The Intelligent System Review
type: channel
description: 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.
publisher: Factum Research Institute (SMC-Private) Limited
language: en
publication name: The Intelligent System Review
items: ADVANCEMENTS IN SOIL NUTRIENT AND CONTAMINANT DETECTION USING LASER-INDUCED BREAKDOWN SPECTROSCOPY
items: IDENTIFICATION OF PREGNANCY LOSS RISK FACTORS USING MACHINE LEARNING ALGORITHMS
items: LEVERAGING STRUCTURAL PATTERNS TO IDENTIFY OPTIMAL LINKS IN COMPLEX HUMAN NETWORKS
items: CMIP6-BASED ASSESSMENT OF HISTORICAL AND FUTURE TEMPERATURE AND RAINFALL TRENDS IN QUETTA VALLEY, PAKISTAN
items: ENHANCING WHEAT (TRITICUM AESTIVUM L.) PERFORMANCE THROUGH COMBINED USE OF BIOFERTILIZERS AND ZINC SULPHATE
items: LOW-THD SINGLE-PHASE FULL-BRIDGE INVERTER WITH PI REGULATION, 10 KHZ SPWM CONTROL, AND LC FILTERING FOR 110 V RMS, 60 HZ OUTPUT
items: A FRAMEWORK FOR ENHANCING CYBERSECURITY IN INTERNET BANKING SYSTEMS
items: IMPROVING SOLAR ENERGETIC PARTICLE PREDICTION WITH MACHINE LEARNING
items: SAFESPEAK: AN ANONYMOUS DIGITAL MENTAL HEALTH COMPANION FOR PAKISTANI TEENS
items: AI-DRIVEN PREDICTIVE MAINTENANCE FOR ROAD TRANSPORT INFRASTRUCTURE
items: NLP FOR CYBERSECURITY: TEXT ANALYSIS IN THREAT INTELLIGENCE
items: ONTOLOGY-BASED REAL-TIME SENTIMENT ANALYSIS FOR PRODUCT REPUTATION
items: EVALUATING MODELS FOR ESTIMATING REFERENCE EVAPOTRANSPIRATION ACROSS PAKISTAN’S CLIMATIC ZONES
items: SMART AVIATION: SECURE, COLLABORATIVE FLIGHT DELAY PREDICTION WITH MACHINE LEARNING
items: QUANTUM COMPUTING FOR TEST OPTIMIZATION IN SAFETY-CRITICAL SYSTEMS
items: EMPOWERING PATIENTS WITH DIGITAL SECOND OPINIONS: A MULTI-DOCTOR REVIEW PLATFORM PROPOSAL
items: KNOWLEDGEBOT: ENHANCING CHATBOT INTELLIGENCE VIA FEDERATED LEARNING ON WIKIPEDIA LLM
items: FINANCIAL FEASIBILITY OF SMART SAFETY TECHNOLOGIES IN ELECTRICAL ENGINEERING PROJECTS: CURRENT STATUS AND FUTURE PROSPECTS
items: IDENTIFYING PREGNANCY LOSS RISK FACTORS USING MACHINE LEARNING
items: OPTIMAL LINK PREDICTION IN COMPLEX HUMAN NETWORKS VIA STRUCTURAL PATTERN ANALYSIS
items: CMIP6-BASED ANALYSIS OF HISTORICAL AND FUTURE TEMPERATURE AND RAINFALL TRENDS IN QUETTA VALLEY, PAKISTAN
items: LOW-COST TWO-FINGER LINKAGE-DRIVEN EXOSKELETON FOR HAND REHABILITATION: DESIGN AND PROTOTYPE

ADVANCEMENTS IN SOIL NUTRIENT AND CONTAMINANT DETECTION USING LASER-INDUCED BREAKDOWN SPECTROSCOPY
type: item
description: 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.
subject: Articles
  topic: section
subject: Agriculture
  topic: keywords
subject: Contamination
  topic: keywords
subject: Detection
  topic: keywords
subject: Environmental
  topic: keywords
subject: LIBS
  topic: keywords
subject: Nutrient
  topic: keywords
subject: Parameters
  topic: keywords
subject: Soil
  topic: keywords
subject: Technique
  topic: keywords
creator: Haris Yaseen
creator: Zubair Sajid
rights: Copyright (c) 2025
date: 2025-09-30
publication date: 2025-09-30
starting page: 83
ending page: 92

IDENTIFICATION OF PREGNANCY LOSS RISK FACTORS USING MACHINE LEARNING ALGORITHMS
type: item
description: 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.
subject: Articles
  topic: section
subject: Pregnancy loss
  topic: keywords
subject: Risk factors
  topic: keywords
subject: Machine learning
  topic: keywords
subject: KNN
  topic: keywords
subject: SVM
  topic: keywords
subject: Decision Tree
  topic: keywords
subject: Extra Trees Classifier
  topic: keywords
creator: Ejaz Ahmed
creator: Hussain Bux
creator: Muhammad Tahir
rights: Copyright (c) 2025
date: 2025-09-30
publication date: 2025-09-30
starting page: 73
ending page: 82

LEVERAGING STRUCTURAL PATTERNS TO IDENTIFY OPTIMAL LINKS IN COMPLEX HUMAN NETWORKS
type: item
description: 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.
subject: Articles
  topic: section
subject: Link Prediction
  topic: keywords
subject: Social Networking
  topic: keywords
subject: Complex Network
  topic: keywords
creator: Nasreen Jawaid
creator: Arif Warsi
creator: Abdul Salam
rights: Copyright (c) 2025
date: 2025-09-30
publication date: 2025-09-30
starting page: 58
ending page: 72

CMIP6-BASED ASSESSMENT OF HISTORICAL AND FUTURE TEMPERATURE AND RAINFALL TRENDS IN QUETTA VALLEY, PAKISTAN
type: item
description: 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.
subject: Articles
  topic: section
subject: Climate variability
  topic: keywords
subject: Temperature dynamics
  topic: keywords
subject: Precipitation trends
  topic: keywords
subject: Quetta Valley
  topic: keywords
subject: CMIP6 projections
  topic: keywords
subject: SSP climate scenarios
  topic: keywords
creator: Muhammad Raheel Bawani
creator: Asad Ayub Rajput
rights: Copyright (c) 2025
date: 2025-09-30
publication date: 2025-09-30
starting page: 38
ending page: 57

ENHANCING WHEAT (TRITICUM AESTIVUM L.) PERFORMANCE THROUGH COMBINED USE OF BIOFERTILIZERS AND ZINC SULPHATE
type: item
description: 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.
subject: Articles
  topic: section
subject: Wheat
  topic: keywords
subject: Biofertilizers
  topic: keywords
subject: Zinc supplementation
  topic: keywords
subject: Growth traits
  topic: keywords
subject: Yield performance
  topic: keywords
creator: Waheed Ahmad
creator: Uzair Ahmad
rights: Copyright (c) 2025
date: 2025-09-30
publication date: 2025-09-30
starting page: 24
ending page: 37

LOW-THD SINGLE-PHASE FULL-BRIDGE INVERTER WITH PI REGULATION, 10 KHZ SPWM CONTROL, AND LC FILTERING FOR 110 V RMS, 60 HZ OUTPUT
type: item
description: 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.
subject: Articles
  topic: section
subject: Closed-loop proportional–integral (PI) control
  topic: keywords
subject: DC–AC conversion
  topic: keywords
subject: High-frequency switching
  topic: keywords
subject: LC output filter
  topic: keywords
subject: Single-phase full-bridge inverter
  topic: keywords
subject: Sinusoidal pulse-width modulation (SPWM)
  topic: keywords
subject: Total harmonic distortion (THD)
  topic: keywords
subject: Voltage regulation
  topic: keywords
subject: Renewable energy systems
  topic: keywords
subject: Uninterrupted power supplies (UPS)
  topic: keywords
creator: Shah Faisal Afridi
rights: Copyright (c) 2025
date: 2025-09-30
publication date: 2025-09-30
starting page: 10
ending page: 23

A FRAMEWORK FOR ENHANCING CYBERSECURITY IN INTERNET BANKING SYSTEMS
type: item
description: 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.
subject: Articles
  topic: section
subject: Cybersecurity
  topic: keywords
subject: Cyber Defense Architecture
  topic: keywords
subject: Digital Information Security
  topic: keywords
subject: Online Banking Security
  topic: keywords
subject: Fraud Prevention
  topic: keywords
subject: Data Protection
  topic: keywords
creator: Mujahid Hussain
creator: Nashitah Alwaz
creator: Muhammad Musawir Saeed
rights: Copyright (c) 2025
date: 2025-09-30
publication date: 2025-09-30
starting page: 1
ending page: 9

IMPROVING SOLAR ENERGETIC PARTICLE PREDICTION WITH MACHINE LEARNING
type: item
description: 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
subject: Articles
  topic: section
subject: Machine Learning (ML)
  topic: keywords
subject: Solar Energetic Particles (SEPs)
  topic: keywords
subject: Space Weather Prediction.
  topic: keywords
creator: Muhammad Amir
rights: Copyright (c) 2025
date: 2025-06-30
publication date: 2025-06-30
starting page: 45
ending page: 56

SAFESPEAK: AN ANONYMOUS DIGITAL MENTAL HEALTH COMPANION FOR PAKISTANI TEENS
type: item
description: 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
subject: Articles
  topic: section
subject: Safe speak
  topic: keywords
subject: adolescents
  topic: keywords
subject: Mental health and Natural Language Processing
  topic: keywords
creator: Sasuee Rajper
rights: Copyright (c) 2025
date: 2025-06-30
publication date: 2025-06-30
starting page: 40
ending page: 44

AI-DRIVEN PREDICTIVE MAINTENANCE FOR ROAD TRANSPORT INFRASTRUCTURE
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: Convolutional Neural Networks
  topic: keywords
subject: Light Detection and Ranging
  topic: keywords
creator: Muhammad Umer
rights: Copyright (c) 2025
date: 2025-06-30
publication date: 2025-06-30
starting page: 30
ending page: 39

NLP FOR CYBERSECURITY: TEXT ANALYSIS IN THREAT INTELLIGENCE
type: item
description: 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
subject: Articles
  topic: section
subject: Natural Language Processing
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Threat Intelligence
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Pakistan
  topic: keywords
subject: Multilingual AnalysisX
  topic: keywords
subject: Text Mining
  topic: keywords
creator: Amir Ali
rights: Copyright (c) 2025
date: 2025-06-30
publication date: 2025-06-30
starting page: 19
ending page: 29

ONTOLOGY-BASED REAL-TIME SENTIMENT ANALYSIS FOR PRODUCT REPUTATION
type: item
description: 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
subject: Articles
  topic: section
subject: Sentiment Analysis
  topic: keywords
subject: Semantic Web
  topic: keywords
subject: Ontology Modeling
  topic: keywords
subject: SPARQL Query
  topic: keywords
subject: E-commerce Reviews
  topic: keywords
subject: Product Reputation
  topic: keywords
creator: Zeeshan Raza
rights: Copyright (c) 2025
date: 2025-06-30
publication date: 2025-06-30
starting page: 1
ending page: 18

EVALUATING MODELS FOR ESTIMATING REFERENCE EVAPOTRANSPIRATION ACROSS PAKISTAN’S CLIMATIC ZONES
type: item
description: 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
subject: Articles
  topic: section
subject: Reference Evapotranspiration
  topic: keywords
subject: NASA-POWER data
  topic: keywords
subject: Penman-Monteith FAO56
  topic: keywords
creator: Zia Ul Haq
rights: Copyright (c) 2025
date: 2025-03-31
publication date: 2025-03-31
starting page: 54
ending page: 67

SMART AVIATION: SECURE, COLLABORATIVE FLIGHT DELAY PREDICTION WITH MACHINE LEARNING
type: item
description: 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
subject: Articles
  topic: section
rights: Copyright (c) 2025
date: 2025-03-31
publication date: 2025-03-31
starting page: 43
ending page: 53

QUANTUM COMPUTING FOR TEST OPTIMIZATION IN SAFETY-CRITICAL SYSTEMS
type: item
description: 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.
subject: Articles
  topic: section
subject: QUANTUM COMPUTING FOR
  topic: keywords
subject: TEST OPTIMIZATION IN SAFETY
  topic: keywords
subject: -CRITICAL SYSTEMS
  topic: keywords
creator: Dr. Kashif Saghar
rights: Copyright (c) 2025
date: 2025-03-31
publication date: 2025-03-31
starting page: 31
ending page: 42

EMPOWERING PATIENTS WITH DIGITAL SECOND OPINIONS: A MULTI-DOCTOR REVIEW PLATFORM PROPOSAL
type: item
description: 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
subject: Articles
  topic: section
subject: EMPOWERING PATIENTS WITH
  topic: keywords
subject: DIGITAL SECOND OPINIONS
  topic: keywords
subject: A MULTI-DOCTOR
  topic: keywords
subject: REVIEW PLATFORM PROPOSAL
  topic: keywords
creator: Saba Baloch
rights: Copyright (c) 2025
date: 2025-03-31
publication date: 2025-03-31
starting page: 28
ending page: 30

KNOWLEDGEBOT: ENHANCING CHATBOT INTELLIGENCE VIA FEDERATED LEARNING ON WIKIPEDIA LLM
type: item
description: 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.
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Chatbot
  topic: keywords
subject: Factual Checking
  topic: keywords
subject: Multi-hop Retrieval System
  topic: keywords
subject: Wikipedia
  topic: keywords
subject: Medical Dataset
  topic: keywords
creator: Rayyan Shabbir
rights: Copyright (c) 2025
date: 2025-03-31
publication date: 2025-03-31
starting page: 12
ending page: 27

FINANCIAL FEASIBILITY OF SMART SAFETY TECHNOLOGIES IN ELECTRICAL ENGINEERING PROJECTS: CURRENT STATUS AND FUTURE PROSPECTS
type: item
description: 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.
subject: Articles
  topic: section
subject: Smart safety technologies, electrical engineering projects
  topic: keywords
subject: financial feasibility
  topic: keywords
subject: implementation costs
  topic: keywords
subject: return on investment
  topic: keywords
subject: cost-benefit analysis
  topic: keywords
subject: safety economics
  topic: keywords
subject: smart technologies ROI
  topic: keywords
subject: electrical safety standards
  topic: keywords
subject: implementation challenges
  topic: keywords
creator: Tehmina Aslam
rights: Copyright (c) 2025
date: 2025-03-31
publication date: 2025-03-31
starting page: 1
ending page: 11

IDENTIFYING PREGNANCY LOSS RISK FACTORS USING MACHINE LEARNING
type: item
description: 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.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: 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)
creator: Sajid Hafeez
rights: Copyright (c) 2024
date: 2024-12-31
publication date: 2024-12-31
starting page: 63
ending page: 73

OPTIMAL LINK PREDICTION IN COMPLEX HUMAN NETWORKS VIA STRUCTURAL PATTERN ANALYSIS
type: item
description: 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.
subject: Articles
  topic: section
subject: Link Prediction
  topic: keywords
subject: Social Networking
  topic: keywords
subject: Complex Network
  topic: keywords
creator: Zulfiqar Ali
rights: Copyright (c) 2024
date: 2024-12-31
publication date: 2024-12-31
starting page: 47
ending page: 62

CMIP6-BASED ANALYSIS OF HISTORICAL AND FUTURE TEMPERATURE AND RAINFALL TRENDS IN QUETTA VALLEY, PAKISTAN
type: item
description: 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.
subject: Articles
  topic: section
subject: Climate change
  topic: keywords
subject: temperature trends
  topic: keywords
subject: rainfall patterns
  topic: keywords
subject: Quetta Valley
  topic: keywords
subject: CMIP6
  topic: keywords
subject: SSP scenarios
  topic: keywords
creator: Saqib Mahmood
rights: Copyright (c) 2024
date: 2024-12-31
publication date: 2024-12-31
starting page: 26
ending page: 46

LOW-COST TWO-FINGER LINKAGE-DRIVEN EXOSKELETON FOR HAND REHABILITATION: DESIGN AND PROTOTYPE
type: item
description: 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.
subject: Articles
  topic: section
subject: Hand Exoskeleton
  topic: keywords
subject: Rehabilitation Robotics
  topic: keywords
subject: Physiotherapy
  topic: keywords
subject: Assistive Devices
  topic: keywords
subject: Post-Stroke Recovery
  topic: keywords
subject: Low-Cost Design
  topic: keywords
subject: Linkage Mechanism
  topic: keywords
subject: Servo Motor Actuation
  topic: keywords
subject: Frugal Innovation
  topic: keywords
creator: Safiullah Samo
rights: Copyright (c) 2024
date: 2024-12-31
publication date: 2024-12-31
starting page: 1
ending page: 12