International Journal of Innovations in Science & Technology
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Barriers to Maternal Health Care Accessibility and Its Causal Determinants in Faisalabad, Pakistan: A Geospatial Assessment
Full text linkAccess to maternal healthcare is a critical determinant of maternal and neonatal health outcomes, yet it remains a neglected issue in many developing regions, including Pakistan. This study investigates the spatial distribution and accessibility of maternal healthcare facilities in Faisalabad District using Geographic Information System (GIS) techniques, including point distance analysis and multiple ring buffer analysis. A total of 230 female respondents from six tehsils were surveyed using a structured questionnaire, with data gathered from two major public hospitals in the district. There are 155 Basic Health Units (BHUs) operating within Faisalabad District. 45.2% of respondents reported good accessibility to maternal health facilities, while 43% reported bad and 11.7% worse access. The highest concentration of cases (78 respondents) was within 6–10 km of a health facility. The findings reveal significant spatial disparities in access to healthcare facilities, particularly in rural and peripheral areas such as Tandlianwala, Chak Jhumra, and Samundri. Many women reside more than 20 kilometers from the nearest well-equipped hospital, and road conditions—ranging from poor to non-existent—further limit physical accessibility. The multiple ring buffer analysis demonstrates that several remote settlements fall outside the 12-mile buffer, indicating critical service gaps. Additionally, economic constraints and lack of public transportation exacerbate the situation, limiting women\u27s ability to seek timely antenatal care. The overcrowding of urban public hospitals and the high cost and low quality of private healthcare further restrict options for low-income groups. The study underscores the urgent need for decentralized planning, infrastructure improvement, and equitable distribution of maternal health services, especially in underserved rural regions
Geo-Spatial Analysis to Access Land Slide Susceptibility in Tehsil Balakot, District Mansehra, Pakistan
Full text linkLand slides are one of the recurrent natural problems that are widespread throughout the world, especially in mountainous areas, which cause significant injuries and loss of human lives, damage to properties and infrastructures. The term “landslide” is the movement of a mass of rock, debris, or earth down a slope under the influence of gravity. Landslide hazard mapping is a fundamental tool for disaster management activities in fragile mountainous terrains. The main purpose of this study was to find out landslide hazard assessment by bivariate statistical modelling and prepare an optimized mitigation map of the tehsil Balakot. The modelling was performed using a geographical information system (GIS) to derive a landslide hazard map of the tehsil Balakot. To achieve the objectives of the study, two types of variables, that is, dependent variables and independent variables, were used. The dependent variable that was selected for study was landslide occurrences. As a mandatory part of the study, the sites of previous landslides were collected from Google Earth Pro software, and a consecutive field visit was also conducted to validate the landslide sites on the ground. The Independent variables were the landslide causal factors. The causal factors that were used to achieve the objectives are Slope, Aspect, Curvature, NDWI, NDVI, Geological map, Elevation, and River distance network. The DEM data, Sentinel-2 data, and regional geologic map were used to process the landslide causal factors. The information value model was used for assessing the landslide susceptibility. The landslide susceptibility map was evaluated using the ROC curve. The result of the AUC curve was 78.71% which indicated good accuracy in the identification of the landslide susceptibility zone in a regio
Effects of Polyethylene Glycol (PEG) Simulated Drought Stress on Physio-Agronomic Characteristics in Myhco Variety of Sorghum Bicolor L
Full text linkThe present study was aimed at determining the differential interactive effects of Ca/Mg quotient and PEG-simulated drought in Sorghum bicolor at the vegetative stage. Sorghum bicolor collected variety Myhco from Persabaq Nowshera were sown in earthen pots (lower inside diameter, 18cm upper inner diameter, 20 cm height and 2 cm thickness) filled with 2 kg of air-dried soil and silt (2:1) pH, moisture content and field capacity in triplicates in the green house of the Department of Botany, University of Peshawar in 2019. The designed experiment contains seven treatments each having three replicates, among these treatments first three are control, the second three are treated Ca/Mg quotient 4+PEG0.6 Ca/Mg quotient 4+PEG0.2, Ca/Mg quotient 2+PEG0.6, Ca/Mg quotient 2+PEG0.2, Ca/Mg quotient 0.18+PEG0.6 Ca/Mg quotient 0.18+PEG0.2, while the last three treatments are treated Ca/Mg quotient 0.18+PEG0.2. Conclusions We conclude that there is a reduction in the agronomy, i.e., leaf area, leaf fresh and dry weight, and a similar reduction also occurred with all other vegetative parts. There is a clear difference between control and PEG drought, and a greater reduction is observed in 0.6 MPa drought. The biochemical characters were also affected in the same manner; a clear reduction was observed in chlorophyll, sugar, and protein, and occurred while the Ca/Mg quotient had no significant effect on Sorghum bicolor L.in Varity myco
Industry 5.0: An Energy-Efficient Smart Task Offloading Mechanism for Multi-Access Edge Computing
Full text linkThe 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
Digital Credibility and Social Gratification: Understanding How Generation Z in Pakistan Engages with Misinformation and Algorithmic Influence in the Contemporary Social Media Landscape
Full text linkIn 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
Enhancing Skin Cancer Detection: A Study on Feature Selection Methods for Image Classification
Full text linkVisually comparable images can be easily recognized by the human eye, but specialist knowledge is needed to correctly describe medical images, such as those showing skin afflicted by cancer. As skin cancer is becoming more commonplace worldwide, there is a growing need for qualified specialists to help with its diagnosis. Several intricate genetic abnormalities lead to cancer, one of the most serious illnesses. Skin cancer is the most frequently diagnosed type of cancer. The present research examines two main methods: segmentation and feature extraction, since early identification is essential to enhancing treatment results. Our research focuses on identifying malignant melanoma, which is caused by an overabundance of melanocytes in the dermis layer of the skin. We used the well-known dermatological approach known as asymmetry, border, color, and differential (ABCD) dermoscopy to aid in early identification. Asymmetry (differences in shape and structure), border irregularity (uneven or jagged borders), color variation (differing pigmentation inside the lesion), and differential structure (development in size and appearance over time) are the criteria used in this technique to analyze skin lesions. CNN-based deep learning models are used for image pre-processing, segmentation, feature extraction, and classification in the organized process of the suggested framework. Additionally, sophisticated digital image processing methods like size estimates, color identification, border analysis, and symmetry detection are included. By using CNNs to collect texture-based information, feature extraction is improved and skin lesions can be precisely categorized. We suggest using a Backpropagation Neural Network (BPNN) to increase classification accuracy and make efficient decisions when distinguishing between benign and malignant skin diseases. To overcome this difficulty, machine learning classifiers have surfaced as a viable way to automate the classification of images for skin cancer. In this paper, deep convolutional neural networks (CNNs) are used to construct a predictive model for skin cancer diagnosis. Using the HAM10000 dataset, the suggested method produced a 92% accuracy rate
Sustainable Energy Solution for Mobile Robotics: Modeling and Power Management
Full text linkRobotics is increasingly addressing inefficiencies in diverse applications, including healthcare, mining, and defense. This research focuses on energy optimization for a Two-Wheel Mobile Robot (TWMR) using a novel framework that analyzes component-level power consumption and evaluates battery performance. Simulations conducted in Gazebo Classic 11, powered by ROS (Noetic), assessed the TWMR\u27s battery discharge rates across different configurations. Results identified the Lidar sensor as the primary power consumer, with a 300mAh battery significantly extending operational duration (65.83 seconds) compared to a 100mAh battery (21.41 seconds). The study also examined how component integration impacts energy usage, providing valuable insights for future robotic system designs. These findings highlight the critical role of battery size in optimizing energy efficiency and ensuring prolonged functionality of robotic systems in practical applications
Analysis of Machine Learning Models to Automate the Early Detection of Alzheimer Disease
Full text linkAlzheimer\u27s disease is an advanced neurological illness that primarily affects those over 65. It is characterized by memory loss and cognitive deterioration. Although there isn\u27t a known cure, early intervention can greatly delay the disease\u27s progression, which emphasizes how crucial a prompt and precise diagnosis is. Early-stage identification is still a difficult and time-consuming procedure, though. This study uses machine learning (ML) to improve and speed up Alzheimer\u27s disease detection. The National Alzheimer\u27s Coordinating Center (NACC) dataset, which consists of clinical and genomic data, was subjected to three ML algorithms: Elastic Net Classifier (ENC), Random Forest (RF), and Artificial Neural Network (ANN). Unlike established methodologies that largely rely on Magnetic Resonance Imaging (MRI) paired with other modalities, this research highlights the utilization of limited datasets and comparatively underexplored clinical-genomic data. The models were trained and assessed using the Scikit-learn and Tensor Flow frameworks. With an accuracy, F1 score, and recall of 92%, ANN outperformed the other models, indicating its potential for early Alzheimer\u27s identification. This study demonstrates the feasibility of addressing difficulties in early-stage Alzheimer\u27s diagnosis by combining clinical and genomic data with machine learning algorithms
The Patient Monitoring and Alert System Using Mobile and Wearable Technology
No full textInterest in remote patient monitoring has grown significantly in recent years. We present a Patient Monitoring and Alert System that uses mobile and wearable technology to track vital signs like heart rate and blood oxygen levels (SpO₂). The system detects abnormal readings and sends instant alerts to caregivers or healthcare professionals through push notifications, SMS, and email. The mobile app is built using Flutter and connects to a wearable device using Bluetooth Low Energy (BLE) for real-time data transfer. We tested the system with 30 users and achieved a 95% success rate in delivering notifications, with fast data transmission (under 200ms via BLE), proving it to be a reliable solution for real-time health monitoring
AI Vision for Health Care: Virtual Keyboard and Mouse Empowering Partially Disabled Patients
Full text linkThis paper introduces a machine-learning-based virtual keyboard and mouse system designed to assist individuals with physical disabilities. The system recognizes hand gestures using computer vision techniques and translates them into keyboard inputs and mouse controls. By utilizing Convolutional Neural Networks (CNNs) and the YOLOv8 model, the system achieves real-time performance with an average accuracy of 92%, enabling touchless interaction with computers. The solution uses widely available hardware like standard webcams, making it accessible, affordable, and easy to deploy. This system improves the usability of computing devices for people with motor impairments, offering an innovative, touchless alternative to traditional input methods. It also supports essential tasks such as scrolling, clicking, and zooming through simple gestures. The framework is adaptable to various environments, ensuring it is easy to use in different settings. Our system offers a complete virtual keyboard and mouse solution using a common webcam and real-time gesture recognition, making computer use easier and more affordable for users with motor impairment