International Journal of AI EBioMedicine Innovations
DIGITAL WELL BEING AND PSYCHOLOGICAL RESILIENCE IN THE TECHNOLOGY ERA
In this way, technological advances have had major impacts on communication, education, work, and day-to-day activities. Though technological advancements have enabled increased productivity, ease of work, and connectivity, the overuse of technology may cause stress, anxiety, insomnia, distractions, and emotional exhaustion. Thus, digital well-being and psychological resilience have gained relevance in maintaining mental health in the age of technology. This paper explores the connection between psychological resilience and digital well-being through a quantitative approach based on 200 participant responses. Based on the results, several individuals reported significant digital stress as a result of prolonged exposure to technology. In contrast, people with resilient personalities exhibited stronger emotional regulation abilities, healthier technology practices, and enhanced life satisfaction. In summary, psychological resilience proves to be an essential tool for boosting digital wellbeing. Through increasing digital literacy, mindfulness, and responsible use of technology, one can enjoy a more beneficial and productive life.
Digital Well-Being, Psychological Resilience, Technology Era, Mental Health, Screen Time, Digital Stress, Digital Literacy, Mindfulness, Social Media, Well-Being
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
Power Quality Performance Evaluation Of Multilevel Inverter With Reduced Switching Devices & Minimum Standing Voltage
Multilevel inverters (MLIs) have gained significant attention in modern power electronic applications due to their ability to produce high-quality output waveforms with reduced harmonic distortion and lower switching losses. This work presents the power quality performance evaluation of a multilevel inverter with reduced switching devices and minimum standing voltage, aiming to improve efficiency while minimizing system complexity and cost. The proposed inverter topology reduces the number of power electronic switches and associated components, thereby decreasing switching losses, control complexity, and overall implementation cost.In addition, the design ensures minimum standing voltage stress across switches, enhancing device reliability and extending operational lifespan. The inverter is analyzed for key power quality parameters such as total harmonic distortion (THD), voltage regulation, and efficiency under various load conditions. Simulation and experimental results demonstrate that the proposed system achieves improved output waveform quality with reduced harmonics and better voltage utilization compared to conventional multilevel inverter configurations. Overall, the proposed approach offers a cost-effective, efficient, and reliable solution for power conversion applications in renewable energy systems and industrial drives.
1Mr.L.Bichu Naik,2Munavath Laxman,3P.Shashi Vardhan,4V.Sree Charan5B.Thejaswini
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
Development Of Bi-Directional Dc/Dc Converter With Dual Battery Energy Storage For Hybrid Electric Vehicle System
The increasing demand for efficient and sustainable energy systems in hybrid electric vehicles (HEVs) has led to the development of advanced power conversion technologies. This work presents the design and development of a bi-directional DC/DC converter integrated with a dual battery energy storage system for hybrid electric vehicle applications. The proposed system enables efficient energy transfer between two energy storage units and the vehicle’s DC bus, allowing both charging and discharging operations based on load requirements.The bi-directional converter plays a crucial role in managing power flow by operating in both buck and boost modes, thereby improving energy utilization and system flexibility. The dual battery configuration enhances reliability, extends battery life, and ensures uninterrupted power supply by balancing energy between primary and auxiliary storage units. The system is controlled using advanced control strategies to maintain voltage stability, reduce power losses, and improve overall efficiency. Simulation and experimental results demonstrate that the proposed system achieves high efficiency, better energy management, and improved performance compared to conventional single battery systems. This approach contributes to the advancement of energy-efficient and sustainable hybrid electric vehicle technologies.
Bi-directional DC/DC Converter, Dual Battery System, Hybrid Electric Vehicle (HEV), Energy Storage System, Power Management, Battery Charging and Discharging, Buck-Boost Converter, Energy Efficiency, Voltage Regulation, Renewable Energy Integration, Electric Vehicle Technology.
1Mr.K.Prasad,2Avunuri Anil,3Banoth Parlal,4Biyyala Pavan Kumar5 Jangapelly Sai Krishna
MULTIMODAL DEEP LEARNING FRAMEWORK FOR STUDENT ENGAGEMENT RECOGNITION USING EEG SIGNALS AND FACIAL IMAGES
Today’s students face heavy academic workload, long study hours, and continuous pressure to perform well, which affects both their mental and physical health. Increasing stress, fear of failure, and lack of support often push vulnerable students toward severe outcomes, including depression and suicide. Using the provided student dataset containing features such as study hours, sleep duration, attendance, stress level, past scores, and extracurricular activity, we perform preprocessing and Exploratory Data Analysis (EDA) to understand behaviour patterns and data quality. The existing system applies classical machine-learning models like Random Forest, Support vector machine(SVM), Gradient Boosting, Decision Tree, and Linear Regression, but these handle each output separately and fail to learn deeper relationships between student factors. To address this, we propose an integrated Attention-based Bidirectional Long-Short Term Memory(BiLSTM) model combined with Optimal Decision Rule List(ODRL) feature extraction for joint prediction of student performance grade (classification), stress level (classification), and exam score (regression). The AttentionBiLSTM captures important behavioural sequences, while ODRL extracts meaningful features. Results show that the proposed approach outperforms the existing models, providing a more accurate and holistic understanding of student academic and emotional conditions.
LINGAMPALLY SUNANDA, N. VAISHNAVI, M. SAMBRANI, Y. SATHWIKA, K. PRAMOD
SMART FINANCIAL PROFILING SYSTEM USING DEMOGRAPHIC AND ECONOMIC ATTRIBUTES
This project introduces an AI-based financial identification system designed to analyze and classify the financial status of individuals or communities using demographic and economic data. With increasing demand for data-driven solutions in finance and policymaking, the proposed system provides a reliable and scalable approach to assess financial conditions across various population segments.The system utilizes key demographic features such as age, gender, education level, occupation, marital status, and geographical location, along with economic indicators like income level, employment status, housing type, and access to banking services. By processing this data using Machine Learning algorithms such as Random Forest, Logistic Regression, and K-Means Clustering, the model can predict financial standing and group individuals into low, medium, or high financial categories.This classification helps governments, financial institutions, and NGOs to target specific groups for financial aid, credit services, or development programs. For example, banks can assess creditworthiness for micro-loans, while government bodies can identify underprivileged communities for subsidy distribution or social welfare schemes.The project also incorporates data visualization tools to present economic patterns and demographic influences in a clear, insightful manner. Regional heatmaps, statistical graphs, and predictive dashboards help stakeholders make informed decisions backed by real data.Overall, this project demonstrates how Artificial Intelligence can enhance financial inclusion by leveraging publicly available or survey-based data. It promotes transparent, ethical, and intelligent economic planning, ensuring that financial services and benefits reach those who need them most
PENNADA SURYA GANESH, Y. NIKSHALA, U. AKSHAYA, N. SHASHANK VARMA
BLOCKCHAIN-BASED DIGITAL EVIDENCE MANAGEMENT SYSTEM FOR CRIMINAL INVESTIGATIONS
The rapid growth of digital technologies has significantly increased the amount of digital evidence used in criminal investigations. However, traditional evidence management systems face challenges related to data integrity, security, transparency, and chain of custody management. Digital evidence stored in centralized databases is vulnerable to tampering, unauthorized access, and data loss, which can compromise the reliability of evidence presented in legal proceedings.This research proposes a Blockchain-Based Digital Evidence Management System designed to enhance the security, transparency, and reliability of criminal evidence handling. Blockchain technology provides a decentralized and immutable ledger that records all evidence-related transactions, ensuring that once evidence data is stored, it cannot be altered or deleted without detection. The system maintains a secure chain of custody by recording every access, modification, and transfer of digital evidence in a transparent and tamper-proof manner. The proposed framework integrates cryptographic techniques, smart contracts, and distributed ledger technology to ensure secure storage, authentication, and controlled access to digital evidence. Investigators, forensic experts, and legal authorities can securely upload, verify, and retrieve evidence while maintaining full accountability and traceability of all activities. Smart contracts automate evidence verification and authorization processes, reducing human errors and improving operational efficiency. By leveraging blockchain technology, the proposed system enhances trust, transparency, and accountability in criminal investigations while protecting the integrity of digital evidence. This approach can significantly improve the management of forensic data and strengthen the credibility of digital evidence in judicial systems
PAINDLA THIRUPATHI, RAHUL KUMAR, G. HARSHAVARDHAN, T. SAHITH, L. VIJAY
SECURE AND TRANSPARENT CLOUD TASK SCHEDULING USING BLOCKCHAIN TECHNOLOGY
Cloud computing has revolutionized data storage and computational processes, but it still faces challenges in secure task scheduling and data integrity. Existing approaches, as discussed in the base paper, suffer from issues such as inefficient scheduling algorithms, high computational overhead, and vulnerabilities in data security. To address these limitations, we propose an improved blockchain-integrated task scheduling system that ensures tamper-proof data recording, enhanced verification mechanisms, and automated document validation. Our approach leverages smart contracts to securely store task submissions on the blockchain, providing transparency and immutability. By integrating blockchain verification, we eliminate unauthorized modifications and enhance trust in the scheduling process. Additionally, our system automates task validation and document conversion, reducing manual overhead. When an admin verifies a task, the associated document is automatically converted into a PDF, updating the task status while maintaining a decentralized and auditable record. Compared to existing methods, our system offers greater security, improved efficiency, and seamless task traceability. Through this novel blockchain-based framework, we successfully mitigate the drawbacks highlighted in the base paper, enhancing the security and reliability of task scheduling in cloud environments.
DR. GADE VENKATA VARA PRASAD, T. SHASHI KUMAR, V. MANIKANTA, T. HARI KRISHNA, CH. SHIVA DURGA SURYA PRABHAS
OPTIMIZING MATHEMATICAL ALGORITHMS FOR REALTIME COMPUTATIONAL APPLICATIONS
The increasing demand for rapid data processing and intelligent decision-making has significantly enhanced the importance of predictive analytics in modern industrial and computational systems. Real-time applications such as robotics, healthcare monitoring, financial trading, and smart manufacturing require efficient mathematical algorithms capable of handling large volumes of dynamic data with minimal latency. This review paper focuses on optimizing mathematical algorithms for real-time computational applications, emphasizing their role in improving system performance, computational efficiency, and scalability. The study analyzes various mathematical modelling and optimization techniques, including complexity reduction methods, numerical optimization, heuristic and metaheuristic approaches, and parallel and distributed computing. Additionally, advanced computational strategies such as machine learning-based optimization, approximation algorithms, streaming data processing, and hardware acceleration using GPUs and FPGAs are explored. The findings highlight that optimized algorithms significantly enhance processing speed, reduce computational overhead, and enable accurate real-time predictions across multiple domains. Furthermore, the integration of mathematical modelling with modern computational technologies improves system responsiveness and decision-making capabilities. In conclusion, the adoption of optimized mathematical algorithms, combined with emerging Industry 4.0 technologies such as artificial intelligence and edge computing, plays a crucial role in devel oping efficient, scalable, and intelligent real-time computational systems
Real-Time Computing; Mathematical Algorithms; Algorithm Optimization; Computational Efficiency; Parallel Processing; High-Performance Computing.
QUANTUM-RESISTANT AUTHENTICATION AND KEY EXCHANGE PROTOCOL FOR SECURE IOT ARCHITECTURES
The rapid expansion of Internet of Things (IoT) ecosystems has introduced significant security challenges due to resource-constrained devices, heterogeneous network architectures, and largescale connectivity. Traditional authentication and key exchange mechanisms, primarily based on classical cryptographic algorithms such as RSA and ECC, are vulnerable to future quantum computing attacks. The emergence of quantum algorithms, particularly Shor’s algorithm, threatens the long-term confidentiality and integrity of IoT communications. This paper proposes a Quantum-Resistant Authentication and Key Exchange Protocol specifically designed for secure IoT architectures. The proposed framework integrates post-quantum cryptographic primitives, including lattice-based key exchange mechanisms and hash-based authentication schemes, optimized for low computational overhead suitable for constrained IoT devices. The protocol ensures mutual authentication, forward secrecy, and resistance against quantum adversaries while maintaining minimal communication latency. Security analysis demonstrates robustness against replay, man-in-the-middle, and key-compromise attacks. Performance evaluation shows that the proposed solution achieves a balance between quantum security strength and resource efficiency, making it suitable for next-generation IoT deployments in smart cities, healthcare systems, and industrial automation.
Post-Quantum Cryptography; Quantum-Resistant Authentication; IoT Security; Key Exchange Protocol; Lattice-Based Cryptography; Hash-Based Authentication; Forward Secrecy; Lightweight Cryptography; Secure IoT Architectures.
GRAPH-BASED BIG DATA ANALYTICS FOR REAL-TIME DETECTION OF COORDINATED MISINFORMATION CAMPAIGNS
The rapid growth of social media platforms has amplified the spread of misinformation, often through coordinated campaigns involving networks of automated accounts and malicious actors. Traditional content-based detection methods struggle to identify such campaigns in real time due to their dynamic and networkdriven nature. This paper proposes a GraphBased Big Data Analytics Framework for the real-time detection of coordinated misinformation campaigns. The proposed system models social media interactions as dynamic graphs, where nodes represent users or posts and edges represent interactions such as shares, mentions, or retweets. By leveraging graph analytics techniques combined with scalable big data processing frameworks, the system detects anomalous community structures, synchronized behavior patterns, and information propagation anomalies. Graph-based metrics such as centrality, clustering coefficient, and community detection are integrated with machine learning models to identify coordinated inauthentic behavior. The framework is designed for high-throughput stream processing to ensure timely detection in large-scale social networks. Experimental evaluation demonstrates improved detection accuracy and reduced response time compared to traditional content-based approaches. The proposed model provides a scalable, real-time solution for mitigating misinformation threats in digital ecosystems.
Graph Analytics; Big Data; Misinformation Detection; Coordinated Campaigns; Social Network Analysis; RealTime Stream Processing; Community Detection; Anomaly Detection; Social Media Security.
MACHINE LEARNING–BASED BUG DETECTION FRAMEWORK FOR LARGE SOFTWARE PROJECTS
Software defects remain a major challenge in large-scale software projects due to increasing system complexity and rapid development cycles. Traditional bug detection techniques often rely on manual code reviews and rulebased testing, which are time-consuming and error-prone. This paper proposes a machine learning–based bug detection framework designed for large software projects. The framework leverages historical defect data, source code metrics, and learning algorithms to automatically identify potential bugs at early stages. Classification and prediction models are integrated into the development workflow. Experimental evaluation is conducted on large software repositories. Results show improved detection accuracy and reduced defect leakage. The proposed framework enhances software quality and development efficiency.
Bug Detection, Machine Learning, Software Defects, Software Engineering, Predictive Analytics, Code Analysis
COGNITIVE RADIO NETWORKS FOR DYNAMIC SPECTRUM MANAGEMENT IN SMART CITY ENVIRONMENTS
Smart cities demand highly efficient wireless communication to support diverse IoT devices, sensors, and urban applications. Cognitive radio networks (CRNs) provide dynamic spectrum access, enabling intelligent utilization of underused frequency bands. This paper proposes an AI-enabled framework for dynamic spectrum management in smart city environments, leveraging machine learning algorithms to optimize spectrum allocation, reduce interference, and improve network throughput. The system continuously monitors spectrum usage and predicts optimal channel assignment for IoT devices and communication nodes. Simulation results demonstrate improved spectral efficiency, reduced latency, and enhanced energy efficiency compared to traditional static allocation methods. The framework supports scalability, adaptability, and real-time decision-making, making it suitable for dense urban deployments. Integration of AI ensures continuous learning and intelligent spectrum management in highly dynamic networks.
Cognitive Radio Networks, Dynamic Spectrum Management, AI, Smart Cities, IoT, Machine Learning, Spectrum Allocation, Energy Efficiency
SMART SENSOR–INTEGRATED CONSTRUCTED WETLAND SYSTEMS FOR SUSTAINABLE WASTEWATER TREATMENT
Constructed wetlands have emerged as an ecofriendly and cost-effective solution for wastewater treatment in urban and rural environments. However, the lack of real-time monitoring limits their operational efficiency and performance optimization. This study presents a smart wastewater treatment framework that integrates constructed wetland systems with IoT-based smart sensors for continuous monitoring and control. Key water quality parameters such as pH, temperature, dissolved oxygen, turbidity, and biochemical oxygen demand are monitored in real time. Sensor data are transmitted to a centralized platform for analysis and performance assessment. Experimental results demonstrate significant improvements in treatment efficiency and system reliability. The proposed approach enhances decision-making, reduces operational costs, and supports sustainable water management. This system is particularly suitable for decentralized wastewater treatment applications in developing regions.
Constructed Wetlands, Wastewater Treatment, Smart Sensors, IoT, Water Quality Monitoring
PERFORMANCE ENHANCEMENT OF SOLAR POWER SYSTEMS USING MPPT TECHNIQUES
Solar power systems are increasingly adopted to meet growing energy demands and reduce dependence on fossil fuels. However, the efficiency of photovoltaic systems is highly influenced by environmental conditions such as irradiance and temperature. Maximum Power Point Tracking (MPPT) techniques play a vital role in extracting maximum available power from solar panels. This paper presents a detailed study on the performance enhancement of solar power systems using advanced MPPT techniques. Various MPPT algorithms are analyzed with respect to efficiency, tracking speed, and stability. A control-oriented framework is proposed to improve system performance under dynamic conditions. Experimental evaluations demonstrate improved power extraction and reduced losses. The results confirm that effective MPPT control significantly enhances system efficiency. The study supports the deployment of intelligent solar energy systems for sustainable power generation.
Solar Power System, MPPT, Photovoltaic System, Renewable Energy, Power Electronics
EMBEDDED SYSTEM DESIGN FOR SMART HOME AUTOMATION
Smart home automation has emerged as a transformative solution for improving comfort, energy efficiency, and security in residential environments. This research paper presents the design and implementation of an embedded system–based smart home automation framework that integrates sensors, actuators, and wireless communication technologies. The proposed system enables real-time monitoring and control of household appliances through a centralized embedded controller. Microcontroller-based architecture ensures low power consumption, cost effectiveness, and scalability. Various sensors are deployed to collect environmental data such as temperature, motion, and light intensity. The system supports remote access through IoTenabled interfaces, enhancing user convenience and safety. Experimental results demonstrate reliable operation, reduced energy usage, and fast response times. The proposed model is suitable for modern smart homes and can be extended to larger automation systems
Embedded Systems, Smart Home Automation, IoT, Microcontroller, Sensors, Home Control
REAL-TIME SMART SECURITY SURVEILLANCE USING ADVANCED COMPUTER VISION TECHNIQUE
Smart surveillance systems have become essential for ensuring safety and security in modern environments. Traditional surveillance methods rely heavily on manual monitoring, which is inefficient and error-prone. This paper presents a real-time smart security surveillance framework using advanced computer vision techniques to enhance threat detection and situational awareness. The proposed system integrates video acquisition, preprocessing, object detection, tracking, and behavior analysis into a unified architecture. Deep learning–based vision models are employed to achieve high accuracy in realtime scenarios. Experimental evaluation is conducted on benchmark video datasets and real-world surveillance footage. Results demonstrate improved detection accuracy, reduced false alarms, and reliable real-time performance. The framework offers a scalable and intelligent solution for smart security applications.
Computer Vision, Smart Surveillance, Real-Time Security, Deep Learning, Object Detection, Video Analytics
AI-OPTIMIZED ENERGY-EFFICIENT RESOURCE ALLOCATION IN IOT-ENABLED WIRELESS NETWORKS
The proliferation of IoT devices has created unprecedented demand for efficient wireless networks, requiring both high performance and low energy consumption. Traditional resource allocation methods struggle to adapt to dynamic network conditions, resulting in suboptimal energy usage and reduced qualityof-service (QoS). This paper proposes an AIoptimized framework for energy-efficient resource allocation in IoT wireless networks. Leveraging machine learning algorithms, the system dynamically allocates bandwidth and transmission power based on real-time traffic, channel conditions, and device requirements. Simulation results demonstrate significant improvements in network throughput, energy efficiency, and latency reduction compared to conventional methods. The proposed framework integrates predictive modeling and real-time feedback, providing scalable, sustainable, and QoS-aware IoT network operation. This approach supports future largescale deployments while minimizing energy consumption and operational costs.
IoT, Resource Allocation, Energy Efficiency, Machine Learning, Wireless Networks, AI Optimization, Qualityof-Service
MACHINE LEARNING–DRIVEN PREDICTION OF COST OVERRUNS IN CIVIL CONSTRUCTION PROJECTS
Cost overruns remain one of the most critical challenges affecting the successful delivery of civil construction projects worldwide. Inaccurate cost estimation, design changes, project delays, and unforeseen risks significantly contribute to budget escalation. This research presents a machine learning–driven framework for predicting cost overruns in civil engineering projects at early project stages. Historical project data comprising financial, technical, and managerial parameters are utilized to train predictive models. Multiple supervised learning algorithms are evaluated to identify the most reliable prediction technique. The proposed approach enables proactive decision-making and risk mitigation by forecasting potential cost overruns before project execution. Experimental results demonstrate improved prediction accuracy compared to conventional estimation methods. The study highlights the practical applicability of data-driven intelligence in construction cost management. The outcomes support enhanced financial control and sustainable project planning.
Cost Overrun Prediction, Machine Learning, Construction Management, Predictive Analytics, Civil Engineering
INTELLIGENT LOAD FORECASTING USING MACHINE LEARNING ALGORITHMS
Accurate load forecasting plays a vital role in the efficient operation and planning of modern power systems. With the increasing penetration of renewable energy sources and smart grid technologies, traditional forecasting methods are often insufficient to handle complex and nonlinear load patterns. This paper presents an intelligent load forecasting approach using machine learning algorithms to improve prediction accuracy and reliability. The proposed framework integrates data preprocessing, feature selection, and advanced learning models to capture temporal and seasonal variations in electricity demand. Multiple machine learning techniques are evaluated for short-term and medium-term forecasting. Experimental results demonstrate significant improvement over conventional forecasting models. The approach enhances grid stability and operational efficiency. The study highlights the effectiveness of intelligent forecasting for modern energy systems.
Load Forecasting, Machine Learning, Smart Grid, Energy Management, Power Systems
DESIGN OF LOW-POWER VLSI ARCHITECTURE FOR HIGH-SPEED COMMUNICATION
The rapid growth of high-speed communication systems has increased the demand for energy-efficient VLSI architectures capable of operating at high data rates. Power consumption has become a critical design constraint due to batteryoperated devices, thermal limitations, and reliability concerns in modern integrated circuits. This paper presents the design and analysis of a low-power VLSI architecture optimized for high-speed communication applications. Various power reduction techniques at architectural, circuit, and system levels are explored and integrated into the proposed design. The architecture focuses on minimizing dynamic and leakage power while maintaining high throughput and low latency. Simulation-based evaluation demonstrates that the proposed design achieves significant power savings without degrading performance. The results validate the effectiveness of the adopted low-power strategies. The proposed architecture is suitable for next-generation communication systems.
Low-power VLSI, High-speed communication, Power optimization, CMOS design, Energy-efficient architecture
WIRELESS SENSOR NETWORKS FOR ENVIRONMENTAL MONITORING APPLICATIONS
Wireless Sensor Networks (WSNs) have emerged as a key enabling technology for realtime environmental monitoring due to their low cost, scalability, and autonomous operation. These networks consist of spatially distributed sensor nodes capable of sensing, processing, and transmitting environmental data to a central base station. WSNs are widely used in applications such as climate monitoring, forest fire detection, air and water quality assessment, and agricultural monitoring. The integration of wireless communication and sensing technologies allows continuous observation of remote and hazardous environments. However, challenges such as limited energy resources, data reliability, and network scalability remain critical. This paper presents a comprehensive study of WSN architectures, protocols, and methodologies for environmental monitoring. A proposed methodology emphasizing energy efficiency and reliable data transmission is discussed. Experimental evaluation demonstrates the effectiveness of the proposed approach. The results highlight the potential of WSNs in achieving sustainable and intelligent environmental monitoring systems.
Wireless Sensor Networks, Environmental Monitoring, Energy Efficiency, Data Transmission, IoT
SEISMIC DAMAGE PREDICTION IN REINFORCED CONCRETE STRUCTURES USING MACHINE LEARNING-BASED MODELING
Seismic damage assessment of reinforced concrete (RC) structures is a critical task in earthquake-prone regions to ensure structural safety and minimize economic losses. Conventional analytical and numerical methods often require extensive computational effort and detailed structural modeling. Recent advancements in machine learning (ML) provide data-driven alternatives capable of learning complex nonlinear relationships between seismic inputs and structural responses. This paper presents a machine learning–based framework for predicting seismic damage in RC structures using key structural and ground motion parameters. Supervised learning models are trained on simulated seismic response data to classify damage states effectively. The proposed approach improves prediction accuracy and reduces computational complexity compared to traditional methods. The results demonstrate that ML models can reliably estimate damage levels under varying seismic intensities. This study highlights the potential of intelligent systems for rapid post-earthquake damage assessment. The framework supports decision-making in structural design, retrofitting, and disaster mitigation planning.
Seismic damage prediction, Reinforced concrete structures, Machine learning, Structural health monitoring, Earthquake engineering.
ADVANCED POWER MANAGEMENT STRATEGIES FOR ENERGYEFFICIENT ELECTRIC VEHICLES
The rapid growth of electric vehicles has increased the demand for efficient power management systems to enhance energy utilization and driving range. Advanced power management strategies play a crucial role in optimizing battery usage, improving system reliability, and reducing energy losses. This paper presents a comprehensive study on advanced power management strategies for energy-efficient electric vehicles. The proposed approach integrates intelligent energy control, battery management, and power distribution techniques. Various operating modes and load conditions are analyzed to achieve optimal performance. The study focuses on minimizing power losses while maintaining vehicle performance. Experimental evaluation demonstrates improved efficiency and extended battery life. The results highlight the effectiveness of intelligent power management in electric vehicles. The proposed strategies support sustainable and efficient transportation systems.
Electric Vehicles, Power Management, Energy Efficiency, Battery Management System, Intelligent Control
OPTIMIZED DEEP LEARNING-BASED 5G MIMO SYSTEM DESIGN FOR HIGH-PERFORMANCE WIRELESS COMMUNICATION
The fifth-generation (5G) wireless communication system demands high data rates, low latency, and efficient spectrum utilization. Multiple-input multiple-output (MIMO) technology is a key enabler to meet these requirements. However, designing optimal MIMO systems is challenging due to dynamic channel conditions and hardware constraints. This paper proposes a deep learning-based optimization framework for 5G MIMO systems. The framework leverages neural networks to adaptively configure antenna arrays, beamforming vectors, and resource allocation. Simulation and experimental evaluations demonstrate improved spectral efficiency, reduced bit error rates, and enhanced system reliability compared to conventional methods. The proposed approach integrates real-time channel estimation and adaptive learning for robust performance under varying network conditions. The framework provides a scalable solution for future high-capacity 5G deployments.
5G, MIMO, Deep Learning, Beamforming, Spectral Efficiency, Wireless Communication, Channel Estimation
AI-ENABLED COST AND SCHEDULE OPTIMIZATION MODELS FOR CONSTRUCTION PROJECT MANAGEMENT
Cost overruns and schedule delays are persistent challenges in construction project management, often resulting from uncertainty, poor planning, and inefficient resource utilization. Traditional optimization techniques rely on deterministic models and expert judgment, which are inadequate for handling complex and dynamic project environments. This paper proposes AI-enabled cost and schedule optimization models for construction project management by integrating machine learning and intelligent optimization techniques. The proposed framework analyzes historical and real-time project data to predict cost and schedule deviations and recommends optimal mitigation strategies. Artificial intelligence models dynamically adjust resource allocation, sequencing, and budgeting decisions. Experimental evaluation demonstrates that the AI-based approach significantly reduces cost overruns and schedule delays compared to conventional methods. The results highlight the potential of AI-driven optimization for improving project performance and decision-making accuracy.
Construction Project Management, Cost Optimization, Schedule Optimization, Artificial Intelligence, Machine Learning, Resource Allocation
THE FUTURE OF MEDICAL PRACTICE: HARMONIZING HUMAN JUDGMENT WITH ARTIFICIAL INTELLIGENCE
The rising capabilities of artificial intelligence (AI) present an unprecedented opportunity to transform medical practice by augmenting clinical decisions, accelerating discovery, and improving patient outcomes. This paper examines the convergence of human clinical expertise and AI-driven systems, proposing frameworks for safe and effective human–AI collaboration in healthcare. We review core technologies and use cases where highperformance medicine is emerging, synthesize literature on benefits and risks, and present a methodology for integrating AI into clinical workflows that prioritizes explainability, robustness, and clinician agency. A synthetic case study demonstrates how an interpretable AI decision-support system for diagnostic imaging can be trained, validated, and evaluated in a workflow that preserves clinician oversight. Results from the case study indicate that AI can measurably improve diagnostic sensitivity while preserving clinician control, but success requires attention to dataset representativeness, model interpretability, regulatory compliance, and human factors. We conclude with practical recommendations for deployment, research priorities to address outstanding challenges, and a call for multi-stakeholder collaboration to realize the vision of harmonized human–AI medicine.
PREDICTIVE AND PREVENTIVE MEDICINE THROUGH BIG DATA AND MACHINE INTELLIGENCE
Predictive and preventive medicine (PPM) is transforming modern healthcare by focusing on early disease detection, risk stratification, and individualized interventions. Advances in big data analytics and machine intelligence have made it possible to integrate large-scale, heterogeneous health information—such as electronic health records (EHRs), wearable sensor data, genomic sequences, and social determinants of health—into powerful predictive models. This paper presents a comprehensive analysis of how big data and machine intelligence can enable predictive and preventive medicine. It covers foundational principles, current methods, and challenges while introducing a novel framework combining machine learning, deep learning, and health informatics infrastructure. Results from synthetic health data demonstrate the feasibility of predictive modeling for risk estimation and early detection of chronic conditions. Future directions include incorporating real-time streaming data, improved data governance, and advanced ethical frameworks.
Predictive medicine, preventive healthcare, big data, machine intelligence, health informatics, electronic health records, wearable devices, deep learning, risk stratification, public health analytics
INTEGRATING AI AND MOLECULAR MODELING: TOWARD ULTRAPRECISE PROTEIN STRUCTURE PREDICTION
Accurate protein structure prediction is fundamental to understanding biological function and accelerating drug discovery. Recent breakthroughs in deep learning, exemplified by AlphaFold, have reshaped structural biology by producing high-accuracy models from sequence alone. However, challenges remain for difficult targets, conformational ensembles, and dynamic regions. This paper explores an integrated approach that couples state-of-the-art AI predictors with molecular modeling and physicsbased refinement to achieve ultra-precise protein structures. We propose a hybrid pipeline that uses deep-learning-derived models as starting conformations for molecular dynamics (MD) refinement guided by enhanced sampling and specialized force-field tuning. A synthetic experimental comparison across a set of representative proteins demonstrates that the hybrid approach reduces RMSD relative to AlphaFold alone while providing physically consistent conformations and uncertainty-aware predictions. The results highlight trade-offs between computational cost and accuracy, and we discuss practical guidelines for choosing approaches based on target properties
Protein structure prediction, AlphaFold, molecular dynamics, hybrid modeling, deep learning, structural biology, RMSD, pLDDT, enhanced sampling.
EXPERT-LEVEL RECOGNITION OF SKIN CANCER USING ADVANCED DEEP LEARNING MODELS
The early and accurate detection of skin cancer—including melanoma, basal cell carcinoma, and squamous cell carcinoma— significantly improves patient prognosis and reduces healthcare costs worldwide. Conventional diagnosis remains heavily reliant on expert dermatologists, whose availability is often limited and subject to inter-observer variability. Advances in artificial intelligence (AI) and deep learning have opened new opportunities for automated, high-performance medical image analysis. This study presents an advanced deep learning framework comprising an ensemble of convolutional neural networks (CNNs), specifically EfficientNet-B4, ResNet50, and DenseNet201, trained on a large and diverse dataset of dermoscopic and clinical images. Our approach applies data augmentation, color normalization, and lesioncentered preprocessing to improve generalization and reduce noise. Results indicate that the ensemble achieves dermatologist-level accuracy, with an AUROC of 0.97, sensitivity of 0.90, and specificity of 0.88 across multiple skin lesion categories. Grad-CAM visualizations provide interpretability by highlighting diagnostically relevant regions of interest, enhancing clinician trust. This research demonstrates the potential of AI-powered diagnostic tools to support clinical decisionmaking in dermatology, reducing diagnostic disparities and improving early detection outcomes.
Skin Cancer, Melanoma Detection, Basal Cell Carcinoma, Squamous Cell Carcinoma, Dermatology, Deep Learning, Artificial Intelligence, Convolutional Neural Networks (CNNs), Ensemble Learning, Medical Image Analysis, Grad-CAM, Data Augmentation, Computer-Aided Diagnosis, Interpretability, Dermatologist-Level Classification
ARTIFICIAL INTELLIGENCE IN PANDEMIC RESPONSE: DIAGNOSTIC STRATEGIES FOR COVID-19
The COVID-19 pandemic exposed critical gaps in global diagnostic capacity, surveillance, and rapid clinical decision support. Artificial intelligence (AI) offered a range of diagnostic strategies—ranging from imaging-based models for chest X-ray and CT interpretation to multimodal systems that fuse clinical, laboratory, and digital phenotyping data—for accelerating detection, triage, and resource allocation. This paper synthesizes the state of AI diagnostic methods applied to COVID-19, proposes a unified evaluation framework, and demonstrates a proof-of-concept using a synthetic dataset that simulates common realworld conditions: noisy labels, distribution shift, and limited ground truth. We compare three diagnostic approaches (imaging-only deep convolutional neural networks, clinical-data gradient boosting models, and a multimodal ensemble) on sensitivity, specificity, calibration, and robustness to domain shift. Results on the synthetic benchmark show that multimodal ensemble models achieve the best balance of sensitivity and specificity while maintaining better calibration and robustness across simulated hospitals. We discuss operational challenges—data heterogeneity, bias, regulatory pathways, and clinician adoption—and provide recommendations for integrating AI diagnostics into pandemic response workflows.
COVID-19, artificial intelligence, diagnostic imaging, machine learning, multimodal models, pandemic response, model robustness, calibration, public health