International Journal of Artificial Intelligence and Computer Electronics
Secure And Decentralized Health Data Management Using IoMT And Blockchain
The rapid growth of the Internet of Medical Things (IoMT) is transforming modern healthcare by enabling continuous patient monitoring, early disease detection, and improved clinical decision-making. However, healthcare data is often distributed across multiple hospitals, medical institutions, and digital platforms, creating significant challenges related to interoperability, secure data sharing, privacy, and efficient data management. To address these issues, this project proposes a blockchain-enabled framework for the secure, interoperable, and privacy-preserving validation and storage of IoMT-generated healthcare data. The proposed system utilizes Hyperledger Fabric, a permissioned blockchain platform, to provide controlled access, tamper-resistant data management, secure transactions, and transparent auditing within healthcare environments. Edge computing is also incorporated to process data closer to IoMT devices, thereby reducing communication latency, minimizing network congestion, and improving the efficiency of real-time healthcare services. The decentralized architecture enables authorized healthcare stakeholders to securely exchange patient information while maintaining data integrity, confidentiality, and privacy. The framework can support important applications such as remote patient monitoring, preventive healthcare, and timely clinical intervention. Experimental evaluation indicates that the proposed approach has the potential to enhance healthcare workflow efficiency, accelerate clinical decision-making, and support a more patient-centered model of healthcare data management. Overall, the proposed framework contributes toward the development of a secure, scalable, interoperable, and efficient digital healthcare infrastructure for future IoMT-based healthcare systems.
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by-nc-nd/4.0/
Deep Learning Algorithms For Optimized Thyroid Nodule Classification
The growing incidence of thyroid cancer has increased the need for accurate and efficient methods for the early detection and classification of thyroid nodules. Ultrasound imaging is widely used for thyroid examination; however, manual interpretation can be time-consuming and may be affected by variations in image quality and the complexity of nodule characteristics. Automated classification systems can therefore provide valuable support to physicians by improving diagnostic speed, consistency, and accuracy. Nevertheless, the limited availability of medical image datasets and the difficulty of extracting informative features remain significant challenges. This study presents an advanced framework for thyroid nodule classification based on the extraction of meaningful, discriminative, and clinically relevant features from thyroid ultrasound images. The proposed approach integrates image preprocessing, effective feature extraction, and machine learning-based classification techniques to improve the identification of different thyroid nodule categories. The extracted features capture important characteristics such as morphology, texture, shape, echogenicity, and boundary patterns, enabling the system to identify subtle differences associated with thyroid abnormalities. The framework is designed to distinguish benign and malignant nodules while also recognizing normal thyroid classifications. By reducing the effects of image noise, redundant information, and variations in ultrasound data, the proposed system improves the reliability of the classification process. The combined classification approach provides a comprehensive representation of thyroid nodules and demonstrates promising performance in preliminary evaluations. Overall, this research contributes to the development of an intelligent and efficient diagnostic support system that can assist healthcare professionals in the timely and accurate detection of thyroid cancer.
Data-Driven Crop Suitability Recommendation Using Random Forest And Soil-Climate Feature Analysis
Efficient crop selection is a crucial component of sustainable agriculture, improved productivity, and long-term food security. Selecting an appropriate crop depends on multiple soil and environmental conditions, while conventional farming practices often rely on farmers' experience and traditional cultivation patterns. This study presents a data-driven crop suitability recommendation system using the Random Forest algorithm and soil-climate feature analysis to support accurate and reliable crop selection. The proposed system uses key agricultural parameters, including Nitrogen, Phosphorus, Potassium, soil pH, temperature, humidity, and rainfall, to evaluate soil fertility and prevailing climatic conditions. Data preprocessing and exploratory analysis are performed to improve data quality and identify significant relationships between agricultural features and crop suitability. A Random Forest Classifier is then employed to learn complex patterns from historical agricultural data. By combining the predictions of multiple decision trees, the model improves classification performance while reducing the risk of overfitting. The developed system can recommend 22 different crop varieties based on varying soil and climatic conditions. Its performance is assessed using accuracy, precision, recall, F1-score, and a confusion matrix. Experimental results show that the Random Forest model achieves approximately 99% accuracy on the test dataset, demonstrating strong predictive performance and reliability. The proposed approach can support farmers and agricultural stakeholders in making informed, data-driven decisions, thereby contributing to efficient crop planning, sustainable farming practices, and enhanced agricultural productivity.
Applying Machine Learning Algorithm For The Classification Of Sleep Disorders
Sleep disorders such as Insomnia and Sleep Apnea significantly affect health, well-being and daily functioning. The project develops a machine-learning based classification system that predicts sleep-disorder categories from demographic, sleep, lifestyle and vital-health information. The source thesis describes a dataset of 400 samples with 13 relevant features and three target classes: Insomnia, None and Sleep Apnea. The proposed approach uses a Random Forest classifier. The model is selected because ensemble learning reduces the risk of overfitting, supports mixed data, requires comparatively modest computational resources and provides feature-importance information. The thesis reports an accuracy of 95% and evaluates the model using accuracy, precision, recall, F1-score, confusion matrix and ROC analysis.
Detecting AI-Generated Fake News Using Multi-Layer Perception Classifier
The rapid advancement of large language models (LLMs), such as GPT, has increased the generation and spread of AI-created fake news, creating significant challenges for reliable information dissemination. Conventional text-classification techniques often have limited ability to distinguish authentic news from artificially generated or manipulated content. To address this challenge, this study proposes a Multi-Layer Perceptron (MLP) classifier integrated with Natural Language Processing (NLP) techniques for the effective detection of AI-generated fake news. The textual data undergoes preprocessing steps, including tokenization, stop-word removal, and vectorization, to extract meaningful linguistic features. These features are subsequently provided to the MLP model, where multiple hidden layers and nonlinear activation functions learn complex patterns associated with fabricated news content. A dedicated dataset consisting of AI-generated news samples created using GPT-4 across 42 different news categories was developed for training and evaluation. Experimental results indicate that the proposed MLP-based approach achieves high classification accuracy and strong F1-score performance, outperforming several traditional machine-learning methods. The study demonstrates the effectiveness of combining NLP-based feature extraction with deep learning for identifying AI-generated misinformation and contributes toward improving the reliability and integrity of online information environments.
Synergizing High-Dimensional Technical Metrics And Crowd Sentiment For Bitcoin Volatility Classification Using Cart
This study presents a hybrid approach for forecasting the next-day Bitcoin price range by integrating Natural Language Processing (NLP)-based sentiment analysis with a Long Short-Term Memory (LSTM) deep learning model. The proposed framework combines historical Bitcoin market data and high-dimensional technical indicators with sentiment information extracted from a large volume of Twitter posts. Advanced NLP techniques are used to analyze public opinions and capture positive, negative, and neutral market sentiment, while the LSTM network learns temporal dependencies and complex sequential patterns from both numerical and textual features. The experimental analysis is conducted using six years of Bitcoin market data along with millions of relevant Twitter posts. By incorporating technical indicators and public sentiment simultaneously, the proposed model provides a more comprehensive understanding of market behavior than conventional approaches based only on historical price information. Sensitivity analysis is further performed to evaluate and optimize the contribution of sentiment features to the forecasting process. The results demonstrate that the inclusion of sentiment-driven information improves the accuracy, robustness, and adaptability of next-day Bitcoin price range prediction. Overall, the study highlights the potential of combining deep learning, financial indicators, and social media sentiment analysis for developing more effective and dynamic cryptocurrency forecasting systems.
A Secure Multi-Layer Framework For File Upload Validation, Encryption, And Controlled Access
File upload functionality is widely used in modern applications and communication platforms, including Internet of Things (IoT) devices, smart homes, and smart city environments, to support efficient data exchange and content sharing. However, insecure file upload mechanisms can create unrestricted file upload (UFU) vulnerabilities that threaten the confidentiality, integrity, and availability of communication systems. This study examines the major security risks, underlying causes, and potential consequences associated with UFU vulnerabilities. Such weaknesses may be exploited without extensive user interaction or privileged access, potentially leading to unauthorized file execution, system compromise, service disruption, and reduced network reliability. The study highlights the importance of implementing robust file validation, access control, secure storage, and monitoring mechanisms to strengthen upload security. The findings demonstrate the need for continued research and improved defensive approaches to protect communication systems and maintain reliable service delivery in emerging smart environments.
Quantum Vision–Guided Xception for Robust Visual Feature Learning Under Image Distortions
This work proposes a novel Heavy QV-Xception model for advanced object recognition by integrating the wave-centric principles of Quantum Vision (QV) theory with the efficient architecture of Xception. Inspired by the particle-wave duality of quantum physics, QV theory interprets visual objects as dynamic information waves rather than static pixel-based images. The proposed framework incorporates a robust QV transformation block at the frontend to convert conventional spatial images into high-dimensional wave-function representations. This transformation enables the model to capture complex structural patterns, non-local statistical relationships, and dynamic contextual information that may not be effectively represented through conventional localized pixel processing. The transformed wave-based features are subsequently processed through the Xception backbone, which employs depthwise separable convolutions to efficiently separate spatial feature extraction from cross-channel feature correlation. By combining quantum-inspired information representation with efficient deep convolutional learning, the Heavy QV-Xception model provides enhanced feature extraction and improved object recognition capability. Experimental evaluation on multiple benchmark datasets demonstrates that the proposed model consistently outperforms standard Xception and other conventional convolutional neural network models. The results highlight the potential of integrating Quantum Vision theory with advanced deep learning architectures to achieve more accurate and robust object recognition.
Multi-Agent AI Chief of Staff for Turbine Fleet Operations
Modern wind turbine fleets generate an overwhelming volume of telemetry, weather, maintenance and market signals, and the operators who must act on that volume are increasingly stretched thin. This paper proposes a multi agent artificial intelligence system, framed as an AI chief of staff, that coordinates six specialised agents, a forecasting agent, a SCADA monitoring agent, a predictive maintenance agent, a grid compliance and trading agent, a reporting and human approval agent, and an orchestrator that plans and routes work between them. The system integrates a hybrid CNN LSTM attention forecasting model for wind speed and power prediction, a shared context bus for short term coordination, a vector store for retrieval augmented generation over manuals and grid codes, and a human in the loop approval gate before any command reaches the physical fleet. We describe the business context, stakeholders, problem statement and objectives that motivate the design, present the agent architecture and interaction flow, and provide a full, runnable Python reference implementation in the appendix. Simulated experiments across an eight turbine fleet show that the proposed forecasting model reduces mean absolute error from 1.92 metres per second under a persistence baseline to 0.71 metres per second, and that the composite multi agent system improves curtailment loss reduction, maintenance downtime reduction and grid penalty reduction relative to a rule based baseline. The results suggest that multi agent orchestration, when combined with structured outputs and human oversight, is a practical path towards safer and more efficient autonomous operation of renewable energy fleets.
Copyright (c) 2026 Author https://creativecommons.org/licenses/by-nc-nd/4.0
Safe And Energy Efficient Jerk Controlled Speed Profiling For On Road Autonomous Vehicles
Efficient speed planning is crucial for the safe and comfortable navigation of autonomous vehicles in dynamic environments. This paper introduces a novel energy-efficient, jerk-controlled speed planning approach based on quintic polynomial generation. We present a systematic methodology to determine the dynamic speed of autonomous vehicles by integrating several factors, including the relative velocity with dynamic obstacles, the curvature of the base frame and optimal selected path, road adherence, and road gradient. The direct integration of road adherence and gradient into the speed profiling approach contributes to improving vehicle safety. Comparative analysis with literature methods demonstrates the significant impact of jerk smoothness on energy efficiency. Simulations are conducted in a joint simulation between Simulink and SCANER Studio vehicle dynamics simulator, followed by validation on a real-world dataset. Our findings elucidate the significance of the proposed planning method in enhancing safety, energy economy, driving comfort, and computational efficiency, while effectively addressing a wide range of critical.
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
Agriculture contributes one-third of India's Gross Domestic Product, and a large portion of the population depends on agriculture for subsistence. Farmers' annual income is heavily based on the yield of the crops they produce, which has been steadily declining owing to a variety of issues, one of which we are focused on is bird intrusion in crop fields. The Farmers face massive difficulty in protecting their crops from invading swarms of birds. The most notable birds in this regard are peacocks and parrots, which destroy the harvest in agricultural farm fields such as Corn, Ground nut, etc., Considering a statistical survey of farmers on the proportion of crop damage caused by birds, we would like to present a Internet of Things (IoT) based automated bird detection and repeller system. This work aims to chase peacocks and parrots from the crop fields. The proposed approach has two modules: birds' intrusion detection using a Passive Infrared (PIR) sensor and a Repeller Unit, which consists of an audio amplifier and megaphone to make predator noises that will drive the birds away from the field.
Embedded System For Efficient Monitoring And Management Of Solar Applications
In recent years, the use of solar technology as an alternative to conventional methods of electricity generation has increased. However, they have low conversion efficiency; one way to increase energy production is by using Solar Tracking Systems (STS), which cause energy expenditure. Monitoring systems are required to calculate the energy balance between produced and consumed. In this research, the design and implementation from a concurrent approach of an embedded system for energy monitoring in solar applications is presented, obtaining a low energy consumption, high connectivity, scalable, modular, and open architecture system. Experimental tests were carried out considering five proposed energy-saving strategies. These tests recorded the energy consumption of actuators, electronic hardware, and generated power, resulting in a 16.47% increase in the energy budget and a reduction in the global power consumption of 7.27%. Notably, the developed embedded system exhibited a low energy consumption of 0.326Wh.
IoT-Enabled Smart Guard System For Rail Safety With Automated Crack Detection
In the modern era of transportation using ordinary vision to detect cracks in track puts passengers in danger because these problems must go unnoticed for accidents to happen. To improve track evaluation our system employs infrared sensors combined with microcontrollers GSM, GPS and cloud computing for monitoring railway tracks in real time. IR sensors mounted along the tracks scan for damaged areas without human intervention including tiny unnoticeable fractures. The microcontroller analyses the crack data and sends alerts using a GSM module to both local railway stations and maintenance teams. The system sends precise GPS data about the crack’s location along with its severity to help repair teams respond faster. The cloud-based system stores track condition data securely, enabling maintenance teams to plan future maintenance and monitor track status over time. When repairs are not completed within the required 48-hour period, the system activates a secondary alert mechanism to notify higher authorities, ensuring timely action to prevent train accidents. The system enhances railway safety by detecting track cracks at an early stage, reducing risks and minimizing maintenance costs while improving overall repair efficiency.
Global System for Mobile Communications (GSM)
Solar Based Smart Cropping Defence System Using IoT Based On Birds And Animals
Preserving their harvest from wild animals and unauthorized intrusions is a significant difficulty for farmers. The Farm Guardian, a solar-powered, IoT-enabled automated crop protection system, helps solve this. This technology uses an microprocessor alongside touch sensors and ultrasonic sensors to detect movement and touch. Should dangers be discovered, the HCSR04 ultrasonic sensor's continuous object distance monitoring sets off a buzzer and warning system. A solar-powered electric fence keeps animals or trespassers off the perimeter, hence enhancing security by means of a slight deterrent shock. the system sends alerts and actual data to an Android phone belonging to a farmer, therefore enabling remote monitoring effective operation with a 12V solar photovoltaic power source changed to 5V DC for the control unit and sensors. Output modules for buzzers, LED indicators, a servo motor, and a user interface oLED screen constitute the system. Automated, affordable, environmentally friendly means of crop protection using renewable energy is provided by Farm Guardian. The suggested strategy maintains ecologically sustainable farming methods, secures the farm efficiently, decreases dependence on traditional fences, and lowers human interference.
Advanced Track Detection And Obstacle Avoidance System For Autonomous Vehicles
This project presents the design and development of an autonomous robotic vehicle equipped with track sensing and obstacle avoidance capabilities using an L298 motor driver and a microcontroller. The vehicle is designed to move autonomously in different environments while detecting obstacles and making avoidance decisions. It is fitted with an HC-SR04 ultrasonic sensor, which continuously measures the distance for the presence of an obstacle and changes the path of the vehicle accordingly. The motor driver L298 controls the two DC motors, which aid in controlling the movement of the robot accurately. The microcontroller then processes this information from the ultrasonic sensor and instructs the motor driver to make adjustments to achieve a smooth navigation and obstacle avoidance in real-time. Designed for handling various types of terrains and obstacles, the robot showcases very advanced techniques of collision avoidance. The control software is written in the tonny and C++ programming, integrating sensor information with motor commands. This project is for improving efficiency and autonomy in terms of robotic vehicles, with absolutely no manual intervention to the least possible while improving operational reliability.
Digital Twin Based Smart Building Management System
Transforming smart building management into a digital twin technology is operational efficiency and safety enhancement. This paper presents a digital smart building management system comprising an Raspberry Pi Model designed to successfully achieve communication between sensors and device-related activities. Focused features include gas detection alerts, theft detection based on vibration, air quality monitoring (MQ135), occupancy detection (IR sensors), adaptive lighting control (LDR), and temperature/humidity control via DHT11. Early hazard detection is ensured from fire sensors. Real-time data processing with ThingSpeak integrated it contributed to continuous real-time monitoring. Emergency response would thus use a GSM module. The L293D driver controlled ventilation with both a CPU fan and DC motor. Feedbacks are relayed via real time on an oLED display too. This is a truly reliable, efficient, and safe building management system made possible through integrating digital twin technology with an IoT-based automation mechanism.
Power Bi-Integrated Drone System For Environmental Hazard Detection
This paper presents an advanced embedded system for hazardous gas detection and environmental monitoring, designed to improve public safety and industrial risk management through real-time sensing and intelligent analytics. The proposed framework integrates multiple gas sensors, including MQ-2, MQ-7, and MQ-135, along with temperature, humidity, and fire sensors to detect combustible gases, smoke, toxic emissions, and fire hazards. These sensors are connected to an embedded microcontroller that continuously monitors environmental conditions and triggers instant alerts whenever abnormal readings are detected.To enhance operational efficiency, the system is integrated with Microsoft Power BI for real-time data visualization, dashboard reporting, and predictive analysis. The Power BI interface enables continuous monitoring of gas concentration, environmental temperature, humidity levels, and fire threats through interactive charts and alerts. Historical trends and analytical insights further support preventive decision-making and rapid response in hazardous situations.The proposed system is also deployed on a UAV platform, extending its capabilities to aerial surveillance and remote monitoring. This integration enables safe and efficient inspection of industrial facilities, disaster-affected regions, and inaccessible environments where human intervention is risky. The drone-based embedded system provides live environmental assessment, improving mobility, coverage, and situational awareness.Overall, the proposed solution offers a low-cost, scalable, and intelligent framework for hazardous gas detection, environmental safety, and smart city applications. By combining embedded sensing, wireless communication, UAV mobility, and business intelligence tools, the system delivers an effective approach for real-time monitoring, risk reduction, and improved emergency management.
Calmnest: An IoT-Enabled Intelligent Zebra Crossing System With PIR Sensor And Camera-Based Pedestrian Counting
The Smart Automated Zebra Crossing System operationalizes Passive Infrared (PIR) sensors and Internet of Things (IoT) technology with IP camera-based headcount capabilities to foster pedestrian safety. A data-driven control system protected pedestrian safety by integrating adaptive management structures and intelligent traffic methods. The system actively switches on its crossing systems only after PIR sensors detect approaching pedestrians.
Exploring Deep Learning And Machine Learning Approaches For Brain Hemorrhage Detection
Brain hemorrhage is a serious neurological condition that can lead to severe disability or death if not diagnosed promptly. Early identification of intracranial bleeding significantly improves patient survival rates and treatment outcomes. In clinical practice, computed tomography (CT) imaging is widely used for diagnosing neurological abnormalities due to its speed and accuracy. However, manual examination of CT scans by radiologists can be time-consuming and prone to human error, particularly when large numbers of images must be analyzed.Recent advances in artificial intelligence have enabled the development of automated systems capable of detecting and classifying brain hemorrhages with improved efficiency and reliability. This study presents a comprehensive review of existing approaches for brain hemorrhage detection based on both traditional machine learning and modern deep learning techniques. The review focuses on the major stages involved in automated detection systems, including image preprocessing, feature extraction, and classification. Furthermore, the performance of different algorithms is analyzed and compared based on evaluation metrics reported in previous studies.In addition, this paper discusses commonly used benchmark datasets employed for training and evaluating hemorrhage detection models. The advantages, limitations, and challenges associated with current methodologies are also highlighted. Finally, potential research directions are suggested to enhance detection accuracy, reduce computational complexity, and improve clinical applicability of automated diagnostic systems.
Brain Hemorrhage Detection, Computed Tomography (CT), Machine Learning, Deep Learning, Medical Image Processing, Feature Extraction, Classification, Artificial Intelligence in Healthcare.
Marine Debris Detection System Using Yolov11
Marine debris has become a major environmental concern that threatens aquatic ecosystems and coastal sustainability. Efficient and accurate detection of marine waste is therefore essential for effective monitoring and cleanup efforts. This study proposes an enhanced object detection approach that integrates attention mechanisms with the YOLOv11 architecture to improve the identification of marine debris in complex ocean environments.The proposed framework combines the strong instance detection capability of YOLOv11 with attention modules, including Coordinate Attention and a Bottleneck Transformer. While YOLOv11 with Coordinate Attention demonstrates consistent performance across diverse environmental conditions, the Bottleneck Transformer contributes to identifying debris regions that may be overlooked during manual annotation. Although slightly less stable in some scenarios, the transformer-based approach shows improved performance in detecting larger debris objects, indicating its potential usefulness for specialized monitoring tasks.Experimental results demonstrate that incorporating attention mechanisms into YOLOv11 enhances detection capability and adaptability in maritime environments. The findings suggest that attention-based object detection models can effectively support environmental monitoring systems by addressing different operational requirements. Furthermore, the study highlights the importance of selecting detection architectures according to specific deployment conditions, as different models may provide advantages depending on the characteristics of the debris and the monitoring environment.
Marine Debris Detection; YOLOv11; Attention Mechanisms; Coordinate Attention; Bottleneck Transformer; Environmental Monitoring; Deep Learning; Computer Vision.
The increasing use of Artificial Intelligence (AI) in financial decision-making has significantly improved the efficiency of loan approval systems. However, many AI models operate as “black boxes,” making it difficult for financial institutions and customers to understand how decisions are made. This lack of transparency can lead to issues related to trust, fairness, and regulatory compliance. Explainable Artificial Intelligence (XAI) addresses this challenge by providing interpretable insights into the decision-making process of AI models.This study explores the application of Explainable AI techniques in loan approval systems to enhance transparency and accountability. Machine learning models such as decision trees, logistic regression, and ensemble methods are evaluated alongside explanation techniques including SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations). These approaches help identify the key factors influencing loan approval decisions, such as income level, credit history, employment status, and debt-to-income ratio.The proposed framework demonstrates how XAI can improve model interpretability while maintaining predictive performance. By providing clear explanations for automated decisions, financial institutions can build greater trust with customers and ensure compliance with regulatory requirements. The findings highlight the importance of integrating explainability into AI-driven financial systems to support fair, transparent, and responsible lending practices.
Explainable Artificial Intelligence (XAI), Loan Approval, Machine Learning, SHAP, LIME, Financial Decision Systems, Model Interpretability, Responsible AI
Secure Video Processing And Water Mark Embedding Using Shamir Secret Rule
With an emphasis on user identification, video encryption, and decryption capabilities, this project makes safe video processing, encryption, and watermark insertion possible. Registering, logging in, and uploading videos with watermarks for processing are all possible. Every submitted video is encrypted using the cryptography library, and Shamir's Secret Sharing is used to partition the encryption key, guaranteeing safe key distribution and storage. In order to be retrieved and decrypted later, the encrypted frames are stored independently. By reassembling key shares, decryption is accomplished, enabling the reconstruction of the original movie while removing the watermark from the first frame. Additionally, the application offers the ability to examine split frames, download the decrypted video, and browse contact and performance information pages. With a user-friendly interface and strong back-end encryption mechanisms, this system guarantees data confidentiality and integrity by using OpenCV for video processing and secure file management techniques. Sensitive data, such as encrypted frames and key shares, are uploaded and stored securely by the program in designated folders. To further prevent unwanted access to the decryption process, key shares are kept apart.
Secure Video Processing, Digital Watermarking, Shamir’s Secret Sharing, Video Encryption, Frame-Level Encryption, Cryptography, OpenCV, Key Management, Data Security, Secure Multimedia Transmission.
Population Health Management in Saudi Arabia: AI Integration Review
Saudi Arabia's Vision 2030 has initiated a comprehensive restructuring of the national healthcare system, positioning Population Health Management (PHM) as a central pillar for achieving sustainable, value-based healthcare delivery. This study presents a systematic narrative review of the evolution, policy framework, implementation strategies, and early performance outcomes of PHM within the Saudi healthcare system. Guided by PRISMA-based methodological rigor, the review synthesizes foundational population health theory, international implementation science literature, national reform policies, and empirical evidence from cluster-level implementation. Findings indicate that Saudi Arabia has developed a structured PHM framework anchored in established population health theory and operationalized through the Council of Health Insurance (CHI) five-step PHM cycle. The Qassim Health Cluster case demonstrates large-scale screening implementation, structured disease prioritization under the 5x5 model, and regional governance alignment. Comparative macro-health indicators reveal that Saudi Arabia achieves population health outcomes comparable to high-expenditure systems while maintaining lower per capita healthcare spending, indicating strong structural efficiency. However, despite policy alignment and early operational success, several implementation gaps persist, including limited dedicated PHM financing mechanisms, emerging digital interoperability infrastructure, workforce readiness constraints, and insufficient longitudinal outcome reporting. Benchmarking against international PHM implementation domains suggests that Saudi Arabia is currently in a Structured Early Expansion Phase. This review further examines the critical role of Artificial Intelligence (AI) in advancing the PHM agenda — including AI-enabled population segmentation, predictive risk stratification, clinical decision support, and hospital management information systems (HMIS) — highlighting both proven applications and unresolved implementation challenges within the Saudi context. Strengthening financing models, digital integration, AI infrastructure, workforce specialization, and performance monitoring systems will be critical to scaling PHM nationally. With sustained governance commitment and strategic alignment under Vision 2030, PHM has the potential to enhance cost-efficiency, equity, and long-term health system sustainability across the Kingdom.
Population Health Management (PHM); Vision 2030; Saudi Healthcare Reform; Value-Based Healthcare; Health Clusters; Chronic Disease Management; Artificial Intelligence (AI); Hospital Management Information Systems (HMIS); Digital Health Integration; Predictive Analytics; Health System Transformation; Healthcare Sustainability.
An Interpretable Hybrid Machine Learning Framework for Robust Type II Diabetes Prediction Using Electronic Health Records
Diabetes mellitus type II (T2DM) happens to be a fast-emerging international health issue that needs timely and accurate risk identification to mitigate chronic conditions. EHRs offer an excellent platform for longitudinal clinical data, but predictive modeling with EHRs is plagued by data quality challenges, highdimensional feature spaces, and limited generalizability across healthcare facilities. The paper presents a proposed interpretable hybrid machine-learning architecture for predicting T2DM with high accuracy using structured EHR data. The proposed pipeline combines systematic data preprocessing (imputation, outlier handling, normalization), a hybrid feature selection strategy (Random Forest importance and SHAP-based explainability), and domain-conscious model validation to improve generalization. Evaluation of the framework is conducted on benchmark datasets such as Pima Indians, UCI Diabetes 130-US Hospitals, MIMIC-III, and a simulated longitudinal EHR dataset. The experimental evidence illustrates a gradual improvement in performance across processing stages, reaching 91% accuracy and an AUROC of 0.93 with the Random Forest + SHAP model. The analysis reveals that the clinically established predictors identified by feature importance in the present research are glucose, A1C, insulin, BMI, and blood pressure, which align with diagnostic guidelines. A comparative study of the proposed methodology with LSTM and transformerbased models demonstrates that it strikes a good balance between predictive accuracy and interpretability. The findings show that hybrid model explainability and structured preprocessing are highly effective in increasing robustness, fairness, and clinical usability. The prescribed framework includes a scalable, reliable blueprint for practical implementation in digital health and clinical decision support systems.
Electronic Health Records (EHRs), Medical Data Analytics, Predictive Modeling, Explainable AI, Random Forest, SHAP, Domain Adaptation, Digital Health Systems.
Robust Image Recognition using Quantum-Inspired Algorithms on Low-Power Electronic Platforms
Image recognition has become an essential component of artificial intelligence (AI) applications, from autonomous vehicles to smart surveillance systems. However, conventional deep learning models such as convolutional neural networks (CNNs) require significant computational resources, making them inefficient for low-power or embedded devices. This research introduces a quantum-inspired computing framework for image recognition designed to function effectively on low-power electronic platforms. By leveraging quantum annealing principles, amplitude encoding, and hybrid optimization techniques, the study proposes models that achieve high recognition accuracy with minimal energy consumption. Experimental simulations conducted on embedded GPUs and neuromorphic chips demonstrate that quantum-inspired algorithms outperform classical CNNs in energy efficiency by up to 45% while maintaining competitive accuracy levels above 92% across multiple datasets. This paper highlights how quantum-inspired methodologies can bridge the gap between computational efficiency and accuracy in next-generation AI systems.
Quantum-Inspired Computing, Image Recognition, Low-Power Electronics, Energy-Efficient AI, Quantum Algorithms, Edge Computing, Neuromorphic Hardware
Copyright (c) 2025 Author https://creativecommons.org/licenses/by-nc-nd/4.0
AI-Driven Power Optimization in IoT Devices: Enhancing Efficiency through Intelligent Electronic Control Systems
With the exponential growth of the Internet of Things (IoT), optimizing power consumption has become a critical challenge in ensuring the sustainability and efficiency of connected systems. Traditional power management techniques often rely on static algorithms and fail to adapt to dynamic workloads and environmental changes. This study explores the application of Artificial Intelligence (AI) in optimizing power utilization across IoT devices through intelligent electronic control systems. By integrating machine learning algorithms, real-time analytics, and adaptive control strategies, AI enables predictive power regulation, extending battery life and improving energy efficiency. Experimental results indicate that AI-based optimization can reduce power consumption by up to 35% compared to traditional models. The findings highlight the transformative potential of AI in enhancing the reliability, performance, and scalability of IoT networks for industrial, healthcare, and smart home applications.
Artificial Intelligence, IoT, Power Optimization, Machine Learning, Energy Efficiency, Embedded Systems, Predictive Control
Neuromorphic Computing: Bridging Artificial Intelligence and Next-Generation Electronic Architectures
Neuromorphic computing represents a paradigm shift in the design and functionality of artificial intelligence (AI) systems, offering a pathway toward highly efficient, brain-inspired electronic architectures. Unlike traditional von Neumann architectures, which separate memory and processing units, neuromorphic systems emulate the neural structure of the human brain by integrating computation and memory in a single framework. This study explores the role of neuromorphic computing in advancing AI performance, reducing energy consumption, and enabling real-time adaptive learning. Using case analyses of emerging neuromorphic processors such as Intel’s Loihi, IBM’s TrueNorth, and BrainChip’s Akida, this research highlights the potential of neuromorphic systems in applications ranging from autonomous robotics to edge computing. The findings reveal that neuromorphic architectures achieve up to 100× energy efficiency improvements and 50× faster processing speeds compared to conventional AI hardware. The paper concludes by discussing the challenges of scalability, material innovation, and algorithmic design that must be addressed to fully realize the promise of neuromorphic intelligence.
Neuromorphic Computing, Artificial Intelligence, Spiking Neural Networks, Brain-Inspired Architecture, Edge Computing, Low-Power Electronics
Deep Learning-Based Fault Detection in Industrial Electronic Systems: A Predictive Maintenance Approach
In recent years, industrial systems have experienced a paradigm shift with the introduction of Artificial Intelligence (AI) and Deep Learning (DL) techniques for predictive maintenance. Traditional fault detection approaches, relying on threshold-based monitoring and manual inspections, often fail to identify early-stage anomalies, leading to unplanned downtimes and financial losses. This study investigates the role of deep learning algorithms—particularly Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Autoencoders—in fault detection and predictive maintenance of industrial electronic systems. The paper analyzes sensor-based datasets from various industrial environments to evaluate model performance. Results show that deep learning models can detect potential faults with up to 98% accuracy, enabling early intervention and reduced maintenance costs. The findings highlight the transformative impact of deep learning in achieving reliability, operational efficiency, and sustainability in modern industrial settings.
Deep Learning, Fault Detection, Predictive Maintenance, Industrial Electronics, Neural Networks, Anomaly Detection, Industry 4.0
Edge Intelligence for Smart Devices: Integrating AI with Embedded Electronics for Real-Time Decision Making
The emergence of Edge Intelligence (EI) represents a transformative leap in computing, merging Artificial Intelligence (AI) capabilities directly into embedded systems and Internet of Things (IoT) devices. Unlike traditional cloud-based models, EI enables real-time data processing and decision-making at the device level, reducing latency, bandwidth dependency, and security risks. This research explores how integrating AI algorithms with embedded electronics enhances efficiency, adaptability, and energy optimization in smart devices. Through case studies in healthcare, autonomous vehicles, and industrial automation, the study demonstrates the practical impact of edge intelligence on system responsiveness, fault tolerance, and computational autonomy. Findings indicate that EI not only revolutionizes data analytics but also sets the foundation for the next generation of self-learning, context-aware devices.
Edge Intelligence, Embedded Systems, Artificial Intelligence, IoT, Real-Time Processing, Edge Computing, Smart Devices, Data Latency, System Optimization