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
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
ENHANCING ROOFTOP PV PERFORMANCE IN DESERT CLIMATES THROUGH ADAPTIVE O&M: SAUDI ARABIA CASE STUDY
This paper explores adaptive operation and maintenance (O&M) strategies to improve rooftop photovoltaic (PV) system performance in Saudi Arabia’s harsh desert climate. Despite high solar irradiance, factors like dust, extreme heat, and humidity reduce system efficiency and lifespan. The study evaluates smart technologies including AI-driven predictive maintenance, robotic cleaning, and IoT-based monitoring. Key performance indicators such as performance ratio (PR), utilization factor, and Levelized Cost of Electricity (LCOE) were analyzed through case studies in Riyadh and Dhahran. Results show that adaptive O&M can boost energy output by at least 12%, maintain PRs above 90%, and lower LCOE to $0.014/kWh. These strategies prove more cost-effective and efficient than traditional reactive approaches. The paper concludes with recommendations for real-world implementation and highlights research gaps for desert PV systems in the MENA region.
Rooftop PV systems; adaptive maintenance
PHOTOCATALYTIC DEGRADATION OF ORGANIC POLLUTANTS VIA METAL-OXIDE NANOSTRUCTURES
Increasing contamination of freshwater by persistent organic pollutants calls for efficient and eco-friendly treatment methods. This study explores photocatalytic degradation of organic dyes using metal-oxide nanostructures (TiO₂, ZnO, Fe₂O₃) synthesized via sol-gel and hydrothermal techniques. Characterization by SEM, FTIR, XRD, and UV-Vis confirmed well-dispersed nanostructures with favorable structural and optical properties. Under UV and visible light, significant degradation of dyes like methylene blue, Rhodamine B, and methyl orange was observed. Photocatalytic efficiency was tested across catalyst dosage, dye concentration, pH, and light conditions. Kinetic analysis indicated pseudo-second-order behavior, suggesting surface chemisorption as the key degradation mechanism. Reusability tests showed catalyst stability over multiple cycles. These results demonstrate the potential of green-synthesized metal-oxide nanostructures as sustainable catalysts for effective wastewater treatment.
LOW-THD 110 V RMS, 60 HZ PI-REGULATED SINGLE-PHASE FULL-BRIDGE INVERTER WITH 10 KHZ SPWM AND LC FILTERING
This study presents a single-phase full-bridge inverter converting DC to stable 110 V RMS, 60 Hz AC using 10 kHz sinusoidal PWM for precise control of IGBT switches. An LC low-pass filter (4.06 mH, 6.23 µF) significantly reduces harmonic distortion (THD ≈ 0.22%). A high-bandwidth PI controller dynamically adjusts PWM duty cycles to maintain output stability within ±2% despite load variations and DC-bus disturbances. The system demonstrates fast recovery and excellent dynamic response, making it suitable for high-quality AC power applications like renewable energy and UPS systems.
Closed-loop proportional–integral (PI) control, DC–AC conversion
High-frequency switching, LC output filter
Sinusoidal pulse-width modulation (SPWM)
OPTIMIZED CYBERSECURITY FRAMEWORK FOR SAFEGUARDING PERSONAL DATA IN PAKISTAN’S INTERNET BANKING
This study addresses cybersecurity challenges in Pakistan’s growing Internet banking sector, where increasing digital transactions expose vulnerabilities to sophisticated fraud. Using a survey of 350 account holders, the research validates a four-layer Cyber Defense Framework aimed at protecting personal financial data. Results highlight gaps in user awareness, security practices, and trust, emphasizing the need for robust, adaptive cybersecurity measures. The proposed framework offers practical guidance for banks and regulators to enhance system resilience and legal protections, providing a tailored strategy to safeguard Pakistan’s digital banking ecosystem.
LEVERAGING FREE SURFACE VORTICES FOR CLEAN ENERGY: REVIEW OF GRAVITATIONAL WATER VORTEX POWER PLANTS
The extracted discharge from fossil fuel operational systems and power generating plants is considered a huge concern for the developed countries since they are accountable for several epidemics, lung diseases, and environmental hazards. Hydroelectric power-generating plants provide an alternative solution to the recent energy contingency of the world, since they utilize the stored energy in a free stream of water to deliver electricity instead of utilizing oil and other fuels. Gravitational Water Vortex Power Plant (GWVPP) under the class of Mini Micro Pico Hydropower have been observed as a suitable technology to produce power from the flow of water with a hydraulic head of 0.7 - 3.0 m with a flow rate as low as 0.05 m.s-1. This type of turbine is used in regions where the water-powered head isn't high, with high to medium stream rates. Since this technology is still in its infancy, experimental validation of theoretical work is the utmost necessity to cover the gap between claimed and achieved efficiencies. In this review work, an attempt has been made to gather the findings of experimental studies carried out by different researchers & pilot projects installed worldwide, to present the actual scenario and determine the gap between the claimed and desired results. Technical, economic, and ecological parameters of GWVPP are compared with those of other micro hydro power plants, and it is concluded that GWVPP is the most efficient of all these, having low cost, being environmentally friendly, and most useful for aquatic life.
REAL-TIME DRIVER DROWSINESS DETECTION VIA EYE STATE RECOGNITION
This paper presents a real-time driver drowsiness detection system based on eye state recognition, achieving 97% accuracy. The system combines CNN and RNN models to analyze live video feeds and issue timely alerts to prevent accidents caused by driver fatigue. The model is trained on a balanced dataset of 4,760 images, equally split between open and closed eyes, captured under varied driving conditions.
DEEP LEARNING FRAMEWORK USING 2D CNN FOR SUNSPOT-BASED SPACE WEATHER PREDICTION
Accurate sunspot prediction is vital for space weather forecasting to protect space-based infrastructure. This study leverages deep learning models—LSTM, GRU, and 2D Convolutional Neural Networks (2D-CNN)—within a cloud computing framework to enhance prediction accuracy and scalability. Using time-series sunspot data, LSTM and GRU capture sequential patterns, while optimization techniques reduce complexity and overfitting. Experimental results show 2D-CNN achieves the highest accuracy at 99.39%, with strong precision and recall, highlighting its superior spatial feature extraction. GRU outperforms LSTM in sequential data handling. These results demonstrate the effectiveness of deep learning, especially 2D-CNNs, for robust sunspot forecasting.