, and predictive modeling while preserving privacy through federated learning architectures. In financial intelligence, UAIF supports fraud detection, credit scoring, and market trend analysis by synthesizing multi-source financial data. In healthcare, the framework facilitates patient monitoring, disease prediction, and personalized treatment plans by analyzing distributed medical records while ensuring compliance with privacy regulations. The proposed framework emphasizes modularity, scalability, and interoperability across heterogeneous data sources. Experimental results demonstrate enhanced predictive accuracy, reduced data transmission overhead, and improved privacy preservation compared to conventional centralized models. This research highlights the potential of unified AI systems to bridge the gap between computational intelligence and real-world applications, contributing to smarter decision-making in finance and healthcare while promoting ethical and secure AI practices.
Design and Implementation for an Improved Version of Cloud Computing Architecture by using Concept of Ontology with Query Retrieval and Refinement Mechanism
The conception of cloud computing has been prevailing in minds of each individuals, business likewise as scientific organizations. The promotional material of cloud is at high pace. Technology has become thus advanced that the majority in operation systems square measure compatible to figure with cloud suppliers as a result of cloud computing is predicated on pay per usage and market bound policy. They supply facilities and simple accessibility to users for accessing resources in multiple environments. During this paper, varied aspects of cloud computing and its applications square measure conferred. The paper conjointly presents execs and cons of ANEKA-one of .NET application framework to develop cloud computing platform in multiple virtual setting likewise as physical setting. It’s compatible with Windows and UNIX each. It describes varied programming models that convert applications into cloud application so facultative ANEKA cloud infrastructure in system. ANEKA is verified to be one in every of scalable and reliable technologies in cloud word.
Programming Models (Task, Thread, Map Reduce and Parameter Sweeping)
From Open Information Extraction to Probabilistic Fusion: Semantic Retrieval Pipelines for Enterprise Knowledge Graph Construction
<span style="font-size: 10.0pt; line-height: 115%; font-family: 'Times New Roman','serif'; mso-fareast-font-family: Cambria; color: #262626; mso-themecolor: text1; mso-themetint: 217; mso-style-textfill-fill-color: #262626; mso-style-textfill-fill-themecolor: text1; mso-style-textfill-fill-alpha: 100.0%; mso-style-textfill-fill-colortransforms: 'lumm=85000 lumo=15000'; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">The exponential growth of unstructured enterprise data spanning documents, logs, emails, reports, and web content has intensified the demand for scalable mechanisms capable of extracting, organizing, and retrieving knowledge in machine-interpretable forms. Knowledge Graphs (KGs) have consequently emerged as a foundational representation for modeling entities, relationships, and contextual semantics across heterogeneous and distributed information sources, enabling more advanced analytics, reasoning, and decision support. This article presents a systematic exploration of semantic retrieval pipelines for enterprise knowledge graph construction, tracing their evolution from early Open Information Extraction (OpenIE) systems to more sophisticated probabilistic knowledge fusion architectures. Drawing upon seminal systems such as TextRunner and SigmaKB, we analyze how successive pipeline stages including large-scale text ingestion, relation extraction, semantic filtering, entity normalization and disambiguation, and probabilistic knowledge fusion work in concert to transform noisy, unstructured data into coherent and reliable enterprise knowledge graphs. The discussion synthesizes recurring architectural patterns observed across foundational systems, examines practical challenges encountered in large-scale enterprise deployments such as noise management, ambiguity resolution, scalability, and trust and highlights emerging directions toward AI-augmented semantic retrieval, where machine learning and neural representations increasingly complement symbolic knowledge representations to enhance robustness, adaptability, and semantic depth
An Real-Time Deep Fakes and Face Forgery Using Transfer Learning Algorithm
A novel deep learning architecture for deepfake detection was proposed that combines LSTM and CNN methods, implemented using Python on the Kaggle platform. The model was evaluated using two datasets: DFDC and Ciplab. Dataset preprocessing involved 19,148 real images and an equal number of fake images, with 80% allocated for training using 128 × 128 image sizes. Binary cross-entropy function 5.4 was used to calculate error rates during training iterations.The results demonstrated high accuracy rates of 97.32% and 98.24%, with low error rates of 0.15% and 0.26% for the respective datasets. These results confirm the model's adaptability for accurate deepfake prediction. The effectiveness stems from the complementary strengths of both approaches: LSTMs effectively capture sequential temporal dependencies between frames to identify deepfake inconsistencies, while CNNs evaluate frame content to detect visual artifacts, inconsistencies, or unusual patterns indicating manipulation. The hybrid approach leverages the strengths of both methods, resulting in a more efficient and effective model capable of adapting to various deepfake generation techniques. By combining temporal analysis capabilities of LSTMs with the spatial feature extraction abilities of CNNs, the architecture achieves a more comprehensive deepfake detection system.
Divya Bharathi G , Vijay P , Nishanth S, Pavithran S, Vijay Sarathy R
Rice Leaf Disease Detection and Remedies using Deep Learning
Rice, a critical food staple cultivated globally, faces significant productivity challenges from leaf diseases that reduce yields and cause economic damage. Traditional disease identification methods are typically manual, slow, and require specialized expertise. Automated detection systems offer farmers a way to identify leaf diseases with minimal human involvement. Earlier research in rice leaf disease identification primarily employed image processing techniques combined with machine learning algorithms. These approaches extracted specific features such as color attributes, textural patterns, vein structures, and lesion morphology from images of diseased leaves. Machine learning models then classified diseases based on these extracted characteristics. Deep learning approaches have emerged as particularly effective for disease detection, as they can identify complex patterns from large datasets without requiring explicit feature extraction. This systematic review examines various deep learning methodologies applied to rice leaf disease detection, including transfer learning, ensemble approaches, and hybrid techniques. The review also evaluates how effectively these methods address various challenges in the field. The review provides detailed information on model architectures, hyperparameter configurations, fine-tuning strategies, and performance evaluation metrics used across different studies. Additionally, it addresses limitations in existing research and suggests potential directions for developing more accurate and efficient rice leaf disease detection systems in the future.
Murali S, Arun S, Logeswaran S, Vignesh S
Ensuring Application Resilience in Cyber Security with Artificial and Machine Learning
As cyber threats get more complex, application resilience is a critical concern. Conventional security solutions are useless against adaptive threats that change over time, necessitating the use of AI and machine learning technologies. AI improves cybersecurity by anticipating vulnerabilities, detecting anomalies, and automating threat response. Machine learning systems analyse massive volumes of security data to identify trends in threats, hence improving real-time responses. Deep learning, image processing, and behavioural analysis improve defence capabilities against attackers. Nonetheless, concerns about adversarial assaults and data poisoning persist. The convergence of AI with cybersecurity infrastructures such as Zero Trust Architecture (ZTA) increases resilience. This article provides an overview of AI/ML technologies in cybersecurity, including their advantages, problems, and future trends in application protection.
Comparative Performance Analysis of MTCMOS- Based Combinational Logic Circuits for Low- Power Applications
analysis of various MTCMOS-based combinational logic circuits, evaluating their effectiveness in terms of power Efficient power management is a critical aspect of electronic systems, significantly influencing the evolution of modern technology.In modern electronic systems, power consumption plays a crucial role in determining performance, efficiency, and sustainability. Multi-Threshold Complementary Metal-Oxide-Semiconductor(MTCMOS) technology has emerged as a promising solution for reducing leakage power while maintaining circuit performance. This paper presents a comparative dissipation, propagation delay, and overall performance. The study involves simulations of key combinational circuits, such as logic gates, multiplexers, and arithmetic units, under different operating conditions. Results demonstrate the trade-offs between power savings and speed in MTCMOS implementations, highlighting the optimal design choices for low-power applications. The findings contribute to the development of energy-efficient digital circuits, making MTCMOS a viable candidate for next-generation low-power VLSI designs.
Multi - Threshold Complementary Metal – Oxide – Semiconductor (MTCMOS)
Adaptive Machine Learning Frameworks for Data Quality Monitoring: From Anomaly Detection to Continuous Pipeline Validation
Data quality monitoring (DQM) has become a critical requirement in modern data-driven systems, especially in machine learning (ML) pipelines where poor-quality, inconsistent, or drifting data can directly degrade model performance, reliability, interpretability, and fairness. As organizations increasingly rely on automated decision-making systems, even subtle data anomalies such as distributional shifts, missing-value spikes, schema mismatches, or feature correlation changes can propagate downstream and produce significant operational and reputational risks. Traditional rule-based validation approaches, including static thresholds, manual audits, and predefined integrity constraints, are often inadequate in dynamic, large-scale, and streaming environments where data characteristics evolve continuously. Consequently, machine learning techniques have emerged as adaptive and scalable solutions for automated data quality monitoring, enabling systems to detect complex anomalies, context-sensitive outliers, and temporal drift patterns without exhaustive manual specification. This article surveys key ML-driven approaches to DQM, including statistical anomaly detection, density-based outlier detection, isolation-based methods, and concept drift detection frameworks, while also examining their integration into continuous ML pipelines. Foundational techniques such as the Local Outlier Factor (LOF) and Isolation Forest are discussed alongside modern validation architectures that embed automated profiling, distribution comparison, and alerting mechanisms into production workflows. By synthesizing algorithmic foundations, system design principles, and operational best practices, this article presents a structured framework for implementing robust ML-based DQM systems capable of maintaining data integrity in complex, high-volume environments.
AI Driven Multi-Cloud Data Consistency and Security Architecture for Always-On Digital Enterprise Systems
Modern enterprises increasingly rely on multi-cloud infrastructures to ensure scalability, high availability, and operational flexibility. However, managing data consistency and ensuring robust security across distributed cloud platforms presents significant challenges. Data replication, synchronization delays, heterogeneous security policies, and dynamic workloads complicate traditional management approaches, exposing critical enterprise systems to risks such as data breaches, inconsistencies, and compliance violations. This research proposes an Artificial Intelligence (AI) driven multi-cloud data consistency and security architecture designed for always-on digital enterprise systems. The framework integrates AI-powered monitoring, predictive anomaly detection, automated data synchronization, and adaptive security policy enforcement. By continuously analyzing transactional patterns, network flows, and system interactions, the architecture identifies potential inconsistencies, predicts security threats, and autonomously enforces corrective actions across multi-cloud environments. The methodology includes designing the architecture, implementing AI-based data management and security modules, simulating multi-cloud enterprise workloads, and evaluating performance metrics such as data consistency, latency, throughput, security breach detection, and compliance adherence. Results demonstrate that the AI-driven framework significantly improves cross-cloud data consistency, enhances security posture, reduces manual management overhead, and supports uninterrupted operation of critical digital enterprise services. This approach enables enterprises to maintain secure, consistent, and always-on operations while dynamically optimizing resources across multiple cloud platforms.
Modern Middleware for Real-Time Banking Enhancing Transaction Speed and Reliability
This document provides a detailed presentation of the Real-time Service Integration (RSI) Middleware Architecture. This cloud-native solution aims to help financial institutions modernize the banking transaction process for large numbers of consumers. Engineered for high-speed transaction processing and performance, this architecture supports transaction latency of under 100 milliseconds, with the ability to process over 10,000 transactions per second while providing an uptime availability rate of 99.99% with ACID compliance between the old legacy systems and the new CORE banking systems using a canonical data model and orchestration technologies such as Kafka Streams and Dynamic Payment Rails. Among the many significant advances provided by this architecture are Delta-Lake Analytics, NewSQL with CockroachDB, Microservices built on Spring Footprint and Containers (Kubernetes), and a Disaster Recovery Plan with a Recovery Time Objective (RTO) of fewer than 15 minutes, estimated at saving at least $200 million per year in cloud infrastructure costs. The document also emphasizes how useful Agile Methodologies are for Continuous Delivery and engagement with stakeholders while also fully specifying potential future enhancements to this architecture including Edge Computing; Central Bank Digital Currency (CBDCs) based on Blockchain technology; and Artificial Intelligence (AI) driven Fraud Detection to improve Banking Security and Efficiency. The RSI Middleware architecture allows financial institutions to operate more efficiently and modernize their data systems resulting in greater financial savings and improved performance metrics. As the platform is modular in design, the platform is expected to support a wide range of technological advances in the future.
A Real-Time Analytics Architecture for Enterprise Order Lifecycle Visibility and Backlog Management
Enterprise order management systems traditionally rely on batch-oriented data pipelines that introduce significant latency between transactional events and analytical insights. In large-scale global supply chains, delays in reporting order status, fulfillment availability, and backlog trends can hinder operational responsiveness and reduce customer satisfaction. This paper presents the design and implementation of a real-time analytics architecture that enables near-instant visibility into the order lifecycle across distributed enterprise systems. The proposed architecture integrates change data capture (CDC), distributed streaming pipelines, scalable NoSQL storage, and search-based indexing to deliver sub-second analytics for order tracking, backlog management, and fulfillment decision support. The system replaces legacy batch reporting processes and enables continuous synchronization between transactional enterprise resource planning (ERP) systems and analytical dashboards. Experimental results demonstrate a reduction in data latency from approximately 24 hours to sub-second availability, a reduction in report load time from several minutes to seconds, and improved scalability supporting large-scale operational analytics across global business units. The architecture has been deployed across multiple enterprise operational units and serves as a reference implementation for event-driven analytics in supply chain environments.
AI-Integrated Smart Infrastructure Architecture for Autonomous Data Centers Secure Networks and Intelligent Resource Management
Artificial Intelligence (AI) has emerged as a transformative technology in modern computing infrastructures, enabling intelligent automation, predictive management, and enhanced security within large-scale digital ecosystems. The increasing complexity of data centers, network infrastructures, and distributed computing environments has created the need for smart architectures capable of autonomously managing resources and maintaining operational stability. This research focuses on the development of an AI-integrated smart infrastructure architecture designed to support autonomous data centers, secure network operations, and intelligent resource management. The proposed architecture integrates machine learning algorithms, software-defined infrastructure, predictive analytics, and automated orchestration mechanisms to enhance the efficiency and resilience of digital platforms. By leveraging AI-driven analytics, the system can monitor infrastructure performance, predict workload fluctuations, detect potential failures, and optimize resource allocation in real time. Additionally, the architecture incorporates advanced cybersecurity frameworks that use intelligent threat detection and behavioral analysis to protect network environments from evolving cyber threats. The study also explores the implementation of autonomous decision-making processes within infrastructure management systems, reducing the need for manual intervention and minimizing operational errors. The findings demonstrate that AI-integrated infrastructures significantly improve scalability, operational efficiency, and system reliability while strengthening security mechanisms. This research contributes to the development of next-generation intelligent infrastructure capable of supporting the growing demands of modern digital services.
AI-Based Big Data Systems for Real-Time Disaster Response and Resource Allocation
AI-based big data systems support near real-time disaster response and resource allocation. These systems shorten reaction times by leveraging scarce resources (data, bandwidth, processing, and decision-making) more efficiently in disaster management. A layered architecture provides distributed computing, data fusion, and decision management for near real-time deployment. Future implementations will prototype alarm generation using social-media and sensor data. Large-scale disasters place severe and often insurmountable demands on emergency agencies. Under such conditions, resource allocation plays a critical role in minimizing disaster impact, but conventional methods based on historical data often cannot meet real-time requirements. The integration of geographical information with data from social networks, sensor networks, and other sources can facilitate the relevant prediction and decision-making processes, but the available data, computing, and human resources are all severely constrained—with the same scarcity of responses and alerts as for other data types. For these reasons, proposed methods shorten response times by applying AI-based big-data systems.
AI-Powered Big Data Analytics for Smart Retail Customer Journey Optimization
Intensifying of competition among modern retail companies necessitates a transition from big data to enhanced intelligence through optimization of the still-unknown customer journey by means of artificial intelligence and business analytics. Such a transition calls for a rethinking of conceptual foundations and development of a data ecosystem. Data constitute a fundamental part of modern intelligent systems. The new-age retail environment produces tremendous amounts and varieties of data about customer experiences during their journey, both online and offline. A model-based customer-journey-mapping and touchpoint-optimization technique and its supporting architecture are presented. Other needed technologies encompass a wide spectrum of artificial intelligence techniques for machine learning, deep learning, and natural language processing. Regulatory and ethical aspects of the exploitation of such a data ecosystem support the smart retail business. Customer journey mapping defines all stages of customer experience with a company or brand and includes its online and offline components. Every stage encompasses several touchpoints for customer experience assessment: awareness, consideration, intent, evaluation, purchase, consumption, and advocacy. For a comprehensive customer-journey model, advanced customer experience metrics are required in addition to those for each touchpoint: net promoter score, customer satisfaction score, customer lifetime value, customer engagement score, and customer effort score. All customer-journey metrics determine the optimization of touchpoint impact on profit.
Scalable Cloud Data Processing Models for Digital Banking Transaction Monitoring
Electronic banking offers new opportunities for credit unions and other community financial institutions. By allowing members to bank 24 hours a day from virtually anywhere in the world, the benefits of electronic banking must be weighed against the potential for increased fraud and illicit transactions. Regulatory requirements mandate that as a result of the Money Laundering Control Act of 1986, banks need to have a formal anti-money laundering program that includes procedures to monitor electronic banking activity. Cloud computing technologies provide scalable computing resources at a reduced operational cost, but their use for anti-money laundering systems has not yet been demonstrated. The characteristics of digital banking transactions indicate these systems may benefit from cloud-based resources. Appropriate cloud architectures and data ingestion and transformation methods are proposed to support real-time rule-based compliance system detection and machine-learning-based anomaly detection. Cloud processing scalability creates processing capacity for compliance requirements without negatively impacting the underlying economic advantage. Cloud computing has emerged as a disruptive technology enabling scalable and affordable data processing in a rapidly evolving environment, and organizations have seized the opportunity to move data storage and processing outside the enterprise firewall. Scalable data processing can be a significant advantage for organizations with irregularly occurring, high-volume access patterns. For compliance monitoring and operational reporting, however, regulatory requirements necessitate monitoring and reporting within a defined period. Such rigid time constraints reduce the potential operational cost benefit of cloud-scale processing resources. Digital transaction characteristics suggest compliance monitoring and anomaly detection can be separated and deployed in a hybrid processing model. The rule-based detection models of the operational system provide anomaly detection ground truth and a source of training data for machine-learning models. Cloud processing enables a scalable architecture to meet current and future capacity demands.
a cloud data management and processing system model
a bank transaction monitoring system that adheres to regulatory requirements
real-time transaction monitoring of digital banking transactions
near real-time monitoring of banking transactions
rule-based monitoring of banking transactions
machine-learning-based monitoring of banking transactions
full-text data storage for banking transactions
Cloud-Native Security and Governance Framework for SAP Enterprise Systems Enabled by Machine Learning
The rapid evolution of digital technologies has significantly transformed the way enterprises operate, manage data, and deliver services. Organizations today generate enormous volumes of data from enterprise resource planning systems, customer platforms, supply chains, Internet of Things devices, and digital business applications. However, many enterprises struggle to integrate these diverse data sources effectively, leading to fragmented systems and inefficient decision-making processes. Artificial Intelligence (AI) combined with cloud-based enterprise platforms offers a promising solution for addressing these challenges. SAP cloud technologies provide an integrated environment that supports intelligent data management, advanced analytics, and scalable digital infrastructures. This research paper presents an AI-driven SAP cloud architecture designed to facilitate intelligent enterprise data integration and enable scalable digital transformation. The proposed architecture combines SAP Business Technology Platform, machine learning services, cloud-native microservices, and automated data pipelines to create an intelligent enterprise ecosystem. The paper discusses the architectural framework, integration strategies, implementation methodology, benefits, and challenges associated with deploying AI-enabled SAP cloud architectures in modern enterprises. The study demonstrates how organizations can leverage AI-powered cloud technologies to improve operational efficiency, enhance real-time decision-making, and achieve sustainable digital transformation.
Secure Enterprise Ecosystems for AI-Enabled Financial Healthcare Intelligence Platforms and Autonomous DevSecOps Automation
The transformation of enterprise digital infrastructures through cloud computing has enabled organizations to deliver services more efficiently, scale operations, and integrate advanced analytics. However, this shift also introduces critical challenges in security, compliance, and operational efficiency, especially in industries handling sensitive data, such as finance and healthcare. Traditional security and operations models are insufficient for dynamic cloud environments, where continuous monitoring, rapid threat mitigation, and automated governance are required. This research proposes an AI-enabled secure cloud ecosystem framework for enterprise platforms, financial technologies, and healthcare intelligence, integrating autonomous DevSecOps automation. The proposed architecture leverages artificial intelligence for real-time threat detection, predictive analytics, and intelligent automation of security and operational workflows. Cloud-native microservices, container orchestration, and continuous integration/continuous deployment (CI/CD) pipelines are combined with AI-driven monitoring to enhance enterprise agility and resilience. The framework also incorporates autonomous DevSecOps principles, enabling automated security policy enforcement, vulnerability remediation, and operational scaling without human intervention. By integrating secure cloud-native infrastructure, AI analytics, and autonomous automation, the proposed ecosystem supports financial transaction integrity, healthcare data privacy, and enterprise operational efficiency. The study provides architectural design principles, integration strategies, and evaluation methodologies for implementing AI-driven secure cloud ecosystems that enable scalable, intelligent, and resilient enterprise platforms.
Liposome-Based Drug Delivery Systems for Targeted Treatment of Breast Cancer
Liposome-based drug delivery systems have emerged as an effective nanotechnological strategy to overcome these limitations by improving drug solubility, stability, pharmacokinetics, and tumor targeting. Liposomes are biocompatible phospholipid vesicles capable of encapsulating both hydrophilic and hydrophobic anticancer agents, enabling controlled drug release and reduced off-target effects. This review provides a comprehensive overview of liposome-based drug delivery systems for targeted treatment of breast cancer. It discusses breast cancer pathophysiology and molecular targets relevant to liposomal design, followed by an in-depth analysis of liposome structure, formulation strategies, and physicochemical properties influencing therapeutic performance. Passive targeting via the enhanced permeability and retention (EPR) effect, active ligand-mediated targeting, and stimuli-responsive liposomal systems are critically examined. The review further highlights therapeutic applications including liposomal chemotherapeutics, combination drug delivery, and gene/siRNA delivery approaches. In vivo pharmacokinetics, biodistribution, clinical progress of approved liposomal formulations, and ongoing clinical developments are also discussed. Additionally, safety, toxicity, and regulatory considerations are addressed, along with key challenges hindering clinical translation. Finally, emerging trends such as multifunctional liposomes, personalized nanomedicine, and scalable manufacturing strategies are explored. Overall, liposome-based drug delivery systems represent a promising and clinically relevant platform for improving the efficacy and safety of breast cancer therapy.
Cloud-First AI Security Architecture for Protecting Enterprise Digital Ecosystems and Financial Networks
The rapid digitalization of enterprise ecosystems and financial networks has accelerated the adoption of cloud-first strategies, enabling scalability, flexibility, and operational efficiency. However, this digital transformation also introduces heightened cybersecurity risks, including advanced persistent threats, ransomware attacks, and vulnerabilities in distributed cloud and networked environments. This research proposes a cloud-first AI security architecture designed to protect enterprise digital ecosystems and financial networks through real-time threat detection, adaptive risk mitigation, and intelligent decision-making. The framework integrates artificial intelligence and machine learning models to continuously monitor system activity, network traffic, and user behavior, detecting anomalies and predicting potential attacks. Cloud-native technologies, including containerization, microservices, and orchestration platforms, support scalable deployment, high availability, and resilience. Zero-trust security principles enforce strict identity verification, multi-factor authentication, and behavioral access control across users, devices, and applications. Additionally, the architecture incorporates intelligent data governance to ensure compliance with financial regulations, secure data handling, and privacy protection. By combining AI-driven analytics, cloud-first deployment, and proactive incident response, the framework provides a comprehensive solution for securing complex enterprise infrastructures and financial platforms. The research demonstrates that integrating AI with cloud-first security strategies enhances operational resilience, reduces cyber risk exposure, and strengthens trust in digital financial ecosystems.
Next Generation AI-Integrated Cloud Systems for Secure Digital Transformation in Healthcare, Finance, and Smart Infrastructure
The rapid digital transformation of critical sectors such as healthcare, finance, and smart infrastructure has significantly increased the demand for secure, scalable, and intelligent cloud computing systems. Traditional security frameworks are insufficient to handle evolving cyber threats, data privacy requirements, and the complexity of distributed cloud-native architectures. This paper proposes a next generation AI-integrated cloud system architecture designed to support secure digital transformation across critical industries. The proposed framework integrates artificial intelligence, machine learning analytics, cloud-native infrastructure, and automated cybersecurity mechanisms to enhance threat detection, infrastructure automation, and continuous compliance management. The architecture incorporates intelligent monitoring, predictive vulnerability analysis, and automated response capabilities to ensure resilient and secure digital ecosystems. The proposed model demonstrates how AI-driven cloud systems can improve operational efficiency, protect sensitive data, and enable reliable digital services in healthcare, financial systems, and smart infrastructure environments.
Secure HR Data Exchange between SAP SuccessFactors and Payroll Using AI-Optimized Encryption, Masking, and Data Minimization Controls
Secure exchange of human resource data between cloud based human capital management platforms and downstream payroll systems has become a critical enterprise concern as organizations increasingly rely on distributed integration architectures. This study examines the confidentiality, integrity, and exposure risks inherent in data flows between SAP SuccessFactors and payroll platforms, with particular attention to personally identifiable and financial information subject to stringent regulatory obligations. The paper argues that traditional perimeter based security and static encryption alone are insufficient to address overexposure, misuse, and operational leakage of sensitive HR attributes during routine payroll processing. To address this gap, the study proposes a unified control framework that combines strong cryptographic protection, policy driven masking, and strict data minimization, augmented by AI optimized decision logic appropriate to the technological landscape of 2019. The framework leverages statistical risk scoring, rule augmented learning, and anomaly detection techniques to dynamically select encryption strength, masking profiles, and attribute level payload composition based on data sensitivity, purpose limitation, and operational context. A reference architecture is presented to demonstrate how these controls can be embedded across extraction, transformation, transmission, and ingestion layers without disrupting payroll accuracy or timeliness. Evaluation considerations focus on measurable reductions in data exposure, improved audit evidence generation, and enhanced governance transparency rather than speculative automation claims. The findings suggest that AI optimized security orchestration can materially improve trust and compliance in HR data exchanges while remaining compatible with established enterprise integration patterns. This work contributes a practical and extensible foundation for secure HR system interoperability and offers a defensible baseline for future research on adaptive data protection in enterprise information systems.
From Reactive Alerts to Predictive Intelligence: AI-Assisted Monitoring in Modern Cloud Environments
Modern distributed systems operating across cloud-native, microservices-based, and containerized environments generate massive volumes of high-velocity telemetry data, including logs, metrics, traces, configuration changes, and event streams. As system architectures grow increasingly decentralized and elastic, traditional rule-based monitoring approaches dependent on static thresholds and manually crafted alerts struggle to cope with dynamic workloads, ephemeral infrastructure, and evolving failure modes. These limitations often result in excessive false positives, alert fatigue, delayed root cause identification, and reactive incident management practices. Artificial Intelligence (AI) and Machine Learning (ML) techniques have therefore emerged as critical enablers of proactive incident detection, leveraging unsupervised, semi-supervised, and statistical learning models to automatically identify anomalous behavior patterns before they escalate into service degradation or outages. By incorporating scalable anomaly detection algorithms such as Isolation Forest, structured log parsing mechanisms like Drain, and real-time statistical learning frameworks for continuous anomaly scoring, AI-assisted monitoring systems transform raw telemetry into actionable insights. This paper presents a comprehensive overview of these AI-driven monitoring architectures, examining their algorithmic foundations, data preprocessing pipelines, streaming inference capabilities, and operational trade-offs, while highlighting how they collectively support early detection, adaptive learning, and resilience in modern production environments.
Machine Learning-Enabled Predictive Security and Governance Frameworks for SAP-Integrated Cloud-Native Enterprise Systems
The rapid transformation of enterprise digital ecosystems has led organizations to increasingly adopt cloud-native architectures integrated with enterprise resource planning platforms such as SAP. While these advancements provide greater scalability, operational efficiency, and real-time analytics capabilities, they also introduce complex security and governance challenges. Enterprise environments today must manage large volumes of sensitive data, distributed workloads, and multiple access points across hybrid and multi-cloud infrastructures. As a result, organizations require intelligent and predictive security mechanisms capable of proactively detecting threats, ensuring regulatory compliance, and maintaining governance standards across enterprise systems. Machine learning technologies have emerged as powerful tools for analyzing large-scale operational data and identifying patterns associated with potential cyber threats or governance violations. This research proposes a machine learning-enabled predictive security and governance framework designed specifically for SAP-integrated cloud-native enterprise environments. The framework combines AI-driven anomaly detection models, identity-centric governance policies, and automated monitoring mechanisms to improve enterprise security posture and operational reliability. The study evaluates the proposed framework through simulated enterprise infrastructure environments that generate large-scale operational logs, user activity records, and system transaction data. Experimental results demonstrate that predictive machine learning models significantly enhance threat detection accuracy and reduce response times compared to traditional rule-based security systems. Furthermore, the framework improves enterprise governance by enabling continuous monitoring of access control policies and compliance requirements. The research findings highlight the potential of integrating machine learning with cloud-native enterprise architectures to create resilient, scalable, and intelligent digital ecosystems capable of addressing modern cybersecurity and governance challenges.
Intelligent Data Lakehouse and MLOps Pipelines for Scalable Predictive Analytics in Cloud-Based Enterprise Systems
Enterprise systems increasingly rely on large-scale, cloud-based data platforms to support decision-making, operational efficiency, and strategic planning. Traditional data warehouses and lakes often struggle with scalability, integration, and real-time analytics requirements. The emergence of data lakehouse architectures, combined with machine learning operations (MLOps) pipelines, provides an integrated solution for scalable, high-performance predictive analytics in modern enterprise environments. This research proposes an intelligent data lakehouse architecture integrated with MLOps pipelines to enable scalable predictive analytics in cloud-based enterprise systems. The framework unifies structured, semi-structured, and unstructured data while supporting real-time ingestion, preprocessing, feature engineering, and model deployment. MLOps pipelines automate model training, testing, versioning, deployment, and monitoring, ensuring reproducibility, reliability, and continuous improvement of predictive models. The architecture leverages cloud-native technologies, including distributed storage, containerized services, and orchestration tools, to optimize resource allocation and scalability. Predictive analytics models provide insights for operational optimization, financial forecasting, customer behavior analysis, and risk assessment. The study highlights the benefits of combining intelligent data lakehouses with MLOps pipelines, including improved model performance, operational efficiency, and governance, while addressing challenges such as data heterogeneity, pipeline complexity, and cross-cloud interoperability.