International Journal of Science, Engineering and TechnologyAnalysis and Prediction of Crime Detection Techniques Using Machine Learning Approach
Authors – Sameeksha Bhati, Assistant Professor Priyanshu Dhameniya
Abstract- – The field of study known as machine learning examines how machines can learn to act autonomously. Self-driving cars, speech recognition, web search, and a deeper understanding of the human genome are just a few of the recent applications of machine learning. In addition, it has made it possible to make crime forecasts using historical data. Using nominal class labels, classification is a supervised prediction method. Weather forecasting, medical care, banking, homeland security, and corporate intelligence are just few of the numerous fields that have benefited from classification [6]. Data gathering, classification, pattern recognition, prediction, and visualization are typical steps in a machine learning-based approach to analyzing criminal behavior. Association analysis, classification and prediction, cluster analysis, and outlier analysis are examples of classic data mining methods that focus on structured data; newer methods can also extract useful insights from unstructured data.
Analysis Of Symptoms And Severe Outcomes Of Covid-19
Authors – Kavita Sheoran, Geetika Dhand, Shaily Malik, Nishtha Jatana
Abstract- – An outbreak of pneumonia, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) commonly known as COVID-19 started in Wuhan, China, in December 2019 has become a global pandemic. It has many similarities with Severe Acute Respiratory Syndrome Coronavirus(SARS-CoV) and Middle East Respiratory Syndrome Coronavirus (MERS).In this paper, analysis of severe complications observed in other organs is done. Comparison of immune response, various symptoms, general advisory and recovery status of patients in India is also observed.
The Role of Imaging Modalities in Lung Cancer Treatment
Authors – Research Scholar V.Juliet Rani , Asst. Prof. Dr. K.K.Thanammal
Abstract- – Lung cancer is the most deadly disease in the world. This paper overviews one of the most important and challenging problems in oncology, the problem of lung cancer diagnosis using computer information system. Lung cancer is the dangerous disease that is being spread all over the world. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance. An effective CAD system can diagnose the lung cancer in an accurate manner. CAD systems for lung cancer have been explored in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. In order to accomplish the above four steps we must have the appropriate imaging modalities.
Design and Thermal Analysis of Double Pipe Heat Exchanger by Changing Mass Flow Rate
Authors – M.Tech. Scholar Naveen Kumar, Prof. Abhishek Bhandari
Abstract- – Heat exchangers are employed in a variety of applications, included power plants, nuclear reactors in energy production, RAC systems, self-propelled industries, food industries, heat retrieval systems, & chemical handling. The techniques of upgrading can be divided into two categories: active and passive ways. The active approach necessitates the use of peripheral forces. Discrete surface geometries are required for passive approaches. These strategies are commonly utilized to increase heat exchanger performance. Helical tubes have already been designated as among the passive heat transfer enhancement materials. Due the short construction and high heat transfer coefficient, and they will be widely employed in various industrial applications. The thermo-hydraulic performance of various configurations of gas- to-liquid double-pipe heat exchangers featuring helical fins was investigated that used a computational model based on CFD. The heat transmission, pressure drop, unit weight, and overall performance of helical or longitudinal fin configurations studied numerically simulated and the results. The effects of increasing the number of fins and Reynolds number on thermo-hydraulic performance also were investigated
Total Harmonics Distortion Performance Analysis between Micro-Inverter and Single Phase Inverter Photovoltaic Systems
Authors – Sonali Mathur, Assistant Professor Mukesh Kumar
Abstract- – Before the direct current (DC) input voltage can be turned into alternating current (AC), each of the current topologies of solar micro inverters uses a number of steps. One or more power converters could be built into each of these stages. A transformer, a filter, and a diode rectifier might also be in it. There are a very large number of both active and passive parts. In the scope of this thesis, a brand-new architecture for a solar micro inverter is made. This new micro inverter is made up of a new single switch inverter, which is made by changing the single ended primary inductor DC-DC converter that was already there. This new inverter can take DC power and turn it into a clean sinusoidal waveform. A lot of research is being done on how the new inverter is built and how it works. With the help of a controller, this new inverter can make almost any kind of output waveform. The inverter was found to be able to work in a total of four different ways. The new inverter was designed using a modelling method called “state space averaging.” Due to the switching that is built into the circuit, the system is a non-linear fourth order system. This makes the system not follow a straight line. Before the system can be looked at as a linear system, it must be linearized around a certain point. It has been found that the inverter’s control-to-output transfer function does not have a minimum phase. To find out about transfer functions, the root locus method is used. From the point of view of control, the presence of right half zero makes it harder to build the structure of the controller. The cell equations are used to make a model of the photovoltaic (PV) cell in MATLAB. The maximum power point tracking (MPPT) method is used to make sure that the PV cell’s output power is always at its highest level. This lets the power from the PV cell be used to its fullest potential. The easiest way to solve this problem is to change something and then watch what happens. When you use this new inverter, you don’t need the different phases that a traditional solar micro inverter needs. The proposed design of the inverter was confirmed by both simulation and the results of experiments done on the set-up.
Remote Substation Monitoring in a Distribution Power Grid
Authors – M. Tech. Scholar Jasti Raja, Prof. Katragadda Swarnasri, Asst. Prof. Ponnam Venkata K Babu
Abstract- – Monitoring and control of distribution transformers is desired by any distribution utility company in view of many reasons. Distribution transformers are located in remote places in order to supply power to few categories. In order to provide good and reliable power automation has become an essential part in the distribution network. Monitoring and controlling essentially require the data from the grid, analysis of the data and controlling the devices on the network on the basis of evaluated results. Objective of this paper is to design the cost effective model for monitoring the remote electrical parameters like voltage, current, temperature of a transformer and send these real time values over network to a remotely located substation or a device. This system can automatically update the real time electrical parameters periodically (based on time settings). This system can be designed to send alerts whenever the relay trips or whenever the voltage or current exceeds the predefined limits. This experimental setup is a prototype of the proposed project, for demonstration purpose we have used Arduino and Raspberry Pi here. The controller can efficiently communicate with the different sensors being used by detecting the abnormal operating conditions.
A Survey on Image Water Marking Techniques and Attacks
Authors – Dilesh Khairwar, Asst. Prof. Sumit Sharma
Abstract- – Digital information may be transferred from one location to another with the least amount of difficulty than any other media. Text, music, video, and image data, among other types of data, can all be transferred using the same media and the same methods. However, certain precautions have been made by the owner of the data by embedding some signature or validating information at the receiver end. The security of these data is greatly dependent on the protocols. This article does a comprehensive review of several approaches to the protection of digital image data that have been offered by researchers. In the study, signature embedding techniques and their attributes were broken down in detail in order to better the reader’s grasp of the subject field. In this research, different network assaults that may have an effect on the data that was received were also elaborated upon. The study also provided an explanation of the various aspects that researchers make use of to secure digital data. This is because each feature has its own significance and area of use that varies according to the type of image and the attacks that are being made.
AI-Augmented User Access Analytics In Centrify-Managed Environments
Authors: Pooja Sharma, Ankit Mehra, Shalini Nair, Rohit Chauhan
Abstract: As enterprises grow increasingly reliant on identity-centric security models, managing and auditing privileged access has become paramount especially in regulated environments such as healthcare, government, and finance. Centrify, a leading privileged access management (PAM) platform, offers comprehensive vaulting, session control, and policy enforcement. However, static access control methods alone often fail to detect nuanced insider threats, credential misuse, or abnormal behavioral patterns. This review explores the integration of artificial intelligence into Centrify-managed UNIX and hybrid environments to enhance user access analytics and proactively detect risks. We examine how machine learning techniques ranging from supervised classification to anomaly detection and time-series modeling can be used to analyze session metadata, command histories, vault activity, and authentication behavior. The paper outlines the architecture of AI-enhanced pipelines, data collection strategies, real-time alerting systems, and integration points with Centrify’s policy engine. We also evaluate the implications of AI-based adaptive access controls, context-aware role adaptation, and forensic replay for audit and compliance. Through detailed sections on threat modeling, deployment strategies, and federated learning approaches, this review positions AI as a transformative layer over traditional access control. Ultimately, AI-augmented user access analytics enable more intelligent, responsive, and resilient identity governance—essential for maintaining Zero Trust postures and meeting modern regulatory requirements.
DOI: http://doi.org/10.5281/zenodo.15847029
Implementing Samba Clustering Techniques To Achieve High Availability And Fault-Tolerant File Sharing In Enterprise Network Environments
Authors: Aruni Kashyap
Abstract: As enterprises increasingly demand continuous data availability and resilience in their file-sharing infrastructures, clustering Samba has emerged as a critical strategy for achieving fault tolerance and high availability. Samba, a powerful open-source software suite, provides seamless file and print services across various operating systems, notably integrating Linux/Unix servers into Windows-based environments. However, single-node Samba configurations pose significant risks of service disruption due to hardware or software failures. Clustering Samba mitigates these risks by deploying multiple redundant nodes that ensure uninterrupted access to shared resources. This article explores the conceptual and technical underpinnings of clustered Samba configurations, examining how they bolster file-sharing reliability, maintain service continuity, and simplify management within enterprise ecosystems. We discuss key architectural designs such as active-active and active-passive clustering, delve into the technologies enabling Samba clustering—including CTDB (Clustered Trivial Database), Pacemaker, and Corosync—and analyze their roles in sustaining high availability. Additionally, the article investigates best practices, real-world deployment models, performance considerations, and security implications. With digital infrastructure demands evolving rapidly, the clustering of Samba for fault-tolerant file sharing represents a critical enabler of IT service continuity. By synthesizing architectural guidance with practical implementation strategies, this article offers a comprehensive blueprint for IT architects and system administrators aiming to optimize Samba for resilience and uptime in both on-premises and hybrid cloud environments.
DOI: http://doi.org/10.5281/zenodo.16750805
Enhancing Samba Performance For High-Bandwidth Media Streaming Platforms Through Efficient Configuration And Network Resource Management
Authors: Jerry Pinto
Abstract: Samba, the open-source implementation of the SMB/CIFS protocol, has become a vital component in enabling file sharing across heterogeneous systems. In media streaming platforms where high throughput, low latency, and efficient concurrency are paramount, Samba's optimization directly influences performance, user satisfaction, and system scalability. This article explores the technical intricacies and performance tuning strategies for Samba in the context of media streaming, including caching mechanisms, transport-layer considerations, and filesystem interactions. Media streaming demands sustained data transfer rates for large media files, making Samba's configuration and tuning critically important for ensuring uninterrupted playback and robust access control. By drawing on real-world implementations and performance benchmarks, this article identifies bottlenecks in default Samba deployments and presents engineering solutions to enhance stream-read efficiency, reduce CPU utilization, and optimize memory handling. Moreover, it investigates the synergy between Samba and network file systems (NFS), SSD storage, and modern Linux kernel features like asynchronous I/O and systemd enhancements. As video consumption and digital content delivery grow exponentially, refining Samba for media workloads becomes essential. This paper serves as a practical guide for system architects, DevOps teams, and media IT infrastructure planners aiming to align Samba services with the stringent demands of modern streaming platforms.
DOI: http://doi.org/10.5281/zenodo.16750827
Implementing Blockchain Technology For Secure, Transparent, And Decentralized Access Control In Modern File System Architectures
Authors: Namita Gokhale
Abstract: In the digital era, data breaches and unauthorized access to sensitive information have become critical concerns, prompting the need for robust access control mechanisms. Traditional access control models such as Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) have served as the foundation of enterprise security strategies. However, these models often exhibit vulnerabilities tied to centralization, including single points of failure, susceptibility to insider threats, and limited audit capabilities. Blockchain technology, known for its decentralized architecture and immutable ledger, introduces a paradigm shift in how access control can be enforced across distributed systems. This paper delves into the concept of blockchain-based access control systems tailored for file systems, exploring the foundational technologies, implementation strategies, security implications, and future outlook. Through the use of smart contracts, access decisions can be enforced automatically, eliminating the need for human intervention and enhancing policy adherence. Furthermore, blockchain’s transparency enables comprehensive and tamper-proof auditing of access logs, ensuring accountability across all levels of the organization. Key considerations such as scalability, identity management, policy customization, and integration with traditional infrastructure are thoroughly discussed. Challenges, including transaction throughput limitations, storage constraints, and regulatory compliance, are addressed with emerging solutions such as Layer 2 protocols and privacy-preserving technologies. Real-world implementations and use cases further underscore the practical viability of the approach. In summary, blockchain-based access control for file systems offers a future-ready solution that aligns with the security, transparency, and auditability demands of modern enterprises.
DOI: http://doi.org/10.5281/zenodo.16750850
Implementing Micro-Segmentation Strategies To Strengthen Security And Isolate Cloud Workloads In Virtualized And Multi-Tenant Environments
Authors: Nayantara Sahgal
Abstract: Micro-segmentation has emerged as a pivotal strategy in securing cloud workloads in modern enterprise environments. As cloud adoption accelerates, traditional perimeter-based security models are proving inadequate against increasingly sophisticated threats that target lateral movement within data centers. Micro-segmentation enables fine-grained policies that isolate workloads and control traffic based on identity, context, and application-level logic. This minimizes the attack surface and significantly reduces the risk of breaches propagating across systems. By using software-defined networking (SDN) and policy-driven automation, organizations can dynamically segment workloads without physical network changes, thus ensuring operational efficiency. This paper explores the conceptual framework of micro-segmentation, its technical implementation in multi-cloud and hybrid environments, and its synergy with identity and access management (IAM), zero trust principles, and DevSecOps practices. We also discuss challenges such as policy sprawl, visibility constraints, and compliance mapping, while presenting use cases that illustrate real-world benefits. The increasing complexity and dynamism of cloud-native applications make micro-segmentation not just an enhancement, but a necessity in cloud workload security strategies.
DOI: http://doi.org/10.5281/zenodo.16750875
Designing And Deploying Energy-Efficient Server Infrastructure To Optimize Power Consumption In Densely Populated Urban Environments
Authors: Neel Mukherjee
Abstract: With the rapid expansion of digital infrastructure and cloud computing services, urban centers are experiencing an unprecedented demand for data processing and storage capabilities. This escalating requirement places immense stress on energy resources, especially when server deployments are not optimized for efficiency. The need for sustainable energy practices in data centers and server farms becomes paramount to mitigate environmental degradation, reduce operational costs, and support resilient urban ecosystems. This article explores the multifaceted approach required to achieve energy-efficient server deployment in densely populated areas. It examines the intersection of technology, urban planning, regulatory policies, and innovative cooling and power management techniques. Emphasis is placed on next-generation server hardware, modular deployment strategies, edge computing architectures, and renewable energy integration. Additionally, the study addresses socio-economic and environmental impacts, proposing a comprehensive roadmap to sustainability. By highlighting best practices and real-world case studies, the article aims to contribute to a paradigm shift in how cities manage their digital infrastructure. Ultimately, energy-efficient server deployment is not only a technological imperative but also a critical step toward achieving smarter, greener, and more livable urban environments.
DOI: http://doi.org/10.5281/zenodo.16750903
AI-Augmented Platform Engineering: Transforming Developer Experience Through Intelligent Automation And Self-Optimizing Internal Platforms
Authors: Shravan Kumar Reddy Padur
Abstract: The evolution of enterprise software delivery has entered a new epoch where platform engineering and artificial intelligence (AI) converge to fundamentally reimagine the developer experience (DX) as a data-driven, intelligent ecosystem. Traditional DevOps models—once reliant on manual pipelines, ad hoc integrations, and static toolchains—are being replaced by internal developer platforms (IDPs) that offer standardized, self-service environments abstracting away infrastructure and compliance complexity. These platforms, exemplified by Spotify’s Backstage, Google’s Cloud Build, and Humanitec’s orchestration layer, provide a unified interface that empowers developers while embedding policy-as-code, observability, and automation into every workflow. Building on Google’s Site Reliability Engineering (SRE) principles and the empirical rigor of DORA and SPACE frameworks, organizations are now augmenting developer workflows with AI-assisted coding, predictive capacity planning, and AIOps-based observability. This convergence of intelligent automation and platform governance establishes a feedback-driven development environment where human creativity and machine intelligence collaborate to optimize productivity, resilience, and operational excellence at scale.
Tail-Latency-Oriented Quality Assurance for Microservices: A System-Aware, SLO-Driven Approach
Authors: Srikanth Chakravarthy Vankayala
Abstract: Tail latency, the phenomenon in which a small fraction of requests exhibit disproportionately high response times presents a critical and often underestimated challenge in microservices-based architectures. As distributed systems scale and individual user operations begin to traverse dozens of interconnected services, even rare latency outliers can propagate and amplify across these call chains, ultimately degrading user experience, violating Service-Level Objectives (SLOs), and affecting overall system reliability. Foundational research such as The Tail at Scale (2013) demonstrated mathematically how small variances at the component level can lead to dramatic increases in end-to-end latency at scale, while subsequent studies like IC2E 2019 revealed that container-level interference, resource contention, and scheduling variability introduce additional layers of unpredictability in higher-percentile latency profiles. Modern frameworks such as FIRM (OSDI 2020) further show that tail latency is not merely a performance artifact but a dynamic systems phenomenon that requires continuous monitoring, adaptive resource allocation, and intelligent SLO-driven control loops. Together, these insights highlight that tail latency emerges from the interplay of architectural decomposition, microservice communication patterns, orchestration policies, and cloud infrastructure behavior. Building on this body of work, this article proposes a comprehensive engineering paradigm for “Tail-Latency-Oriented Quality Assurance,” integrating rigorous performance testing, predictive analytics, interference-aware validation, and automated mitigation mechanisms to ensure that complex microservices environments remain reliable, predictable, and scalable under real-world conditions.
DOI: https://doi.org/10.5281/zenodo.17920534
System-Level Analysis of Wireless Communication Technologies Supporting Cloud-Based IoT Applications
Authors: Vihaan Oberoi
Abstract: The rapid expansion of the Internet of Things (IoT) has necessitated a robust communication backbone capable of bridging physical sensors with cloud-based analytical engines. This review article provides a comprehensive system-level analysis of the wireless technologies enabling this integration as of 2025. We evaluate the multi-tier architecture—spanning perception, gateway, edge, and cloud layers—to identify critical performance bottlenecks in latency, energy efficiency, and scalability. The analysis contrasts short-range standards like Wi-Fi 7 and Bluetooth 5.4 with Long-Range Low-Power (LPWAN) solutions such as LoRaWAN and NB-IoT, while highlighting the transformative role of 5G/6G and Non-Terrestrial Networks. Key systemic challenges, including security at the resource-constrained edge and the interoperability of heterogeneous protocols via the Matter standard, are examined in depth. Furthermore, we explore the emerging synergy between edge intelligence and cloud orchestration, specifically through the use of digital twins and data offloading strategies. The review concludes by forecasting future research directions in AI-native wireless and zero-energy IoT, offering a strategic framework for selecting and implementing wireless technologies in a cloud-centric IoT ecosystem.
Integrating SAP HANA with IoT Analytics for Real-Time Healthcare Decision Support
Authors: Raghav Senmor
Abstract: Real-time decision-making is critical for enhancing patient outcomes, reducing medical errors, and optimizing healthcare operations. The proliferation of IoT-enabled medical devices, including wearables and connected sensors, generates massive streams of heterogeneous patient data, presenting both opportunities and challenges for healthcare analytics. This paper explores the integration of SAP HANA with IoT analytics to enable real-time healthcare decision support. SAP HANA’s in-memory computing and high-speed data processing capabilities allow for rapid ingestion, preprocessing, and analysis of streaming IoT data. By combining predictive and prescriptive analytics, healthcare providers can identify early warning signals, detect anomalies, and deliver timely interventions. The paper discusses system architecture, integration workflows, performance evaluation metrics, and key challenges such as data privacy, interoperability, and scalability. Emerging trends, including edge computing, AI-driven predictive analytics, and cloud-based healthcare platforms, are also highlighted. The findings demonstrate that integrating SAP HANA with IoT analytics facilitates proactive, data-driven clinical decision-making, improves operational efficiency, and supports personalized patient care.
Secure Patient Data Intelligence in SAP Systems Powered by Artificial Intelligence
Authors: Navika Purohitam
Abstract: The management of patient data in healthcare is increasingly complex due to growing volumes of sensitive information and evolving regulatory requirements. Traditional security measures in enterprise systems like SAP are often insufficient to address sophisticated cyber threats and ensure compliance. This article explores the integration of artificial intelligence with SAP systems to enhance patient data intelligence and security. AI technologies, including machine learning, predictive analytics, and natural language processing, enable real-time monitoring, anomaly detection, automated compliance checks, and intelligent decision-making. The combination of SAP’s robust data management infrastructure with AI-driven analytics provides healthcare organizations with a proactive approach to securing patient information, optimizing workflows, and improving operational efficiency. Case studies demonstrate that AI-enhanced SAP implementations reduce security breaches, streamline compliance, and support accurate, timely insights into patient care. The article also examines challenges such as technical complexity, data quality, privacy concerns, and cost, while highlighting emerging trends that promise more secure, intelligent, and scalable healthcare data management solutions. Integrating AI with SAP systems represents a forward-looking strategy for safeguarding patient information while enhancing the quality and efficiency of healthcare delivery.
Adaptive Connectivity and Control Models in Wireless IoT Cloud Environments
Authors: Kunal Vireksha
Abstract: The proliferation of Internet of Things (IoT) devices has created dynamic wireless networks that generate massive volumes of data requiring efficient and reliable management. Integrating these networks with cloud platforms enables scalable storage, processing, and advanced analytics, but also introduces challenges such as latency, energy constraints, and variable connectivity. This article explores adaptive connectivity and control models as solutions for optimizing performance, reliability, and energy efficiency in wireless IoT cloud environments. Adaptive connectivity models dynamically adjust communication pathways, protocols, and resource allocation to maintain seamless operation, while adaptive control models utilize real-time monitoring, predictive analytics, and feedback mechanisms to optimize network and device behavior. Cloud integration provides centralized orchestration, AI-driven decision-making, and scalable analytics to enhance these adaptive strategies. The article highlights applications across smart cities, industrial IoT, healthcare, and agriculture, demonstrating improved latency, throughput, reliability, and energy efficiency. Challenges such as security, interoperability, scalability, and technical complexity are discussed, along with emerging trends including edge AI, 5G/6G networks, digital twins, and autonomous network management. The study emphasizes that combining adaptive models with cloud orchestration is critical for creating intelligent, resilient, and efficient IoT ecosystems capable of supporting large-scale, real-time applications.
DOI: https://doi.org/10.5281/zenodo.18161066
Designing Intelligent Healthcare Information Systems Using AI, IoT, And Cloud Computing Technologies
Authors: Kairav Desai
Abstract: The transition from traditional healthcare records to intelligent Health Information Systems (IHIS) represents a fundamental shift in medical engineering, driven by the convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and Cloud Computing. This review article provides a comprehensive analysis of the design principles and architectural methodologies required to build autonomous, end-to-end medical ecosystems. We explore the system engineering lifecycle, beginning with the design of high-fidelity, energy-aware Internet of Medical Things (IoMT) sensing layers that ensure continuous, non-intrusive data acquisition. Central to the discussion is the architectural transition toward a microservices-based, cloud-native infrastructure that leverages the edge-fog-cloud continuum to satisfy both real-time latency requirements and long-term big data analytics. The processing layer is analyzed through the lens of clinical-grade AI design, emphasizing the importance of algorithm selection, explainability (XAI), and validation against medical regulatory standards. Furthermore, we address critical design challenges, including semantic interoperability via HL7 FHIR standards, security-by-design through blockchain and federated learning, and the ethical mitigation of algorithmic bias. By synthesizing recent case studies in intelligent intensive care and telemedicine, this review identifies future research frontiers such as 6G-enabled tactile internet and personalized digital health twins. The findings offer a roadmap for engineers and clinicians to design resilient, interoperable, and human-centric systems that transform raw biometric data into life-saving clinical intelligence.
DOI: https://doi.org/10.5281/zenodo.18221694
Security And Privacy Challenges In Cloud-Integrated IoT Systems: A Risk Management Perspective
Authors: Aryvik Patil
Abstract: The convergence of the Internet of Things (IoT) and Cloud Computing has revolutionized data-driven industries, yet it has simultaneously introduced an expansive and complex attack surface. This review article provides a comprehensive analysis of the security and privacy landscape within Cloud-IoT systems from a risk management perspective. We categorize vulnerabilities across a multi-layered taxonomy, spanning the physical perception layer, the communication network layer, and the virtualized cloud layer. By examining the inherent conflict between the resource constraints of IoT devices and the high overhead requirements of traditional cloud security, this article highlights the necessity of shifting toward a decentralized, risk-based defense strategy. We evaluate the efficacy of current risk management frameworks, such as STRIDE and ISO/IEC 27001, in identifying and mitigating threats unique to cyber-physical systems. Furthermore, the review explores advanced technical solutions, including lightweight cryptography, edge-based anomaly detection using machine learning, and the application of blockchain for decentralized identity management. Through various case studies in smart healthcare and industrial automation, we demonstrate how risk priorities shift across different vertical applications. The article concludes by identifying future research directions, such as post-quantum cryptography and autonomous self-healing security agents, emphasizing that the long-term viability of Cloud-IoT ecosystems depends on the integration of security-by-design principles and a continuous lifecycle of risk assessment.
DOI: https://doi.org/10.5281/zenodo.18221767
Machine Learning Approaches For Optimizing Cash Flow And Liquidity Management In SAP Financial Modules
Authors: Yashvik Pai
Abstract: In the volatile landscape of modern corporate finance, traditional spreadsheet-based liquidity management often fails to provide the real-time precision required for strategic decision-making. This review article evaluates the integration of machine learning (ML) methodologies within SAP financial modules specifically SAP S/4HANA Finance and SAP Treasury and Risk Management to optimize cash flow and liquidity. We examine how the transition to a unified data model, the Universal Journal, provides a high-fidelity training environment for predictive algorithms. The review categorizes ML approaches into three primary functional areas: time-series forecasting for predicting liquidity trends (utilizing models such as ARIMA, Prophet, and LSTMs), classification models for analyzing customer payment behavior to optimize Accounts Receivable, and Natural Language Processing (NLP) for automating bank-to-ledger reconciliation. Furthermore, we analyze the architectural synergy between SAP’s "One Exposure from Operations" framework and embedded AI, which allows for the continuous refinement of cash position forecasts. The article also addresses significant implementation hurdles, including the challenge of data fragmentation in hybrid SAP landscapes, the necessity for model interpretability in audited financial environments, and the shift toward "Autonomous Treasury" operations. By synthesizing current literature and technical documentation, this review provides a roadmap for CFOs and treasury professionals to leverage ML for reducing idle cash, mitigating foreign exchange risks, and enhancing organizational resilience through data-driven liquidity planning.
DOI: https://doi.org/10.5281/zenodo.18228239
Performance Analysis Of Cloud-Based Architectures For Real-Time Processing Of Biomedical And Sensor Data
Authors: Ronav Shetty
Abstract: The rapid expansion of the Internet of Medical Things (IoMT) has necessitated a transition from localized medical monitoring to high-throughput, cloud-integrated analytical frameworks. However, the inherent "best-effort" nature of traditional cloud computing often conflicts with the stringent requirements of real-time biomedical applications, where processing delays can jeopardize patient safety. This review article provides a comprehensive performance analysis of various cloud-based architectures—centralized, edge-fog, and serverless—tailored for the continuous processing of high-frequency sensor data such as ECG, EEG, and PPG signals. We evaluate these architectures against a rigorous set of performance metrics, including end-to-end latency, jitter, packet loss ratio, and signal-to-noise ratio (SNR) preservation. The analysis highlights the critical role of the edge-fog-cloud hierarchy in mitigating network congestion and reducing the computational overhead of data security and interoperability protocols (e.g., HL7 FHIR). We explore specialized optimization strategies, such as lightweight virtualization using Docker, hardware acceleration through cloud-based GPUs, and adaptive task-offloading policies. Furthermore, we examine the performance impact of emerging communication standards like 5G URLLC (Ultra-Reliable Low-Latency Communications) and their potential to enable tactile internet applications like remote robotic surgery. By synthesizing empirical benchmarking data and qualitative case studies from smart ICU and telecardiology environments, this review establishes a set of design best practices for engineering "guaranteed-performance" clinical infrastructures. The findings underscore that the future of biomedical data processing lies in a decentralized, autonomous architecture capable of maintaining sub-second responsiveness within a highly scalable and secure global network.
DOI: https://doi.org/10.5281/zenodo.18228279
LLM-Powered Incident Intelligence: Cognitive Augmentation For Cloud-Native Operations
Authors: Ramani Teegala
Abstract: By September 2022, cloud-native systems operating at enterprise and internet scale had reached a level of architectural and operational complexity that fundamentally challenged traditional approaches to incident detection, diagnosis, and response. Microservices proliferation, dynamic infrastructure provisioning, continuous deployment pipelines, and deeply interconnected service dependencies produced failure modes that were increasingly emergent rather than deterministic. While observability platforms provided extensive access to logs, metrics, and distributed traces, the practical bottleneck during incidents shifted from data availability to human sense making. Incident response workflows continued to rely heavily on manual correlation, institutional memory, and ad hoc reasoning performed under severe time pressure, resulting in prolonged mean time to diagnosis and inconsistent operational outcomes. During this period, advances in large language models demonstrated a growing capacity to interpret, summarize, and synthesize natural language and semi-structured information. These capabilities aligned closely with the nature of operational artifacts such as alerts, logs, incident timelines, architectural documentation, and post-incident analyses. This paper introduces the concept of LLM-powered incident intelligence as an emerging operational discipline appropriate to the state of industry practice as of September 2022. LLM-powered incident intelligence refers to systems that apply large language models, constrained by retrieval, governance, and human-in-the-loop design principles, to assist operators in understanding and reasoning about complex incidents rather than executing remediation autonomously. The paper positions LLM-powered incident intelligence as a cognitive augmentation layer that sits between observability tooling and human decision making. Rather than replacing human judgment, these systems aim to reduce cognitive load, accelerate contextual understanding, and support evidence-driven reasoning during high-severity incidents. The discussion is grounded in the maturity of transformer-based language models, semantic retrieval techniques, and enterprise observability platforms available by late 2022. Security, operational correctness, and accountability are treated as first-class constraints. By framing LLM-powered incident intelligence as an assistive and governed capability, this paper outlines a pragmatic approach to enhancing incident response effectiveness without undermining trust or operational control.
DOI: http://doi.org/10.5281/zenodo.18694701
Cloud-Native Platform Engineering For Enterprise Applications
Authors: Fathimath Naseema
Abstract: The rapid evolution of enterprise information systems, driven by digital transformation and large-scale cloud adoption, has intensified the need for scalable, resilient, and continuously deployable application architectures. Traditional infrastructure management models, characterized by siloed operations and manual provisioning, struggle to accommodate the dynamic requirements of distributed and microservices-based systems. In response, cloud-native platform engineering has emerged as a strategic paradigm that restructures enterprise IT operations through automation, standardization, and developer-centric design. Rather than merely managing infrastructure, platform engineering focuses on building Internal Developer Platforms (IDPs) that abstract complexity, embed governance controls, and enable self-service capabilities across development teams. This review systematically examines the foundational principles and architectural constructs underpinning cloud-native platform engineering. It analyzes the integration of containerization technologies, microservices architectures, orchestration frameworks such as Kubernetes, Infrastructure as Code (IaC), and DevOps practices within enterprise ecosystems. Particular emphasis is placed on how these enabling technologies collectively support scalable workload management, automated deployment pipelines, policy-driven governance, and comprehensive observability frameworks. The review further explores the role of IDPs in reducing cognitive load, enforcing compliance, and standardizing operational workflows across multi-team and multi-cloud environments. In addition to technological enablers, the study critically evaluates key challenges associated with cloud-native platform adoption, including multi-cloud heterogeneity, expanded security attack surfaces, regulatory compliance pressures, cultural transformation barriers, and skill shortages. By synthesizing recent scholarly literature and industry best practices, the review identifies recurring architectural patterns, governance models, and maturity pathways that support successful enterprise implementation. The findings highlight that platform engineering acts as a structural bridge between development and operations, institutionalizing DevOps principles into scalable, product-oriented platforms. This transformation not only accelerates application delivery but also strengthens resilience, operational consistency, and security posture in distributed environments. Furthermore, the review outlines emerging directions such as AI-driven operations (AIOps), policy-as-code automation, FinOps integration, and edge-cloud orchestration, which are expected to redefine the next generation of enterprise cloud-native strategies. Overall, cloud-native platform engineering is positioned as a foundational discipline for modern enterprise IT, enabling organizations to balance agility with governance while navigating the increasing complexity of distributed digital ecosystems.
DOI: https://doi.org/10.5281/zenodo.18708365
Automation And Performance Optimization In Enterprise Distributed Systems
Authors: Ravindu Samarasinghe
Abstract: Enterprise distributed systems have evolved into the foundational infrastructure supporting modern digital ecosystems, enabling large-scale applications across sectors such as finance, healthcare, e-commerce, telecommunications, and cloud-based services. These systems operate across geographically dispersed environments and heterogeneous platforms, managing massive volumes of transactions, real-time data streams, and globally distributed user interactions. As enterprises increasingly adopt cloud-native architectures, microservices models, and containerized deployments, system complexity has grown substantially, creating new operational challenges related to scalability, resilience, latency control, and fault tolerance. In response to these challenges, automation and performance optimization have emerged as indispensable pillars of enterprise system management. Automation frameworks—including Infrastructure as Code (IaC), Continuous Integration and Continuous Deployment (CI/CD) pipelines, orchestration platforms, and dynamic auto-scaling mechanisms—enable reproducible infrastructure provisioning, rapid application delivery, and adaptive resource management. These technologies reduce human intervention, minimize configuration errors, and accelerate recovery from system disruptions, thereby improving operational consistency and reliability. Simultaneously, performance optimization techniques ensure that distributed systems maintain efficiency under fluctuating workloads and unpredictable traffic patterns. Strategies such as intelligent load balancing, multi-layer caching, distributed tracing, observability integration, fault-tolerant design, and optimized resource scheduling collectively enhance throughput, minimize latency, and prevent cascading failures. These mechanisms allow enterprises to sustain high service availability while controlling infrastructure costs and maintaining compliance with service-level objectives. The review further examines emerging paradigms, including AI-driven automation, self-healing infrastructures, predictive scaling, and AIOps-based decision support systems. By leveraging machine learning algorithms and advanced analytics, modern distributed systems increasingly transition from reactive maintenance models to proactive and autonomous operational frameworks. These advancements signal a shift toward intelligent infrastructure ecosystems capable of continuous self-monitoring, adaptation, and optimization. By synthesizing contemporary research findings and industry best practices, this review provides a comprehensive and structured analysis of automation and performance optimization strategies in enterprise distributed environments. It highlights architectural evolution, technological enablers, operational challenges, and future research directions, offering a conceptual and practical foundation for designing resilient, scalable, and efficient enterprise systems in an increasingly digital and distributed world.
DOI: https://doi.org/10.5281/zenodo.18708450