International Journal of Innovations in Science & Technology
Not a member yet
813 research outputs found
Sort by
Impact of Urbanization on Land Use and Land Cover: A Geospatial investigation of Taluka Khairpur (2000-2020)
Get PDF Urbanization is a major driver of land use and land cover (LULC) changes, profoundly affecting agricultural lands and promoting urban expansion. Recent studies indicate that urban development often occurs on the most fertile and productive lands, contributing significantly to the reduction of arable land in the outskirts of cities. This research study analyzes the LULC changes in Taluka Khairpur using GIS and remote sensing techniques. It provides a detailed 20-year (2000-2020) analysis that has not been previously addressed. Satellite images for the years 2000, 2005, 2010, 2015, and 2020 were downloaded from the United States Geological Survey (USGS). ArcGIS was utilized for supervised classification and LULC calculation, including categories like built-up areas, agricultural land, barren land, desert, and waterbodies. The study revealed significant changes in LULC over 20 years. The built-up area in Taluka Khairpur increased by 131.59 km² (221%), during the study period which resulted in transformations in other LULC categories. Such as, agricultural land decreased by 34.40 km² (47.25%), barren land by 80.89 km² (34.74%), desert area decreased by 6.74 km² (2.56%), and waterbodies by 9.57 km² (3.64%). This study highlights the significant urban expansion and reduction in agricultural and natural land cover in Taluka Khairpur, underscoring the need for sustainable urban planning and environmental conservation
Hierarchical Modeling of Barriers to Sustainable Development in the Mining Industry of Pakistan: An ISM and MICMAC-Based Approach
Get PDFMining contributes to economic development but relies on finite and non-renewable resources, posing sustainability challenges. Achieving long-term economic stability and environmental preservation requires a balanced approach that integrates effective resource management with sustainable development strategies. However, sustainable development in mining is complex, as it faces multiple barriers related to governance, economic, structural, and environmental challenges. This study applies Interpretive Structural Modeling (ISM) to explore these barriers and analyze their interdependencies. Data was collected from the literature and analyzed through expert opinions via a structured questionnaire, and an ISM-based model was developed to determine the hierarchical structure of these barriers. The MICMAC (Cross Impact Matrix Multiplication Applied to Classification) analysis further classifies barriers based on their driving and dependence power, providing insights into their relative importance within the system. Findings reveal that all thirty-two barriers influence the sustainability process, with some controlling as a key driving force while others function as dependent factors. Lack of top management commitment and lack of enforcement of rules and regulations emerge as the most influential barriers due to high driving power and low dependence. The absence of autonomous barriers indicates that all identified factors significantly affect the sustainable development of mining. The hierarchical ISM-based model emphasizes the necessity for targeted interventions at different barrier levels. This research contributes to sustainability efforts by offering a structured approach to understanding barrier interrelationships, aiding policymakers and industry stakeholders in formulating effective strategies for responsible and sustainable mining practices
Quantifying Crop Residue Burning in Punjab, Pakistan: A GEE-Based Assessment of Air Pollution
Get PDFCrop residue burning has become a common agricultural practice in developing countries due to numerous economic and social factors. To delineate the current pollution generated from crop residue burning in Punjab, Pakistan, a detailed study was conducted based on district-level crop production data from 2020 to 2024. The extraction of agricultural land was gathered from the European Space Agency/Climate Change Initiative (ESA/CCI), and the active fire count data were acquired from the VIIRS 375 m FIRMS standard active fire product. Air quality parameters, including CO and CH4, were assessed using Sentinel 5SP Tropomi. It was determined from the results that approximately 27% of the burned area increased from the years 2020-2021, accounting for a rapid increase from 31,984.15 sq km to 40,651.86 sq km, correlated with high CO and CH4 concentrations in 2021 and 2024, respectively, whereas a 17% decline occurred from the years 2022 to 2023, accounting for a decrease from 37,008.54 sq km to 33,710.85 sq km. However, it increased again by approximately 5.9% from 2023 to 2024 (33,710.85 sq km to 35,691.10 sq km). Using GEE, our study demonstrates the application of satellite data to map agricultural residue burning, and this information can provide valuable insights for policy formulation and managing crop residue practices
Estimating Soil Erosion Risk in District Diamer, Pakistan Using RUSLE Model: A Spatial Analysis Approach
Get PDFSoil erosion is a critical issue in the hilly regions of Diamer, Pakistan, due to the region\u27s varying topography and significant precipitation patterns. This study uses an effective combination of Geographic Information System (GIS) technologies and the Revised Universal Soil Loss Equation (RUSLE) model to calculate soil erosion rates within the region\u27s complex topography. Different GIS layers, such as rainfall erosivity (R), slope length and steepness (LS) factor, soil erodibility (K), conservation practices (P), and cover management factor (C), were merged by utilizing satellite data and the Normalized Difference Vegetation Index (NDVI). The resulting map showed a maximum soil loss of 2279.3 t/ha/year over the region. Notably, the greatest soil loss was observed in the western regions of Diamer, where rainfall and rainfall erosivity are also recorded as high in these areas. Five separate categories of soil erosion were identified, with a mean soil loss rate of 27 t/ha/year. According to the GIS analysis, 95% of the overall area experienced less severe erosion than the severe erosion classes, accounting for 5%. Additionally, the study included the computation of composite NDVI estimates for 2023 using Google Earth Engine (GEE). This method improved both the scalability and usability of the study by enabling effective processing and storage of data in the cloud. GEE enables the computation of NDVI quickly and precisely. This pioneering study is an important step toward understanding and resolving soil erosion issues in Diamer, Pakistan. The study offers valuable insights for decision-making and management planning initiatives by utilizing cutting-edge GIS tools and RUSLE modeling
Performance Analysis of HCEDV-Hop Localization Algorithm in Anisotropic Wireless Sensor Network
Get PDFAccurate and energy-efficient localization is an ongoing challenge in Anisotropic Wireless Sensor Networks (AWSNs), especially when AWSNs are deployed in irregular topologies (like valleys, coastlines, and mountainous terrain) versus regular topologies. This extended work presents additional performance evaluation of the previously introduced Hop-Correction and Energy-Efficient DV-Hop (HCEDV-Hop) algorithm. The HCEDV-Hop combines an error-correcting step with a hop-constrained broadcasting approach to improve localization accuracy and reduce energy consumption. In this study, we evaluate the HCEDV-Hop in anisotropic contexts where radio irregularities are direction-dependent and deployments in C-shaped fields are representative of real-world scenarios. The efficacy of the HCEDV-Hop is assessed using both regular and random deployments for a range of node densities, DOI values, and hop thresholds. Simulation results showed that localization errors increased in anisotropic fields but were still significantly reduced compared to conventional DV-Hop. While random deployment at DOI = 0.2 performed best, regular deployment maintained consistent accuracy. Broadcasting t hops decreased energy use without diminishing accuracy. Overall, the HCEDV-Hop performed better in ideal circumstances but remained reliable enough for real-world applications such as disaster management, environmental monitoring, and military surveillance
Security Issues and Research Opportunities in Wireless Body Area Networks
Get PDFWireless Sensor Networks (WSNs) have found application in diverse fields, one of is Wireless Body Area Networks (WBANs). WBANs are essential networks for fitness diagnostics, observation, and flexible actuators, which rely on data gathered from numerous wireless sensors installed in or above the human body. Due to the Ad hoc nature of WBANs, there are security concerns, which can affect the confidentiality, authenticity, and integrity of data. Security and privacy play a critical role in ensuring secure communication by helping networks prevent unauthorized access and avoid fraudulent activities. Despite its significance, no survey has been conducted in WBANs in terms of computation and communication overheads, Man in the Middle attack (MIMA), Denial of Service (DoS), and Spoofing attacks. This paper helps the new researcher in WBANs security to better understand the area and the need for designing new schemes that focus on the aforementioned parameter
Improving Software Security Through an LLM-Based Vulnerability Detection Model
Get PDFThe 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
Get PDFManual 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
Quantifying Confidence in Diabetic Retinopathy Diagnosis: A Comparative XAI Study of Deep Learning and Bayesian Neural Networks
Get PDFDiabetic 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
Education Access System (EAS): A Low-Latency Learning System for Low-Bandwidth Education Access
Get PDFOnline 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