International Journal of Innovations in Science & Technology
International Journal of Innovations in Science and Technology (ISSN 2618-1630) is an international peer-reviewed and "Open Access" journal designed to explore advances in various disciplines of Science and Technology wherein computer-based algorithms and syntaxes have been utilized. The researchers can submit their field observations and the results of laboratory experiments to IJIST within "Aims and Scopes" of the journal. The journal publishes issues quarterly. Rapid Publication: manuscripts are transparently reviewed by international and local reviewers and the first decision is provided to authors within 10-15 Days. High Visibility: indexed in HEC, MDPI, REPEC and other database...... Recognition of Reviewers: Reviewers providing timely decisions and thorough peer-review reports are acknowledged with a discount in Article Processing Charges.
Assessment of Soil Fertility in Jhelum, Punjab, Pakistan, using Geospatial Technologies
Soil fertility is a key factor influencing agricultural productivity and sustainability. This study evaluates the spatial distribution of essential soil chemical properties-pH, electrical conductivity (EC), available phosphorus (P), available potassium (K), organic matter (OM), and saturation percentage in Tehsil Jhelum, Pakistan. A total of 160 topsoil samples (0–15 cm depth) were collected using random sampling, with GPS coordinates recorded for each site. Laboratory analysis was conducted to assess the nutrient status of the soils, and Ordinary Kriging interpolation was used within a GIS framework to generate spatial distribution maps. The findings revealed notable variability across the region. Soil pH ranged from 4.3 to 7.8 (mean: 7.44), indicating mostly neutral to slightly alkaline conditions. EC values ranged from 0.49 to 1.40 dS/m, suggesting low to moderate salinity. Available phosphorus varied between 1.2 and 7.8 mg/kg, while available potassium ranged from 60 to 180 mg/kg, showing moderate fertility levels. Organic matter content was uniformly low (0.20–0.66%), with a mean of 0.42%, highlighting poor organic inputs. Saturation levels varied from 22% to 72%, displaying a layered spatial pattern. The spatial heterogeneity observed in soil nutrients underscores the need for site-specific nutrient management and precision agriculture practices. The generated maps serve as valuable tools for farmers, agronomists, and policymakers to make informed decisions aimed at improving crop productivity and maintaining soil health in the region.
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An Innovative Machine Learning (ML) Approach in Fabric Defect Detection and Quality Assurance
The garment and textile industries are essential sectors that significantly contribute to a nation's economic development. Fabric defect detection is a complex problem in the textile and technology industries since the efficacy and efficiency of automatic defect detection determine the quality and cost of any textile product. In the past, the textile industry used manual labor to find flaws in the fabric production process. The primary disadvantages of the manual fabric flaw identification technique are human weariness, lack of focus, and time consumption. This article introduces an innovative automated system for detecting garment defects powered by machine learning to revolutionize the traditional system and replace the manual inspection system. This innovative advanced system is trained and assessed using the 500-image dataset from the Artistic Milliners Company in Pakistan. The machine learning algorithm and image processing techniques form the foundation of AI technology, offering the best flaw detection accuracy. This work presents an automated fabric defect detection system driven by a supervised machine learning algorithm, i.e., SVM, that can accurately and precisely detect "hole" and "stain" faults. The system achieves a 72% precision and 74% recall for holes and an 85% precision and 83% recall for stains by utilizing a machine learning algorithm, i.e., SVM. The proposed method throws up vital issues like scalability and fabric sort flexibility. Compared to traditional manual processes, this new method lowers inspection costs by 65%, increasing productivity and setting a standard for automated and sustainable textile quality monitoring.
Artificial Intelligence-Augmented Intrusion Detection Systems for Advanced Threat Taxonomy in Cloud Computing Environments
Over the past few decades, cyber-attacks have emerged as a grave form of criminal activity and a subject of intense scholarly and policy debate. The rapid proliferation of cloud computing services— particularly Software as a Service (SaaS)—has further motivated research to classify security threats and their corresponding countermeasures. Scholars have increasingly focused on the risks, vulnerabilities, and malicious intrusions inherent in such environments, with particular emphasis on MITM (MITM) attacks and their mitigation and detection mechanisms. Host-based virtual software has demonstrated considerable efficacy in detecting malware within localized environments. Building on this foundation, the present study classifies Man-in-the-Middle (MITM) attacks in SaaS platforms through the deployment of Cloud-based Intrusion Detection Systems (CIDS). Our investigation concentrates specifically on attacks that target cloud hosts deployed within SaaS infrastructures. The proposed methodology incorporates the roles of the source cloud, destination cloud, and directional flow of the attack vector. In this context, the cloud ecosystem is understood as a dynamic environment where any participating entity, equipped with sufficient technical expertise, may both launch and be subjected to sophisticated intrusions. Accordingly, adaptive CIDS monitoring architectures are essential to safeguard communication between cloud actors. Moreover, CIDS frameworks furnish modular components capable of aggregating alerts, conducting analysis, and notifying administrators of potential breaches. To further illustrate the threat landscape, we present a statistical analysis of vulnerabilities most frequently exploited in MITM scenarios. This classification not only highlights the evolving tactics of adversaries but also equips readers with a structured understanding of MITM attacks, thereby fostering greater familiarity with contemporary cloud security challenges.
Optimization of Production Planning with Python’s SciPy: A Computational Study
Production planning optimization is the act of effectively distributing limited resources, including labor, materials, and equipment, to achieve production targets while optimizing profit and reducing waste. This study analyzes how optimization methods can be applied to production planning models in the cooking oil sector, with a particular emphasis on how linear programming (LP) can be used to handle usable quality limitations to maximize gross profit. The goal of this study is to find the best values for decision variables across a variety of inventory-based production frameworks. It is important in a manufacturing zone where input bound must be weighed against consumer needs, such as the industry of cooking oil. In order to provide a computational method for determining the perfect production levels, the study establishes a linear programming (LP) model and solves it using Python’s SciPy package. This optimization method uses objective functions involving dense matrices and numerical equations to solve the production planning problem. In calculating output levels and profit margins, the numerical results show a significant convergence, rating the effectiveness and credibility of the suggested approach in providing the optimal solution for practical industrial planning.
DECS: A Deep Learning Approach for EEG Channel Selection in Emotion Classification
The non-stationary nature of Electroencephalogram (EEG) signals often leads to high computational complexity in emotion recognition systems. To address this, we propose a novel framework that integrates optimal channel selection with efficient feature extraction. Our method begins by converting preprocessed EEG signals into two-dimensional spectrograms using a Continuous Wavelet Transform (CWT). These spectrograms are then processed by a GoogLeNet model for deep feature extraction. A key contribution is the Differential Entropy-based Channel Selection (DECS) technique, which identifies and retains the most informative channels. To manage dimensionality, the extracted features are encoded using the Bag-of-Deep-Features (BoDF) method, which employs k-means clustering to create a visual vocabulary and represents features as histograms. Finally, these histogram features are classified using a Support Vector Machine (SVM). Evaluated on the SJTU SEED and DEAP datasets, the proposed model achieves state-of-the-art classification accuracies of 95.1% and 81.1%, respectively, demonstrating its effectiveness and efficiency.
Design of a Novel Compact and High-Efficiency T-Slot Microstrip Antenna for 28 GHz
In this paper, a novel microstrip patch antenna incorporating a T-shaped slot in the radiating patch is proposed to achieve high radiation efficiency and excellent impedance matching for 28 GHz millimeter-wave 5G applications. Utilizing the Rogers RT5880 substrate with a dielectric constant of 2.2, loss tangent of 0.0009, and 0.8mm thickness, the proposed antenna achieves a radiation efficiency of 81.18%, total efficiency of 81.17%, and a peak gain of 7.23 dB over a 2 GHz impedance bandwidth (27–29 GHz). A T-shaped slot is incorporated in the radiating patch to enhance impedance matching and bandwidth. Comparative analysis across ten substrates demonstrates the superiority of Rogers RT5880 in balancing performance, cost, and compactness for mm Wave 5G applications. This innovative microstrip patch antenna design marks a significant advancement in the field, delivering enhanced performance tailored for 5G wireless communication systems.
Python and GLO-DEM Pixel-Based Hypsometry in Upper Indus Basin
Northern Pakistan’s Hunza and Shyok headwaters, where the Karakoram, Himalaya, and Hindukush ranges converge, host some of the largest mid-latitude valley glaciers outside the polar regions and play a decisive role in runoff and hazards of the Upper Indus Basin. Hypsometry provides a rapid, terrain-based approach to assess basin condition in such high mountain settings. In this study, a 30 m digital elevation model was used to delineate 31 sub-catchments, and for each unit, the hypsometric curve and hypsometric integral (HI) were derived. Methods were kept consistent across scales, with HI also recalculated on 1-4 km grid tiles, and spatial organisation tested through Global Moran’s I and the Getis-Ord Gi* statistic. Results reveal coherent belts of high HI aligned with the Main Karakoram Thrust, the Main Mantle Thrust, and the Karakoram Fault, indicating actively rising terrain and focused incision. Lower HI corridors occur in wider valley floors and recent fills, reflecting more mature landscapes and enhanced storage. HI distributions remain stable across tile sizes with mean values below one-half, while significant clustering confirms that these belts are intrinsic terrain signals. Harder crystalline and intrusive lithologies show higher HI on average, though wide variance suggests the combined influence of structure, rock strength, and relief. These geomorphic patterns carry direct hydrological meaning: high-HI belts imply fast translation of snow and ice melt with sharper seasonal peaks, whereas low-HI corridors favour storage and delay. Hypsometry, therefore, offers a cost-effective and reproducible tool for identifying active belts and providing priors for hydrological modelling and hazard planning in the Upper Indus Basin.
Remote Sensing and Machine Learning-Driven Flood Inundation Mapping of September 2025 Ravi Watershed Using Sentinel-1 SAR
Floods is among the most devastating natural hazards in South Asia. The September 2025 flood in the Ravi Basin was triggered by heavy monsoon rainfall and the release of water from cross-border dams. This study utilized Sentinel-1 SAR data, including both ascending and descending passes in VH polarization, to map flood inundation across the basin using a Random Forest classifier. Pre-flood and post-flood composites were prepared for April-May and 27 August to 5 September, respectively. The predictors feature includes VH_pre, VH_post, VH_diff, and VH_ratio. Terrain correction using the NASA DEM and landcover filtering with ESA WorldCover at 10m improved classification accuracy. Results showed that 1,885 km² of land was inundated, representing 5% of the total basin area. Approximately 260 settlements were impacted, including Dera Baba Nanak, Kartarpur, and the low-lying regions of Lahore. Croplands were the most affected class, with 1,610 km² flooded, followed by grasslands (90 km²) and sparse vegetation (62 km²). Built-up areas accounted for 0.7 km² of inundation, though the socio-economic impact was disproportionately high. Precipitation analysis from NOAA CPC confirmed rainfall clustering in the Sialkot and Narowal corridor. The peaks exceeding 800 mm/day cause this region as the epicenter of the flood. News reports corroborated satellite findings, noting that over 2.5 million displaced and more than 100 lives were lost. The study highlights how tributary floods involving the Ravi, Sutlej, and Chenab are emerging as severe hazards for Punjab. Findings underline the need for improved monitoring, resilient agricultural strategies, and disaster preparedness to mitigate future economic and food security risks.
A Hybrid Machine-Learning Framework for Intrusion Detection: Comparative Evaluation and Statistical Validation
The increasing sophistication and frequency of cyberattacks have intensified the need for Intrusion Detection Systems (IDS) that are both accurate and adaptive. Traditional IDS, whether signature-based or anomaly-based, provides foundational protection but faces well-documented limitations: signature-based systems struggle against zero-day exploits, while anomaly-based systems often produce high false positive rates. To address these challenges, researchers and practitioners are increasingly turning to Machine Learning (ML) as a means of enhancing IDS capabilities. This paper explores the integration of ML techniques—supervised, unsupervised, and deep learning—into IDS frameworks and evaluates their effectiveness using widely recognized datasets, including NSL-KDD and CICIDS2017. Supervised learning methods such as Random Forest and Support Vector Machines (SVM) demonstrate strong classification abilities, while unsupervised clustering approaches offer promise in identifying novel attacks. Deep learning models, particularly Recurrent Neural Networks (RNNs), show state-of-the-art performance in capturing sequential traffic patterns and detecting subtle anomalies. In addition to model comparisons, this study emphasizes the practical relevance of ML-enhanced IDS by examining its integration with established tools like Snort and Zeek. Our results highlight that ML-driven IDS consistently outperforms traditional approaches, with RNNs and Random Forest achieving the highest balance of accuracy and efficiency. The findings underscore the potential of ML-based IDS to serve as the next frontier in cybersecurity, offering improved detection accuracy, reduced false alarms, and adaptability to evolving threats. At the same time, challenges remain in terms of dataset representativeness, computational demands, and the interpretability of deep learning models. By situating the analysis within both academic research and real-world deployment contexts, this paper contributes to a clearer understanding of the opportunities and trade-offs in advancing IDS through machine learning.
Socially Shared Metacognition of Students in Computer-Supported Programming Tasks and Their Stance on the Difficulty of the Task
The internet has brought much emphasis to online collaborative learning, where learning is connected to co-constructing understanding and knowledge about subjects and tasks through collaboration and conversation. This research centers on several groups of students undertaking a programming project in a Zoom-based environment” or “via Zoom meetings. The paper proposes that socially shared metacognition is most effective in group-based problem-solving. It is a process in which one member of the group helps regulate the whole group’s process of solving a problem and elicits other members’ reactions to this proposal. The feeling of difficulty in performing the task helps ascertain and display the role of group interaction in individual learning. The paper also proposes that the increase in socially shared metacognition decreases the level of difficulty of a problem and thus alleviates individuals’ feelings of task difficulty.
Deep Learning Based Sentiment Analysis on Instagram Insights of Consumer Behavior for Improving Business Decision Making
The increasing use of social media platforms such as Instagram has made them a significant source of consumer insights for businesses, highlighting the importance of automated sentiment analysis. This study aims to address the challenge of accurately classifying consumer sentiments in Instagram posts, where informal language, slang, and sarcasm often reduce the effectiveness of traditional models. To overcome this gap, two deep learning approaches were employed: a Bidirectional Long Short-Term Memory (BiLSTM) network as a classical recurrent baseline and transformer-based architectures (BERT and DistilBERT) as state-of-the-art models. A dataset of 184,010 Instagram posts was preprocessed, tokenized, and mapped into positive and negative sentiments, and the models were trained and evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrices. The results demonstrated that BERT achieved the highest performance with an accuracy of 0.91 and an F1-score of 0.91, outperforming BiLSTM (accuracy 0.87, F1-score 0.86), while DistilBERT provided a competitive balance between accuracy (0.89) and efficiency. These findings confirm that transformer-based models, particularly BERT, are better suited for capturing nuanced sentiments in social media text. The study concludes that models can provide actionable insights into consumer behavior, enabling businesses to enhance brand monitoring and customer engagement.
Artificial Intelligence Meets Endocrinology: A Machine Learning-Based Approach to Thyroid Disease Diagnosis Using Feature Selection Methods
Thyroid Disease (TD) arises when the thyroid gland either grows abnormally or does not generate enough thyroid hormones, and might cause serious health issues and consequences. Early and efficient identification of thyroid disease is important for improved clinical intervention and disease management. By combining sophisticated and advanced machine learning models with a range of advanced feature selection strategies, this research study aims to enhance the classification of thyroid disease based on a machine learning based diagnostic system. The preprocessed dataset used in this study and the trials were taken from the machine learning repository at the University of California, Irvine (UCI). We employ two popular feature selection techniques- Chi-Square, and Recursive Feature Elimination, and a dimensionality reduction technique Linear Discriminant Analysis (LDA), and to choose the best features from the dataset for experiments. After selecting the most suitable features, they were then used to train and test the machine learning models: Multi-Layer Perceptron (MLP), Gradient Boost (GB), and Recurrent Neural Network (RNN). Evaluation matrices, accuracy, precision, recall, and F1-score were used to assess models' performance. The experimental results show that the machine learning model Gradient Boost (GB) outperformed the other models and yielded an accuracy of 99%, indicating its ability to classify the Thyroid Disease (TD) accurately. The proposed research work helps to create an intelligent decision-support system for medical diagnostics by offering an understandable and reliable framework for Thyroid Detection.
Industry 5.0: An Energy-Efficient Smart Task Offloading Mechanism for Multi-Access Edge Computing
The industry 5.0 heralds a transformation of industrial systems by integrating artificial intelligence (AI), the Industrial Internet of Things (IIoT), and Multi-Access Edge Computing (MEC) to foster resilience, efficiency, and sustainability. However, managing the massive volume of computation-intensive tasks generated by heterogeneous IIoT devices presents major challenges, particularly in optimizing both latency and energy consumption under dynamic industrial conditions. This research proposes a hybrid task offloading framework Computational Genetic Particle Swarm Optimization Algorithm (CGPCA) to intelligently balance energy efficiency and latency in MEC-enabled IIoT networks. CGPCA integrates the global search capability of Genetic Algorithms (GA) with the fast convergence of Particle Swarm Optimization (PSO), forming a two-layer optimization approach for effective task-device associations and power-bandwidth allocation. The framework is evaluated using iFogSim and Edgelands simulation environments, reflecting realistic industrial scenarios with variable workloads, device capabilities, and server conditions. Results indicate that CGPCA reduces average latency by up to 24%, lowers energy consumption by 18–25%, and maintains a task offloading success rate of 94% surpassing conventional GA, PSO, and heuristic baselines. The framework also achieves improved load balancing and faster convergence time, confirming its suitability for time-sensitive and energy-constrained IIoT environments. This study contributes to the realization of Industry 5.0 by offering an adaptive, intelligent solution that enhances computational efficiency while supporting sustainable and human-centered industrial automation. Future directions include extending CGPCA to highly mobile IIoT contexts and integrating predictive analytics for further performance gains.
Analysis of Periodic Permeability on Free Convective Three-Dimensional Flow with Cattaneo-Christov heat transfer and Slip Effect
The present research paper contributes the slip effects on a three-dimensional viscous fluid flow for free convective boundary conditions with periodic permeability. Free convection fundamentally involves some heat transfer methods. In this work, the Cattaneo-Christov heat transfer method has been employed to develop the knowledge of heat transfer actions in complex flow porous system with periodic permeability. Moreover, the impact of the slip effect is investigated to more effectively deal with the boundary conditions. The mathematical model has designed for incompressible, viscous and laminar flow with free stream specifications. By using the regular perturbation approach, governing highly nonlinear partial differential equations are transferred into the ordinary differential equations in linear form together with certain linear partial differential equations. The separation variables approach is then used for transforming the linear PDEs to ODEs. Analytical solutions are obtained for the pressure, velocity field, components of skin friction, and temperature field. The influence of physical attributes existing in the mathematical representation of the physical occurrence is investigated and illustrated. Both the slip parameter and the Cattaneo-Christov heat flux have an impact of thickness on the thermal boundary layer of observed fluid flow.
Design Evolution and Feature Enhancement Strategies for Advanced Digital Stethoscopes
A stethoscope is a fundamental auscultation and diagnostic device that plays a major role in medication and assists in the identification of sounds inside the body to predict cardiovascular and respiratory diseases. The advent of electronic and artificial intelligence (AI)-enhanced digital stethoscopes is prompted by the limitations of traditional auscultation performance, such as the need for a clinician's experience, failure to detect the required sounds in noisy conditions, and the inability to store patient data. This study focuses on the evolution in the design, relative performance characteristics, and areas of future improvement of stethoscopes, including digital devices that incorporate AI. Several studies show the employment of advanced filters to acquire important auscultating frequency bands, a high-gain amplifier to boost low-frequency internal body sounds, a noise cancellation circuit to block out background noise, Bluetooth for data sharing in real-time signal processing, and syncing with other medical devices. Key features that could be introduced in future versions are adaptive frequency filters, AI-based clustering to classify the sound, remote diagnostic functionality, and an improved data storage system. Protection circuits that take the form of lithium-ion batteries, wireless modules, and processing based on a microcontroller are some of the resource components highlighted in terms of portability and efficiency. This research seeks to develop stethoscopes by incorporating innovations while managing limitations, ultimately enhancing tools for healthcare professionals.
Impact of Empty Nest Syndrome on Parental Mental Health: Moderating Role of Coping Styles
When the kids depart, parents may experience empty nest syndrome (ENS), which is a depressing and negative emotional disturbance, and it in turn affects their mental health. According to studies, there is a need to study single parents and elderly parents who are living in shelter homes. According to attachment theory, the mental health (MH) of parents is greatly impacted by their children as an outcome of the bond between parents and their children. Coping styles assume a pivotal part in how the elderly adjust to the difficulties of (ENS) and keep up with their mental health. Thus, in the recent study moderating role of coping styles was studied. The research design that was used was a cross-sectional survey. A sample of 200 parents was collected, including single parents as well, through purposive sampling techniques. Individuals aged 60 years were included in the study. The Empty Nest Syndrome Questionnaire-Indian Form (ENS-IF), Mental Health Inventory (MHI-5), and Simplified Coping Styles Questionnaire, alongside the demographic data sheet and consent form, were administered. Collected information was analysed through SPSS and Process Macro using correlation, Regression, t-test, and moderation analysis. Future researchers can develop interventions to improve coping styles so that the mental health of empty-nest parents can be enhanced.
Bridging the Divide of Formal and Informal Transit in Urban Areas - Considering Multidimensional Aspects of Sustainability
Public transport in cities across the developing world is fundamentally shaped by the dualism of formal and informal services. Informal transport modes, including minibusses, shared taxis, and auto-rickshaws, are not merely supplementary but are essential components of the urban mobility ecosystem, providing critical connectivity for marginalized communities. Contemporary scholarship advocates for a multifaceted evaluation of these systems to capture their full socio-economic, environmental, and operational impact. This paper conducts a systematic literature review to synthesize existing assessment frameworks for public transport. The findings reveal a significant gap: current methodologies often fail to integrate the core dimensions of sustainability—social, economic, and environmental—with emerging imperatives like climate resilience and comprehensive regulatory and technological considerations. By mapping the state of the art, this review underscores the necessity for a more holistic evaluation paradigm, focusing on frameworks that move beyond a simple formal-informal divide to foster comprehensive understanding and strategic integration.
IoT-Enabled Smart Agriculture: Architectures, Applications, and Future Directions
Introduction/Importance of Study: The integration of Internet of Things (IoT) technologies into agriculture has become essential to tackle challenges of food security, climate change, and resource optimization. Novelty statement: This study introduces the novelty of a unified, low-cost, and modular IoT framework that addresses gaps in scalability, interoperability, and affordability, particularly in developing agricultural regions. Material and Method: A systematic literature review was conducted across IEEE Xplore, SpringerLink, ScienceDirect, MDPI, and Google Scholar, focusing on twelve peer-reviewed studies published between 2019 and 2024. Comparative thematic analysis was applied to examine IoT architectures, communication protocols, and practical implementations. Result and Discussion: Findings highlight that IoT systems commonly adopt a three-layer architecture (perception, network, and application), with LoRa, Zigbee, and fog computing models offering reliable rural connectivity. Reported outcomes include 30–40% water savings through smart irrigation, 15–20% yield increases with IoT-based monitoring, and up to 16% energy efficiency improvements in optimized wireless sensor networks. Despite these advances, challenges remain in cost, interoperability, farmer training, and security mechanisms. Current frameworks also lack adaptability across diverse farming contexts, limiting scalability and long-term sustainability. Concluding Remarks: IoT-enabled agriculture offers significant potential to enhance sustainability and productivity, but future research must prioritize modular platforms, lightweight AI integration, energy harvesting, and context-specific deployment strategies.
Bridging Global Frameworks and Local Realities: Towards Localizing the City Essentials Approach in Pakistan’s Urban Planning Systems
Cities across the Global South are increasingly exposed to compound and cascading risks—ranging from climate-induced disasters to governance, infrastructure, and institutional failures. Global frameworks such as the UNDRR Making Cities Resilient 2030 (MCR2030) Campaign, the City Resilience Index (CRI), UN-Habitat’s City Resilience Profiling Tool (CRPT), and the ISO 37123 Indicators for Resilient Cities have collectively redefined resilience as a governance-driven, system-wide process. However, their translation into the planning and institutional realities of developing countries remains partial and fragmented. This paper bridges these global frameworks with local contexts through a comparative synthesis that identifies areas of convergence—such as governance, preparedness, and coordination—and divergence in adaptability, innovation, and modularity. Focusing on Pakistan as a representative case, the study examines how the City Essentials Approach under MCR2030 can be embedded within national and local urban planning systems to operationalize resilience. Findings from the comparative review reveal that frameworks like MCR2030 and LGSAT align with Pakistan’s disaster management architecture (NDMA–PDMA), while data-intensive tools such as CRI and ISO 37123 remain constrained by limited institutional capacity. The paper proposes the City Essentials Localization Pathway (CELP) as a conceptual bridge to integrate global principles into local governance, enabling performance-based resilience assessment, policy coherence, and data-driven decision-making within Pakistan’s urban systems.
Performance Analysis of Task Distribution Mechanism in Multi-User’ Collaborative Assembly Task' In 3d Virtual Environment
CVEs are real-time, computer-simulated environments where two or more actors can mutually complete a task using synthetic objects. User performance is one of the major problems that arise due to coordination problems, a good mechanism to divide tasks, or less understanding or interaction among users collaborating. The impact of multi-user collaboration on using the task distribution mechanism remains unexplored. In this study, the impact of TDM on multi-users’ collaborative virtual environment is investigated. The TDM model assigns the task to collaborating users in CVEs on a static or dynamic manner. In static distribution, there exists weak coupling, and the amount of communication during the actual execution of a task is low, while in dynamic distribution, users are tightly coupled and hence need to communicate more. To study the effect of static and dynamic task distribution strategies on user’s performance in CVEs on multi users, a CVE prototype was developed using C++ and OpenGL, simulating an assembly task with distinct roles for multiple users, where twenty (20) group (each consists of two users) perform a task in collaboration under both strategies (static and dynamic) on two users and three users using arrow-casting and audio aids. The result shows that static with arrows-casting for two users takes an average time of 331.15 sec, and for three users, 321.45sec, and for audio (342.73sec and 326.34sec, respectively. Similarly, the dynamic with arrow casting for two users takes 347.76 sec, and for three users, 333.24 sec, and for audio, 350.12 sec and 344.4 sec, respectively. The findings provide valuable insights into how multi-user collaboration, task distribution methods, and cognitive aids can influence task efficiency and teamwork. However, when the number of users increased to three users, there is a chance that the performance will be degraded because, from the experimental data, a lower improvement was observed for three users than for two users. This research contributes to improving task management and collaboration in CVEs, with potential applications in training, education, and remote teamwork.
A Robust Deep Learning Model for Early Glaucoma Detection Using Retinal Imaging
The Glaucoma Detection System is developed in such a way that it can enable early diagnosis of glaucoma by incorporating the latest technology with the patient-centric healthcare paradigm. It uses a user-friendly interface written in the Tkinter language and a Convolutional Neural Network (CNN) model, and is mostly useful in processing medical images. The purpose of the methodology is to democratize ocular care, focus on the insidious nature of glaucoma, and emphasize the need to have a highly accurate CNN model to detect the disease at the earliest stage. The key features are preset structures and real-time image processing, which will speed up detection and allow healthcare professionals to prioritize severe cases. The system encourages the development of multimodal integration and feedback of data in order to promote efficacy, proactive eye health, as well as the principles of fair access to care.
Harnessing Unconventional Malware Detection Techniques to Equip Proactive and Resilient Cyber Defense Strategies Against the Constantly Changing Landscape of Sophisticated Cyber Threats
The rapidly changing intricacy of malware, specifically in the case of highly secured air-gapped networks, necessitates proactive and resilient detection mechanisms that are highly capable of detecting sophisticated, modern, and obfuscated malware. Instant work focuses on a model for malware detection that is vibrant and resilient and uses deep learning models to look at Windows API call patterns that come from executable files. The original dataset from Kaggle had a big class imbalance (malicious: 42,797; benign: 1,079), but the SMOTE approach helped balance the training data. In this regard, a comparison of seven deep learning models, including Simple ANN, MLP, DropConnect Improved ANN, Residual ANN, DenseNet ANN, RBF Network, and hybrid CNN-LSTM, has been conducted over both 50 and 150 training epochs on various metrics such as recall, accuracy, F1-score, precision, and ROC-AUC. As a result, the CNN-LSTM model, enhanced by an attention mechanism, exhibited superior efficacy in differentiating between benign and malicious samples. In this context, the accuracy improvement is minimal at +0.08%, but the most substantial increase in Class 0 recall is +4.1%, and the F1-score shows an enhancement of +2.7%. The most significant contribution of this study is the attention-augmented architecture that apparently diminishes interpretability and enhances focus on significant behavioral attributes.
Computation of Rotational Flow of the Sun Using Satellite Data and Doppler Shift Calculations
Solar rotational flow governs the Sun’s magnetic activity, space weather variability, and long-term dynamo processes. Traditional tracer-based techniques offer limited precision in mapping these flows, creating the need for direct spectroscopic velocity measurements. This study presents a computational framework for deriving full-disk Doppler velocity maps of the Sun using high-resolution Hα spectra from the Chinese H-alpha Solar Explorer (CHASE) mission. The H-alpha Imaging Spectrograph (HIS) data cube (2304 × 2313 × 46 pixels) was processed through a workflow of preprocessing, continuum normalization, Voigt profile fitting, and pixel-wise Doppler conversion to retrieve line-of-sight velocities. The resulting field of ~5.3 million pixels shows clear differential rotation, with blue shifts up to −7.89 km s⁻¹ on the approaching limb and red shifts up to +2.19 km s⁻¹ on the receding limb, corresponding to equatorial and polar rotation periods of ~25 and ~31 days, respectively. Localized asymmetries in active regions further reveal small-scale velocity perturbations. These results validate CHASE–HIS spectroscopy as a reliable tool for global solar flow diagnostics and highlight the utility of Voigt-based Doppler modeling in resolving fine-scale plasma dynamics. The developed approach bridges spectroscopic and Helio seismic methods, offering a reproducible foundation for future studies on solar dynamo modeling and space weather prediction.
Digital Credibility and Social Gratification: Understanding How Generation Z in Pakistan Engages with Misinformation and Algorithmic Influence in the Contemporary Social Media Landscape
In the contemporary digital landscape, Generation Z increasingly relies on social media as a primary source of information, communication, and self-expression. While these platforms foster connectivity, learning, and creativity, they also amplify the circulation of misinformation due to limited regulation and inadequate fact-checking practices. This study investigates the motivations and behavioral patterns of Generation Z in Pakistan concerning online information engagement, focusing on the balance between social gratification and information credibility. Employing a qualitative exploratory design, data were collected through five focus group discussions (FGDs) comprising 25 participants across diverse academic disciplines, including Media Studies, Art & Design, Computer Science, Business Administration, and Allied Health Sciences. Thematic analysis revealed that social validation and entertainment are dominant motivators for content sharing, whereas critical evaluation and fact-checking remain secondary concerns. Instagram and WhatsApp emerged as the most frequently used platforms, followed by X (formerly Twitter), TikTok, and Facebook. Although participants acknowledged the prevalence of misinformation, only 52% consistently verified content prior to sharing. The study highlights how algorithmic reinforcement and emotional engagement contribute to selective exposure and echo chambers, intensifying the challenge of discerning credible information. Findings underscore the need for comprehensive digital literacy initiatives that integrate fact-checking, ethical sharing, and critical thinking into educational frameworks. The research contributes to the broader discourse on media ethics, algorithmic influence, and the sociocognitive dimensions of digital engagement among youth in developing contexts.
Interactive Impacts of Heavy Metals and Soil Amendments on Enzymatic Activities and Microbial Biomass
Both organic and inorganic soil additives are frequently used to increase the bioavailability of lead (Pb) and cadmium (Cd) in polluted soils, but these amendments may also affect microbial activity in soils by modifying heavy metal solubility. This research assessed the influence of different soil additives on enzymatic activity and the solubility of Pb and Cd in spiked soils. Soils were spiked with Pb (0, 1000, 1500 mg kg⁻¹) and Cd (0, 100, 150 mg kg⁻¹) artificially. Incubation experiments were carried out with various amendments, such as citric acid (CA; 0, 10 mmol kg⁻¹), ammonium nitrate (AN; 0, 10 mmol kg⁻¹), EDTA (0, 5 mmol kg⁻¹), compost (CO; 0, 10%), and titanium dioxide nanoparticles (TNPs; 0, 100 mg kg⁻¹). The microbial biomass carbon (Cmic) and dehydrogenase activity (DHA) declined by 66% and 47% in Pb₁₅₀₀, and by 54% and 35% in Cd₁₅₀ treatments, respectively. In control soil, compost addition gave the highest value of Cmic and DHA, followed by TNPs, CA, AN, and EDTA. But the mixed application of Pb, Cd, and soil additives caused an overall reduction in microbial activity. Among all the treatments, EDTA alone and in combination with Pb and Cd showed maximum toxicity to soil microorganisms.
Data Mining for Smarter Administration of TVET Institutes
Retaining the trainees is a major problem for the TVET institutes today. The trend of TVET education in Khyber Pakhtunkhwa province has improved in recent decades. Despite its high cultural barriers, resistance in women's education, and dropout rates, on the basis of annual admission, Khyber Pakhtunkhwa holds the 2nd position among all other provinces of Pakistan. In this research, we have tried to decrease the dropout ratio by enhancing the Daily attendance of the trainees and improving their results. Monthly Fee slip, Date Sheet, and Results will be shared with the parents/guardians through SMS/WhatsApp. New TVET institutes will be able to check the trainee's educational record from the previous TVET institute. The Data Mining for Smarter Administration of TVET Institutes will be a Mobile and Web-Based Application and will keep a close relationship between Parents, Teachers, and Administration of the TVET institute.
Advanced Deep Learning-Based Potato Defect Identification Leveraging YOLOv8 for Smart Agriculture
This paper presents the design of an effective deep learning model to identify and rank potato defects, enabling intelligent farming and post-harvest tasks. The primary goal is to automate the quality measurement of potatoes in several categories: healthy, damaged, defective, fungal-diseased, and sprouted, with the help of an optimized YOLOv8 model. The data set on potato images was annotated and gathered in the real-world agricultural conditions in a wide variety of images. Data augmentation and transfer learning were used to train the model and enhance generalization and detection rates in different conditions. The experiment showed that the detection performance was high and it reached 95.3% training accuracy, 93.8% validation accuracy, and 92.5% test accuracy with an F1-score of 92.9. The results verify that the suggested approach plays a crucial role in detecting defects in potatoes in real time, which can be used to support comprehensive, computerized, and accurate agricultural surveillance.
Improving Software Security Through an LLM-Based Vulnerability Detection Model
The risks to modern digital infrastructures posed by software vulnerabilities are critical and include data breaches, unauthorized access, and losses in revenue. Although traditional static and dynamic analysis tools are effective in discovering vulnerability patterns, they are not able to recognize complex, context-dependent, logic-based, and security-embedded flaws that evolve within software systems. This research offers a Large Language Model-based Vulnerability Detection Model (LLM-VDM) focused on enhancing software security with intelligent, context-aware code analysis. Leveraging transformer-based architecture adapted to the Juliet, Big-Vul, and Devign benchmark datasets to assess the performance and integration of code semantic and code contextualization methods, the proposed model was evaluated. Experimental results demonstrated LLM-VDM’s superiority to both baseline and deep learning competitors SonarQube, Devign, CodeBERT, and CodeT5, attaining 91.2% accuracy, 90.0% F1-score, and 0.94 AUC. Furthermore, the integrated explainability module improves explainability by pinpointing vulnerable code and outlining remediation strategies. The findings showed LLM-based technology provides software developers with more secure, adaptive, explainable, and scalable systems, meeting the needs of contemporary software development.
Microcontroller-Based Automated Cloth Folding Machine for Domestic and Industrial Textile Applications
Manual cloth folding is a repetitive and time-consuming process that limits productivity and efficiency in both domestic and industrial contexts. This work presents the design and implementation of a microcontroller-based automatic T-shirt folding machine aimed at achieving a fully autonomous operation without manual intervention. The proposed system employs lightweight acrylic boards for structural strength and portability. It integrates sensors to ensure precise garment detection and controlled folding operations. Experimental evaluation demonstrates a folding time of 9.2 seconds per shirt, ensuring operational efficiency and precision. The system effectively addresses the limitations of previous designs, particularly in terms of automation, sensing capability, and user safety, and provides a scalable foundation for future advancements in automated garment handling.
Analysis of Web Application Security: Integrating WAF and SSL/TLS for Enhanced CMS Protection
Content Management Systems (CMS) such as WordPress, Joomla, and Drupal power a significant portion of the web, making them prime targets for cyber threats, including TLS downgrade attacks, SQL injection (SQLi), cross-site scripting (XSS), and brute force attempts. Traditional security mechanisms often fail to mitigate these sophisticated attacks, leading to data breaches and unauthorized access. This research implements a multi-layered security framework integrating Web Application Firewalls (WAFs), TLS 1.3 enforcement, AI-driven vulnerability detection, and enhanced security headers on a WordPress test environment. Security audits using OWASP ZAP, Nessus, and Burp Suite validated the effectiveness of each component. Results demonstrate an 80% reduction in brute force attacks, a 93% decrease in SQL injection attempts, and a 100% elimination of XSS vulnerabilities. The implementation of WAF filtering, real-time monitoring, and strict access controls significantly reduced the attack surface. This study provides a scalable, adaptive security model capable of evolving with emerging cybersecurity challenges, offering a vital contribution to web application security.
Auscultation-Based Pulmonary Disease Detection and Classification Using Deep Neural Networks
Pulmonary diseases like Pneumonia, Bronchiectasis, and Chronic Obstructive Pulmonary Disease cause a large number of deaths worldwide. For such diseases to be treated and managed effectively, an early and accurate diagnosis is essential. In this work, we propose a deep learning model based on Recurrent Neural Networks (RNN) that can detect three different pulmonary diseases, as well as healthy lung sounds, using only auscultation recordings. The model was trained using the ICBHI dataset, which contains 920 recordings from 126 people and covers more than 6,800 respiratory cycles. To uniform the data, the audios are padded to equal length. To tackle class imbalance in the dataset, augmentation techniques of Gaussian noise injection, time-shifting, and time stretching are used. We employ a simplified version of the Gated Recurrent Unit (GRU)-based RNN architecture to deal with the padded sequences, along with a dropout layer to avoid overfitting. The model is trained using the Adamax optimizer with categorical cross-entropy loss, along with a model checkpoint to ensure learning consistency. Apart from the evaluation of model accuracy, we also evaluated the F1-score, accuracy, and loss graphs to ensure the competitive performance of our approach. Out of the six different experiments, with different data variations and two different model architectures, the outperforming model exhibited an accuracy of 98.53%, a precision of 98.57%, a recall of 98.53%, and an F1-score of 98.52%.
Leveraging Machine Learning for Spreading Factor Optimization in Lora WAN Networks
The Internet of Things (IoT) has witnessed exponential growth and widespread integration across diverse sectors such as agriculture, logistics, smart cities, and healthcare. Among various IoT communication paradigms, the Long-Range Wide Area Network has emerged as a prominent and preferred technology, attributed to its extended transmission range, energy efficiency, and cost-effectiveness. Nevertheless, the escalating proliferation of IoT endpoints has amplified the complexity of efficient resource orchestration, particularly in Spreading Factor (SF) optimization within infrastructures. To mitigate this challenge, this study introduces a Machine Learning–driven Adaptive Data Rate (ML-ADR) framework for dynamic SF management. A Long Short-Term Memory (LSTM) neural network was meticulously trained using a dataset synthesized via ns-3 network simulations to achieve optimal SF classification. The pre-trained LSTM model was subsequently deployed on end-device nodes to enable intelligent and adaptive SF allocation using real-time data during simulation. Experimental evaluations reveal significant enhancements in packet delivery ratio and notable reductions in energy consumption, thereby validating the efficacy and scalability of the proposed ML-ADR approach.
Energy Harvesting Implementation in WBAN Routing Protocols with Multi-Relay Co-Operation
Mostly simulations are used to evaluate the performance of Wireless Body Area Networks (WBANs). The recent researches are focused on channel modelling and energy conservation at the Network/MAC layer. Normally, collaborative learning, path loss, and energy harvesting are ignored in these schemes of studies. In this research, we will try to use an Energy Harvesting (EH) mechanism to recharge the batteries instead of replacing them time and again. In contrast with the existing studies, the proposed scheme considers collaborative learning and energy harvesting. Cost functions are used to identify the most feasible wireless route from a given node to the sink while sharing each other’s distance and residual energy information. The human body temperature (thermal energy) and the pumping of the heart can be used for energy harvesting within the body, while solar energy can be used for energy harvesting of nodes on the human body.
UAV-Based Flood Mapping and Damage Assessment in Harnai Khawar, Swat, Khyber Pakhtunkhwa, Pakistan
Floods is among the most destructive hydrological hazards in Pakistan, particularly across the steep, data‑sparse basins of Khyber Pakhtunkhwa (KPK). The 2022 monsoon produced catastrophic damage in the Swat Valley, disrupting transport, irrigation, and housing. This article demonstrates an Unmanned Aerial Vehicle (UAV) workflow for rapid, high‑resolution flood mapping, damage quantification, and risk zonation in the Barwai Khwar sub‑watershed of the Swat River Basin. Pre‑event context was assembled from Google Earth Pro imagery (12 June 2022), and post‑event aerial surveys were flown using a DJI Phantom 4 Pro (v2.0) with GNSS‑supported Ground Control Points (GCPs). Imagery was processed in Agi soft Meta shape to generate Ortho mosaics and surface products, then analyzed in a GIS to delineate inundation, channel widening, structural damage, and agricultural losses. The floodplain width locally expanded from approximately 7 m to 76 m; damaged linear infrastructure includes ~2.18 km of retaining walls and 39 m of bridges. Surface impacts include ~6,271 m² of residential area and ~22.56 ha of cropland affected. The approach provided near‑centimeter spatial detail, enabling precise polygonal accounting for recovery planning and identification of high‑risk margins where unprotected construction coincides with steep banks and tight meanders. Findings confirm the value of UAV photogrammetry as a fast, replicable, and cost‑effective complement to satellite‑based disaster assessment in Pakistan’s mountain valleys, supporting preparedness, reconstruction, and resilient land‑use decisions.
Impact of Anticipatory Grief on Quality of Life among Caregivers of Thalassemia Patients: Mediating Role of Physical Activity
Caregivers of patients with chronic illness, such as thalassemia, face unique challenges in their lives. During the caretaking of the patient, caregivers face anticipatory grief due to the fear of loss. The goal of the current study was to investigate the impact of anticipatory grief on the quality of life of caregivers of thalassemia patients with the mediating role of physical activity. Differences across genders in a proposed relationship were also studied. Anticipatory grief scale (Theut et al., 1991), international physical activity questionnaire (Geneva, 1998) & quality of life scale (Flanagan, 1970), alongside the demographic sheet and written informed consent form, were employed for collecting data among 300 caregivers using cross cross-sectional survey research design that included multiple cities of Punjab. Results indicate that anticipatory grief has a significant negative relationship with quality of life and physical activity. Physical activity has a significant positive relationship with quality of life. Results also indicate that women show significantly higher levels of anticipatory grief than men. Men show a higher level of quality of life than women. The findings of the study provided strong empirical support for the predicted role of anticipatory grief on quality of life among caregivers. These findings further highlight that physical activity should be part of parents' lives to deal with grief related to the health of a child.
Transformers as the Foundation of Large Language Models: A Comprehensive Review
The transformation of Transformer architecture has led the way into a new era for NLP, as it broke the traditional RNNs, LSTMs, Seq2Seq models, etc. As their main feature, the Revolution of Transformers was the hybridization of self-attention and multiheaded attention, which allowed the models to learn dependencies across time spans of any length through positioning methods. This resulted in a quick and efficient process for training large-scale Language Models (LLMs) that could handle the data very well and simultaneously learn the long-term dependencies. This paper is titled "Transformers as the Foundation of Large Language Models: A Comprehensive Review", and it not only reflects but also presents a critically reviewed path taken by LLMs from BERT to GPT-4 and beyond, along with the better reasoning, arithmetic, and instruction following attributed to the scaling up of architecture. The review further indicates and discusses the current concerns regarding efficiency, bias, interpretability, and domain specialization, and warns that settling these issues might dictate the fate of T-bases improvements. The authors aim through this project to provide an exhaustive comprehension of the setting in which Transformers enabled LLMs and actively directed the development of contemporary AI research.
Unsupervised Detection of Credential Stuffing and Account Takeover Attempts through User Behavioral Biometrics in Web Applications
Credential stuffing and account takeover (ATO) attacks can still be stated as being one of the most ongoing risks of contemporary web applications, as they use reused credentials and defy traditional authentication procedures. Conventional supervised detection approaches rely on labeled attack data (which is frequently non-existent or incomplete) in the real world. In this paper, a credential stuffing and ATO adversarial behavior multi-layer detector is suggested through modeling user behavioral biometrics based on the web-session navigation patterns and chaining information during the login procedure. The framework combines Isolation Forest and Local Outlier Factor to train a normal behavioral distribution and detect deviations that suggest abnormal use of the accounts, which are either automated or reports of hacked accounts. Analysis of three different datasets RBA, MSNBC, and CERT Insider Threat, has shown that the framework is highly detection sensitive with an ROC-AUC of up to 0.936 and an F1-score of 0.842 in login-level anomaly detection, and cross-domain generalization on enterprise data. The findings validate the practicability of unsupervised behavioral modelling as a lightweight defense mechanism that is scalable and resistant to credential abuse. The suggested solution ensures that labelling is reduced, the privacy of users is maintained, and a scalable basic infrastructure is provided to accommodate the adaptive and risk-centric authentication models in massive web applications.
Effect of Different Drying Methods on Quality Characteristics of Wheat Grains
Wheat is one of the main staple cereals used worldwide, providing 50% of food energy and protein consumed globally. Drying, the oldest preservation technique used for agricultural products, is the removal of moisture to its certain desired requirement. The drying process aims to reduce the grain's moisture content for safe storage. The present study was carried out at the Department of Farm Structures and Postharvest Engineering, Sindh Agriculture University, Tandojam, Pakistan. Freshly harvested wheat grain varieties TD-1, Imdaad-2005, and Sindhu were collected from the Latif Experimental Farm, Sindh Agriculture University, Tandojam. The wheat grain samples were subjected to three different drying methods i.e. sun drying, convective drying, and intermittent drying. Data in terms of quality for wheat grain was observed initially, and then after being subjected to different drying methods. The results revealed that moisture content, length, width, thickness, thousand kernel weight, weight loss, bulk density, germination, and drying rate for all wheat varieties decreased with respect to time in all drying methods. The results in terms of quality after drying were observed to be better for intermittent drying, followed by convective and sun drying. The farmers and grain processing industries are suggested to adopt the intermittent drying method, which enhances grain quality and reduces energy consumption.
Hydraulic Modelling of Flood Assessment in Chenab Basin Pakistan
Flooding represents one of the most recurrent and economically damaging natural hazards in Pakistan, particularly within the Indus River system, where extensive floodplains and dense human settlement exacerbate vulnerability. This study presents an integrated flood-assessment framework combining optical remote sensing, geographic information systems (GIS), and physically based hydraulic modelling to delineate and quantify flood inundation in the Chenab Basin, Punjab. Multi-temporal Landsat 8 OLI imagery acquired during pre-, peak-, and post-flood stages was processed to derive the Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Water Ratio Index (WRI) for flood detection. Comparative accuracy assessment using field observations and high-resolution reference imagery demonstrated that MNDWI outperformed other indices, achieving an overall accuracy of 91% compared to 83% for NDWI and 85% for WRI. Supervised maximum-likelihood land-use/land-cover (LULC) classification yielded an overall accuracy of 91.6% with a Kappa coefficient of 0.89, confirming strong agreement between classified outputs and ground reference data. A 30 m SRTM-derived Digital Terrain Model was employed to develop a one-dimensional hydraulic model in HEC-RAS, simulating flood scenarios for return periods ranging from 2.5 to 100 years (455,000–1,665,000 cusecs) along the Chenab River reach between Head Trimmu and Head Panjnad. Modelled water-surface elevations showed close correspondence with GPS-recorded flood marks, with positional deviations below 50 m and sensitivity analysis indicating a maximum ±0.15 m variation in water level for ±0.01 changes in Manning’s roughness coefficient. Results indicate that approximately 68% of the study area was inundated during the 2010 flood, with cropland accounting for nearly 61% of the affected area and settlements for 18%. The integration of satellite-derived water indices with hydraulic simulation proved effective for accurate flood delineation and hazard zoning, providing a robust and operationally scalable framework for flood-risk assessment and spatial planning in data-scarce river basins of Pakistan.
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An Enhanced Similarity Measure–Driven K-Nearest Neighbor Framework for Categorical Data Classification
Machine learning provides effective answers to real-world classification issues by combining supervised approaches (e.g., regression, SVMs, decision trees, neural networks) and unsupervised techniques (e.g., clustering, PCA). Comparing categorical data to numerical data reveals that the former is still understudied. This study compares three variations of the K-Nearest Neighbors (KNN) algorithm, Dice Coefficient KNN (DKNN), Overlap Coefficient KNN (OKNN), and Simple Match Coefficient KNN (SMKNN) on three categorical datasets: Malware Detection, Hospital Readmission (Kaggle) and Mushroom (UCI Repository). Each variation improves classification performance by incorporating a unique similarity metric. Recall, accuracy, precision, and F1-score were used to evaluate the models. According to experimental results, SMKNN consistently performed better than the other variations, obtaining an average F1-score of 93.3%, accuracy of 88.29%, precision of 89.33%, and recall of 98%. With an F1-score of 91% and an average accuracy of 83.89%, OKNN came in second, while DKNN did worse with an accuracy of 73.74%. These results demonstrate the stability and promise of SMKNN as a dependable model for categorical data classification, highlighting its exceptional and flexible performance across a variety of datasets. The study gives useful information for identifying the best KNN variations for data-driven applications.
Half a Century of Warming in Punjab, Pakistan: Statistical Evidence from 1970–2019
Regional temperature gradients affect how climate change is defined and assessed from varying perspectives. In this research, temperature trends in the Province of Punjab from 1970 to 2019 were examined. To assess the changes in temperature, the monthly means of temperature (Tmean), maximum temperature (Tmax), and minimum temperature (Tmin) were analyzed using Sen's slope estimator method. Several empirical techniques are applied to assess whether the trends are indeed significant, either positively or negatively, and to what extent diversity exists among different weather stations. Also considered are the expected values in the determination of a comprehensive account of temperature fluctuation and variation. The analysis indicates a significant increase in the mean temperature (Tmean) across Punjab, with a sharper increase from Northern Punjab to Southern Punjab. While maximum temperature (Tmax) shows a steep increase in southern and western regions, minimum temperature (Tmin) shows a predominantly increasing trend in Central Punjab. These findings are going to be useful to those making national policy who are trying to formulate strategies for climate change mitigation and adaptation. This study examines long-term temperature trends in Punjab, Pakistan, from 1970 to 2019 using Mann–Kendall and Sen’s slope estimator methods. Results show a statistically significant warming trend, with mean temperature increasing at 0.04°C per year. Southern and Western Punjab experienced higher rates of warming compared to Northern regions. Maximum temperatures increased more sharply in the south, while minimum temperatures rose more prominently in central Punjab, indicating a declining diurnal temperature range. These findings highlight regional climate disparities and underscore the need for targeted adaptation strategies.
Smart Farming with AI: Comparative Evaluation of CNN Models for Tomato Leaf Disease Classification
Tomato is a major agricultural crop cultivated worldwide; however, its production is severely threatened by a wide range of plant diseases, necessitating accurate and timely detection methods. In recent years, neural network–based computer software and mobile applications have emerged as effective tools for plant disease detection. In this study, three advanced convolutional neural network (CNN) architectures—ResNet-50, DenseNet-121, and InceptionV3—are comparatively analyzed to evaluate their effectiveness in identifying and classifying tomato diseases using the PlantVillage dataset. To enhance model robustness against real-world variability, comprehensive image preprocessing and data augmentation techniques were employed, including rotation, horizontal and vertical flipping, rescaling, shear transformation, and zooming. A systematic hyperparameter tuning strategy was adopted by experimenting with various combinations of learning rates, batch sizes, and optimizers to optimize training performance. Experimental results demonstrate that hyperparameter optimization significantly improves classification accuracy, with the ResNet-50 model achieving the highest accuracy of 98.2%, along with superior F1-score, precision, and recall values. DenseNet-121 and InceptionV3 also exhibited strong performance, although their results were comparatively lower than those obtained with ResNet-50. These findings underscore the effectiveness of transfer learning and fine-tuning strategies in the development of automated systems for plant disease detection and classification. The study highlights the strong potential of CNN-based architectures for scalable and accurate disease detection, offering valuable support to farmers for early diagnosis and improved crop management. Furthermore, the study identifies future research directions, including deployment under real field conditions and the exploration of additional deep learning architectures.
EEG Based BCI for Intelligent Wheelchair Control System Using Deep Learning
This research study presents the design of an Electroencephalography (EEG) based Brain Computer Interface (BCI) for intelligent wheelchair control to assist patients with mobility disorders. The concept of this research is to enable a direct communication link between the human brain and the machine without physical movement. This study used the BCI Competition IV 2a dataset, which contains EEG recordings of nine subjects performing four motor imagery (MI) tasks that were mapped to wheelchair navigation commands such as turning left, right, moving forward, and stopping. In this study, a deep learning architecture, TCFormer (Temporal Convolutional Transformer), was implemented to learn the spatial and temporal correlations between EEG channels. A lightweight Fusion Head module was added to enhance performance. It consisted of one-dimensional convolution and adaptive pooling operations for improved local temporal feature extraction. The proposed TCFormer-Fusion model achieved an overall classification accuracy of 75%, outperforming the baseline TCFormer model by 72%. Overall, this research study demonstrates that transformer-based models can learn complex EEG signal representations for motor imagery classification. The proposed model contributes toward developing an intelligent wheelchair control system that operates on brain signals, reducing external assistance. This work, with further optimization and real-time implementation, can contribute significantly to the assistive technology and human-computer interaction fields.
Preliminary Medical Diagnosis Using Voice-Based Urdu Language Interface
Expert knowledge is stored in the knowledge base through an externalizing process in the form of facts, procedures, heuristics, and rules. The knowledge base helps to refine the present knowledge and insert new knowledge without recompiling a program. Medical diagnosis is one of the first knowledge-based areas in which expert system principles are applied. Almost all knowledge-based medical diagnostic systems take input symptoms in the form of text and rely on the English language. This is a hindrance to illiterate and non-native English speakers of developing countries to utilize the system, and unfortunately, Pakistan is one of them. In this connection, this paper proposed an indexing method for integrating the medical diagnostic knowledge base with a Pakistani National Language-based voice-oriented user interface for accommodating the illiterate.
Decoding Cognitive States and Emotions Using the Electroencephalogram
Emotions are essential in human communication, social interaction, and decision-making. However, accurately classifying emotions is difficult with many applications in various domains such as psychology, psychiatry, neuroscience, and human-computer interaction. Emotion detection is one of the key challenges in current research, especially when emotional words are used. It is already known that positive and negative words have an impact on human behaviour and emotions, but very rare study that focus on emotions based on the words. In this study, we propose a novel approach for emotion classification based on electroencephalogram (EEG) data elicited by text stimuli, which are various English words. Text stimuli can evoke rich and diverse emotions, but they have been less explored than other modalities for emotion elicitation. In this study, EEG data of 25 participants were used, which were collected using a 128-channel EGI system. The collected data was pre-processed, and features were extracted using four methods: Convolutional Neural Network (CNN), Wavelet Transform (WT), Power Spectral Density (PSD), and the raw data itself was used as features. The results showed that CNN features achieved an average accuracy of 80%, followed by WT with 75%, PSD with 72%, and raw data with 65%. Our study shows the feasibility and effectiveness of using CNN, PSD, and WT with SVM for emotion classification based on EEG data and text stimuli. Lastly, a hybrid model was proposed based on the combination of CNN for feature extraction and SVM for classification.
A Modified K-Nearest Neighbors Algorithm for the Detection of Heart Disease
The leading cause of mortality worldwide is heart disease, sometimes referred to as cardiovascular disease. It is a dangerous illness that impacts the heart and blood arteries. A significant amount of research and analysis has been done recently with the goal of improving the accuracy and dependability of heart disease data. In this discipline, machine learning is crucial since it provides medical diagnostic tools that may be used to forecast illness and enhance healthcare. In this study, heart disease detection is proposed by combining KNN with Jaccard and Cosine similarities. Further, the results of Jaccard and cosine integrated KNN are compared with the results of state-of-the-art models like KNN and decision trees. Python and its libraries are used for simulation purposes. After the simulation, it was found that Jaccard-based KNN (JKNN) had the best accuracy (97%) according to the study's analysis of the Cleveland heart disease dataset. With 91% accuracy, the Cosine-based KNN (CKNN) likewise demonstrated strong performance. In a similar vein, the decision tree is inadequate for classifying heart disease because of its poor accuracy rate as 85%. Likely, KNN shows average results in the form of accuracy, as 86%. According to the results, the JKNN technique is the best model for this task, closely followed by CKNN. The use of machine learning in the diagnosis and prognosis of heart disease is affected by these discoveries.
Quantifying Confidence in Diabetic Retinopathy Diagnosis: A Comparative XAI Study of Deep Learning and Bayesian Neural Networks
Diabetic Retinopathy remains the primary microvascular complication of diabetes and a leading cause of irreversible blindness globally. While deep learning models offer high diagnostic accuracy, their widespread clinical integration is profoundly limited by two fundamental, unresolved deficiencies in previous literature: the absence of comprehensive, fair comparative analysis across diverse architectures and the pervasive lack of transparent, quantifiable prediction confidence necessary for clinical acceptance. This study directly addresses these challenges by presenting a highly optimized and rigorous comparative evaluation of three powerful models: the high-capacity EfficientNetB0, the computationally efficient MobileNetV3Small, and a novel Custom Bayesian Neural Network (BNN) framework. Through robust methodology, all models achieved exceptional generalization, stabilizing with impressive final F1-Score > 0.91. The Custom BNN demonstrated clear superiority as the most reliable diagnostic tool, securing the highest Accuracy 0.9294 and F1-score 0.9289 on the objective test set. Most significantly, this work delivers a breakthrough in safety assurance by integrating sophisticated Explainable AI (XAI) and probabilistic modeling: Grad-CAM and Local Interpretable Model-agnostic Explanations (LIME) confirmed anatomically grounded decision-making, while the BNN uniquely provides quantifiable uncertainty metrics, offering a crucial 95% confidence interval (CI) for every diagnosis. These results validate a new generation of high-performance models, led by a transparent BNN architecture, that are ready for implementation to deliver reliable, trusted, and efficient Diabetic Retinopathy screening solutions worldwide.
Prediction of Molecular and Physical Properties of Non-small Cell Lung Cancer (NSCLC) Drugs using Mathematical Modelling and M-Polynomial Indices
The computation of M-Polynomial indices for Erlotinib, a tyrosine kinase receptor inhibitor and most widely recognized anti-cancer drug for the treatment of patients with NSCLC and advance pancreatic cancer is the main focus of this study. In order to efficiently calculate these M-polynomial indices, we used a graph-based method which renders use of the edge partitioning technique based on adjacent matrices and vertex degrees. Using Python software, we applied numerous regression models, such as numerous Linear Regression (LR), Elastic Net Regression (ENR), Lasso Regression (LR), Ridge Regression (RR) and Support Vector Regression (SVR), to develop Quantitative Structure-Property Relationships (QSPR). Based on the M polynomial indices, these models were utilized to forecast the physical properties such as melting point, enthalpy of vaporization, molar refractivity, molar volume, and polarizability, molecular weight, molecular mass, surface area, chemical hardness of NSCLC medications. According to our research, the M-polynomial indices predict these physical attributes with remarkable accuracy, providing crucial information on structural traits that maximize anticancer effectiveness. Additionally, we suggested predictive models for every physical attribute examined, proving the value of the M-polynomial index in comprehending molecular behaviour and directing the creation of innovative therapeutic medicines. This study not only facilitates the accurate prediction of physical properties for known NSCLC drugs but also holds the potential to fasten the novel drug discovery and development, uncharacterized anti-cancer compounds, thus contributing to the advancement of cancer therapeutics.
Understanding the Role Of Emotional Intelligence in Agile Teams in Context of Requirement Change Management
Requirement changes are inevitable in Agile software development, wherein flexibility is the key. Although Scrum offers defined change management procedures, but they tend to overlook the emotional aspects involved in Requirement Change Management (RCM) success. This systematic literature survey investigates the application of Emotional Intelligence (EI) in Agile RCM, drawing conclusions from 27 studies. Results emphasize recurring issues like ineffective change planning, uncertain prioritization, inadequate stakeholder involvement, resistance to changes, and affective barriers in the form of fear, lack of trust, and low motivation. The review finds shortcomings in disciplined RCM practices, role-based EI integration, and alignment for performance. To fill these gaps, the research recommends a role-centric RCM framework incorporating EI concepts to enhance communication, trust, and flexibility with Agile adaptability.
Dynamic Behavior of a Magnetized Multi-Component Hybrid Nanofluid on an Oblique Elongating Interface Affected by Extraction and Permeable Media Interactions
The current study explores the mechanism of heat transfer in non-Newtonian Maxwell tri-component nanofluid flow past an inclined stretching sheet embedded in a permeable medium. The electrically conducting nanofluid is considered under the impact of the Lorentz force. The nanoparticles of three types: Silver, Copper, and Ferric oxide, are considered and mixed with the water taken as a base fluid. The proposed phenomenon in the form of differential equations is solved numerically for the numerical outcomes. These results reflect that the Maxwell fluid parameter has an increasing impact on the velocity of the fluid and a decreasing effect on the temperature. The increasing magnetic force effects highlight the increasing trend in temperature of the fluid and the decreasing impact on the velocity of the fluid. The increasing number of nanoparticles has an increasing thermal effect on the fluid. Similarly, the skin friction and rate of heat transfer are dependent functions of pertinent parameters. The differential equations are solved using the exact solver bvp4c.
Maximum Value Attribute based Decision Tree and Random Forest for COVID-19 Prediction
The COVID-19 pandemic emerged as one of the most disruptive global health crises of the century, affecting social and economic systems worldwide. The rapid rise in infections placed immense pressure on healthcare infrastructures, demanding fast and reliable diagnostic tools. In recent years, Machine Learning (ML) has gained considerable importance in the medical field, supporting the diagnosis of conditions such as heart failure, pneumonia, dengue, breast cancer, and diabetes. In a similar way, clinical symptoms related to COVID-19 can be utilized to support early prediction, helping limit transmission. Although ensemble learning techniques such as Decision Trees and Random Forests have shown strong predictive performance for COVID-19, they often require more time and a larger number of iterations, which can be challenging when rapid detection is needed. This study focuses on improving the efficiency of COVID-19 prediction by integrating Rough Set Theory (RST) through the Maximum Value Attribute (MVA) approach with classical Decision Tree (DT) and Random Forest (RF) models. The objective is to reduce computation time and iterations while maintaining reliable diagnostic accuracy. The proposed method classifies patients as COVID-19 positive or negative based on eight key clinical symptoms. A dataset containing clinical records of 136,294 patients, collected from an open-source GitHub repository, was used for evaluation. Four models—DT, RF, MVA-DT, and MVA-RF—were implemented in Python using Jupyter Notebook. Standard evaluation metrics were applied to assess performance. Overall, the MVA-DT model achieved the most efficient execution, while the MVA-RF model demonstrated strong predictive capability with an accuracy of 95.82%, precision of 81.90%, recall of 59.28%, and an F1 score of 68.77%.
Microbial-Plant-Biochar System for the Removal of Pollutants from Effluents and Contaminated Soil
Industrial activities released wastewater containing heavy metals and synthetic dyes that remain in the environment for a longer period. These pollutants disrupt ecosystems, pose risks to human health, and continue to accumulate if they are not treated properly. Many conventional remediation methods are costly and often generate secondary waste, which limits their practical use. As a result, researchers are increasingly exploring sustainable and environmentally friendly alternatives. This review discusses an integrated-microbial-biochar system as a promising approach for wastewater and soil remediation. Biochar produced from materials such as sewage sludge, oil-field drilling mud, and agricultural residues offers a highly porous and chemically active surface that can effectively bind heavy metals (Cd, Cu, and Zn) as well as a wide range of industrial dyes. In addition, microbial strains such as Acinetobacter sp. and Bacillus subtilis play an important role in degrading organic pollutants, restoring enzymatic activity and contaminated soils, and improving nutrient cycling. Recent developments, including biochar-microbe beads and composite bioreactor systems, have shown better performance than biochar or microbes used alone. These combined systems enhance microbial survival, reduce toxicity, and significantly improve pollutant removal efficiency. By summarizing recent findings on pyrolysis conditions, microbial immobilization techniques, and pollutant removal behavior, this review highlights the potential of hybrid remediation strategies. Emerging modifications, such as magnetic chitosan-modified biochar, are also discuss future directions to further strengthen integrated remediation systems.
Education Access System (EAS): A Low-Latency Learning System for Low-Bandwidth Education Access
Online learning worldwide requires well-performing Learning Management Systems (LMS); however, most web-based application packages have no opportunities to execute their task in the low-bandwidth areas due to the infrastructure factor, the absence of digital literacy, and the barriers of institutions. It applies this digital divide to the countryside and the developing world and limits access to quality education. Existing LMS systems such as Moodle and Blackboard are designed to support high-bandwidth systems, and they are not applicable in locations where 2G/3G is the predominant network. This gap can be addressed by a low-bandwidth, lightweight, and low-latency LMS named Education Access System (EAS), which is developed, engineered, and tested in this research project. It also worked through four phases of methodology: requirement analysis and stakeholder mapping, data analysis and system architecture built upon a layered architecture, implementation of the application using React, node.js, MongoDB, and optimized APIs, and assessment of the project via manual/automated testing and User Acceptance Testing (UAT) with students. The findings show login and usable databases, 95.8% respondents had improved learning with the platform, but valid performance under the simulated conditions of a 2G/3G (100-500 kbps) was still simulated (low forum interaction 12.5%). EAS explains how LMS optimization solutions can be used to the advantage of equitable digital education, comprising SDG 4, and how the future will center on mobile deployment, offline adaptations, and long-term collaborative capabilities.
Agentic AI for Autonomous Soil and Fertilization Management for Agriculture Sustainability
Soil fertility loss and excessive chemical fertilization are major environmental and economic issues in developing regions such as Punjab, Pakistan. This paper proposes an Agentic AI framework for autonomous soil and fertilization management that combines (i) IoT soil sensing and drone-based crop monitoring for real-time perception, (ii) predictive modelling for short-horizon nutrient and moisture forecasting, and (iii) multi-agent reinforcement learning (MARL) for adaptive decision-making. The system operates with operational autonomy, executing daily management decisions without routine human-in-the-loop control. Agronomic expert knowledge is incorporated only offline as safety constraints and initialization priors (e.g., allowable nutrient ranges and stress-avoidance rules) to bound the action space and prevent unsafe behavior, rather than to prescribe actions. Experiments were conducted across two seasons at two sites (Sheikhupura and Multan) under four treatments: Farmer Practice (FP), Rule-Based Control (RBC), Machine Learning Predict (ML-Predict), and the proposed Agentic AI. Results show that Agentic AI reduces nitrogen fertilizer use while maintaining/improving yield proxy and improving soil indicators (including residual nitrate reduction and improved Soil Health Index). We also analyze irrigation outcomes as a sustainability objective and show how water usage must be treated as a constrained or multi-objective term in the reward function to avoid over-irrigation. Overall, the framework supports scalable, data-driven soil management with bounded autonomy, preserving expert-defined agronomic safety.
Clear Tic-AI: Detection of Dysarthria and its Severity Analysis
Dysarthria and other motor speech disorders result from abnormalities in the neural or muscular processes that actually control speech production; conversely, these disorders affect the strength, coordination, and tone of the vocal muscles that ultimately produce less intelligible speech. Because dysarthria can range from moderate distortion of articulation to severe impairment of speech, early and accurate assessment is critical. The paper proposes Clearitic AI, an automated speech analysis platform that leverages artificial intelligence to diagnose vocal disorders. It fuses Wav2Vec2 with traditional acoustic features such as Mel-Frequency Cepstral Coefficients (MFCCs), pitch estimation, and spectral descriptors. Abnormal voice classification and its severity on a framework with a sequential neural network architecture are proposed. Extensive testing of the system is performed using 10,000 recordings of voice samples from the TORGO dataset and the Mozilla Common Voice dataset. Experimental results demonstrate that the proposed model achieves a classification accuracy of 94.2% (±1.3), an F1-score of 0.943, and an Area Under the Curve (AUC) of 0.987 on the test set, thereby establishing the effectiveness of this framework for dysarthric speech detection applications.
A Framework for the Prediction of Parkinson’s Disease Using Agentic Artificial Intelligence
Parkinson’s disease (PD) is a progressive neurodegenerative disorder that is difficult to diagnose, particularly in its early stages. Subtle, slowly evolving symptoms often delay confirmation, reducing opportunities for timely intervention that could improve outcomes and quality of life. Conventional diagnosis relies largely on clinical observation, which can be subjective and insufficiently sensitive for early detection. This thesis proposes an Agentic Artificial Intelligence (AAI) framework for early PD detection and severity assessment using voice-based biomarkers. Biomedical voice parameters are leveraged because vocal changes can reflect early neurological impairment. Two publicly available Kaggle datasets containing voice recordings from individuals with PD and healthy controls are used to train and evaluate the models. For detection, an XGBoost classifier achieves 94.68% accuracy with strong discriminative performance. For severity estimation, XGBoost regression models predict motor and total Unified Parkinson’s Disease Rating Scale (UPDRS) scores with high agreement to clinically reported measurements. A key contribution is an agentic decision-making layer that autonomously interprets model outputs, performs disease staging, and generates stage-dependent monitoring and treatment recommendations. Unlike conventional predictive pipelines that stop at numerical outputs, the proposed system translates predictions into actionable clinical insights to support structured decision-making. Experimental results indicate that the framework can detect PD and estimate severity effectively from non-invasive voice data, highlighting the potential of AAI for earlier diagnosis, personalized monitoring, and intelligent clinical decision support in healthcare. The multi-layer design supports modular updates to models and agent policies, enabling telehealth deployment and longitudinal tracking as additional voice samples become available over time.
Olive Leaf Disease Detection Using Transformer-Based Deep Learning Approach
The use of AI and DL in automated crop health monitoring and disease diagnosis, especially relevant to Pakistan's burgeoning olive growing industry, has gained momentum. This paper proposes a transformer-based deep learning approach for the detection of olive leaf diseases due to significant shortcomings in the robustness and generalization of traditional convolutional neural networks. The proposed system makes use of a Vision Transformer (ViT) architecture to extract both local and global contextual features from the images of leaves using multi-head self-attention mechanisms. The developed Optimized ViT-Small model identifies olive leaves into three classes: Healthy, Aculus olearius, and Olive Peacock Spot. It is trained and tested on a pre-processed dataset of 3,400 high-resolution olive leaf images collected from olive-growing regions of Pakistan. Experimental results show strong performance with a test accuracy of 97% while demonstrating high precision, recall, and F1-scores throughout the classes. Moreover, performance assessment through confusion matrix analysis, ROC AUC, and precision-recall curves supports the developed model's effectiveness. Although the dataset's geographical coverage is limited, the results indicate that transformer-based architectures are an attractive alternative for the applications of precision agriculture in Pakistan.
CLFT: An Optimized Hybrid Cross-Layer Fusion Transformer for Accurate Fake Profile Detection on Social Media
The rapid increase of fake profiles on social media platforms has raised significant concerns regarding online authenticity, user trust, and digital security. Despite various efforts to combat this issue, existing detection methods often fall short due to the evolving nature of fake profiles and the noisy, high-dimensional data involved. This study proposes an optimized Hybrid Cross-Layer Fusion Transformer (CLFT) for detecting fake profiles by analyzing behavioral metadata. The CLFT architecture integrates multi-stage attention mechanisms, including Cross-Layer Fusion Attention (CLFA), Sparse–Dense Hybrid Attention (SDHA), and Temporal-Behavior Embedding Blocks (TBEB), to effectively capture both short- and long-term dependencies in user activities. The model hyperparameters were optimized using the Bayesian Optimization Hyperband (BOHB) framework. Experimental results on a real-world social media dataset show that the proposed model outperforms traditional machine learning techniques and previous Transformer-based models, achieving an accuracy of 99.10%, precision of 99.89%, recall of 99.55%, and an F1-score of 99.72%. Furthermore, the attention mechanisms enhance interpretability by emphasizing the most influential behavioral features, contributing to the model’s transparency and reliability. The findings highlight that Transformer-based models, especially the CLFT, provide a scalable and efficient solution for fake profile detection in noisy environments, with important implications for enhancing social media security. The study emphasizes the need for interpretability in automated detection systems, fostering trust and ensuring better user engagement and platform integrity.
Abstractive Urdu Text Summarization Using Multilingual Transformer Models: A Deep Learning Approach
In contrast to directly copying source text, abstractive text summarization produces short summaries through an understanding of the text. Urdu's low-resource language, which is also characterized by complexities, presents further obstacles. This text investigates the possible extent of deep learning models to automate Urdu text summarization. With respect to the general summary and particular attention to word choice, we try to address the challenges posed by the Urdu language, and we make use of deep learning models for a dataset of Urdu news articles to produce summaries that are accurate and coherent. BERTScore quantitative analysis reveals that the fine-tuned mBART model has an F1 score of 0.497, which is better than mT5 (0.355). As opposed to the most recent Urdu summarization research (2023-2025) in which the majority of reports include ROUGE-based scores, our methodology exhibits a superior semantic consistency and abstractiveness.
A Computational Analysis of Nonlinear Fractional Partial Integro-differential Equation Using Meshfree Multiquadric Radial Basis Function Method
Fractional partial integro-differential equations play an important role in describing physical and engineering systems that exhibit memory and nonlocal effects. Their nonlinear structure and the presence of weakly singular kernels make analytical solutions difficult to obtain, which highlights the need for accurate and flexible numerical strategies. This study develops a meshfree computational method based on multiquadric radial basis functions for solving a nonlinear fractional partial integro-differential equation involving the Caputo derivative. The temporal discretization is carried out using a backward difference formula, and the spatial operators are approximated through radial basis function interpolation. The resulting scheme avoids mesh generation and is suitable for irregular or scattered spatial nodes. Numerical experiments are presented to illustrate the accuracy, reliability, and efficiency of the method for representative test problems. The results indicate that the proposed meshfree approach provides a robust tool for nonlinear fractional models with weakly singular kernels.
Design of a Miniaturized Flexible Patch Antenna with Shorting-Pin Integration for Enhanced Gain in RFID, ISM, and Wearable Biomedical Applications
Antennas are integral parts of wireless communication because they can ensure that signals are transmitted and received effectively, encompassing a variety of frequencies, such as those used in IoT and overall RF systems. This paper introduces a miniaturized single-band antenna of 9 × 31.6 × 0.254 mm3 with a specific design for wearable applications, which was made on flexible Rogers RT5880 substrate. Using the CST Microwave Studio 2024 microchip as the design and analysis tool, the proposed antenna consists of a rectangular slot-based radiating structure with a shorting pin and probe feed, which can achieve stable design performances both in the free-space and on-body situation services. Compliant with the wearable safety requirements and operating at a low level specific absorption rate (SAR) less than 1.6 W/kg at the resonant frequency. The antenna has good radiation performance, with a maximum efficiency of 82% and a maximum gain of 4.1 dBi, having a bidirectional radiation pattern in the elevation plane and an omnidirectional radiation pattern in the azimuth plane. The introduction of shorting-pin as a strategic method of reducing the length of resonance allows the approach of substantial reduction in the resonant length without compromising radiation characteristics. Simulation results also provide further confidence scales of stabilizing impedance performance and omnidirectional radiation pattern properties in the target 5.725-5.875 GHz ISM band, which demonstrates the expectation of shorting-pin strategies for the development of small-size, high biocompatibility, and flexible antennas for next-generation wearable, body-area network, and radio frequency identification (RFID) communication systems.
Design and Validation of a Quantum Software Engineering Lifecycle Framework
Quantum computing promises transformative computational capabilities; however, the absence of structured and standardized software engineering significantly limits its practical scalability. Quantum Software Engineering (QSE) remains fragmented, tool-centric, and largely experimental. This paper proposes a structured Quantum Software Engineering Lifecycle Framework that systematically integrates requirements engineering, hybrid design, quantum development, verification, deployment, and governance. To validate the proposed lifecycle, a two-stage evaluation was conducted. First, a Delphi-based expert validation involving 15 domain experts assessed clarity, feasibility, scalability, and hybrid applicability. Second, a simulation-based comparative analysis using Qiskit and PennyLane evaluated the lifecycle against ad-hoc development workflows across variational and search-based quantum algorithms. Results demonstrate a 24% reduction in development time, 15–18% improvement in execution fidelity, and significant gains in modularity, reusability, and tool interoperability. These findings confirm that adopting a structured lifecycle enables more reliable, scalable, and sustainable quantum software development, positioning QSE as a mature engineering discipline rather than purely experimental practice.
Engineering a Sustainable Future: SWOT Analysis of Pakistan's 2019 Renewable Energy Policy Design
Pakistan, as a developing and densely populated country, faces persistent challenges of electricity shortages, heavy reliance on imported fossil fuels, and rising environmental and economic pressures. Despite possessing substantial renewable energy potential, the share of renewable sources in the national energy mix has remained limited, largely due to weak policy implementation. This study systematically examines Pakistan’s Alternative and Renewable Energy Policy (AREP) 2019 using a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analytical framework. The identification of SWOT factors is based on qualitative analysis of national policy documents, official energy-sector reports, and peer-reviewed literature, supplemented by expert insights from stakeholders within the energy domain to enhance analytical robustness. The results reveal that AREP 2019 demonstrates strong potential through abundant indigenous renewable resources, investor-friendly provisions, and an improved regulatory framework; however, weaknesses persist in delayed project implementation, grid constraints, and procedural complexities. Opportunities lie in hybrid energy systems, private-sector participation, and carbon-credit mechanisms, while threats include political instability, grid unreliability, energy theft, and regulatory uncertainty. By translating SWOT outcomes into actionable policy insights, this study highlights critical implementation gaps and proposes strategic directions for improving energy security, sustainability, and long-term policy effectiveness. The findings offer practical guidance for policymakers, regulators, and investors seeking to strengthen Pakistan’s transition toward a resilient and sustainable energy future.
Position Prediction and Talent Discovery in Football Leagues Using Performance Data
Football has always been dependent on the subjective evaluation of scouts and coaches to find and hire players. Although these methods work to some extent, they usually have restrictions due to human biases, irregularity, and the huge volume of football data. As more data on player performance is made available, data analytics and machine learning represent a chance to introduce objectivity, consistency, and scalability in the recruitment process. This research study suggests a machine learning-based classification model along with a clustering model to classify football players in their main positional roles using statistical performance features. The research is based on the development of models that would help to differentiate among defenders, midfielders, and attackers based on their passing efficiency, contributions to defense, won duels, and attacking indicators. For data extraction, Fbref has been used as the source of data. The player-level data of the 2023-24 season of the Top 5 European Leagues has been extracted using the Python programming language. The data involved various statistical categories addressing all the areas of performance. Position labels were merged with the scraped tables to ensure accurate role mapping. This combination resulted in the creation of an entire dataset with both performance and position features. The dataset was cleaned and prepared using data preprocessing techniques, and selected features were then used in the training process. K-Means Clustering was applied to the PCA-transformed data to cluster similar players based on their playing profile. Different supervised learning algorithms have also been applied, such as Logistic Regression, Decision Tree, K-Nearest Neighbors (KNN), Random Forest, and Voting Classifier. The standard evaluation parameters are used to provide a detailed evaluation of the predictive performance. It was found that ensemble algorithms, in particular Random Forest and the Voting Classifier, performed better than single baseline models and were stronger and more reliable in positional classification. The results suggest the potential of machine learning models when recruiting players in football teams and to facilitate and aid expert judgment. This research sets up a systematic, data-driven framework that helps clubs to screen the enormous number of players effectively in a non-subjective manner.
Design and Implementation of a GSM-Based Car Battery Theft Prevention System Using Arduino Nano
Car battery theft is still a major security threat to people, especially those with standard alarm systems, which are useless after the disconnection of the battery from the vehicle. This study proposes a low-cost car battery theft prevention system based on GSM that can sense battery removal and inform the vehicle owner in real time via an Arduino Nano microcontroller, a SIM800L GSM module, an ESP8266 Wi-Fi module, a sensing circuit relying on a relay, and a buck converter to regulate the voltage. The relay constantly observes the line continuity of the battery, and in the case of battery disconnection, SMS and call signals are sent to the subscribing user. Testing was performed based on experimental testing in normal connection, disconnection of the wire, and power fluctuations. The proposed solution is credible, affordable, and applicable to real-world vehicle security applications that do not require Internet connectivity.
Digital Cardiology: ECG-Based Arrhythmia Detection Using 3D Convolutional Neural Networks
Cardiovascular diseases prove to be a prominent cause of worldwide deaths, and arrhythmias specifically remain a serious threat owing to their unexpected and painless characteristics. This paper presents an ECG-based arrhythmia detection system using a three-dimensional convolutional neural network, or 3D-CNN, architecture. The ECG signals obtained from the MIT/BIH Arrhythmia Database undergo preprocessing techniques like band-pass filtering, R-peak detection by the Pan and Tompkins algorithm, heartbeat segmentation, and volume representation of the heartbeat. The data is divided into training, validation, and testing sets, where 80% of the data is utilized for training and validation, and the remaining 20% for testing independently. The efficiency of the system is tested by accuracy, precision, recall, and F1-score. The proposed system records a validation accuracy of 98.52% and a test accuracy of 98.74%, which is superior to the previously used accuracy of the same database by the 1D CNN and 2D CNN architectures.
Design and Fabrication of Novel Air Conditioning System in CNG Fitted Vehicles
This paper presents a novel air conditioning system for Compressed Natural Gas (CNG)-fitted vehicles that overcomes the high fuel consumption of conventional belt-driven compressors. The system utilizes the Joule–Thomson effect of CNG expansion to generate cooling, captured through a heat exchanger and circulated via a blower, without adding engine load. CNG consumption was measured by recording the initial and final tank pressures and calculating the consumed gas mass based on tank volume and CNG density. Emissions were measured using a gas flow analyzer, with sensors placed in the vehicle’s silencer. Tests were conducted under identical conditions for both the conventional and the proposed air conditioning systems. The main objective of this project was to get a fuel-free air conditioning system in CNG-fitted vehicles. Experimental results show a significant cabin temperature drop to 11°C (284.15K) in 14 minutes (readings were noted by using digital multi meter having temperature sensor), improved fuel economy up to 25% as compared to conventional air conditioning system, and reduced emissions of CO₂ about 55% and CO about 30% (emissions reading were noted by using kane-458s gas flow analyzer). Car Consumption has been calculated by an indirect gravimetric (pressure-density-based) method used to estimate CNG fuel consumption. The findings confirm that the system is a technically feasible, low-cost, and sustainable alternative to conventional AC units for CNG vehicles, especially in hot climates.
Spectral Profiles to Minerals Identification: An Integrated Hyperspectral vs Multispectral Minerals Exploration of the Ophiolitic Belt of Qilla Saifullah District, Baluchistan
Baluchistan, the largest province of Pakistan, hosts abundant metallic and non-metallic mineral resources, particularly copper, gold, chromite, lead-zinc, and rare earth elements, making it a strategic region for mineral exploration and geological characterization. Despite such potential, mineral exploration in the province’s ophiolitic belts remains limited by sparse ground surveys, logistical challenges, and incomplete spatial coverage. To address this gap, the present study aimed to (1) Key ophiolitic minerals (chromite, serpentine, magnesite) and associated lithologies with high spectral precision, and (2) Compare hyperspectral and multispectral sensors for mineral identification and mapping. The methodology integrated ZiYuan-1 02D (ZY-1E) hyperspectral satellite data (spectral range 0.4–2.5 µm) with advanced spectral analysis techniques i.e. Diagnostic Absorption Feature extraction, Spectral Matching against the United States Geological Survey (USGS) Spectral Library, and multiple classifiers including Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF), Random Forest (RF), and targeted band ratios/indices. Mineral point data and lithological maps from the Geological Survey of Pakistan (GSP) were used for accuracy assessment. For comparative analysis, multispectral datasets from ASTER, Landsat-8 OLI, and Sentinel-2 MSI were processed, with a focus on the efficacy of SWIR coverage for mineral detection. Results indicated that ZY-1E hyperspectral data achieved 81.82% mineral classification accuracy and 86.11% lithology mapping accuracy, with Band Ratio techniques emerging as a rapid detection tool. Among multispectral datasets, ASTER outperformed Landsat-8 and Sentinel-2 due to its six SWIR bands, enabling superior discrimination of OH-, Al-, and carbonate-bearing minerals. The study concludes that hyperspectral imagery, due to its high spectral resolution, is indispensable for precise mapping of minerals and lithology in rugged, inaccessible terrains. However, ASTER remains a cost-effective alternative for targeted mapping of alteration minerals where hyperspectral coverage is unavailable. It is recommended to integrate hyperspectral mapping in strategic exploration campaigns to accelerate mineral resource assessment in underexplored regions of the world.
Sustainability-Aware Short-Term Building Cooling Demand Forecasting Using Multi-Source LSTM-Based Deep Learning
Precise short-term forecasting of building cooling demand is a necessary facilitator of power-efficient operation, economic planning, and carbon-conscious decision-making in modern power systems. Nevertheless, many of the current data-driven methods are based on a few input variables, ignore forecast-based exogenous data, or are affected by information leakage in time-series learning pipelines. In order to address these drawbacks, this paper suggests a high-fidelity deep learning model to forecast short-term cooling demand by jointly combining past load patterns, dynamic electricity price signals, grid carbon intensity signals, and multi-horizon weather forecasts. The suggested framework uses a multi-layer Long Short-Term Memory (LSTM) structure that will learn intricate temporal relationships in hourly building energy data throughout a complete annual period. The preprocessing strategy embraces a leakage-free approach and includes the alignment of data with time consistency, normalization of features, and the generation of sliding-window sequences in order to guarantee realistic and reliable performance assessment. The CityLearn dataset is used to train and test the model, and this data is a high-resolution simulated urban building energy environment of 8,760 hourly observations under various seasonal and operational conditions. The results of the experiment indicate that the given approach produces very accurate cooling demand forecasts, as the coefficient of determination (R2) is 0.9823, and the mean error of absolute percentage is below 1, which is much higher than that of traditional baseline models. Additional studies prove that the combination of forecast consistent weather variables, electricity pricing signals, and carbon intensity indicators can significantly boost prediction accuracy and operational relevance. The evaluated leakage-free building energy management system is simulated, but the leakage-free learning pipeline and multi-source input design can be directly applied to real-world systems, enabling the intelligent HVAC control, demand response, and low-carbon operational practices. Altogether, this article may help to fill the gap between deep learning approaches and sustainability-conscious decision-making in the contemporary energy infrastructure. The proposed model is designed for direct multi-step (multi-horizon) prediction of demand for cooling in the form of predictions of demand for several time steps in the future using forecast-aligned input features.