International Journal of Engineering & Extended Technologies Research (IJEETR)
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. Our Vision & Mission · Vision: To be recognized globally as a leading platform for the dissemination of high-quality, innovative, and impactful research in engineering and extended technologies. · Mission: o To promote a culture of scientific excellence and innovation. o To provide open access to research, ensuring equitable knowledge sharing. o To accelerate the application of research findings into real-world practices. o To foster collaboration between academia, industry, and society.
AI-Driven Cloud Governance 2.0: Balancing Agility, Compliance, and Operational Efficiency in Hybrid Multi-Cloud Environments
The paper discusses the way AI-informed cloud governance can enable organisations to control hybrid multi-clouds setting without a disruption in the balance between agility, compliance, and operational efficiency. The paper explains the way AI will aid in policy enforcement, resources control, security surveillance, and automate workloads. The results indicate that AI will cut on manual work, improve the accuracy of the decisions, and develop faster response to risks and performance issues. Resulting challenges also indicate, however, poor quality of data, skills deficiency, and inconsistency in policy implementation. It echoes the findings of the paper that AI-Driven Cloud Governance 2.0 can prove to be a potent asset provided with vivid frameworks, dependable data and ongoing organisational orientation
Large Language Model–Powered Public Service Platforms for Automated Case Assistance and Decision Support
Large Language Models (LLMs) are emerging as a transformative technology in the modernization of public service delivery, enabling intelligent automation, natural language interaction, and context-aware decision support. Traditional public service platforms often rely on manual workflows, rule-based systems, and fragmented data sources, leading to inefficiencies and delays in case handling. This paper presents a comprehensive overview of LLM- powered public service platforms designed for automated case assistance and decision support. It explores how LLMs can be integrated with existing government systems to process unstructured data, interpret citizen requests, and generate actionable recommendations aligned with policy frameworks. The study outlines a layered architectural model incorporating user interaction interfaces, AI processing engines, data integration mechanisms, and governance controls. Key application areas such as social welfare administration, legal advisory systems, healthcare support, and citizen service portals are examined to illustrate practical adoption scenarios. Additionally, the paper discusses critical challenges including data privacy, model bias, explainability, and regulatory compliance, along with mitigation strategies such as human-in-the-loop validation and hybrid AI architectures. The findings suggest that LLM-driven platforms can significantly enhance efficiency, consistency, and accessibility in public services while supporting informed and transparent decision-making. The paper concludes by identifying future research directions focused on scalable deployment, ethical AI governance, and domain-specific model optimization for public sector applications.
Zero-Trust Digital Government Platforms: Secure Identity, API Governance, and Cloud- Native Service Architecture
The rapid digitization of public services has transformed how governments interact with citizens, businesses, and internal stakeholders. However, this transformation has also expanded the attack surface, exposing critical infrastructure to increasingly sophisticated cyber threats. Traditional perimeter-based security models are no longer sufficient in a distributed, cloud-driven ecosystem. In response, the Zero-Trust security paradigm has emerged as a foundational approach for securing modern digital government platforms. This article presents a comprehensive overview of Zero-Trust Digital Government Platforms, focusing on three core pillars: secure identity management, API governance, and cloud-native service architecture. It explores how identity- centric security models leveraging strong authentication, continuous verification, and least-privilege access enable governments to protect sensitive data and services. The paper further examines the role of API governance in ensuring secure, scalable, and compliant data exchange across interconnected systems. Additionally, it highlights the importance of cloud-native architectures, including microservices, containerization, and service meshes, in building resilient and agile public sector platforms. Through an integrated approach, this article outlines architectural patterns, best practices, and implementation strategies for deploying Zero-Trust frameworks in government ecosystems. It also discusses challenges such as legacy system integration, regulatory compliance, and operational complexity. The goal is to provide a generalized, vendor-neutral foundation for designing secure, scalable, and future-ready digital government infrastructures.
Future Trends in AI, Machine Learning, and Big Data: Implications for Technical Leadership
There's no denying that Artificial Intelligence (AI), Machine Learning (ML), and Big Data technologies are profoundly changing the face of software engineering and organizational leadership. As these technologies keep evolving, the design, deployment, and management of software systems are undergoing unprecedented changes. This study discusses the recent developments in Artificial Intelligence, Machine Learning, and Big Data, and their implications for the Technical Leadership in modern business houses. It uses a qualitative analysis approach, drawing on the latest literature, industry reports, and technological trends, to establish the major trends and developments, including autonomous systems, explainable AI, edge computing, data-centric architectures, and AI-driven DevOps. Results indicate that technical leaders are no longer just managers, but must also be strategic innovators able to coordinate and apply intelligent systems, data systems, and teams of interdisciplinary experts. Further, issues like ethical concerns, data privacy, algorithmic bias, and skill gaps are emphasized as essential factors for future leadership. The study finds that the technical leader must be prepared to constantly learn, adapt, and understand the technological and human factors to be effective in the AI era. The findings of this research prove informative for software engineers, IT managers, and organizational leaders seeking to cope with the intricacies of transforming using AI
AI-Augmented API Gateways: Intelligent Traffic Management, Threat Detection, and Adaptive Policy Enforcement
The rapid proliferation of microservices, cloud-native architectures, and distributed digital platforms has significantly increased the complexity of managing Application Programming Interface (API) ecosystems. Traditional API gateways, while effective for routing, authentication, and rate limiting, are increasingly inadequate in addressing dynamic traffic patterns, evolving cyber threats, and the need for real-time decision-making. This paper explores the concept of AI-augmented API gateways, which integrate Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance traffic management, threat detection, and adaptive policy enforcement The article presents a generalized, vendor-neutral architecture for AI-enabled API gateways, highlighting how predictive analytics, anomaly detection models, and reinforcement learning can optimize API performance and security. It examines intelligent traffic routing strategies that dynamically adapt to workload fluctuations, user behavior, and service-level objectives. Additionally, the study investigates AI-driven threat detection mechanisms capable of identifying zero-day attacks, API abuse patterns, and bot-driven anomalies in real time A key contribution of this work is the introduction of adaptive policy enforcement frameworks, where policies evolve autonomously based on contextual insights, risk scoring, and historical data patterns. The paper also discusses challenges such as model drift, data privacy, explainability, and integration complexity within enterprise environments. Practical use cases across finance, healthcare, and large-scale digital platforms are analyzed to demonstrate real-world applicability By combining AI capabilities with API gateway functionalities, organizations can achieve enhanced scalability, resilience, and security in modern digital infrastructures. This paper concludes that AI-augmented API gateways represent a critical evolution in API management, enabling intelligent, self-optimizing, and secure communication layers for next-generation applications
Human-Centered Cloud AI Collaboration Models for Sustainable and Secure Digital Enterprise Innovation
Human-centered cloud artificial intelligence (AI) collaboration models are transforming modern digital enterprises by integrating human intelligence, cloud computing, machine learning, and collaborative automation into sustainable and secure innovation ecosystems. Enterprises increasingly adopt cloud AI systems to improve operational efficiency, enhance decision-making, strengthen cybersecurity, and support sustainable digital transformation. However, the rapid expansion of AI-driven cloud infrastructures also raises challenges concerning ethics, privacy, transparency, trust, workforce adaptation, governance, and environmental sustainability. Human-centered AI emphasizes the integration of human values, explainability, collaboration, and accountability into intelligent systems to ensure that technological innovation aligns with organizational and societal needs. This study examines the role of human-centered cloud AI collaboration frameworks in enabling secure and sustainable enterprise innovation. The paper explores the theoretical foundations of human-AI collaboration, cloud-native architectures, ethical governance, cybersecurity integration, and sustainability-oriented digital transformation. A comprehensive literature review identifies emerging trends, research gaps, and practical applications of collaborative AI systems in enterprise environments. The research methodology adopts a qualitative and conceptual research design supported by systematic literature analysis and framework evaluation. Findings indicate that human-centered cloud AI models significantly improve enterprise resilience, collaborative intelligence, innovation capability, operational sustainability, and adaptive decision-making. The study concludes that integrating ethical AI governance, explainable AI, cloud scalability, and human oversight is essential for creating trustworthy and sustainable digital enterprises in the Industry 5.0 era.
Cloud-Native AI and MDM Framework for Next-Generation Insurance and Retirement Data Products
Ubiquitous digitalization and increasing regulatory scrutiny are creating unprecedented business opportunities and risks for insurance carriers and retirement service providers. Developing next-generation insurance and retirement data products that leverage the cloud, deploy advanced capabilities, adopt a data product mindset, and implement robust data governance can enhance business direction, decision-making speed, performance, and responsiveness to newcomers and disruptors. Integrating these elements into a single master data management (MDM) framework for insurance and retirement domains is essential for orchestrating quality, reliable, consistent, and trustworthy data assets and delivering seamless advanced data services to the organization. Mission-critical business operations depending on data assets in these domains have a long history of data management challenges. As a result, many organizations have addressed the need for structured enterprise-wide data governance, investment in scalable and secure AI/ML platforms for strategical and operational functions, and creation of sharable data products. Growing investment in next-generation data and analytics solutions has informed the integration of cloud-native architecture, AI/ML enablement, and data product enablement into a reference architecture and MDM framework validated with industry benchmarks. Cloud-native architecture is increasingly essential to support the deployment of scalable enterprise-level data solutions. AI/ML investments have expanded well beyond research functions, creating mission-critical production workloads required for business operations or providing customer-facing services. The data product enablement paradigm drives the creation of enterprise-wide MDM capabilities with shared data product lifecycles governed by business need rather than technology requirements.
CARENEXUS: Bridging Insights and Care for Better Clinical Outcomes
In recent years, the rapid growth of healthcare data has created both opportunities and challenges for medical professionals. Doctors are often required to make quick and accurate decisions based on large volumes of patient information, which can lead to increased workload and potential diagnostic errors. This paper presents CareNexus, an AI-powered Clinical Decision Support System designed to assist healthcare professionals in improving diagnostic accuracy and treatment planning. The system integrates machine learning techniques to analyze patient data, identify patterns, and provide predictive insights. It offers features such as disease prediction, risk assessment, and personalized recommendations. By supporting doctors with data-driven insights, CareNexus aims to enhance clinical efficiency, reduce errors, and improve patient outcomes. The proposed system demonstrates how artificial intelligence can act as a supportive tool rather than a replacement for medical expertis
Amsaveni, Monika S, Priyanga V S , Charulatha D V, Joycee K S
Privacy-Preserving Federated Learning Framework with Blockchain-Based Audit Trail for Multi-Hospital Disease Prediction
Electronic Health Record (EHR) fragmentation results in data silos, which hinder the development of strong artificial intelligence in healthcare. Training in a centralized manner is often infeasible owing to stringent data privacy laws such as HIPAA. We propose a novel framework that leverages Horizontal FL and a Permissioned Blockchain-based Audit Layer for facilitating collaboration between multiple hospitals. The predictive models will be based on Long Short-Term Memory (LSTM) networks for predicting patient deterioration using sequential data from vital signs. Additionally, Federated Isolation Forests (FLiForest) will be used for unsupervised anomaly detection. Only model updates will be shared in an encrypted manner, and data will remain within each institution. We will use Differential Privacy (DP) and Homomorphic Encryption (HE) for ensuring algorithmic data privacy. We will also employ a Hyperledger Fabric-based Blockchain for ensuring data provenance and patient consent through smart contracts, thus preventing poisoning attacks. Theoretical analysis of this framework shows that it will achieve a global model prediction accuracy of 4.5% less than the perfect accuracy and will be similar to centralized models. Additionally, it will achieve an accuracy of 96.3% in anomaly detection. The Blockchain will be able to handle high transaction throughput of 110 transactions per second and will have low validation times of 120 ms. Thus, this framework will solve the utility-privacy trade-off and will allow healthcare consortia to develop strong predictive models.
Prem Kumar S, Harish Raj S, Harinivas S, Dukitha M, Hari Rudhran M
AI-Based Intelligent Surveillance System for Criminal Detection and Threat Analysis
This paper proposes an AI-based framework for detecting suspicious activities and criminal faces, which can be used to enhance the capabilities of contemporary surveillance systems. The framework combines the power of advanced computer vision and deep learning algorithms, which can be used to carry out real-time surveillance in public as well as high-security zones. Object detection algorithms, specifically the YOLO algorithm, can be used to identify potential threats, which may include weapons and suspicious objects. Similarly, pose estimation and anomaly detection algorithms can be used to identify abnormal human behaviour , which may include aggressiveness and prolonged loitering. To carry out accurate facial verification, a convolutional neural network based on the Grassmannian algorithm can be used, which can accurately match facial features even in adverse lighting and occlusion. A database can also be created to accurately match criminal faces. The integration of automated threat detection, behavioural analysis, and facial recognition enhances situational awareness and reduces dependency on manual surveillance. This research contributes to the development of intelligent security solutions for smart cities, banking systems, and law enforcement applications, improving response time and overall public safety
Dr. C. Suganthi, Giridharan, Keerthana S, Srikanth S, Tamilmani M
Design And Development of Smart Cono Weeder using IoT
Agriculture plays a vital role in the economy, and efficient weed management is essential for improving crop productivity in paddy cultivation. Traditional manual weeding methods are labor-intensive, time-consuming, and physically demanding for farmers working in waterlogged field conditions. To overcome these challenges, this project presents the design and development of an IoT-based Cono Weeder system integrated with solar power for smart and sustainable agricultural operations. The proposed system aims to automate the weeding process, reduce human effort, and improve operational efficiency through the use of modern embedded and communication technologies. The system is built around the ESP32 microcontroller, which acts as the main control unit for wireless communication, motor control, sensor integration, and web server operation. A web-based control interface enables users to remotely operate the cono weeder using smartphones, tablets, or computers without requiring specialized software installation. The ESP32-CAM module provides real-time live video streaming, allowing farmers to monitor field conditions and navigate the system effectively from a remote location. This remote accessibility improves convenience and reduces the need for constant physical presence in the field. To ensure safe and reliable operation, the system incorporates an HC-SR04 ultrasonic sensor for automatic obstacle detection. When obstacles are detected within a predefined range, the system automatically stops movement to prevent equipment damage and improve safety. The cono weeder and grass cutter mechanisms are independently controlled, allowing flexible operation based on field requirements. The integration of solar energy through a solar panel and rechargeable battery system further enhances sustainability by reducing dependence on conventional power sources and minimizing operational costs. The proposed system is designed using low-cost and easily available electronic components, making it affordable and practical for small and medium-scale farmers. In addition to improving weeding efficiency, the project demonstrates the application of IoT, automation, renewable energy, and embedded systems in precision agriculture. The platform is also scalable and can be extended in the future with additional sensors such as soil moisture, temperature, humidity, and crop health monitoring modules. Overall, the IoT-based Cono Weeder system provides an eco-friendly, cost-effective, and intelligent solution for modern smart farming applications.
Bhavadharani B, Divya S, Divyadharshini A, Hemalatha B
Optimizing InfiniBand Congestion Control for Large-Scale AI Model Training Workloads
This paper examines the issue of InfiniBand congestion in large-scale AI model training systems. The emphasis is made on the impact of congestions on training time, utilization of GPUs, and scaling efficiency in cases where thousands of GPUs are utilized in conjunction. Base system iteration time rose from 320 ms over 256 GPUs to 780 ms over 2048 GPUs and the utilization of GPUs decreased from 78% to 58%. The time of communication was also greater and it demonstrated that congestion of a network is a significant bottleneck. In order to address this issue, the paper uses network counters to identify congestion, and then involves congestion optimization techniques that include subnet manager tuning, virtual lane separation, and load balancing. Upon the implementation of these techniques, the time of iteration decreased to 590 ms at 2048 GPUs with an improvement of approximately 24%. The efficiency of scaling was increased from 58% to 78 %, and the network latency was decreased from 95 µs to 55 µs at peak levels. There was also better equilibrium of the use of links over the network. Based on the results of the conducted investigation, it is possible to increase the system performance by over 20% with the help of the proper congestion control. This assists in the quicker training of AI and optimal utilization of computing assets, which promote huge research and innovations.
IoT Based Smart Bike Accident Detection System
The rapid increase in road accidents and the delay in providing immediate medical assistance remain critical challenges in modern transportation systems, especially for two-wheeler riders. This paper presents a Smart Bike Accident Detection and Emergency Response System using Internet of Things (IoT), designed to automatically detect accidents, minimize false alerts, and ensure rapid communication with emergency services and contacts. The system integrates multiple sensors, including a gyroscope and accelerometer (MPU6050) and a vibration sensor (SW-420), to continuously monitor the bike's motion parameters such as tilt, acceleration, and impact forces. These sensor readings are processed in real time using a microcontroller (ESP32/Raspberry Pi) with sensor fusion techniques to accurately identify accident conditions while reducing false positives caused by road irregularities. Upon detecting a potential accident, the system initiates a confirmation window, allowing the rider to cancel the alert in case of a false detection. If no cancellation is made, the accident is confirmed, and the system retrieves the rider's real-time location using the NEO-6M GPS module. The emergency response mechanism includes both SMS alerts and automated voice call notifications using a GSM module (SIM800L/SIM900A). Alerts are simultaneously sent to police services, ambulance services, and registered emergency contacts. The proposed system operates independently of internet connectivity, relying on GSM networks for communication, making it suitable for deployment in both urban and rural environments. By combining real-time monitoring, intelligent decision-making, reliable alert mechanisms, and data logging, this system significantly reduces emergency response time and enhances rider safety.
Abishek Rathnam C. R, Chezhian Nanmaran A, Kishore Kumar S
Privacy-Preserving Healthcare Intelligence Systems with Cloud-Native Predictive Analytics and Secure Automation
The rapid digitization of healthcare systems has enabled unprecedented data generation from electronic health records (EHRs), wearable devices, medical imaging systems, and IoT-enabled monitoring tools. While this data offers immense potential for predictive analytics and intelligent decision-making, it raises critical concerns regarding patient privacy, data security, and regulatory compliance. This paper proposes a privacy-preserving healthcare intelligence system built on cloud-native architecture integrated with secure automation and predictive analytics capabilities. The system leverages advanced cryptographic techniques such as homomorphic encryption, federated learning, and differential privacy to ensure sensitive medical data remains protected throughout its lifecycle. Cloud-native technologies, including microservices, containerization, and Kubernetes orchestration, enable scalability, resilience, and real-time processing of healthcare workloads. Predictive analytics models powered by machine learning are embedded to support early disease detection, patient risk stratification, and operational optimization in healthcare delivery. Additionally, secure automation workflows using policy-driven access control and AI-assisted decision systems enhance efficiency while maintaining compliance with healthcare regulations such as HIPAA and GDPR. The proposed framework demonstrates how privacy-preserving computation and cloud-native intelligence can coexist to transform healthcare systems into secure, adaptive, and predictive ecosystems. The study highlights architectural design, implementation strategies, and performance considerations for deploying such systems in real-world healthcare environments.
Ayursutra Panchakarma Patient Management and Therapy Scheduling Software- AI Powered Chatbot Assistance
AyurSutra is a cloud-based Panchakarma patient management and therapy scheduling system designed to address the limitations of manual record-keeping commonly followed in Ayurvedic clinical environments. Traditional paper-based processes often lead to fragmented patient data, scheduling conflicts, inaccurate Prakriti assessment, and limited accessibility. To overcome these issues, AyurSutra introduces a fully digital, secure, and scalable full stack solution for managing Panchakarma treatments. The system provides a centralized platform for storing patient details, therapy records, and appointment schedules with real-time updates. Automated Prakriti assessment, a custom health-scoring algorithm, and an AI-powered chatbot enhance diagnostic accuracy and personalized patient interaction. Role-based dashboards ensure that administrators, therapists, and patients access only features relevant to their roles, improving workflow clarity and data security. A modern technical stack supports system reliability and scalability. The frontend is built using HTML, CSS, and JavaScript and deployed on Netlify for fast global access. The backend, developed using Python and Flask, manages core application logic, authentication, and scheduling, and is deployed on Render for secure and scalable cloud execution. Patient data is stored in MongoDB Atlas, enabling high-speed retrieval and strong protection through OTP and JWT-based authentication. The system also includes notification features for appointment reminders, therapy updates, and payment alerts, along with an integrated e-commerce module for purchasing Ayurvedic medicines. By eliminating manual processes and introducing automation, real-time synchronization, and intelligent features, AyurSutra significantly improves operational efficiency, reduces human error, and enhances the overall patient experience in Panchakarma healthcare settings. The proposed system demonstrates the potential for modern web technologies to support the digital transformation of traditional Ayurvedic clinics and deliver more accurate, timely, and reliable patient care.
Sales Guard AI-Driven Decision Intelligence Platform for Business Optimization
The increasing complexity of modern business environments demands intelligent systems capable of transforming large-scale data into actionable insights. This paper proposes SALES GUARD, an AI-driven Decision Intelligence Platform that integrates machine learning, ensemble forecasting, and real-time analytics to optimize business operations. The system leverages heterogeneous data sources, advanced feature engineering, and hybrid predictive models to enhance sales forecasting accuracy and strategic decision-making. In today’s highly competitive retail environment, businesses-ranging from small local shops to medium-scale enterprises-face increasing challenges in accurately predicting sales, managing inventory efficiently, and making timely strategic decisions. Traditional methods of sales analysis often rely on historical averages and manual judgment, which fail to capture complex patterns such as seasonality, demand fluctuations, and market dynamics. Retail businesses, particularly small and medium enterprises (SMBs), face significant challenges in accurately forecasting sales, managing inventory, and making data-driven decisions. A hybrid ensemble model combining XG Boost, Light GBM, and time-series forecasting is implemented to generate robust predictions. Additionally, the system incorporates anomaly detection, real-time streaming, and an interactive “What-If” simulation engine to support proactive decision-making. A multilingual, user-friendly dashboard enables accessibility for diverse users, while explainable AI modules provide transparency in model predictions. In conclusion, Sales Guard AI represents a comprehensive solution that combines machine learning, time-series forecasting, and decision intelligence to address critical challenges in the retail sector. The project not only showcases the practical application of advanced AI techniques but also emphasizes the importance of integrating predictive analytics with strategic business planning. This work lays a strong foundation for future research and development in intelligent retail systems, paving the way for more adaptive, automated, and insight-driven business environments.
Adaptive Deep Learning Image Compression with Test Time Optimization and Importance Guided Quantization
As the problem of compression of digital images continues to grow exponentially with an ever-increasing range of healthcare, social media, telecommunications, and remote sensing applications, the proposal of ADLIC-TTO (Adaptive Deep Learning Image Compression with Test-Time Optimization) arises, which, at test-time, employs per-image fine-tuning to achieve high-quality reconstruction with the same network structure as comparable networks, but at a competitive compression ratio. The model works on 256 x 256 images that are in RGB color, and it reduces the size of the images to a 256-channel latent image with spatial down sampling that is 8x, and this allows the images to be stored effectively in a reduced size. duction and structural fidelity Instagram In-test fine-tuning on Kodak PhotoCD benchmark dataset with 24 high quality natural images having 200 epochs per image, where the evaluation pipeline calculates automatic PSNR, SSIM, bits-per-pixel (BPP), and compression ratio (CR). 42.18 dB and SSIM of 0.9812, much better than JPEG baseline (PSNR 33.94 dB, SSIM 0.9456) yet with an average compression ratio of 3.24x at BPP 7.41, and has SSIM 0.97 at all test images, which proves its suitability in quality critical applications. Medical imaging, digital archiving, and Professional photography is only a few of the applications
Ravichandra D, Mohammed Hanees, Priya Stella Mary I
Food Linker: A Smart System for Global Waste Reduction
Food wastage is a critical global challenge. Large quantities of surplus food from hotels, restaurants, and events are discarded daily while millions face food insecurity. This paper presents FOODLINKER, an Artificial Intelligence-based Sustainable Food Redistribution and Donation Tracking System. The system employs deep learning image recognition (TensorFlow, OpenCV) to assess food freshness and detect spoilage, assigns a color-coded safety status (Green / Yellow / Red), and monitors preparation time to calculate safe consumption windows. Built on a React.js frontend, Node.js/Express.js backend, and MongoDB cloud database, FOODLINKER connects donors with nearby NGOs and volunteers in real time. Automated SMS and push notifications alert stakeholders before food expiry. A gamified incentive mechanism comprising badges, certificates, and trust scores encourages consistent donor participation. The system demonstrates measurable improvements in donation coordination efficiency food safety assessment accuracy, and food wastage reduction, aligning with the United Nations SDG 2 (Zero Hunger) and SDG 12 (Responsible Consumption)
Telemetry Driven Cost Governance for Enterprise Data and AI Platforms
Modern organizations are considerate of enterprise data and AI platforms, which usually provoke excessive and unpredictable operating expenses. Dynamism in cost management Traditional cost management systems use manual reviews, and reports that are backwards looking and which are never effective when dealing with dynamic clouds. The paper is suggesting a Telemetry-Driven Cost Governance model which operates on a real-time platform telemetry, automated policy assessment and controlled execution in order to govern costs on a continuous basis. The model combines the compute, storage, and data pipeline telemetry into a single analytics layer and measures the costs against specific preconceived thresholds. The approval gates and safety checks are automated through recommendations that are carried out in order to maintain system stability. A quantitative analysis presents good outcomes. The total platform cost has been cut by 24 percent, and compute cost is lowered by 28 percent and storage cost has been cut down by 19 percent. Effort of manual analysis of costs was reduced 90%. There was an increase in policy compliance of 76-96 and job success rate was 97.8 percent to 98.6 percent respectively. The findings have shown that Telemetry-based governance can provide a proactive, safe, and quantifiable cost optimization of enterprise-level data and AI platforms.
Designing Next-Generation Enterprise Systems with AI-Augmented Security Analytics and Cloud-Native Intelligence
The rapid evolution of digital transformation has led enterprises to adopt cloud-native architectures and advanced analytics to remain competitive and resilient. However, the increasing complexity of enterprise systems introduces significant challenges in security, scalability, and operational efficiency. Artificial Intelligence (AI)-augmented security analytics has emerged as a critical enabler for designing next-generation enterprise systems that are intelligent, adaptive, and secure. This paper explores how AI-driven techniques can be integrated with cloud-native technologies to enhance threat detection, automate security responses, and optimize system performance. By leveraging machine learning, deep learning, and real-time data analytics, enterprises can proactively identify vulnerabilities, detect anomalies, and mitigate cyber threats. Additionally, cloud-native intelligence enables dynamic resource orchestration, microservices scalability, and continuous monitoring. The study examines current trends, architectural frameworks, and methodologies that combine AI and cloud-native principles for enterprise system design. It also highlights challenges such as data privacy, model explainability, and integration complexity. The findings demonstrate that AI-augmented security analytics, when combined with cloud-native intelligence, provides a robust foundation for building secure, scalable, and future-ready enterprise systems capable of adapting to evolving technological and cybersecurity landscapes.
Intelligent Data Quality Architectures for AI Powered Enterprise Transformation and Compliance
In the era of AI-driven enterprises, data quality has emerged as a foundational pillar for achieving reliable automation, intelligent decision-making, and regulatory compliance. Intelligent Data Quality Architectures (IDQA) integrate advanced analytics, machine learning, metadata management, and governance frameworks to ensure that enterprise data remains accurate, consistent, complete, and trustworthy across heterogeneous systems. This paper explores the design and implementation of AI-powered data quality architectures that enable enterprises to transform legacy systems into intelligent ecosystems capable of real-time validation, anomaly detection, and adaptive data remediation. The study emphasizes the convergence of data engineering, AI governance, and compliance frameworks such as GDPR, HIPAA, and ISO standards. It highlights how intelligent architectures leverage data observability, automated lineage tracking, and predictive quality scoring to reduce operational risk and enhance business agility. Furthermore, it investigates how enterprises can operationalize data quality as a continuous, self-learning process embedded within data pipelines. By synthesizing literature and proposing a structured research methodology, the paper demonstrates how intelligent data quality systems can support enterprise digital transformation initiatives while ensuring regulatory compliance and ethical AI deployment. The findings suggest that organizations adopting AI-enabled data quality frameworks achieve improved decision accuracy, reduced compliance violations, and enhanced scalability in data-driven environments
Explainable AI Driven Frameworks for Enterprise Automation Cloud Native Platforms and Smart Data Systems Integration
Explainable Artificial Intelligence (XAI) is rapidly emerging as a critical enabler for enterprise automation, particularly in cloud-native environments and smart data ecosystems. As organizations increasingly rely on AI-driven decision-making, the need for transparency, interpretability, and trust becomes essential. This paper explores the design and implementation of explainable AI-driven frameworks that integrate seamlessly with enterprise automation systems, cloud-native platforms, and intelligent data infrastructures. It highlights how XAI enhances operational efficiency, regulatory compliance, and decision accountability while supporting scalable and resilient architectures. The study discusses the interplay between microservices, containerization, and AI pipelines, emphasizing how explainability can be embedded into each layer of enterprise systems. Additionally, it examines the role of smart data systems in enabling real-time analytics, adaptive learning, and automated workflows. A comprehensive methodology is proposed for developing XAI-enabled enterprise frameworks, focusing on model transparency, data governance, and system interoperability. The findings suggest that explainable AI not only improves trust in automated systems but also facilitates better human-AI collaboration. However, challenges such as computational overhead, complexity, and trade-offs between accuracy and interpretability remain significant. This research contributes to the growing body of knowledge on building trustworthy AI systems in modern enterprise environments.
AI Driven Multi Layered Security Framework for Autonomous Healthcare Governance and Intelligent Clinical Systems
The increasing digitization of healthcare systems has significantly improved clinical outcomes and operational efficiency, but it has also introduced critical challenges related to data security, privacy, and governance. This paper proposes an AI-driven multi-layered security framework designed to ensure autonomous healthcare data governance while supporting intelligent clinical decision systems. The framework integrates advanced artificial intelligence techniques with layered cybersecurity mechanisms, including identity and access management, encryption, anomaly detection, and behavioral analytics. By leveraging machine learning and deep learning models, the system continuously monitors, analyzes, and adapts to evolving threats in real time. The multi-layered approach ensures defense-in-depth, reducing vulnerabilities across data, application, network, and infrastructure layers. Additionally, the framework supports intelligent clinical decision-making by enabling secure and reliable data access for predictive analytics and diagnostic systems. Cloud-native technologies and interoperable standards facilitate scalability, flexibility, and seamless data exchange across healthcare ecosystems. The proposed solution addresses key issues such as data breaches, insider threats, and fragmented data governance. Ultimately, this framework provides a comprehensive approach to secure, efficient, and intelligent healthcare systems, ensuring trust, compliance, and improved patient care outcomes.
DC-DC Switching Converter with Single Switch and Constant Output Based On Luo Topology
An ultra-high step-up, non-isolated DC–DC converter with a continuous input current was developed as a result of this research. This converter’s architecture consists of a voltage multiplier cell (VMC), a positive output super lift Luo converter (POSLLC), and a quadratic boost converter (QBS) (also referred to as a cascaded boost topology (CBT)). Thus, the bold points of the topologies mentioned earlier enhance the voltage gain of the proposed topology. It is important to note that when the duty cycle is at 50%, the converter attains a voltage gain of ten. Additionally, the constant input current of the topology reduces the current stress on the input filter capacitor. This converter’s topology was investigated and studied under various operating conditions: ideal and non-ideal modes, as well as continuous and discontinuous current modes (CCM/DCM). The converter’s efficiency and voltage gain were also compared to those of newly proposed converters. PLECS and MATLAB software tools were used in the investigation of the proposed topology. A 200 V/200 W prototype was constructed. The experimental results validated the theoretical study and the simulation results. The extracted efficiency was 96%.
G.Neelakrishnan, P.Iraianbu, S. Vishwa, C. Meganatha, P.Gnanaprakash
Adaptive Scheduling of Tasks in the Cloud Environment
Cloud Computing is a computing environment where different services are provided to users over the Web. Task Scheduling is one of the important aspects of Cloud Computing, improving the performance of the cloud system. Task Scheduling involves assignment of resources to a particular task for the task to be completed within possible minimum time. Task Scheduling helps in achieving efficient utilization of resources. In Cloud systems, Virtual Machines (VMs) are scheduled to hosts according to their instant resource usage without considering their overall and long-term utilization. Also, in many cases, the scheduling and placement processes are computational expensive and affect performance of deployed VMs. In this work, the tasks are managed by using the AHP (Analytic Hierarchy process). The AHP assign a rank to each task. After ranking, the tasks are put into the task queue. The virtual machine is created for the task execution. By using divide and conquer method, the task are sub divided and executed in the virtual machine. The divide and conquer method utilize the resources efficiently in the task execution
Human Motion Recognition (HMR) has gained significant attention due to its wide applications in healthcare, sports analysis, rehabilitation, and human-computer interaction. This project focuses on recognizing and classifying human activities using data collected from Inertial Measurement Units (IMUs), which typically include accelerometers, gyroscopes, and sometimes magnetometers. These sensors capture motion dynamics such as acceleration, angular velocity, and orientation in real time. The proposed system involves collecting raw sensor data from wearable IMU devices placed on different parts of the human body. The data is then preprocessed to remove noise and segmented into meaningful time windows. Feature extraction techniques are applied to derive statistical and frequency- domain features that effectively represent motion patterns. Machine learning algorithms such as Support Vector Machines (SVM), Random Forest, or deep learning models like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks are used for classification of activities such as walking, running, sitting, and standing. The system aims to achieve high accuracy and real-time performance while maintaining low computational complexity, making it suitable for embedded and wearable applications. Experimental results demonstrate that IMU-based motion recognition provides a reliable and cost- effective solution compared to vision-based systems, especially in privacy-sensitive and indoor environments. This project highlights the potential of IMU sensors in developing intelligent, portable, and scalable motion recognition systems for real-world applications.
Dr. R. Senthil Kuamr, A.Sathiskumar, Aadhavan S, Dinesh G, Vikash G
Design and Development of an Automated River Cleaning Robot
The rapid increase in plastic and solid waste accumulation in aquatic environments has become a critical environmental challenge, threatening marine ecosystems, water quality, and public health. This paper presents the design and development of an IoT-enabled aquatic garbage monitoring and collection system aimed at improving the efficiency of waste management in water bodies such as rivers and lakes. The proposed system integrates a NodeMCU (ESP8266) microcontroller with ultrasonic and proximity sensors to detect, monitor, and manage floating debris. A conveyor-based mechanism is employed for automated waste collection, followed by segregation using sensor-based identification techniques. Real-time data acquisition and remote monitoring are achieved through cloud integration using the Blynk platform, enabling users to track waste levels and system performance efficiently. Compared to conventional manual and semi-automated cleanup methods, the proposed system reduces human intervention, enhances operational accuracy, and supports data-driven decision-making. Furthermore, the system enables predictive analysis of waste accumulation patterns, contributing to sustainable environmental management. The proposed model is cost-effective, scalable, and adaptable for deployment in diverse aquatic environments, offering a practical solution to mitigate water pollution and promote ecological sustainability.
Dr. R. Senthil Kumar, A.Sathiskumar, Nishanth A, Seshagiri S, Tamilarasu S
An air engine is a pneumatic device that converts the energy of compressed air into useful mechanical work without combustion. It operates on the principle of expansion of compressed air, making it an environmentally friendly alternative to traditional engines. The fabrication of an air engine involves systematic design, material selection, machining, assembly, and testing processes based on concepts from Mechanical Engineering and Thermodynamics. The fabrication process begins with the design phase, where detailed drawings and dimensions of components such as the cylinder, piston, crankshaft, connecting rod, air inlet and exhaust valves, and frame are prepared. Proper design ensures efficient air flow, minimal leakage, and smooth motion transfer. Materials like mild steel, aluminum, and cast iron are selected depending on strength, weight, cost, and machinability. Manufacturing of individual components is carried out using conventional machining processes such as turning, milling, drilling, shaping, and grinding. The cylinder is finished with high precision to reduce friction and air leakage, while the piston is designed to fit accurately within the cylinder. The crankshaft and connecting rod are fabricated to withstand dynamic loads and ensure effective conversion of reciprocating motion into rotary motion. Assembly is a critical stage where all components are fitted together with proper alignment. Bearings, seals, and gaskets are used to reduce friction and prevent air leakage. The compressed air supply system is connected through control valves that regulate the entry and exit of air inside the cylinder. When compressed air enters the cylinder, it pushes the piston, creating reciprocating motion. This motion is transmitted to the crankshaft, producing rotary output. After assembly, the air engine is tested for performance, efficiency, and leakage. Adjustments are made to improve sealing, alignment, and air flow. Lubrication is provided to reduce wear and enhance durability. The overall system is evaluated based on speed, torque, and operational smoothness. The fabricated air engine demonstrates the practical application of pneumatic systems and energy conversion principles. It offers advantages such as low pollution, simple construction, low maintenance, and safe operation. However, limitations such as lower efficiency compared to internal combustion engines and dependency on compressed air supply exist. This project highlights the potential of air engines in small-scale applications and as a sustainable alternative for future energy systems.
Dr. R. Senthil Kumar, A.Sathiskumar, Manikandan B, Manohar S D J, Tamilarasan B
Hand injuries are a significant medical concern, often leading to prolonged rehabilitation periods and increased healthcare costs. Traditional physical therapy requires intensive labor and continuous monitoring. To address these challenges, this paper presents the design and control of a portable hand exoskeleton aimed at improving rehabilitation outcomes while reducing costs. The proposed system supports four-finger degrees of freedom and is adaptable to various hand deformities. A soft exoskeleton glove driven by a pneumatic system is controlled using an Arduino Mega 2560 and relay module. The system enables customizable repetitive motion therapy, making it suitable for stroke patients and individuals with finger-specific injuries. The study highlights the flexibility, usability, and potential applications of the device in modern rehabilitation practices.
Dr. R.Senthil Kumar, Dr. A. Rajasekaran, Gowrisankar.G, Gowtham.R, Sugumar.S
Electrical Steering System in Automobile
This research paper explores advancements in electric power steering (EPS) systems, focusing on electronic control Strategies that enhance vehicle performance and energy efficiency. EPS has become a crucial technology in modern Vehicles, offering significant benefits over traditional hydraulic systems, including reduced energy consumption and Greater integration potential with advanced driver assistance systems (ADAS). This study examines the evolution of Steering systems, highlighting the transition from manual to hydraulic and eventually to EPS, as well as the key Components and architecture of modern EPS systems.The paper further delves into innovative control algorithms, such As model-based, adaptive, and robust control strategies, which contribute to precise steering control and improved Driving experience. In addition, it discusses performance enhancement techniques, including variable steering ratio and Disturbance rejection, alongside energy-efficient motor control techniques and power-on-demand strategies that Optimize system efficiency.Finally, this paper addresses the integration of EPS with ADAS and autonomous driving Technologies, reliability and fault-tolerance measures, and the challenges and future trends shaping the next generation Of steering systems. Through an analysis of current research and industry developments, this study provides a Comprehensive overview of EPS technology and its future prospects.
Advanced Driver Assistance Systems (ADAS)
Dr. R.Senthil Kumar, Dr. Perumal, Murugan M, Sriram G, Seshathri S