International Journal of AI Electrical Civil and Mechanical engineering
ARTIFICIAL INTELLIGENCE BASED CONTROL OF A SUPERCONDUCTING INDUCTOR ASSISTED DC–DC CONVERTER FOR ULTRA-FAST ELECTRIC VEHICLE CHARGING
This work presents the design and analysis of an artificial intelligence (AI)–based controlled superconducting inductor–assisted DC–DC double-boost converter for ultra-fast electric vehicle (EV) charging applications. The increasing demand for rapid and efficient charging solutions has highlighted the limitations of conventional AC–DC charging systems, particularly in terms of energy losses, thermal constraints, and multi-stage conversion inefficiencies. To address these challenges, the proposed system incorporates superconducting inductors using Bi₂₂₂₃ and MgB₂ materials, which offer near-zero resistance, high current-carrying capability, and improved power density. An AI-based controller is implemented to enhance switching performance, ensure stable voltage regulation, and improve dynamic response under varying load conditions. The system is modeled and simulated using MATLAB/Simulink to evaluate its electrical performance, efficiency, and transient characteristics. Simulation results demonstrate that the MgB₂-based converter achieves efficiency exceeding 95% at 15 kW output, outperforming conventional designs. Additionally, the AI controller significantly reduces voltage ripple, minimizes overshoot, and improves system stability. Overall, the proposed intelligent superconducting DC–DC converter provides a high-efficiency, reliable, and scalable solution for next-generation ultra-fast EV charging infrastructure.
Artificial Intelligence Control, Superconducting Inductor, DC–DC Converter, Double-Boost Converter, Electric Vehicle Charging, Ultra-Fast Charging, Power Conversion Efficiency
Mr. MAHAMMAD ILIYAS, NITTURI VENKATA BALAJI, TARUN POLLAI, VELLATURI HARISH, MOGILI SIVAKUMAR
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
DESIGN AND PERFORMANCE ANALYSIS AT 220KV/ 132KV/ 33KV BHOOTPUR SUBSTATION
The rapid growth in electrical power demand necessitates efficient, reliable, and well-designed transmission and distribution systems. This project focuses on the design and performance analysis of a 220/132/33 kV TRANSCO substation, which plays a crucial role in stepping down high transmission voltages to suitable levels for sub-transmission and distribution networks. The primary objective of this project is to analyze the single-line diagram of the 220/132/33 kV TRANSCO substation and evaluate its performance by examining key operational parameters. In addition, the project provides a comprehensive overview of the substation, including its major equipment and operational principles. The design aspect of the project includes detailed consideration of essential substation components such as power transformers, busbar arrangements, circuit breakers, isolators, current transformers (CTs), potential transformers (PTs), lightning arresters and earthing systems. The selection of appropriate equipment ratings is based on load requirements, system voltage levels, fault conditions, and applicable safety standards. Performance analysis is carried out to assess important factors such as load factor, demand factor and line losses of the substation. Overall, this project provides a clear understanding of the design principles, operational performance, and significance of a 220/132/33 kV TRANSCO substation in modern power systems. It highlights how proper planning and design contribute to efficient power transmission, reduced losses, and improved system reliability
DR.B. DHANADEEPIKA, VADLA KALAVATHI, MD SANA SUJAN, GURUJALA AMULYA
A THEORETICAL ANALYSIS OF DATA BREACH DETERMINANTS USING CONTEMPORARY CRIMINAL THEORY
Data breaches have become one of the most critical cybersecurity challenges faced by organizations across the world. With the increasing reliance on digital technologies, sensitive information such as financial records, personal identities, and corporate data is frequently targeted by cybercriminals. Traditional technical approaches alone are often insufficient to fully understand the root causes of data breaches. Therefore, integrating criminological theories with cybersecurity analysis provides a deeper understanding of why such incidents occur. This research explores the determinants of data breaches using contemporary criminal theory frameworks. The study examines how factors such as organizational vulnerabilities, human behavior, opportunity structures, and technological weaknesses contribute to cybersecurity incidents. The research analyzes the applicability of modern criminological theories such as Routine Activity Theory, Rational Choice Theory, and Social Learning Theory in explaining cybercrime behavior. The study also proposes a theoretical framework that combines criminological insights with cybersecurity analytics to better predict and prevent data breaches. By identifying the key determinants that influence cybercriminal activities, this research contributes to the development of more effective cybersecurity policies, risk assessment strategies, and organizational security practices. The proposed framework aims to enhance the understanding of cyber threats and support proactive security mechanisms that mitigate risks associated with data breaches in modern digital environments.
D SRAVAN KUMAR, RASAGNA LAKSHMI, N. NITHIN, M. ANUDEEP, CH. SHIVANI
INTELLIGENT FUEL CONSUMPTION PREDICTION AND DRIVER BEHAVIOR ANALYSIS USING ECU DATA
With the increasing integration of machine learning (ML) in the automotive industry, real-time fuel consumption prediction and driving profile classification have become essential for improving fuel efficiency, reducing emissions, and optimizing vehicle performance. This research presents a machine learning-based approach that leverages Electronic Control Unit (ECU) data to classify driving behavior and predict fuel consumption with high accuracy. The proposed system applies multiple supervised and unsupervised ML models, including K-Means clustering, Logistic Regression, XGBoost for classification, and Ridge Regression, Support Vector Regression (SVR), and XGBoost for fuel consumption prediction. A rigorous feature selection process is implemented to identify the most relevant variables influencing fuel consumption. The model is trained and tested on real-time ECU data collected from a test vehicle, ensuring reliable and actionable insights. The results demonstrate that the classification models achieve up to 100% accuracy in categorizing driving behavior as fuel-efficient or inefficient. Moreover, the regression models predict fuel consumption with an R² value of up to 0.99 and a mean absolute error (MAE) as low as 0.23 L/h, outperforming existing approaches in terms of accuracy and computational efficiency. The real-time data processing capability further enhances its applicability for intelligent transportation systems, eco-driving initiatives, and insurancebased telematics. The study concludes that the proposed MLbased framework provides a robust and efficient method for monitoring and optimizing vehicle fuel consumption, contributing to the development of smart and sustainable automotive solutions.
DR. P MOHAN, S. ROHITHA REDDY, K. VISHNU, D. SAI NIVAS, THIRUPATHI ABHISHEK
DEEP LEARNING-BASED EARLY DETECTION OF MALNUTRITION IN CHILDREN UNDER FIVE YEARS
Malnutrition remains a critical public health concern, particularly among children, necessitating early and accurate diagnostic methods to support timely intervention. This project presents a deep learning-based system for the automatic detection of malnutrition in children using full-body pediatric images. The system classifies images into two categories—Malnutrition and Nutrition—leveraging Convolutional Neural Network architectures. The application is developed using Python for backend processing and Flask as the web framework, with a responsive interface built using HTML, CSS, and JavaScript. Two state-of-the-art deep learning models, ResNet152V2 and MobileNet, were trained and evaluated independently on a balanced dataset comprising 2,010 images (1,005 for each class). The image preprocessing pipeline includes rescaling, resizing, data augmentation, directory-based loading, batching, shuffling, and training-validation splitting, ensuring robust training and model generalization. The ResNet152V2 model achieved a training accuracy of 99% and test accuracy of 96%, while the MobileNet model recorded a training accuracy of 99% and test accuracy of 95%. For performance evaluation, standard classification metrics such as Precision, Recall, F1-score, and Confusion Matrix were utilized. Additionally, model accuracy and loss graphs were plotted to visualize training performance across epochs for both models. The results demonstrate that deep learning can effectively distinguish between malnourished and nourished children using visual cues alone, offering a scalable and non-invasive approach to nutritional screening in clinical and rural settings. This system has the potential to support healthcare professionals and aid in the early detection of malnutrition, thereby improving pediatric health outcomes
DR. PRASANTHI LAKSHMI MALYALA, ALLAM AKSHITHA, SABBANI RISHITHA, SWARAJ MAHENDRA LAKHE, MUCHINTHALA KARTHIK
AI-BASED ACCIDENT ZONE PREDICTION AND RISK MAPPING SYSTEM
The escalating frequency of road accidents, often driven by unpredictable environmental variables, necessitates a shift from traditional retrospective mapping to intelligent, proactive safety technologies. Traditional safety systems, which rely heavily on static historical data, are increasingly insufficient for addressing the dynamic nature of modern road hazards. This project introduces a comprehensive AIBased Accident Zone Prediction and Risk Mapping System designed to mitigate these risks through Real-Time Contextual Analysis. The proposed framework utilizes a Hybrid Risk Scoring Algorithm as a data fusion engine, continuously integrating historical FIR records, live atmospheric conditions, and traffic velocity to identify high-risk zones in real-time. Leveraging robust ensemble learning techniques—specifically Random Forest and XGBoost—the system forecasts accident probabilities with high precision, effectively handling non-linear relationships between road attributes and crash frequency. A key innovation of this system is the integration of a Generative AI Reporting Module powered by Large Language Models (LLMs), which transforms complex analytics into automated, human-readable safety insights for authorities. Additionally, the platform features a SafeRoutemNavigation mechanism that proactively guides commuters away from identified critical danger zones based on live safety scores. By visualizing risks through dynamic heatmaps and interactive geospatial layers, this system bridges the gap between reactive administrative recordkeeping and proactive accident prevention, offering a scalable solution to significantly reduce road fatalities.
Real-Time Contextual Analysis, Hybrid Risk Scoring Algorithm, Ensemble Learning (Random Forest / XGBoost), Generative AI (LLMs), Dynamic Heatmaps, Safe-Route Navigation
DESIGN AND IMPLEMENTATION OF AN XR-ENABLED METAVERSE PLATFORM FOR IMMERSIVE COLLABORATIVE EDUCATION
The rapid evolution of Extended Reality (XR) technologies has opened new possibilities for transforming traditional educational environments into immersive and interactive digital ecosystems. The concept of the metaverse enables persistent, shared virtual spaces where learners can collaborate in real time beyond geographical limitations. This paper presents the design and implementation of an XR-enabled metaverse platform for immersive collaborative education. The proposed system integrates Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) components to create a unified learning environment that supports realtime interaction, virtual classrooms, 3D content visualization, and collaborative problem-solving activities. A modular architecture is developed to handle user authentication, avatar-based interaction, content rendering, spatial audio communication, and session management. The platform leverages cloud-based infrastructure for scalability and low-latency synchronization among participants. Experimental evaluation demonstrates enhanced student engagement, improved knowledge retention, and effective collaboration compared to traditional e-learning systems. The proposed XR-enabled metaverse framework provides a scalable and interactive solution for next-generation immersive education.
Metaverse; Extended Reality (XR); Virtual Reality (VR); Augmented Reality (AR); Immersive Learning; Collaborative Education; 3D Virtual Environment; Cloud-Based Learning Platforms; Real-Time Interaction.
BIAS DETECTION AND MITIGATION USING EXPLAINABLE AI IN INTELLIGENT RECRUITMENT AUTOMATION
The increasing adoption of Artificial Intelligence (AI) in recruitment automation has transformed hiring processes by enabling efficient candidate screening, ranking, and shortlisting. However, AI-driven recruitment systems may unintentionally inherit or amplify societal biases present in historical hiring data, leading to unfair decision-making and discrimination. This paper proposes a Bias Detection and Mitigation Framework that integrates Explainable AI (XAI) techniques into intelligent recruitment automation systems. The proposed model employs machine learning algorithms for candidate evaluation while incorporating fairness-aware metrics to detect demographic bias across protected attributes such as gender and ethnicity. Explainability tools such as SHAP (SHapley Additive exPlanations) are used to interpret model decisions and identify biased feature contributions. A bias mitigation module applies pre-processing and in-processing fairness correction strategies to improve equitable outcomes. Experimental evaluation demonstrates improved fairness metrics, reduced disparate impact, and maintained predictive performance compared to conventional AI-based recruitment systems. The proposed framework enhances transparency, accountability, and ethical compliance in automated hiring processes
Explainable AI (XAI); Bias Detection; Fairness in AI; Recruitment Automation; Machine Learning; Ethical AI; SHAP; Disparate Impact; Algorithmic Transparency; Human-Centric AI..
MECHANICAL AND STRUCTURAL EVALUATION OF ADDITIVELY MANUFACTURED LATTICE COMPONENTS
Additive manufacturing enables the fabrication of complex lattice structures that are lightweight yet mechanically robust, making them ideal for aerospace, automotive, and biomedical applications. This study investigates the mechanical and structural behavior of 3D-printed lattice components fabricated using selective laser melting (SLM) with metallic materials. Different lattice topologies, including body-centered cubic (BCC), face-centered cubic (FCC), and gyroid structures, are analyzed for compressive and tensile performance. Specimens are printed with varied cell sizes and densities to evaluate their effect on stiffness, strength, and energy absorption. Mechanical testing, including tensile, compressive, and hardness tests, is performed to assess performance, while scanning electron microscopy (SEM) is used to examine failure mechanisms and microstructural integrity. Results show that lattice topology and density significantly influence load-bearing capacity and energy absorption. Optimized lattice designs achieve high strength-to-weight ratios, demonstrating the potential of additive manufacturing for lightweight structural components. The study provides insights into design parameters, mechanical optimization, and practical applications of 3D-printed lattice structures in advanced engineering fields.
Additive Manufacturing, Lattice Structures, Mechanical Properties, Lightweight Components, Selective Laser Melting, Energy Absorption
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
DIGITAL SUPPLY CHAIN MANAGEMENT AND ITS INFLUENCE ON ORGANIZATIONAL PERFORMANCE
Digital Supply Chain Management (DSCM) has emerged as a critical factor in improving organizational performance in today’s competitive and technology-driven business environment. This study examines how digital technologies such as ERP systems, big data analytics, IoT, and cloud computing transform traditional supply chains into agile and responsive networks. The research analyzes the relationship between DSCM practices and key performance indicators such as operational efficiency, cost reduction, customer satisfaction, and overall business performance. Primary data were collected through structured questionnaires, and secondary data were gathered from journals and reports. Statistical tools were applied to interpret the data. The findings indicate that effective implementation of digital supply chain practices significantly enhances organizational performance. The study concludes that organizations adopting DSCM gain sustainable competitive advantage and improved decision-making capabilities.
Digital Supply Chain, Organizational Performance, Technology, Efficiency, Innovation
LIGHTWEIGHT AUTHENTICATION SCHEME FOR RESOURCE-CONSTRAINED IOT DEVICES
Resource-constrained IoT devices operate with limited processing power, memory, and energy, making traditional cryptographic protocols impractical for secure authentication. This paper proposes a lightweight authentication scheme tailored for low-power IoT environments, combining hash-based verification, symmetric-key primitives, and reduced handshake overhead. The scheme ensures mutual authentication, protects against replay and impersonation attacks, and maintains low computational cost. Experimental evaluation across microcontroller- and sensor-class hardware demonstrates reduced latency and energy usage compared to existing protocols. The proposed solution provides a secure and efficient mechanism for large-scale IoT deployments in smart homes, healthcare, industrial automation, and remote sensing applications.
Lightweight Authentication, IoT Security, Resource-Constrained Devices, Symmetric Cryptography, Mutual Authentication, Secure Communication.
HIGH-EFFICIENCY DC–DC CONVERTER DESIGN FOR ELECTRIC VEHICLE APPLICATIONS
The rapid growth of electric vehicle (EV) technology has increased the demand for efficient, compact, and reliable power electronic converters to manage energy flow within vehicle power systems. DC–DC converters play a vital role in regulating voltage levels between high-voltage battery packs and low-voltage auxiliary loads. However, conventional converter designs suffer from significant switching losses, thermal stress, and efficiency degradation under dynamic operating conditions. This paper presents a high-efficiency DC–DC converter design tailored for electric vehicle applications. The proposed system employs an optimized converter topology, advanced control strategies, and soft-switching techniques to reduce losses and improve overall performance. MATLAB/Simulinkbased analysis demonstrates enhanced efficiency, reduced ripple, and improved transient response, validating the suitability of the proposed design for next-generation electric vehicles.
AI-ENABLED PREDICTIVE CUSTOMER SEGMENTATION FOR OPTIMIZING DIGITAL MARKETING CAMPAIGNS
The rapid growth of digital marketing has increased the need for intelligent customer segmentation methods capable of predicting consumer behavior with high accuracy. Traditional segmentation approaches such as demographic or RFM grouping fail to capture nonlinear patterns in customer interactions across multiple digital channels. This study proposes an AI-enabled predictive segmentation model that integrates machine learning algorithms to classify customers into actionable segments for improved campaign targeting. Using a dataset containing demographic attributes, purchase history, and online engagement metrics, the model employs clustering techniques followed by predictive classification to identify potential high-value customers. Experimental analysis demonstrates that AI-driven segmentation enhances campaign performance by improving personalization and reducing acquisition costs. The study highlights the potential of AIpowered analytics to revolutionize data-driven marketing strategies.
INVESTIGATION OF MECHANICAL PROPERTIES OF 3D-PRINTED METAL MATRIX COMPOSITES
Additive manufacturing (3D printing) has revolutionized the fabrication of metal matrix composites (MMCs) by enabling complex geometries and tailored material properties. MMCs are widely used in aerospace, automotive, and defense applications due to their high strength-to-weight ratio and superior mechanical performance. This study investigates the mechanical properties of 3Dprinted MMC specimens fabricated with aluminum as the matrix and ceramic particles as reinforcement. Specimens are produced using selective laser melting (SLM) with varying printing parameters such as layer thickness, laser power, and scan speed. Tensile, compressive, hardness, and impact tests are conducted to evaluate the performance of the printed composites. Microstructural analysis is performed using scanning electron microscopy (SEM) to correlate material structure with mechanical behavior. The results indicate a significant improvement in strength and hardness compared to unreinforced aluminum specimens. The study demonstrates that 3D printing parameters strongly influence mechanical performance, with optimized settings providing superior structural properties. The findings highlight the potential of 3D-printed MMCs for highperformance engineering applications, offering a cost-effective and efficient alternative to conventional manufacturing techniques.
3D Printing, Metal Matrix Composites, Mechanical Properties, Additive Manufacturing, Tensile Strength, Hardness
ERROR CONTROL CODING STRATEGIES FOR RELIABLE HIGHSPEED DATA TRANSMISSION
Reliable high-speed data transmission is a fundamental requirement in modern communication systems such as 5G/6G networks, optical fiber links, satellite communications, and high-speed wired networks. As data rates increase, communication channels become more susceptible to noise, interference, and signal distortion, leading to higher bit error rates. Error control coding (ECC) techniques play a crucial role in detecting and correcting transmission errors without requiring retransmission. This paper presents an analysis of widely used error control coding strategies for achieving reliable high-speed data transmission. The performance of different coding schemes is evaluated in terms of error correction capability, bandwidth efficiency, and computational complexity. The study highlights the suitability of advanced coding techniques for next-generation high-speed communication systems
POWER QUALITY ENHANCEMENT IN DISTRIBUTION SYSTEMS USING CUSTOM POWER DEVICES
Power quality issues such as voltage sag, swell, flicker, and harmonics significantly affect sensitive electronic devices and industrial equipment. Custom Power Devices (CPDs), including Dynamic Voltage Restorers (DVRs), Distribution Static Compensators (DSTATCOMs), and Unified Power Quality Conditioners (UPQCs), are designed to mitigate these disturbances. This study presents a comprehensive analysis of CPDs integrated into distribution systems to enhance voltage stability and minimize total harmonic distortion. MATLAB/Simulink simulations are conducted under varying load conditions to evaluate performance metrics such as voltage profile, reactive power compensation, and harmonic mitigation. Results indicate that CPDs can restore voltage sag in milliseconds, reduce harmonic content below IEEE limits, and improve overall system reliability. The study highlights the effectiveness of coordinated CPD deployment and control strategies for modern smart grids. Implementation of these devices provides a robust solution for industrial, commercial, and residential systems requiring high-quality power. Economic considerations, efficiency, and rapid dynamic response are also analyzed. The findings confirm that CPDs are vital for enhancing power quality and maintaining uninterrupted operation of sensitive loads.
Power Quality, Custom Power Devices, Voltage Sag, Harmonics, DVR, DSTATCOM, UPQC, Distribution Systems
INTELLIGENT SPEED CONTROL OF BLDC MOTOR USING ADAPTIVE CONTROL ALGORITHMS
Brushless DC (BLDC) motors are widely used in industrial automation, electric vehicles, robotics, and consumer electronics due to their high efficiency, high torque-to-weight ratio, and low maintenance requirements. However, achieving precise and robust speed control under varying load and parameter uncertainties remains a significant challenge. Conventional proportional–integral (PI) controllers often fail to provide optimal performance under dynamic operating conditions. This paper presents an intelligent speed control approach for BLDC motors using adaptive control algorithms. The proposed system dynamically adjusts control parameters in response to load variations and system nonlinearities, ensuring improved speed regulation and stability. MATLAB/Simulink-based simulations demonstrate enhanced transient response, reduced steady-state error, and superior robustness compared to traditional control methods, validating the effectiveness of adaptive control for BLDC motor applications.
AI-ENABLED SMART AGRICULTURE FRAMEWORK USING MULTISENSOR FUSION ANALYTICS
Smart agriculture increasingly relies on realtime environmental intelligence, crop health analysis, and predictive decision-making to optimize productivity and reduce resource wastage. This paper proposes an AI-enabled smart agriculture framework that integrates multisensor fusion analytics across soil, climate, crop, and machinery sensors to deliver accurate, timely insights. The system combines edge-based data acquisition, clouddriven machine learning, and hybrid fusion strategies to improve detection accuracy, anomaly monitoring, and yield forecasting. Experimental evaluation using fieldmimicking datasets shows enhanced performance in moisture prediction, pest detection, and fertilizer optimization. The framework significantly improves operational efficiency, reduces human intervention, and supports scalable and sustainable agricultural practices.
Smart Agriculture, Multisensor Fusion, Machine Learning, IoT Sensors, Precision Farming, Edge Computing, Crop Monitoring.
LEVERAGING EDGE AI FOR REAL-TIME ANOMALY DETECTION ININTERNET OF THINGS NETWORKS
The rapid growth of the Internet of Things (IoT) has enabled unprecedented connectivity but has also exposed networks to large-scale security and reliability risks. Traditional centralized anomaly detection methods struggle with latency, bandwidth constraints, and privacy concerns. This paper proposes an Edge AI framework for real-time anomaly detection in IoT networks, moving intelligence closer to data sources to reduce latency and preserve privacy. The system combines distributed edge devices running lightweight deep learning models with a cloud-based aggregation server for coordination and model updates. Experimental results show improved detection accuracy, faster response times, and reduced network load compared to centralized systems. These findings highlight the promise of Edge AI as a scalable and privacyconscious solution for securing IoT infrastructures.
Copyright (c) 2025 Arnav Raymond (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
THE DIGITAL ENERGY NEXUS: INTEGRATING AI, IOT, ANDRENEWABLE RESOURCES FOR GRID OPTIMIZATION
The rapid transition toward sustainable energy systems has underscored the need for intelligent, interconnected solutions to manage increasingly complex power networks. This paper explores the Digital Energy Nexus, a paradigm that integrates Artificial Intelligence (AI), the Internet of Things (IoT), and renewable energy resources to achieve advanced grid optimization. By leveraging AI algorithms for predictive analytics, load forecasting, and anomaly detection, utilities can improve operational efficiency and enhance grid resilience. IoTenabled sensors and smart devices provide realtime data on energy production, storage, and consumption, enabling adaptive control strategies. Coupled with renewable resources such as solar, wind, and distributed energy storage, this integrated approach reduces carbon emissions, minimizes system losses, and supports demand-response mechanisms. The study highlights current technologies, architectural frameworks, and policy considerations while identifying challenges such as cybersecurity, data interoperability, and regulatory compliance. Ultimately, the Digital Energy Nexus represents a transformative pathway toward more efficient, resilient, and sustainable energy infrastructures.
Digital Energy Nexus, Artificial Intelligence (AI), Internet of Things (IoT), Renewable Energy, Smart Grid, Grid Optimization, Predictive Analytics, Energy Storage, Demand Response, Sustainability.
Copyright (c) 2025 Jingxu Zheng (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
SMART ELECTRONICS AND EMBEDDED INTELLIGENCE: HARNESSING AI FOR HIGH-PERFORMANCE CIRCUITRY
The convergence of artificial intelligence (AI) and electronics is reshaping the landscape of circuit design and embedded systems. By integrating AI-driven algorithms into electronic design automation (EDA), signal optimization, and embedded decision-making, smart electronics can achieve unprecedented levels of performance, adaptability, and energy efficiency. This paper investigates how embedded intelligence can be leveraged to enhance the design and operation of highperformance circuitry. We review the state-ofthe-art in AI-driven circuit design and embedded optimization, propose a synthetic benchmark to assess different AI methods, and present experimental results comparing reinforcement learning, deep neural networks, and evolutionary algorithms for circuit parameter tuning and embedded system control.
Smart electronics, embedded intelligence, artificial intelligence, circuit design automation, edge computing, reinforcement learning, high-performance circuitry
PUSHING INTELLIGENCE TO THE EDGE: EMBEDDED AI FOR SUSTAINABLE AND ADAPTIVE ELECTRONICS
The rapid evolution of edge computing and artificial intelligence (AI) is reshaping how electronic systems are designed and deployed. This paper explores the paradigm of “Pushing Intelligence to the Edge”—embedding AI directly into smart sensors, ultra-low-power chips, and adaptive control modules to create sustainable and energy-efficient electronic systems. Traditional cloud-based AI architectures suffer from high latency, bandwidth constraints, and increased energy consumption. In contrast, edge AI enables realtime decision-making, data privacy, and reduced power requirements by bringing computational intelligence closer to the data source. The paper examines advanced hardware–software codesign techniques, lightweight neural network architectures, and neuromorphic processors as enablers of adaptive edge devices. Additionally, it addresses sustainability by investigating lowpower design strategies, green semiconductor materials, and dynamic power management. Through a comprehensive review and proposed design framework, this research highlights how embedded AI at the edge can deliver scalable, secure, and adaptive electronics for nextgeneration applications ranging from environmental monitoring to autonomous vehicles.
Edge AI, Embedded Intelligence, Sustainable Electronics, Adaptive Control, Smart Sensors, Low-Power Chips, Neuromorphic Computing, Green Semiconductor Materials, Edge Computing, Energy Efficiency.
DISTRIBUTED LEDGER–BASED ACCESS CONTROL MECHANISM FOR SECURE CLOUD SERVICES
Cloud computing has revolutionized how organizations store, process, and manage data, but centralized access control remains a critical vulnerability. Traditional authentication and authorization mechanisms can be prone to single points of failure, insider threats, and limited transparency. This paper proposes a Distributed Ledger–Based Access Control Mechanism (DLACM) for secure cloud services. By integrating blockchain technology with finegrained access policies, the system decentralizes trust, enhances auditability, and improves resilience against unauthorized access. A prototype was implemented using Hyperledger Fabric integrated with a cloud storage platform. Experimental results demonstrate improved security, transparency, and performance compared with traditional centralized access control models.
ADVANCED ENERGY HARVESTING IN MECHANICAL SYSTEMS VIA MACHINE LEARNING AND SMART MATERIALS
Energy harvesting in mechanical systems has gained prominence as industries seek sustainable and self-powered solutions for sensors, actuators, and embedded electronics. This paper investigates the integration of machine learning (ML) and smart materials to optimize vibrational and thermal energy harvesting from mechanical systems. We develop a hybrid data-driven and physics-based framework to predict and enhance energy output of piezoelectric, triboelectric, and thermoelectric harvesters embedded in rotating and reciprocating machinery. A synthetic dataset representing energy harvester performance under various operating conditions is generated to illustrate methodology. Machine learning models are trained to predict power output and efficiency, and an optimization algorithm is used to maximize energy harvested while maintaining system stability. Results show improved prediction accuracy and demonstrate how ML assisted optimization can identify nonintuitive parameter settings that significantly increase energy recovery.
ADAPTIVE ENERGY MANAGEMENT IN INTELLIGENT BUILDINGS USING AI AND STRUCTURAL MONITORING SYSTEMS
The increasing complexity of modern buildings, combined with rising energy demands and sustainability goals, has led to the development of intelligent energy management systems. By integrating artificial intelligence (AI) with structural health monitoring (SHM), buildings can adaptively optimize energy consumption while maintaining structural resilience. This paper explores AI-driven frameworks for predictive energy management, sensor-based monitoring, and adaptive control strategies in smart buildings. The integration of machine learning algorithms with structural monitoring allows for real-time decision-making, predictive maintenance, and sustainable energy optimization. Case studies, implementation challenges, and performance evaluation metrics are discussed to provide a comprehensive understanding of the state-of-the-art in adaptive energy management.
MACHINE LEARNING–ASSISTED DESIGN OF SMART MATERIALS AND ADDITIVE MANUFACTURED COMPONENTS
The integration of machine learning methods with additive manufacturing (AM) processes and the emerging class of smart materials promises to transform mechanical design and structural performance optimization. This work presents a comprehensive study that combines data-driven modeling, inverse design, and process-parameter optimization to produce additive manufactured components incorporating smart material fractions. We propose a methodological pipeline that spans dataset generation, feature engineering, supervised learning for property prediction, and optimization-driven design to minimize defects while maximizing mechanical performance. An experimental campaign is simulated to demonstrate the approach, producing model predictions and measured outcomes that highlight the potential and limitations of ML-assisted design in AM of smart-material-enabled components. Key outcomes show that machine learning models can predict tensile strength and defect trends with acceptable accuracy and that optimization of laser power, print speed, and smart material fraction yields measurable improvements in structural performance. The results are contextualized within contemporary literature, and practical recommendations for future experimental validation are provided.
PREDICTIVE MAINTENANCE IN HYBRID INFRASTRUCTURE: AI APPROACHES FOR ELECTRICAL AND CIVIL SYSTEMS
The rapid growth of urban infrastructure and electrical networks has created a need for intelligent monitoring systems capable of minimizing downtime, reducing maintenance costs, and enhancing operational safety. Predictive maintenance leverages artificial intelligence (AI) to analyze data from sensors, historical records, and real-time monitoring systems to forecast failures before they occur. Integrating AI across hybrid infrastructure, encompassing both electrical systems and civil structures such as bridges, buildings, and industrial plants, provides an innovative approach to optimize maintenance schedules and ensure structural integrity. This paper explores AI-driven methodologies, frameworks, and applications in hybrid infrastructure predictive maintenance, highlighting challenges, implementation strategies, and performance evaluation metrics.
MACHINE LEARNING APPROACHES FOR PREDICTIVE MAINTENANCE IN INDUSTRIAL ROTATING SYSTEMS
Rotating machinery, including turbines, pumps, compressors, and motors, forms the backbone of modern industrial operations. Unexpected failures can lead to costly downtime, production losses, and safety hazards. Predictive maintenance, powered by machine learning (ML) algorithms, provides a proactive approach to identify potential faults before they manifest. This paper explores state-of-the-art ML techniques for predictive maintenance in industrial rotating systems, integrating sensor data, historical performance records, and environmental conditions. The study highlights key applications, methodologies, and performance evaluation metrics, demonstrating how AI-driven maintenance strategies enhance operational efficiency, reduce downtime, and optimize resource utilization.
Predictive Maintenance, Rotating Machinery, Machine Learning, Industrial Systems, Fault Detection, Digital Twins, Operational Efficiency, AI-Driven Monitoring