Pakistan Journal of Engineering, Technology and Science
The Pakistan Journal of Engineering, Technology, and Sciences The PJETS has been recognized as a "Y-Category" journal by the Higher Education Commission (HEC) of Pakistan. It is a double-blind peer-reviewed, bi-annual, open-access journal of the College of Computer Science & Information Systems(CCSIS). PJETS considers manuscripts that demonstrate scientific excellence and significance in relation to its main theme. Submitted papers should report original, rigorously validated research, including a thorough description of the experimental methods used. It was established in 2011 and covered the areas of Engineering Sciences, Computer sciences, and Mathematics Sciences. This journal aims to provide an international platform for engineers and academicians worldwide to promote, share, and discuss various new issues and developments. The paper submission process is automated by an Open Submission System (OJS), facilitating the authors, editors, and reviewers. Editorial Research Board members are from various universities/institutes in the USA, UK, Netherlands, Malaysia, Canada, France, Finland, UAE, and others.
College of Computer Science and Information Systems, Institute of Business Management, Karachi
An islanding detection and control algorithm based on CVFR for Voltage Source Converter in CIGRE LV Network
Distributed Generators (DGs) are extensively used as Distributed Energy Resources (DERs) and are now an integral part of modern distribution systems. Although these DGs improve the voltage profile and reduce power losses, they have introduced challenges such as islanding and safety issues. This article proposes the algorithm for the islanding detection and control of Voltage Source Converters (VSCs) based on Combined Voltage and Frequency Relays (CVFRs). The CVFR control structure is designed to identify the presence of islanding with minimum detection time and has a capability to change the mode of operation of VSC to provide a stable voltage supply to the connected load. The effectiveness of the proposed algorithm has been studied on the CIGRE LV Test Network with six sets of DGs using passive detection technique. The performance of the algorithm has been tested in a number of scenarios with simulation and comparison made with other detection techniques to validate the model. The system has been modeled and simulated using the software MATLAB/Simulink.
Frequency control, Island detection, Microgrid, Voltage control, VSC
Copyright (c) 2025 Pakistan Journal of Engineering, Technology and Science https://creativecommons.org/licenses/by/4.0
HS-ABSA: An Annotated Dataset for Aspect-Based Sentiment Analysis of Home Services Customer Reviews
With the progression of new technologies, the explosive growth of user-generated content on online platforms has significantly fuelled interest in Aspect-Based Sentiment Analysis (ABSA) for both academic research and commercial applications. While existing ABSA research often utilizes publicly available product-oriented datasets such as those for laptops and cameras, there remains a scarcity of high-quality, publicly available datasets tailored for service-oriented domains. This gap highlights the need to develop new domain-specific resources that can effectively support the evaluation of ABSA models in real-world customer service contexts. In this paper, we present HS-ABSA, a new benchmark human-annotated dataset specifically curated for aspect-based sentiment analysis of home services customer feedback. The dataset was constructed by scraping reviews from the Google Play Store for a mobile app. The scraped reviews were carefully analysed, filtered, and manually annotated. The annotation process focused on opinion terms, aspect categories, and sentiment polarity. Inter-annotator agreement was measured using Kappa’s score. A total of 4,164 reviews were manually annotated. The inter-annotator agreement was calculated using Kohen’s kappa, and scores of 91.92% for opinion terms, 84.00% for aspect categories, and 94.38% for sentiment polarity were obtained. This newly curated ABSA Dataset for the service industry will provide a robust foundation for evaluating ABSA models.
Dataset, Home Service Reviews, Aspect-Based Sentiment Analysis, Sentiment Analysis, Opinion Term, Aspect Category, Sentiment Polarity
MobileNetV1-Inspired Deep Learning Framework for Early Detection and Staging of Alzheimer’s Disease Using MRI
Alzheimer’s disease is one of the most common neuropathological diseases worldwide, with approximately 46.8 million individuals suffering from Alzheimer’s disease with ramifications for caregivers and economies. Various studies describe Alzheimer’s as a progressive disease and focus on the need for early detection to allow for timely intervention and treatment options. Cognitive tests are typically used to evaluate early detection for Alzheimer’s but MRI brain scans are the main detection method for Alzheimer’s diagnosis. Several studies have looked for abnormal brain conditions based on Alzheimer’s disease and dementia disease detection from features extracted from medical images. Methodological approaches to deep learning for brain structure segmentation and Alzheimer’s disease classification are going to become more common due to their better performance on larger datasets and outperforming established machine learning comprehension models. The use of deep learning techniques in this study is based on ‘‘brain MRI scan classification framework’’ for a more precise, efficient, and automated classification of Alzheimer’s Disease stage. The suggested deep learning model uses a convolutional neural network (CNN) architecture with depth-wise separable convolutions based on MobileNetV1 to allow for computational efficiency, pattern recognition and robust feature extraction assistance. The study employed 6400 MRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, categorized as non-demented, mildly demented, moderately demented, and severely demented. The model achieved an overall accuracy of 98% on the test dataset, demonstrating an ability to identify the discrete classes of Alzheimer's disease progression. The average test loss score was remarkably low at 0.0543, demonstrating an effective reduction of the differences between the predicted value and actual value. The model obtained precision scores of 0.95, 0.97, 1.0, and 1.0. The precision score indicates accuracy in the predictions from the classes. The macro and weighted average precision scores were 0.98, which indicates consistency in the precision across all classes.
Alzheimer’s, MRI, CNN, MobileNet v1, deep learning, medical imaging
Prototyping a Cost-Effective, 3D-Printed Transradial Myoelectric Arm: Integration of sEMG Signal Processing, Microcontroller Actuation, and Anthropometric Design
This study provides the design, development, and functional characterization of a new affordable transradial myoelectric prosthesis, specifically designed to tackle the twin challenges of affordability and functional complexity that restrict the availability of commercial prosthetic systems. The overall goal of this work is to develop a completely open-source, anthropometrically optimized, and cost-effective prosthetic platform that facilitates natural motor control with surface electromyography (sEMG) signals from the remaining limb. The design presented utilizes an optimized signal acquisition and decoding architecture that is capable of converting sEMG activity to real-time proportional control commands. The methodological process includes multi-step signal processing, such as acquisition from cutaneous electrodes, analog conditioning, digital decoding using an Arduino Uno microcontroller, and actuation using a five-servo array controlled by a PCA9685 driver. All structural elements, such as the socket and hand structure, were produced using additive manufacturing and dimensionally tested with anthropometric specifications to provide ergonomic comfort and functional stability. Experimental testing ensured an 18 dB enhancement in Signal-to-Noise Ratio (SNR), a mean response time of 210 ± 30 milliseconds, and a static load capacity of 1.8 kg, indicating strong electronic integrity and useful usability for everyday tasks. The overall cost of fabrication, quantified as around USD 65, reflects unparalleled economic viability. Overall, this study sets a significant milestone towards affordable bionic technology with an open-source, high-utility prosthetic system that has the potential to span the gap between performance and price in upper-limb rehabilitation
Benchmarking Modern Evolutionary Algorithms for Multi-Modal Multi-Objective Optimization: A Comprehensive Performance Matrix Analysis
The multi-modal multi-objective optimization (MMO) paradigm has received greater attention because of its applicability and affinity for mapping problems. Multi-objective problems can be solved by classical evolutionary algorithms, also known as EAs, as they are the ideal candidates for solving these complex problems. However, these EAs are deficient in addressing issues related to multi-modal functions. Likewise, modern EAs have been shown to solve various benchmark functions and optimization problems. But the extent of analyzing its performance comprehensively on today’s complex EAs is remarkably scarce in the literature regarding the proposal of the MMO function. This study conducts an extensive analysis of modern EAs on benchmark MMO functions (six functions and three EA algorithms). These include spacing, spread, distance, diversity, convergence, and the run-time identified properties. The properties are then linked with the twelve performance metrics. This work also introduced the optimal EA for managing the MMO benchmark function. The empirical evidence used in the proposed framework is the performance matrix to select the best EA for the function of MMO.
Multi-modal Multi-objective Optimization
A Framework for Real-Time Continual Learning Federated Intrusion Detection Systems
Intrusion Detection and Prevention Systems (IDS/IPS) are vital components of security architecture for protecting the networks against cyberattacks. Traditional IDS/IPS rely on static rules and user configurations, which make them less effective against growing threats. Modern studies have integrated Artificial Intelligence (AI) and Machine Learning (ML) to IDS to improve the accuracy and detection speed. However, such AI/ML based systems still face many issues, which include reliance on the outdated datasets, almost no handling of zero-day attacks, lack of interpretability, and privacy concerns. This paper studies recent AI/ML based IDS/IPS works to identify key shortcomings, and then proposes a real-time, continually learning, federated IDS framework with integrated explainable AI. The proposed framework design addresses the adaptability, privacy, and trustability aspects, which can be used to build more resilient network defense systems
Intrusion Detection and Prevention Systems
AI-Powered Model for Defect Detection and Classification for High-Quality Automotive Manufacturing
This paper presents Detect-iPro, which uses the AI to detect defects at the automotive relay box, assign weights, and track faults in real time. The system relies upon the highly precise but also fast object detection with the YOLOv8 model of deep learning and is easy to integrate with the assembly lines with the help of high-resolution imaging and a convenient graphical user interface. The continuous improvement of the processes and regulatory compliance is also possible through automated data logging as well as real-time feedback that not only ensures a better traceability but also allows the improvement of the process in question. Thorough testing in factory-conditions shows considerable improvements in accuracy, speed and efficiency, and predetermines new standards in automotive quality control. The paper is completed with the discussion of the system limitations, future research principles, and how AI can transform smart manufacturing.
Defect Detection, Artificial Intelligence, YOLOv8, Quality Control, Automation, Automotive Manufacturing, Industry 4.0, Computer Vision, Deep Learning, Traceability
Reinforcement Learning for Climate Change Simulations: A DQN-Based Approach
This research presents a novel reinforcement learning paradigm through application of a Deep Q-Network (DQN) to discover the optimal policies for adaptation to and mitigation of climate change using CMIP6 SSP1-2.6 scenario data. This approach addresses some of the limitations of traditional General Circulation Models (GCMs), which are high on computational resources but can't be adapted to real-time scenarios. The 1032 timesteps are produced by the DQN framework, focusing on surface air temperature (tas), vertical velocity (WAP), and precipitation (pr). After 50 episodes, the cumulative reward of the DQN-optimized actions (carbon capture, reforestation, or inaction) was -12281.33, which represents a 55.6% improvement over the baseline of -27666.14 with a statistically significant improvement (t = 45.72, p < 0.0001). Partial success was indicated by the DQN stabilizing surface air temperature (tas) varying between 6-8°C (mean deviation is 5.9604°C away from the 1.5°C target). Further refinement of the system is envisaged to bring the target closer to 1.5°C. When compared to GCMs, the DQN was computationally efficient, overall training being accomplished in 11.24 minutes. Further improvement is envisaged to include the incorporation of CO? concentrations, sea-level rise, and data integration in real time. This work should thus show the great promise of RL, along with a fair share of insights to support high-value stakeholder governance and sustainable climate-based policy-making.
Comparative Analysis of Transfer Learning and Alliance-Based Approaches in Multi-UAV Swarm Coordination and Control
This paper provides a comparative study of two state of the art methods for coordinated control and swarm formation of unmanned aerial vehicle (UAV). The first method uses a transfer learning-based multi-objective optimization approach in which a hybrid Pigeon-Inspired Optimization (PIO) algorithm combined with transfer learning (TL) facilitates dynamic UAV clusters to execute intelligent path planning, agent swapping, and global synchronization in complex 3D environments. The second approach is based on an alliance-based formation control strategy where an alliance-based formation approach is presented that offers swarm robustness by dividing the UAV network into versatile subgroups driven by consensus laws and thus ensuring resilience in partial communication failures and dynamic topologies. Both methods are compared in this study in various aspects such as system modeling, communication strategy, fault tolerance, reconfiguration ability, optimization performance, and computational complexity. The analysis not only identifies the merits and limitations of each approach but places them in the general research agenda of collective motion in multi-agent systems. The work concludes with potential to integrate transfer learning and alliance-based resilience mechanisms into next-generation adaptive control architectures.
Comparative Analysis, Transfer Learning, Pigeon Inspired Optimization,
AI-Powered UAV-Patrolling Drone For Real-Time Unauthorized Person Detection
In the modern era, the integrity and safety of secure environments have become critically important. To address these challenges, an AI-powered UAV-patrolling drone system for real-time unauthorized person detection has been proposed. This drone executes predetermined flight paths, strategically covering surveillance gaps left by static CCTV cameras or human guards. The system integrates multiple state-of-the-art technologies, incorporating advanced facial recognition using Dlib-HOG, CNN, VGG-Face, Google FaceNet, and OpenFace, along with comprehensive facial analysis, providing real-time analysis of race, age, gender, and facial expressions. This technology is especially valuable for securing large venues, critical infrastructure, and high-profile events where unauthorized access poses significant risks. The system's hybrid architecture allows for optimal performance across different lighting conditions, angles, and crowd densities, setting a new standard for intelligent surveillance systems.
Drone surveillance, Unconstrained Environment , Un-authorized Person Detection, Surveillance Videos, Face Recognition, Facial expression Analysis & Prediction.
Comparative Analysis of Hybrid Attention and Progressive Layering Through a Comprehensive Evaluation of ARU-Net and PLU-Net in Brain Tumour Segmentation
The spectral separation of brain tumour using multimodal Magnetic Resonance Imaging (MRI) is a crucial but laborious undertaking in the field of medical imaging and suggests advanced procedures of automated models in order to mitigate the limitation of manual marking. The current study is an in-depth comparative study of two different deep-learning models the Novel Hybrid Channel Attention Regression U-Net (ARU-Net) and Progressive Layered U-Net (PLU-Net) that build on the U-Net architecture as an enhancement to increase the segmentation accuracy of the BraTS 2021 dataset with respect to the T1, T1ce, T2, and FLAIR modalities. ARU-Net mainly uses the methods of integrating the Convolutional Block Attention Modules (CBAM) and Squeeze-and-Excitation (SE) blocks, adding a regression-based output layer to generate continuous probability maps, which further focuses on feature recalibration and heterogeneity adaptation to tumour regions. On the other hand, PLU-Net uses a cascaded, multi-stage structure enhanced by attention gates and multi-scale data augmentation where the main emphasis is a progressive boosting of accuracy levels at multiple resolutions to guarantee high boundary accuracy. The preprocessing steps are quite different as ARU-Net uses simplified Z-score normalisation and resizing, and PLU-Net involves a full pipeline, involving skull stripping, bias field correction, and two normalisations. The performance analysis reveals that augmentation makes ARU-Net perform better in the specificity (0.949), sensitivity (0.921), and overall accuracy (0.94), and that the overall Dice coefficient (0.91) and Hausdorff95 (2.5) are superior to those of the PLU-Net, which signifies the efficiency of its iterative refinement. Graphical representations of different measures like Dice, specificity and sensitivity validate the fact that ARU-Net excels in terms of healthy-tissue differentiation and that PLU-Net is accurate in the delineation of tumour core. Taken together, the findings open up the synergistic properties of both ARU-Net in terms of its lightweight, attention-driven focus and PLU-Net in terms of its detailed, multi-stage processing on modern hierarchical segmentation paradigms. The presentation of this comparative analysis will not only provide methodological contributions to each of the models, but it will also offer future directions in the development of clinically viable brain tumour segmentation with an aim of finding a balance between efficiency and accuracy in order to support diagnosis.
brain tumour segmentation, deep learning, magnetic resonance imaging, convolutional neural networks, hybrid attention, progressive learning model
Alzheimer’s Disease Detection Using Deep Learning and Federated Learning
Appropriate and precise diagnosis of brain diseases is crucial as many forms of Alzheimer’s disease display similar indications in their initial stages. Most automatic detection or classification systems based on deep learning present confidentiality concerns due to their use of integrated computing and local storage data requirements for training. The goal of this paper is to defend sensitive patient data by proposing a deep learning algorithm that utilizes Federated learning techniques in an IoT-based edge computing framework. It is shown that the secrecy of the patient data can be maintained while still preserving accuracy and efficiency. This method provides a secure edge data protection model where there is no requirement for centralized storage. This paper discusses the strategy and implementation of the federated structure, taking into account the number of devices in the network, memory, and processing capabilities. The success and accuracy of the proposed algorithm are established with empirically defined metrics such as accuracy and defined thresholds.
IoT, Alzheimer’s Classification, federated learning, Deep Learning
Transposition Based Energy Optimization Technique for Internet of Medical Things
Internet of Medical Things comprises of tiny sensors which have the limitations of having small sized batteries. Once these batteries are drained, they need to get recharged. The recharging if these batteries is not a solution in term of human health care as these sensors are implanted on human body. The energy is much more consumed in communication. In this paper a multi hopping and transposition based technique is proposed to make the routing between the sensors to have lesser distance factor which makes the communication energy to be consumed lesser. Euclidean distance formula is used so that the distance of the sensors from the sink is calculated.
An Association Between Product Discount, Rating and Customer Service: An Empirical Study
This study investigates the relationship between buyers' perceptions and empirical data regarding product ratings and discount percentages during e-commerce sales events. Through a mixed-methods approach combining survey analysis and real transactional data, the research reveals a significant discrepancy between consumer beliefs and actual trends. Survey results indicate that the buyers perceive a strong influence of product ratings on discount percentages. However, quantitative analysis of sales data contradicts this perception, demonstrating no statistically significant association between product ratings and discount levels. Instead, the study identifies a moderate positive correlation between customer service quality, measured by chat response time, and product ratings, suggesting that responsive support enhances customer satisfaction and, consequently, product evaluations. Additionally, the research explores three predictive models—linear regression, decision tree, and random forest—to forecast discount percentages based on historical sales patterns and consumer behavior metrics. These models provide actionable insights for e-commerce platforms to refine dynamic pricing strategies, optimize promotional campaigns, and improve overall customer experience. The findings highlight the importance of data-driven decision-making in e-commerce, as consumer intuition may not always align with actual market trends. By leveraging predictive analytics, retailers can better anticipate demand fluctuations, tailor discounts effectively, and foster greater trust through transparent pricing practices. This study contributes to the growing body of knowledge on consumer psychology and pricing optimization in digital marketplaces.
Copyright (c) 2025 Adeel Ansari, Seema Ansari, Ayesha Ghayas https://creativecommons.org/licenses/by/4.0
Dense Neural Network Classification Model for Software Defined Network for Fine Grained Traffic Routing and Flow Analysis
Traffic classification in SDN environments acts as the centerpiece, greatly enhancing network management, security, and overall performance. In essence, the classification of traffic in the SDN environment involves classifying network traffic into specific classes according to certain criteria type, source, or destination. Through good classification of network traffic, network operators can optimize resources with unprecedented efficiency, leverage better routing, and grant more priority to the essential data flows that improve QoS. This study utilizes an SDN dataset from Kaggle and evaluates the performance of a state-of-the-art classification model. The paper introduces a further improved Deep Dense Neural Network (DDNN) model, optimized with the Adam optimizer, having a remarkable classification accuracy of 90%. In this paper, the Adam optimizer has been adopted because it allows for an adaptive learning rate that improves convergence and stability during training. Besides, this model showed high scores for other metrics: Precision, Recall, and F1-score all exceeded 90%, reflecting the model's ability to classify reliably and with balance. The low network loss indicates that the model not only gives an accurate result but is also consistent in its performance, with very few misclassifications. Fine-grained traffic classification cannot be underestimated in SDN, given the wide gamut that runs applications over SDN networks. The DDNN model handles diverse types of traffic with a lot of efficiency through extracting deep features from complex datasets, hence increasing the capability of SDN to meet the ever-evolving demands in networks.
Copyright (c) 2025 Dr. Khaliq Ahmed, Maheen Danish, Asif Raza https://creativecommons.org/licenses/by/4.0
Spectral derived attributes analysis and interpretation using enhanced spectral decomposition method - A Case Study of Penobscot and Stratton Fields
Spectral analysis of data, whether seismic or well-log, provides another dimension of analysis and interpretation. These integrations are expected to lead to more accurate and detailed characterization of subsurface formations, which will have important implications for the oil and gas industry and other fields that rely on geophysical data. Spectral decomposition has been used to extract and determine lithological boundaries, reservoirs, etc. However, the methods fail at providing both high time and frequency resolution. The window length and the fact that the actual signal does not drop to zero outside of the artificially constrained time frame determine the resolution of the technique. Additionally, these techniques typically do not make use of the crucial thickness data that is computed in the Fourier’s phase spectrum. Spectral decomposition is a fundamental mathematical tool that is widely used in machine learning and other fields of data science and engineering and forms the basis of many machine learning algorithms and techniques. We propose to use an improved spectral decomposition technique known as Continuous Amplitude Phase Spectrum (CAPS), which offers high frequency resolution as well as high time/depth resolution of the amplitude and phase spectra. The study used publicly available seismic dataset from two fields, i.e., Stratton and Penobscot, and two wells from the Penobscot field, L-30 and B-41, with gamma-ray, sonic, density, and neutron porosity logs. The areas of interest highlighted by the features derived from CAPS decomposition of the seismic and well log data were found to be in good agreement with the reported findings.
Seismic interpretation, signal processing, spectral decomposition, machine learning
A Comparative Case Study to Control Drones using Hand Gestures
As human-computer interfaces are constantly evolving day by day, hand gesture recognition (HGR) systems are becoming reliable and cost-effective. The interfaces for natural interaction allow users to convert their gestures into commands for a computer system. Multirotor drones are usually controlled using radio controllers, which require good experience and training. It is dif?cult for novice users to properly handle the multirotor using such controllers. This paper builds upon our previously developed architecture for drone control via hand gestures [1]. While the initial work demonstrated the feasibility of using a vision sensor, this study expands its scope by exploring sensor-based and vision-based approaches of HGR. It has been done by integrating and testing three hand gesture data acquisition devices. The aim is to evaluate the performance and usability of these sensors under varying conditions, including indoor and outdoor environments. Experiments, initially conducted in simulation and then with a real drone, show that such devices can be used to train novice users to maneuver the multirotor. Results reveal significant differences in accuracy and practicality, providing actionable insights for selecting sensors based on application needs. This comparative study highlights the adaptability of the existing architecture and its potential for future advancements in human-computer interaction.
Drone, Hand gesture recognition, Microsoft Kinect, Myo Armband, Intel Realsense
Modeling and Up-Gradation of Concentric Circular Shaped Patch Antenna to Array Antenna with the Improved Performance for 5G Millimeter- Wave Communication
This work is primarily devoted to design and aim at high gain of 8×8 array starting from 2×2, 4×4, 8×8 patch array microstrip antennas by upgrading circular shaped antenna to array antenna. It finds applications for utilizing 28 GHz as a licensed bandwidth, such as in wireless communication networks, satellite communications, radar systems, global positioning systems and telemedicine applications. The antenna exhibits a significantly high gain, remarkable directivity, and achieves a peak gain of 15.1 dB for both the E and H planes. It operates independently for horizontal and vertical polarizations. The design uses Rogers RT/Duroid as the substrate, featuring a square shape with a thickness of 0.787 mm and side dimensions of 20 mm. The total size of the antenna is 20 × 20 × 0.787 mm³. It is tuned to resonate at 28 GHz, achieving a return loss of -24.69 dB and offering an impedance bandwidth of 29%. The enhanced performance of this antenna is evident in terms of its return loss, peak gain, radiation pattern, impedance matching, bandwidth, and directivity, making it a promising solution for 28 GHz mm-wave communication systems. Fifth generation (5G) antennas. This problem can be solved by using high gain antenna. Therefore, the antenna structure is designed and simulated using HFSS ANSYS EDT 2021 / R2 Microwave Studio.
Concentric circular antenna, Millimeter-wave, 5G, Array antenna, Impedance bandwidth, Dual polarization.
Development of Automated Fire Control System: An Integrated Solution for Gas, Fire, and Smoke Detection
The major shortcomings of gas, fire, and smoke detection must be addressed to prevent accidents. Gas leaks, fires, and smoke-related incidents pose a significant threat to the country. The main objective of this prototype is to develop a system that detects, manages, and issues immediate alerts for gas leaks, fires, and smoke, thus enabling swift responses to mitigate potential harm. This prototype highlights the importance of household safety by developing an integrated system to detect, control, and manage gas, fire, and smoke incidents. Conventional systems often lack the necessary automation and speed required for effective emergency responses. This project addresses the critical need to automate the detection and control of such dangers. Gas leaks and fires can lead to rapid and severe damage, highlighting the importance of rapid detection and response. This project utilizes an advanced methodology that integrates gas sensors, flame sensors, smoke sensors, and automated control mechanisms. The system automatically secures a leak of gas utilized in cylinders or pipes. For fires, it deploys water sprinklers to suppress flames, whereas fans automatically clear smoke. The implementation of an Arduino-based system automatically controls the system. Flame, gas, and smoke sensors were integrated with the Arduino to control and manage the system. Sensors and Arduino mathematical calculations were also monitored. This prototype is designed to monitor detection over a 100 cm span. Inspired by the latest technologies, this prototype addresses the critical need for household safety by automating the detection, control, and management of gas, fire, and smoke incidents.
The Future of Healthcare: Portable IoT based Patient Monitoring Systems
The healthcare industry is currently the one with the fastest technological and service advancements. The most cutting-edge advancement in this field is the capacity to remotely or portably monitor patients, which is very beneficial given the rapidly expanding global population, rising healthcare costs, and limited access to medical institutions in less developed nations. A patient monitoring system is used to get the patient's vital signs. The patient's vital signs, including blood pressure, temperature, heart rate, ECG, and SpO2, are measured by this instrument. For the improvement of healthcare in the future, we have created and constructed an IoT-based patient monitoring system in this study. The device used in this study can record four parameters: body temperature, heart rate, SpO2, and ECG. The values can be viewed on an LCD, and the device's IoT base allows it to send data to a web application that can be easily accessed from any location. Given the issues, the gadget is designed to be affordable, dependable, and portable, making it simple for patients to roam around the hospital while being monitored for temperature, heart rate, and SpO2. Its accuracy is 98.11%, 98.11%, and 99.73%, respectively. Additionally, because medical gadgets are either unavailable or in restricted supply at hospitals, patients can utilize them at home.
Electrocardiogram (ECG), Vital Signs, Heart Rate, Oxygen Saturation, Body Temperature, Patient monitoring system, Reliable
Copyright (c) 2025 Muhammad Daniyal Maqsood Alvi, Khair Ul Wara, Syed Faizan Raza Shah Zaidi, Komal Tariq, Saad Abdullah, Shahzad Nasim https://creativecommons.org/licenses/by/4.0
Exploring Factors Causing Traffic Congestion through Average Index Method: A Case Study of Hyderabad City
The rapid growth in population and economic activity has primarily led to increased traffic congestion. The system of managing and planning transportation triggers chaos. The study aims to determine the major factors behind Traffic congestion. High urbanization rate, encroachments, public dependency upon private vehicles and giving of public transport, Lack of awareness, and monitoring of transportation adequacy, etc. In this regard, hotspot areas are highlighted, and the factors are ranked, which are assessed using a Likert scale rating-based questionnaire conducted by experts and respondents of Hyderabad, Sindh. These factors are analyzed by applying the Average Index Method Technique, and the sites are identified by generating the Road Network maps on ArcGIS. Findings reveal a clear concept about traffic congestion and the factors blocking traffic flow in the study area. It further explores that the top-ranked factor for traffic congestion, with an average index of 4.50, is Encroachments on roads and Footpaths, whereas the lowest average index is 3.30, Location of Hospitals and other amenities near main roads. This research can help reduce traffic congestion in Hyderabad and other cities with similar scenarios.
Copyright (c) 2025 Fahad Ahmed Shaikh, Nafees Ahmed memon, Imtiaz Ahmed chandio https://creativecommons.org/licenses/by/4.0
Enhancing User-Centered Feature Development: Integrating Design Thinking into Feature-Driven Development
methodologies has gained significant attention in producing user-centric products. Feature-driven development incorporates market demands in a very short period and accomplishes outcomes and interpretations from each iteration. Whereas design thinking is a human-centered approach that keeps the user at the core of the decision. This research aims to propose a framework that establishes a bridge between feature-driven development and design thinking, as user needs are not prioritized during feature-driven development due to the main emphasis on feature completion, and to enhance user-centered feature delivery in agile environments. This research investigates the 40 respondents’ opinions, consisting of low-experienced and highly experienced practitioners, drawing on a mixed-methods research design that includes surveys with agile teams. We explore the proposed framework's effects on user specifications, speculation, differentiation, data dissemination, emotional engagement, level of fidelity, user-centric experience, co-creation, realism, and user contentment. The analysis revealed that user satisfaction is influenced by experience levels, with years of experience showing a significant main effect (p = 0.024). Low-experienced individuals had lower satisfaction scores compared to high-experienced individuals. These findings suggest that experience duration plays a key role in shaping user satisfaction. A mean difference of -0.170 indicates lower user satisfaction among less experienced individuals, suggesting that satisfaction varies with experience duration. These findings suggest that variations in user satisfaction are influenced by the duration of experience.
Copyright (c) 2025 Anum Khalid, Safa Tariq, Rohina Akram, Dr. Maryam Kausar https://creativecommons.org/licenses/by/4.0
Predicting Weather Disruptions for the ICC Champions Trophy 2025 in Pakistan Using Machine Learning and Data Analytics
With an emphasis on the upcoming ICC Champions Trophy 2025, which will be staged in Pakistan, our research paper examines the complex relationship between weather and cricket. Correct forecasting of the weather is crucial for outdoor sporting events to avoid cancellations and guarantee peak athlete performance since climate change has an increasing impact on weather patterns. Pakistan's varied terrain, which encompasses plains, hilly areas, and coastal regions, contributes to the country's notable variety in climate. It is vital for event planners to comprehend local climate dynamics since this variability causes unpredictable weather patterns, such as monsoon rains, intense heat waves, and droughts. Our approach comprised combining real-time analytics with historical weather data from Open-Meteo, as well as using Python tools and the Machine Learning algorithm to predict rain during a game. Power BI is used to show the results, giving a thorough understanding of the climatic trends in three important cities: Karachi, Lahore, and Rawalpindi. The research highlights the significance of data-driven decision-making for event planners, as it empowers them to execute efficient backup plans The main objective of this research is to improve the sports environment for both players and spectators while tackling the urgent problems caused by climate variability.
Climatic Variability in Pakistan, Data Analytics, Machine Learning, Open-Meteo Stat, Rain Prediction
An Experimental Study of Facial Recognition Performance for Real-Time UAV Surveillance in Urban Environments
In today’s era, facial recognition is common in every aspect of life whether in smartphones, security, attendance management, healthcare, and law enforcement. Modern facial recognition uses a state-of-the-art camera mounted on each device, but we need to integrate it at a certain height for security purposes. To solve this problem, this article proposes real-time facial recognition through drone imaging for urban safety and surveillance. In this article, the Unmanned Aerial Vehicle is equipped with sensors and is allowed to fly in urban locations and capture images to find the suspected one through the image processing technique Convolutional Neural Networks (CNNs). In this research, performances of three facial recognition systems, Local Binary Pattern Histogram (LBPH), FaceNet, and Face_Recognition systems have been presented for Frames Per Second, Accuracy, and real-time tracking. A comparative analysis of the proposed work is also presented to validate the work where a minimum confidence threshold of 80% is achieved.
Copyright (c) 2024 Muhammad Shafiq, Beenish Ayesha Akram, Samar Raza Talpur, Leezna Saleem https://creativecommons.org/licenses/by/4.0
A Systematic Study on Recent Evolutionary Algorithms for Multi-Modal Multi-Objective Optimization
The real-world optimization problems are inherently multi-modal and multi-objective (MMMO), such as bio-medical imaging, automotive engine design, plant identification in control systems, inference engine design, etc. This is mainly because of the acute diversity and diffusion of solutions in Pareto space and the multi-modality of the solution set. Therefore, finding the optimal solution for MMMO is a pressing need in the literature. It is evident from the literature that evolutionary Algorithms (EA) are the best candidates for solving MO problems. However, due to massive variants of single-objective EAs (SOEA) and Multi-objective EA (MOEA), the advocacy of the best EA for the MMMO problem needs to be improved in the literature. This work has presented a comprehensive study on the recent variants of EA to solve single-objective and multi-objective optimization problems. In addition, the Uni-modal and multi-modal problems are also considered with the respective EA.
Multi-modal and multi-objective problems
Single Objective Evolutionary Algorithms (SOEA)
Multi Objective Evolutionary Algorithms (MOEA)
Copyright (c) 2024 Muhammad Waqar Khan, Adnan Ahmed Siddiqui, Syed Sajjad Hussain Rizvi https://creativecommons.org/licenses/by/4.0
Cancer Prediction in Healthcare: Leveraging Data Mining Techniques
Data mining methodologies have been widely applied in healthcare research and innovation, and have been proven effective in supporting clinical analysis and predictions. Cancer remains one of the leading causes of mortality worldwide, showing a significant global health challenge. While cancer research has traditionally focused on medical and biological studies, data-driven approaches have emerged as an important complement to conventional methods. Cancer outcome prediction represents a complex and demanding task in healthcare analytics. Data mining methods enable the analysis of cancer incidence across demographic variables such as gender and geographic regions, as well as lifestyle and socioeconomic factors, including diet, education, marital status, and living conditions. These factors contribute significantly to cancer pattern recognition and risk assessment. Many existing predictive systems depend on expert-driven models that achieve high accuracy, but those are computationally intensive and time-consuming. In this research, experimental results indicate that techniques such as clustering and classification can provide high precision; however, optimizing computational efficiency remains a key research challenge. Consequently, the development of accurate and scalable data mining models is essential for effective cancer prediction and decision support.
Cancer disease, Machine Learning, Convolutional Neural Network, Data mining and Bioinformatics.
Code and Beyond: A Review on the Impact of AI on Modern Software Development
This comprehensive review examines the link between AI and software development by examining their interplay throughout all development life cycle stages. The research combines insights from current literature and industry practices, to shed light on AI's impact on processes such as requirement analysis, design, coding, and testing. The study uses the Review Paper technique for examination, covering a wide range of sources, including academic databases, research portals, and publications. The seven-week timeframe includes data analysis, a study of the literature, and a critical examination of the implications and effective role of AI. The study indicates that AI is a collaborative tool in software development, based on historical and contemporary technical realities. The study not only gives more detailed knowledge but also establishes the framework for future research. underlining the need for industry-specific AI applications and a better evaluation of developers' experiences. This research acts as a guidepost for industry executives and academics, providing insights into the changing landscape influenced by AI on software development
Copyright (c) 2024 Aneeta Lohana, Sumbul Ghulamani, Kamran Khowaja, Kazim Raza Talpur, Asadullah Shah https://creativecommons.org/licenses/by/4.0
Harnessing Synergy of Microbial and Plant-Based Systems for Enhanced Bioelectricity Generation in Sustainable Power Systems
The exploration of sustainable and renewable energy sources parallels the computational modeling and simulation of biological systems, both driven by the increasing energy demands of modern society. One promising area of research is the utilization of microorganisms for electricity generation, leveraging their inherent metabolic processes. This study investigates two innovative approaches: Microbial Fuel Cells (MFCs) and Plant Microbial Fuel Cells (PMFCs). MFCs harness the electron transfer capabilities of certain bacteria to generate electricity directly from organic matter. This bio-electrochemical system offers a sustainable and environmentally friendly method of energy production. However, the performance of MFCs can be enhanced by incorporating plant-based systems, leading to the development of PMFCs. In this research, we introduce a novel PMFC design based on the Aloe vera plant, which demonstrates improved stability and increased bioelectricity generation compared to traditional PMFCs. We evaluate the impact of incorporating plants and compost on bioenergy production in PMFCs and present an automated testing framework for the electrical characterization of these systems. By harnessing the synergy between microorganisms and plant systems, this study aims to contribute to the ongoing efforts in developing clean and sustainable energy solutions. The proposed approaches not only address the depletion of fossil resources but also mitigate environmental degradation, aligning with the global sustainability goals.
Bioenergy generation, Chemical energy conversion, Eco-friendly energy, Electron flow, MEA (Membrane electrode assemblies), Microbial Fuel Cells (MFCs)
Copyright (c) 2024 Abid Ali, Mohammad Irfan Anis, Sheraz Mohani, Faraz Ali, Muhammad Ovais Akhter, Asif Aziz https://creativecommons.org/licenses/by/4.0
Leveraging Input-Output Technology to Identify Key Economic Clusters of the Jizan Region
This study ascertains the primary sectors of the Jizan region by analyzing interindustry connections. The study employs input-output tables and input-output modeling to perform a linkage analysis, which evaluates the forward as well as backward connections between sectors. The findings highlight that the main sectors exhibiting strong interdependencies with other sectors are ‘agriculture, forestry and fishing’, ‘manufacturing’, ‘wholesale and retail trade’, and ‘information and communication’. This study emphasizes the role of input-output technology within these sectors, particularly in enhancing productivity, streamlining processes, and fostering innovation. The capacity of these sectors to drive production, create jobs, and facilitate economic development through robust industry connections renders them critical.
Copyright (c) 2024 Ghazala Parveen, Mohammad Arif Riaz, Said Mohammad Jaouadi, Irfan Ahmed https://creativecommons.org/licenses/by/4.0
Assessing Workplace Hazards in Small and Medium Enterprises: An Empirical Study from Southern Pakistan
Employees are undeniably among an organization's most valuable assets. In the course of their daily work, millions of individuals labor in environments, which substantially expose them to various hazards. Mitigating and eliminating these hazards not only ensures workplace safety but also contributes significantly to the attainment of the Sustainable Development Goals (SDGs) set forth by the United Nations. This study was explored the status of occupational health and safety practices related to workplace hazards while focusing on 35 Small and Medium-sized Enterprises (SMEs) situated in the SITE area of Kotri, Sindh, Pakistan. An empirical study was conducted using a questionnaire adapted from the work of Ezekiel M. Makori [1]. Further, data was analyzed in three stages with the help of SPSS. In the first stage, data reliability was checked with Cronbach’s alpha, which was 0.80. In the second stage, statistics was used to compute the mean, standard deviation, and variance of the variables and in the third stage inferential statistics was applied. Results showed that there are many hazards that exist in the working environment of the SMEs notably noise, fire, temperature, and working environment with a greater mean value. There was a negative correlation between occupational health and safety practices and workplace hazards. It is recommended that effective workplace inspections should be conducted on a regular basis to identify the hazards and eliminate them.
Copyright (c) 2025 Miskeen Ali Gopang, Shakeel Ahmed Shaikh, Inayat Ali, Rano Khan Wassan, Safdar Ali Larik https://creativecommons.org/licenses/by/4.0