International Journal of Engineering Research
International Journal of Engineering Research (ISSN Print: 3106-7999 | ISSN Online: 3106-8006) is a peer-reviewed, open-access, quarterly journal published by the Institute for Excellence in Education and Research (IEER), registered with the Securities and Exchange Commission of Pakistan (SECP) under Registration No. 0224595. The journal publishes high-quality, original research in the fields of Artificial Intelligence, Computational Intelligence, Control Systems, and Computer Science, with a strong commitment to ethical publishing practices, rigorous peer-review standards, and global research impact.
Institute for Excellence in Education & Research [SMC – Private] Limited
MACHINE LEARNING-BASED IDENTIFICATION OF RISK FACTORS FOR PREGNANCY LOSS
Pregnancy loss, or spontaneous abortion, is defined as the loss of a fetus before the 20th week of gestation. According to the American College of Obstetricians and Gynecologists (ACOG), approximately 15–20% of clinically confirmed pregnancies result in pregnancy loss. This study utilized cross-sectional data from the Bureau of Statistics Punjab (BSP) to investigate the risk factors associated with pregnancy loss. Multiple machine learning algorithms—including Logistic Regression (LR), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naïve Bayes (NB), Regularized Naïve Classifier (RNC), Classification and Regression Trees (CART), Bernoulli Naïve Bayes (BNB), Passive Aggressive, and Extra Trees Classifier (ETC)—were applied to assess predictive performance. Among these, KNN achieved the highest accuracy at 91%, while all other algorithms exceeded 80% accuracy. Feature selection and importance analysis using LR, CART, and ETC identified the number of children ever born and place of delivery as the most influential factors affecting pregnancy loss risk.
Pregnancy loss, Risk factors, Machine learning, KNN, SVM, Decision Tree, Extra Trees Classifier
STRUCTURAL PATTERN-BASED IDENTIFICATION OF OPTIMAL LINKS IN COMPLEX HUMAN NETWORKS
Link prediction in complex human networks aims to identify missing, future, or potential connections. This study evaluates eight similarity-based algorithms across five human interaction networks to determine the most effective method. Datasets were represented as adjacency matrices and split into training and testing sets, with removed links used to assess predictive performance. Similarity scores were computed during training, and predictive accuracy was measured using the Area Under the Curve (AUC) metric. Comparative results indicate that the Resource Allocation Index (RAI) consistently outperforms other algorithms, particularly in large and complex networks, demonstrating its effectiveness for link prediction in human interaction systems.
Link Prediction, Social Networking, Complex Network
EVALUATING MODELS FOR ESTIMATING REFERENCE EVAPOTRANSPIRATION ACROSS PAKISTAN’S CLIMATIC ZONES
Evapotranspiration is critical in agriculture, irrigation, water resource management, and environmental studies. Accurate estimation of reference evapotranspiration (ET₀) improves water use efficiency and productivity. While in-field measurement methods exist, they are costly, time-consuming, and require complex instrumentation and skilled personnel. As a result, empirical, semi-empirical, and physical models are commonly used. In countries like Pakistan, limited ground-based weather stations and data availability restrict the use of the Penman-Monteith FAO56 (PM-FAO56) equation. Therefore, alternative models need to be evaluated using accessible data sources such as NASA-POWER, which offers global meteorological data. This study compares various ET₀ estimation models against PM-FAO56 using NASA-POWER data across different climatic zones in Pakistan. Three stations from each of four climatic zones were selected. One combination-based, five temperature-based, five radiation-based, and five mass-transfer-based models were assessed. Model performance was evaluated using RMSE, MAE, NSE, Pearson’s r, index of agreement (d), and percentage error (PE). The Valiantzas (combination-based) model showed consistently high accuracy across all stations. Most temperature-based models (except Blaney-Criddle) performed well, followed by radiation-based and mass-transfer models.
Reference Evapotranspiration; NASA-POWER data; Penman-Monteith FAO56
SAFESPEAK: DESIGNING AN ANONYMOUS DIGITAL MENTAL HEALTH PLATFORM FOR PAKISTANI ADOLESCENTS
Adolescent mental health issues are rising in Pakistan, with over half of youth experiencing anxiety or depression. However, stigma, lack of awareness, and limited access to care often prevent them from seeking help. This study presents SafeSpeak, a culturally adapted, AI-powered anonymous chat platform designed to support Pakistani teenagers facing mental health challenges. Utilizing Natural Language Processing (NLP), the platform enables empathetic conversations, detects emotional distress, and refers serious cases to trained counselors. By ensuring privacy, accessibility, and anonymity, SafeSpeak offers a safe, judgment-free space for self-expression. The paper outlines the platform's design, reviews relevant literature on digital mental health interventions, and proposes an implementation framework. It also examines potential impacts, benefits, and challenges in the context of Pakistan’s socio-cultural landscape. SafeSpeak offers a scalable approach to bridging the mental health care gap, especially for underserved and rural youth.
Safe speak, adolescents, Mental health and Natural Language Processing
AN AI-DRIVEN FRAMEWORK FOR THE PREDICTIVE MAINTENANCE OF ROAD TRANSPORT INFRASTRUCTURE
Predictive maintenance is transforming vehicle and infrastructure management by reducing costs and downtime in intelligent transportation systems. This study explores the application of AI in smart transport to anticipate equipment failures and optimize repair schedules. Using real-time sensor data, predictive algorithms—such as neural networks—identify issues before they lead to costly breakdowns. Unsupervised learning enables real-time anomaly detection, while supervised models analyze historical maintenance data for patterns. AI-powered predictive maintenance supports a shift from reactive to proactive practices, enhancing efficiency, safety, and reliability. The study also examines its integration within IoT-connected smart cities, addressing challenges like data integration, algorithm scalability, and cybersecurity. Findings suggest AI can significantly improve operational efficiency, lower maintenance costs, and reduce system disruptions in modern transportation networks.
Artificial intelligence, Machine learning, Internet of Things, Convolutional Neural Networks, Light Detection and Ranging
A SECURE AND COLLABORATIVE APPROACH TO FLIGHT DELAY PREDICTION USING MACHINE LEARNING IN SMART AVIATION
Airlines play a crucial role in global mobility, with passenger satisfaction closely tied to flight punctuality. Leading carriers such as Qatar Airways and Etihad Airways have set high benchmarks for service and innovation. However, delays and cancellations—stemming from weather, congestion, technical issues, or logistical challenges—disrupt schedules, damage reputations, and impact profitability. Traditional manual analysis often struggles to deliver timely insights given the sheer volume of aviation data. This paper introduces a secure, collaborative machine learning platform for predicting flight delays, leveraging supervised learning on publicly available Kaggle data. The model identifies delay patterns, root causes, and potential conflicts before they arise, improving situational awareness and enabling proactive interventions. By facilitating data sharing and collaboration among airlines, airports, and regulators, the platform promotes smarter, more passenger-centric air travel management, transforming flight delay handling into a more efficient and anticipatory process
APPLYING QUANTUM COMPUTING TO TEST OPTIMIZATION IN SAFETY-CRITICAL SYSTEMS
The increasing complexity of software in safety-critical domains like aerospace and automotive demands more efficient testing methods. Traditional test optimization struggles to balance fault detection with resource limits. This research proposes a quantum computing-based framework for test case prioritization and selection, leveraging algorithms such as Grover’s Search and QAOA. By integrating classical fault-based testing with quantum techniques, the approach aims to reduce test suite size while maintaining or improving fault detection. Experiments using benchmark datasets (e.g., Siemens suite) on simulators and IBM Q hardware show that quantum methods can outperform traditional approaches in fault coverage, suite reduction, and efficiency, especially for large test spaces. Statistical analyses validate these improvements. Despite current quantum hardware limitations, the study highlights the promising role of quantum computing in optimizing software testing for safety-critical systems and outlines future directions for advancing this field.
A MULTI-DOCTOR DIGITAL PLATFORM FOR SECOND MEDICAL OPINIONS: A PATIENT EMPOWERMENT PROPOSAL
The rapid rise of digital technologies and mobile health apps is reshaping global medical care. This paper introduces "Second Opinion on Hand," a secure mobile platform offering patients verified second opinions from multiple doctors. Users can upload health records, select chat or video consultations, and receive expert assessments. Prioritizing data privacy, the app utilizes advanced encryption methods like AES-256, TLS, and RBAC. The study details the design, expected benefits, and explores challenges and future prospects in telemedicine.
FOR SECOND MEDICAL OPINIONS: A PATIENT EMPOWERMENT PROPOSAL
KNOWLEDGEBOT: IMPROVING CHATBOT INTELLIGENCE USING FEDERATED LEARNING AND WIKIPEDIA-DRIVEN LLMS
This paper presents KnowledgeBot, a smart chatbot leveraging federated learning for privacy-preserving, accurate medical conversations in both French and English. Combining large language models (LLMs) with a fact-checking system, KnowledgeBot excels at generating informative responses, detecting LLM errors, and mitigating biases through simulated and real user data. Demonstrating strong multilingual capabilities, it achieves factual accuracy rates of 95.2% and 96.1% in human and simulated conversations, outperforming existing baselines. The English model showed higher accuracy than the French counterpart. This research lays the groundwork for secure, reliable, and adaptable chatbots that integrate federated learning with LLMs to advance conversational AI.
Federated Learning, Chatbot, Factual Checking, Multi-hop Retrieval System, Wikipedia, Medical Dataset
ECONOMIC VIABILITY OF SMART SAFETY TECHNOLOGIES IN ELECTRICAL ENGINEERING PROJECTS: CURRENT STATUS AND FUTURE DIRECTIONS
This study evaluates the financial viability of implementing smart safety technologies in electrical engineering projects by analyzing cost-benefit metrics, ROI trends, and implementation challenges across different project sizes. Using a mixed-methods approach—quantitative analysis of 78 projects and qualitative insights from 42 expert interviews—the research shows that mid-to-large projects break even within 18–24 months, while smaller projects take 32–38 months. Key cost factors include initial capital, integration complexity, workforce training, maintenance, and regulatory compliance. The study offers practical guidelines and a framework for stakeholders to balance financial returns with safety priorities, aiding firms in making informed decisions on adopting smart safety technologies.
Smart safety technologies, electrical engineering projects, financial feasibility, implementation costs, return on investment, cost-benefit analysis, safety economics, smart technologies ROI, electrical safety standards, implementation challenges.
LEVERAGING STRUCTURAL PATTERNS TO IDENTIFY OPTIMAL LINKS IN COMPLEX HUMAN NETWORKS
CMIP6-BASED ASSESSMENT OF HISTORICAL AND FUTURE TEMPERATURE AND RAINFALL TRENDS IN QUETTA VALLEY, PAKISTAN
This study investigates historical and projected climate dynamics of temperature and rainfall in Quetta Valley, Pakistan—an arid region highly vulnerable to climate change. Historical analysis reveals a pronounced warming trend in both minimum and maximum temperatures, alongside a modest yet statistically significant decline in annual rainfall, further intensifying regional aridity. Future projections under the high-emission SSP585 scenario suggest continued warming, with maximum temperatures potentially rising by up to 8°C by 2100. Precipitation projections indicate irregular variations but generally point toward a drier climate compared to the baseline, particularly under SSP585. These findings underscore the increasing climatic fragility of Quetta Valley and highlight the urgent need for adaptive strategies in water resource management, agricultural practices, and sustainability planning.
Climate variability, Temperature dynamics, Precipitation trends, Quetta Valley, CMIP6 projections, SSP climate scenarios.
ENHANCING WHEAT (TRITICUM AESTIVUM L.) PERFORMANCE THROUGH COMBINED USE OF BIOFERTILIZERS AND ZINC SULPHATE
A field experiment was conducted during the Rabi season of 2024 at Govt. Post College, Timergara, District Dir Lower, Khyber Pakhtunkhwa, Pakistan, to investigate the effects of biofertilizers and zinc sulphate on the growth and yield of wheat (Triticum aestivum L.). The study included treatments with phosphate-solubilizing bacteria (PSB), Azotobacter, their combination (PSB + Azotobacter), and zinc sulphate applied at 20, 25, and 30 kg/ha. The experimental soil was sandy loam, nearly neutral in pH (7.8), and low in organic carbon (0.35%). Results revealed that the integrated application of PSB, Azotobacter, and zinc sulphate at 30 kg/ha significantly improved growth and yield attributes. The treatment achieved maximum plant height (159.03 cm), plant dry weight (162.70 g/plant), crop growth rate (26.25 g/m²/day), cobs per plant (1.8), rows per cob (16.8), seeds per cob (553.4), 100-seed weight (29.3 g), grain yield (6.5 t/ha), straw yield (12.9 t/ha), and harvest index (33.8%). These improvements can be attributed to the synergistic role of biofertilizers in enhancing nutrient availability, particularly phosphorus and nitrogen, through increased microbial activity in the rhizosphere, alongside the physiological benefits of zinc in enzymatic and metabolic functions. The findings highlight integrated nutrient management, combining biofertilizers with zinc supplementation, as a sustainable approach to improving wheat productivity while reducing reliance on chemical fertilizers.
LOW-THD SINGLE-PHASE FULL-BRIDGE INVERTER WITH PI REGULATION, 10 KHZ SPWM CONTROL, AND LC FILTERING FOR 110 V RMS, 60 HZ OUTPUT
This paper presents a robust and efficient single-phase full-bridge inverter topology capable of converting a DC input into a stable 110 V RMS, 60 Hz AC output. The inverter employs sinusoidal pulse-width modulation (SPWM) with a 10 kHz triangular carrier to precisely control four IGBT switches, thereby generating a high-quality sinusoidal waveform. An LC low-pass filter (L = 4.06 mH, C = 6.23 µF) further suppresses high-frequency components, resulting in exceptionally low total harmonic distortion (THD) of 0.22% in the output voltage. A closed-loop proportional–integral (PI) controller (Kₚ = 21, Kᵢ = 0.03155) ensures stable operation by continuously comparing the filtered output with a 60 Hz reference signal and dynamically adjusting the PWM duty cycle. This adaptive control strategy maintains output voltage within ±2% of the nominal value under load transients (±50%) and DC-bus disturbances, with recovery achieved within a fraction of a cycle. The demonstrated low THD, rapid dynamic response, and high stability make this inverter design well-suited for renewable energy systems, uninterruptible power supplies (UPS), and other high-quality AC power applications.
Closed-loop proportional–integral (PI) control, DC–AC conversion, High-frequency switching, LC output filter, Single-phase full-bridge inverter, Sinusoidal pulse-width modulation (SPWM), Total harmonic distortion (THD), Voltage regulation, Renewable energy systems, Uninterrupted power supplies (UPS)
A FRAMEWORK FOR ENHANCING CYBERSECURITY IN INTERNET BANKING SYSTEMS
The rapid advancement of digital technologies has transformed banking services by enabling automated transactions and reshaping customer interactions through Internet banking. While this shift reduces operational costs and enhances customer satisfaction, it also introduces significant cybersecurity vulnerabilities—particularly in developing countries such as Pakistan, where Internet banking adoption is still emerging. Fraudsters increasingly exploit advanced techniques, exposing banks to internal, external, and regulatory threats. This study adopts a quantitative approach, analyzing survey responses from 350 account holders across Pakistan. Using descriptive statistics, reliability testing, and Cronbach’s Alpha, the research validates a four-layer Cyber Defense Framework for safeguarding digital financial data. The findings highlight critical gaps in user awareness, procedural safeguards, and trust in online transactions, emphasizing the urgent need for comprehensive cybersecurity measures. Practically, the framework offers actionable guidance for financial institutions and regulators to develop resilient system architectures, adaptive defense strategies, and stronger legal mechanisms. By integrating technological innovation with strategic security measures, this study provides a context-specific blueprint for strengthening Pakistan’s banking sector against evolving digital threats.
A COMPREHENSIVE REVIEW OF GRAVITATIONAL WATER VORTEX POWER PLANTS: TECHNICAL AND EXPERIMENTAL PERSPECTIVES
The emissions from fossil fuel–based power plants and industrial systems pose significant challenges for developed nations, contributing to epidemics, respiratory illnesses, and widespread environmental hazards. As a sustainable alternative, hydroelectric power plants offer a reliable solution to the global energy crisis by harnessing the kinetic and potential energy of flowing water instead of relying on oil and other nonrenewable fuels. Within this domain, the Gravitational Water Vortex Power Plant (GWVPP), categorized under Mini, Micro, and Pico hydropower systems, has emerged as a promising technology capable of generating electricity at low hydraulic heads ranging from 0.7–3.0 m and flow rates as low as 0.05 m·s⁻¹. This technology is particularly suitable for regions with low head but moderate to high stream flow conditions. However, as GWVPP is still in its developmental stage, experimental validation is crucial to bridge the gap between theoretical claims and actual performance outcomes. This review consolidates findings from experimental studies and pilot projects implemented worldwide, providing a comprehensive perspective on the current status of GWVPP technology. Furthermore, it compares the technical, economic, and ecological aspects of GWVPP with other micro-hydropower systems. Results suggest that GWVPP stands out as a cost-effective, environmentally sustainable, and ecologically compatible solution, offering distinct advantages for aquatic ecosystems while addressing the growing global energy demand.
INTELLIGENT DRIVER DROWSINESS DETECTION THROUGH REAL-TIME EYE MONITORING
This paper presents a novel architecture for driver drowsiness detection that leverages real-time eye state recognition from live video feeds. The system achieves an accuracy of 97% and is capable of issuing timely alerts to help prevent accidents caused by driver fatigue—one of the major contributors to traffic-related fatalities worldwide. The proposed framework integrates Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs) to enhance feature extraction and temporal sequence analysis. A comprehensive dataset of 4,760 images, evenly divided between open-eye (2,380) and closed-eye (2,380) states, captured under diverse driving conditions, was used to train and validate the model. The results demonstrate the effectiveness of the hybrid deep learning approach in improving detection accuracy, offering a reliable and scalable solution for enhancing road safety.
ADVANCING SPACE WEATHER PREDICTION THROUGH DEEP LEARNING: A 2D CNN FRAMEWORK FOR SUNSPOT FORECASTING
Accurate prediction of sunspot activity is essential for reliable space weather forecasting and the protection of space-reliant infrastructure. Recent advances in cloud computing have accelerated the adoption of deep learning methods, enabling scalable and efficient predictive models. This study investigates the application of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Two-Dimensional Convolutional Neural Networks (2D-CNN) for enhancing sunspot forecasting accuracy. Leveraging a cloud-based framework, the approach improves computational efficiency, supports real-time analysis, and facilitates large-scale model deployment. The dataset comprises time-series records of sunspot activity, making it particularly suitable for recurrent architectures. LSTM and GRU effectively capture sequential dependencies, while optimization strategies such as modified particle swarm optimization and hyperparameter tuning mitigate overfitting and computational overhead. Experimental evaluation demonstrates that 2D-CNN delivers the highest predictive performance, achieving 99.39% accuracy, 99.45% precision, 99.33% recall, and a 98.79% F1-score, underscoring its strength in capturing spatial correlations within sunspot data. Additionally, GRU outperformed LSTM in sequential modeling, with higher precision (98.80% vs. 97.81%) and F1-score (96.21% vs. 96.11%). Overall, the findings highlight the effectiveness of deep learning—particularly 2D-CNNs—in advancing accurate, scalable, and real-time sunspot forecasting for space weather prediction.
PHYSIOLOGICAL AND HISTOLOGICAL IMPACT OF E-CIGARETTE VERSUS CONVENTIONAL SMOKING: A STUDY ON ALBINO MICE
The increasing adoption of electronic cigarettes (e-cigarettes) as alternatives to conventional tobacco products has raised concerns about their systemic health consequences. This study aimed to evaluate and compare the physiological and histopathological effects of e-cigarette (T1) and conventional cigarette (T2) exposure in albino mice. Adult mice were divided into a control group and two experimental groups (T1 and T2) and subjected to nine weeks of exposure. Hematological, metabolic, and hormonal parameters were assessed alongside histological changes. Results indicated reduced hemoglobin levels in both treated groups (6.67 ± 0.356 in T1 and 5.982 ± 0.059 in T2 vs. 6.81 ± 0.09 in controls). White blood cell and platelet counts were significantly higher in the T1 group compared to both control and T2 groups. Testosterone levels increased in T2, while glucose levels rose in both groups, with a greater elevation in T1. Estrogen levels showed divergent responses, increasing in T1 but decreasing in T2. Histological examinations revealed pathological alterations in the myocardium, kidneys (glomerulosclerosis), liver (hepatocellular ballooning), and lungs (emphysematous changes), with more severe structural damage observed in the T2 group. Both exposures also led to progressive weight loss, more pronounced in male mice. Overall, the findings demonstrate that both e-cigarettes and conventional cigarettes exert adverse systemic effects; however, e-cigarettes appear to cause greater disruption of immune responses and hormonal balance, while conventional cigarettes induce more extensive structural damage. These results underscore the need for careful evaluation of the long-term health risks associated with e-cigarette use.
INTELLIGENT WEED DETECTION IN SMART AGRICULTURE USING FUZZY INFERENCE AND IMAGE PROCESSING
Weed detection through image processing is an emerging and rapidly advancing field with the potential to revolutionize modern agriculture. This technology enables farmers to accurately identify and monitor weed growth, facilitating targeted and efficient weed control practices. This study presents the development of an image acquisition and processing system, integrated with a fuzzy logic–based decision-making platform, to determine the appropriate pesticide dosage and application areas within agricultural fields. MATLAB was employed as the processing environment for analyzing field images and identifying grassy weed regions. The fuzzy inference system utilizes weed coverage and patch values, supported by membership functions, to guide herbicide application rates for specific field zones. By enabling precision targeting, this approach reduces chemical overuse, enhances sustainability, and supports healthier agricultural practices. With growing global food demand and increasing pressure on natural resources, sustainable farming methods are essential. The findings of this work highlight the potential of combining image processing with fuzzy logic as a practical, intelligent solution for advancing smart and sustainable agriculture.
INTELLIGENT SMART GLOVE SYSTEM FOR ROBOTIC HAND FINGER MOVEMENT PREDICTION
Robot-assisted surgeries have advanced significantly, enabling complex procedures with minimal invasiveness and enhanced flexibility. This research proposes a machine learning (ML)–based approach for predicting finger movements of a robotic hand using a Smart Glove equipped with Light Dependent Resistor (LDR) sensors. The system employs an ESP-WROOM-32 microcontroller, integrated with Arduino IDE and Jupyter, to capture and refine real-time finger motions, including flexion and extension. These movements are mapped to the robotic hand through real-time integration. The dataset generated represents multi-output regression challenges, as multiple finger movements must be predicted synchronously. To address this, a K-Nearest Neighbors (KNN) regressor was applied, leveraging its ability to handle multi-output regression effectively. Model performance was evaluated using Root Mean Square Prediction Error (RMSPE), and real-time implementation demonstrated that the robotic hand accurately replicated finger motions corresponding to the Smart Glove inputs. The proposed method enhances control precision, reduces latency, and improves overall user interaction, offering potential applications in advanced prosthetics, artificial limb control, and remote robotic operations.
AI-DRIVEN ANOMALY DETECTION FOR SECURING CRITICAL INFRASTRUCTURE
Critical infrastructure, including electrical systems and communication networks, faces increasing risks from cyber-attacks and system failures, making reliable anomaly detection essential for operational security and resilience. Traditional rule-based monitoring methods often fail to capture the complexity and evolving nature of modern threats. This study explores the application of artificial intelligence (AI) and machine learning techniques for anomaly detection to safeguard critical infrastructure. By leveraging deep learning models such as recurrent neural networks (RNNs) and Transformers, the proposed approach captures temporal and contextual dependencies in system data, enabling early detection of irregular patterns. In addition, unsupervised and self-supervised learning methods are employed to address challenges related to scarce labeled data, while reinforcement learning supports adaptive threat response strategies. Experimental evaluations on benchmark datasets demonstrate that AI-driven models significantly outperform conventional methods in terms of detection accuracy, precision, recall, and response time. The findings underscore the potential of AI to provide proactive, scalable, and adaptive defense mechanisms, thereby enhancing the reliability, availability, and security of critical infrastructure systems. Future research directions include improving model interpretability, reducing computational overhead, and enabling real-time deployment in large-scale, heterogeneous environments.
Artificial Intelligence (AI), Machine Learning (ML), Cybersecurity, Critical Infrastructure, Threat Prediction, Anomaly Detection
ADVANCED PHOTOTRANSISTOR DESIGN USING NANOSTRUCTURED SEMICONDUCTORS
Phototransistors are crucial in optoelectronics for their high sensitivity and broad spectral response, yet improving responsivity, detectivity, and response speed remains a challenge. This study reports the design and fabrication of high-performance phototransistors using GaN–ZnO nanowires and WS₂–InGaZnO heterojunctions, focusing on band gap engineering, synthesis parameters, plasmonic enhancement, and heterostructure integration. GaN–ZnO nanowires with varying ZnO concentrations were synthesized and incorporated into bottom-gate phototransistors with Ti/Au electrodes, and device performance was evaluated under different synthesis conditions; Ag nanoparticles were further introduced for plasmonic enhancement. Separately, WS₂–InGaZnO heterojunctions were fabricated using CVD and sputtering. Band gap tuning from 3.4 eV (GaN) to 2.6 eV (Zn-rich nanowires) enhanced visible light absorption, with optimized nanowires at 850 °C achieving responsivity of 95.3 A/W and detectivity of 2.1 × 10¹¹ Jones, further improved to 131.7 A/W and 5.9 ms response time with Ag decoration. The WS₂–InGaZnO heterojunction device demonstrated superior performance, achieving 122.5 A/W responsivity, 3.8 × 10¹¹ Jones detectivity, and 91.7% stability over 50 cycles. These results highlight that combining band gap engineering, plasmonic enhancement, and heterostructure design significantly advances phototransistor performance, providing a pathway toward next-generation broadband, high-sensitivity photodetectors for practical optoelectronic applications.
photoresponsivity, semiconductor nanomaterials
DETERMINANTS OF CLOUD ADOPTION IN SMES: A PLS-SEM APPROACH
Cloud computing has emerged as a transformative technology offering cost efficiency, scalability, and flexibility, yet its adoption among small and medium-sized enterprises (SMEs) remains uneven. This study investigates the determinants influencing cloud computing adoption in SMEs using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. Drawing on technology adoption theories, the research model incorporates factors such as perceived usefulness, ease of use, cost-effectiveness, security concerns, top management support, and external pressure. Survey data collected from SMEs were analyzed to assess both the measurement and structural models. The results reveal that perceived usefulness, management support, and external pressure significantly drive adoption, while concerns regarding cost and security act as barriers. The findings provide empirical evidence that strategic, technological, and organizational factors jointly shape adoption decisions. This study contributes to the literature by offering a comprehensive model of SME cloud adoption and provides practical insights for policymakers, vendors, and SME managers to enhance cloud adoption strategies.
Cloud Computing Adoption, SMEs, Perceived Usefulness, Security Concerns, Cost Efficiency, Organizational Readiness, PLS-SEM, Digital Transformation
MACHINE LEARNING FOR LOG-BASED ANOMALY DETECTION AND SELF-HEALING
Modern software systems generate enormous volumes of log data to support monitoring and fault diagnosis, far exceeding the capacity of manual analysis. This paper proposes an architecture for detecting anomalous patterns in software logs using advanced machine learning and deep learning techniques, enabling proactive fault diagnosis and self-healing capabilities. Traditional rule-based approaches are inadequate for handling the scale and complexity of contemporary log data, whereas deep learning models such as Long Short-Term Memory (LSTM) networks and Transformer architectures excel at capturing contextual dependencies within log sequences. To address challenges such as limited labeled data, this study leverages self-supervised and contrastive learning methods. In addition, reinforcement learning and rule-based automation are incorporated to dynamically correct faults, thereby minimizing system downtime. The proposed models are evaluated on benchmark log datasets using metrics including precision, recall, F1-score, and AUC-ROC. Experimental results demonstrate that Transformer-based models achieve superior performance compared to conventional machine learning techniques, though at higher computational costs. Notably, self-healing mechanisms reduce downtime by up to 68.2%, underscoring the potential of AI to significantly enhance system reliability and availability. However, challenges such as model interpretability, computational efficiency, and real-time adaptability remain. This paper provides a state-of-the-art review of AI-based log anomaly detection approaches and outlines future research directions, emphasizing lightweight architectures, explainable AI, and scalable deployment as key enablers for advancing AI-powered anomaly detection and self-healing systems in safety-critical domains.
AI-powered anomaly detection, software logs, machine learning, deep learning, self-healing systems, log analysis, LSTM,
AI-BASED SPEECH ANALYSIS FOR IMPROVING COMMUNICATION IN GLOBAL SOFTWARE DEVELOPMENT
This article explores the biology of aging and strategies to promote healthy longevity. It highlights the roles of hormone balance, stress management, sleep, and physical activity in slowing aging and preventing related diseases. Regular exercise, quality sleep, and stress reduction improve cognitive and physical function, while supplements like DHEA, melatonin, and NAD+ precursors support cellular repair. Emerging therapies such as senolytics, gene therapy, and personalized medicine offer promising avenues for extending lifespan. Together, these approaches combine lifestyle, technology, and medical advances to enhance health and independence with age.
SAMPLE SIZE EFFECTS ON ML CLASSIFICATION ACCURACY
The performance of machine learning classification models is strongly influenced by training dataset size. This study analyzes how varying sample sizes affect five popular classifiers: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and Naïve Bayes. Using simulated datasets from 50 to 5,000 samples, models were evaluated on Accuracy, Precision, Recall, and F1-score. Results show that all models improve with more data, but sensitivity to sample size differs. Logistic Regression and SVM perform consistently well across sizes, while Naïve Bayes excels even with limited data. Decision Trees are unstable with small datasets but improve notably with larger samples. Random Forests improve gradually, achieving competitive results at scale. These insights guide practitioners in choosing appropriate algorithms based on data availability, highlighting the need to match model complexity to dataset size for optimal performance.
DEEP LEARNING FOR SIGN LANGUAGE INTERPRETATION TO ENHANCE COMMUNICATION IN SPEECH-IMPAIRED INDIVIDUALS
MACHINE LEARNING FOR FAULT DETECTION IN THREE-PHASE TRANSMISSION LINES AND ELECTRIC MACHINES
This research explores machine learning algorithms for fault detection and classification in electrical machines and three-phase transmission lines. Using MATLAB Simulink, fault scenarios were simulated to generate datasets, which were preprocessed and divided into training, validation, and testing sets. Algorithms including Decision Trees, XGBoost, k-Nearest Neighbors (KNN), and Random Forest were evaluated. The best-performing model was integrated with Simulink for real-time fault detection. Results indicate that KNN and Random Forest outperform other methods in accurately identifying and classifying faults, enhancing system reliability and reducing downtime.
REINFORCEMENT LEARNING IN IOT-ENABLED HEALTHCARE: OPPORTUNITIES, CHALLENGES, AND FUTURE DIRECTIONS
Reinforcement Learning (RL), a powerful machine learning approach, is transforming healthcare by enabling intelligent systems that learn and adapt through continuous interaction with dynamic data environments. Unlike static heuristic methods, RL updates decisions based on real-time feedback, making it well-suited for medical settings where patient conditions evolve and treatment effects are delayed. This review examines 27 validated studies showcasing RL applications in real-time monitoring, critical care, drug dosing, personalized treatment, and more. It highlights how RL enhances clinical outcomes and automates complex decisions while addressing key challenges such as interpretability, ethical concerns, and integration with traditional healthcare systems. The study also discusses explainable RL and human-in-the-loop learning as vital steps toward bridging the gap between algorithmic intelligence and practical clinical use.
Smart Healthcare, Deep Reinforcement Learning