The Intelligent System Review (ISSN Print: 3106-7832 | ISSN Online: 3106-7840) is a peer-reviewed, open-access, quarterly scholarly journal published by Factum Research Institute (SMC-Private) Limited, a legally registered entity with the Securities and Exchange Commission of Pakistan (SECP) under Registration No. 0310960. 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, a rigorous peer-review process, and the promotion of global research visibility and impact.
Factum Research Institute (SMC-Private) Limited
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-BASED SPEECH ANALYSIS FOR IMPROVING COMMUNICATION IN GLOBAL SOFTWARE DEVELOPMENT
Aging is a complex biological process influenced by genetic, environmental, and lifestyle factors, and understanding its mechanisms is essential for promoting healthy longevity. This article examines the interplay between hormonal regulation, stress management, sleep quality, and physical activity in delaying age-related decline and reducing the risk of chronic diseases. Hormone balance, particularly involving DHEA, melatonin, and NAD⁺ precursors, plays a vital role in maintaining cellular repair, metabolic efficiency, and circadian rhythm alignment. Lifestyle strategies such as regular physical exercise, restorative sleep, and effective stress reduction are shown to enhance both cognitive and physical performance, thereby supporting resilience in aging populations. In addition to these established approaches, emerging biomedical interventions—including senolytics to remove senescent cells, gene therapy to address molecular drivers of aging, and personalized medicine tailored to individual biological profiles—offer promising opportunities to extend healthspan and potentially lifespan. By integrating lifestyle optimization with technological and medical advances, a comprehensive framework for longevity emerges, one that emphasizes prevention, cellular health, and the preservation of independence in later life. This holistic perspective underscores the potential for combining behavioral, pharmacological, and genomic strategies to transform aging from an inevitable decline into a manageable and healthier stage of human life
ADVANCEMENTS IN SOIL NUTRIENT AND CONTAMINANT DETECTION USING LASER-INDUCED BREAKDOWN SPECTROSCOPY
Laser-Induced Breakdown Spectroscopy (LIBS) has emerged as a fast, versatile, and efficient analytical technique with increasing applications in soil analysis, contributing to sustainable agricultural practices. This review provides a comprehensive overview of recent advancements in LIBS for soil characterization, emphasizing its role in detecting and quantifying essential nutrients and contaminants. Key developments focus on addressing limitations such as matrix effects, moisture content, and particle size variability, with optimized techniques including spatial confinement, addition of conductive materials, and laser-induced fluorescence (LIF) enhancing detection limits and analytical precision. Integration of machine learning, deep learning, and chemometric approaches has further improved predictive modeling, enabling robust analysis across diverse soil matrices. Additionally, portable and handheld LIBS systems facilitate real-time, in-field soil monitoring. Standardization efforts using certified reference materials and interlaboratory protocols are enhancing reproducibility and broader scientific acceptance. Collectively, these innovations establish LIBS as a powerful tool for precise soil nutrient management, contaminant detection, and environmentally sustainable agriculture.
IDENTIFICATION OF PREGNANCY LOSS RISK FACTORS USING MACHINE LEARNING ALGORITHMS
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.
LEVERAGING STRUCTURAL PATTERNS TO IDENTIFY 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.
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.
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
Sinusoidal pulse-width modulation (SPWM)
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.
IMPROVING SOLAR ENERGETIC PARTICLE PREDICTION WITH MACHINE LEARNING
This study advances Solar Energetic Particle (SEP) prediction through advanced machine learning techniques, mitigating the risks SEPs pose to space missions, satellites, and terrestrial systems. Using historical and real-time data from NASA and ESA, models based on Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and Random Forest algorithms were developed. Careful dataset preparation, hyperparameter tuning, and cross-validation ensured robust model performance. Among these, CNNs demonstrated superior accuracy and precision, making them a valuable tool for SEP forecasting. Overall, this work enhances machine learning capabilities for space weather prediction, contributing to safer and more reliable space operations
SAFESPEAK: AN ANONYMOUS DIGITAL MENTAL HEALTH COMPANION FOR PAKISTANI TEENS
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
Mental health and Natural Language Processing
AI-DRIVEN PREDICTIVE MAINTENANCE FOR 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.
NLP FOR CYBERSECURITY: TEXT ANALYSIS IN THREAT INTELLIGENCE
This study explores the use of Natural Language Processing (NLP) for cybersecurity threat intelligence in Pakistan’s multilingual and evolving digital landscape. Using a mixed-methods approach, 50,000 text samples (60% Urdu, 40% English) from local cybersecurity sources were analyzed. Techniques such as tokenization, named entity recognition, sentiment analysis, and topic modeling were applied using SVM, Random Forest, and LSTM algorithms. Results showed 87.3% accuracy in threat classification, a 65% increase in processing speed, and a 72% improvement in identifying emerging threats. Culturally adapted NLP models outperformed generic ones by 23%. Key challenges included code-switching, shifting threat vocabularies, and data privacy. The study offers actionable insights for building effective, localized cybersecurity solutions in multilingual settings
ONTOLOGY-BASED REAL-TIME SENTIMENT ANALYSIS FOR PRODUCT REPUTATION
Social media platforms have become influential channels for user interaction, enabling the creation and exchange of content within virtual communities. E-commerce sites like Amazon, eBay, and CNET leverage user-generated reviews and ratings to influence purchasing decisions. However, current review systems are often centralized, static, and vulnerable to false evaluations, lacking the adaptability to reflect real-time consumer sentiment. This research proposes a dynamic, ontology-driven reputation system that continuously evolves to provide accurate and timely product ratings. The system collects online reviews, performs sentiment analysis to extract opinions, and uses a semantic layer to build contextual profiles for decision-making. An adaptive ontology integrates with a database to map relevant structures and generate semantic queries, delivering actionable insights. By enabling real-time adaptation and greater transparency, this approach empowers consumers and manufacturers to make informed decisions based on up-to-date reputational data
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
SMART AVIATION: SECURE, COLLABORATIVE FLIGHT DELAY PREDICTION WITH MACHINE LEARNING
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
QUANTUM COMPUTING FOR 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.
EMPOWERING PATIENTS WITH DIGITAL SECOND OPINIONS: A MULTI-DOCTOR REVIEW PLATFORM 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
KNOWLEDGEBOT: ENHANCING CHATBOT INTELLIGENCE VIA FEDERATED LEARNING ON WIKIPEDIA LLM
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.
FINANCIAL FEASIBILITY OF SMART SAFETY TECHNOLOGIES IN ELECTRICAL ENGINEERING PROJECTS: CURRENT STATUS AND FUTURE PROSPECTS
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
IDENTIFYING PREGNANCY LOSS RISK FACTORS USING MACHINE LEARNING
Pregnancy loss, or spontaneous abortion, affects 15–20% of clinically diagnosed pregnancies. This study uses cross-sectional data from the Bureau of Statistics Punjab to identify key risk factors through various machine learning algorithms, including Logistic Regression, KNN, LDA, SVM, and others. Among these, KNN achieved the highest accuracy at 91%, while most models exceeded 80%. Feature selection methods revealed that "total children ever born" and "place of delivery" were the most influential factors. These findings highlight the potential of ML models in predicting pregnancy loss and identifying critical risk indicators.
Bureau of statistics Punjab: K nearest neighbor (KNN); Decision tree classifier; Gaussian N.B.; Support vector machines (SVM); Bernoulli N.B.; Passive-Aggressive classifier; Radius Neighbors classifier (RNC); Extra tree classifier (ETC); Linear Discriminant Analysis (LDA)
OPTIMAL LINK PREDICTION IN COMPLEX HUMAN NETWORKS VIA STRUCTURAL PATTERN ANALYSIS
This study investigates optimal link prediction methods in complex human networks by applying eight similarity-based algorithms across five real-world human interaction datasets. Networks were converted into adjacency matrices, with links removed for testing. Each method was evaluated using AUC scores to assess predictive performance. Results showed that the Resource Allocation Index (RAI) consistently outperformed other techniques, particularly on large and complex networks, highlighting its effectiveness for predicting missing or future connections in human-centric systems.
CMIP6-BASED ANALYSIS OF HISTORICAL AND FUTURE TEMPERATURE AND RAINFALL TRENDS IN QUETTA VALLEY, PAKISTAN
This study analyzes historical and projected temperature and rainfall trends in Quetta Valley, Pakistan—an arid region highly vulnerable to climate change. Historical data show a clear warming trend in both minimum and maximum temperatures, alongside a slight but significant decline in annual rainfall, intensifying regional aridity. Under the high-emission SSP585 scenario, maximum temperatures could rise by up to 8°C by 2100, while precipitation patterns are projected to become more erratic and drier. These findings highlight the region’s growing climate fragility and the urgent need for adaptive strategies in water management, agriculture, and sustainability.
LOW-COST TWO-FINGER LINKAGE-DRIVEN EXOSKELETON FOR HAND REHABILITATION: DESIGN AND PROTOTYPE
This study presents the design and prototype of a low-cost, linkage-driven two-finger exoskeleton for hand rehabilitation, aimed at improving motor function in patients with neurological injuries. Using a planar six-bar linkage, laser-cut acrylic parts, and standard servo motors controlled by an Arduino, the device delivers precise finger flexion and extension. Weighing around 100 grams and costing under $50, the prototype demonstrates mechanical reliability and effective control without specialized components. This affordable, simple design offers a promising solution to expand access to robotic-assisted therapy in resource-limited healthcare settings.