International Journal of Innovations in Science & Technology
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Frontal System Track Variation and Its Impact on Water Availability in Northern Pakistan Using Remote Sensing and Ground Data During Monsoon Season- 2019
Get PDFPakistan, which is positioned in the South Asian sub-continent, occupies a significant climatological location. It is included among the world\u27s few countries which experience a comprehensive transformation from summer all the way to winter season. The variation in precipitation has direct and significant consequences on society. In this ongoing research, the latitudinal variation in the track of the frontal system and trends in Pakistan during the monsoon period have been examined. Meteorological data (monthly rainfall, maximum temperature, satellite images, upstream data for Tarbela, Mangla, Rasool, and Marala) has been taken to conduct the ongoing research. Consequently, the focus of the study is the frontal weather system that moves North of Pakistan and energizes the monsoon rainfall over the Indus Basin which makes it a source of flooding. The rainfall is the cause of flooding downstream of rivers in the plains of Punjab and Sindh. Varying trends in rainfall were observed across the selected stations in Pakistan. The ongoing research is conducted across Pakistan with Gilgit and Skardu being the cities in Northern Pakistan. Among all the water reservoirs, Tarbela exhibited an increased upstream flow due to the snow melt factor over glaciers in Gilgit and Skardu because of an increase in maximum temperature
Python Based Modelling of Flood Damage Assessment Using High-Resolution Aerial Imagery
Get PDFFlood is a natural disaster that can cause devastating impacts on the community, infrastructure, and the environment. UAVs enable to compute the extent of the flood and to identify the vulnerable areas prone to future flooding, assisting in the formulation of effective mitigation strategies. This study presents a case study of Barwai Khwar, Swat, Khyber Pakhtunkhwa (KPK), pre-flood image attained from Google Earth Pro and the post-flood aerial imagery was collected by using unmanned aerial vehicles (UAVs). To capture the detailed visual information of the flood-affected region and to assess the extent of the flood damage the acquired imagery was then processed by using advanced image processing algorithms to extract essential information, such as inundation extent, floodwater depth, and changes in land cover. This procedure assists in evaluating the precise damage assessment and development of effective recovery and mitigation strategies. Results revealed that the 2022 flood in Barwai Khowar\u27s large agricultural land was submerged (14758.9 perimeters), leading to a significant loss in crop yield and potential long-term impacts on food security. Additionally, critical infrastructure, including roads, bridges, and buildings suffered substantial damage. The destructed area of the retaining wall is 2184m (2km), housing damage is 1074.9m and 82.6 m of Nullah was calculated in this region. Moreover, the application of such technologies can facilitate more informed and timely responses to natural disasters, enhancing the overall resilience of communities and ecosystems
Effective Model for CoAP Inspired Trust Aware Scheme in Internet of Things with AES Algorithm
Get PDFIOT networks have been developed in the realm of technology to make connectivity among the things around us. Networks could only connect computer devices before the Internet of Things (IoT) became a reality. Security is the key issue with these devices. There are significant risks of data loss or hacker assault because these gadgets communicate their data via internet. Challenges from the start have accompanied IoT adoption. In this paper, some of the major difficulties on the way to communicate between gadgets are explored. IoT networks protect user privacy with various types of personal data that are made available for these IoT-based connected devices. To protect IoT-based systems, a trust-aware approach utilizing the CoAP and AES algorithms has been proposed in this paper. The AES method has very robust security, shielding the data and architecture in comparison to others. The utilization of AES coupled with CoAP will enhance the efficacy of the system
Remote Sensing-Based Prospectivity Maps Generation for Exploration of Minerals in Pakistan Using Machine Learning Techniques
Get PDFThe objective of this study is to generate and compare prospectivity maps that show the presence of Limestone in a specific area using remotely sensed data and machine learning techniques, in order to determine the most precise map that accurately depicts the presence of Limestone in that area. Remotely sensed data often utilize machine learning techniques to identify mineral formations and map geological features. Furthermore, machine learning techniques can also be used to generate prospectivity maps for mineral exploration. In this study, we utilized band ratios and principle component analysis (PCA) in conjunction with machine learning techniques to effectively identify Limestone formations and generate prospectivity maps for Limestone exploration using satellite imagery. Support Vector Machines (SVM) and Neural Networks (NN) were the machine learning techniques utilized on multispectral imagery from Sentinel-2 and Landsat-8. To assess the accuracy of the identification, the confusion matrix and kappa coefficient were employed. It was determined that the accuracy of the Neural Networks (NN) techniques was significantly better than the accuracy of the Support Vector Machines (SVM) techniques. The Neural Networks (NN) achieved an accuracy of 94.92% with a kappa value of 0.929, whereas the Support Vector Machine (SVM) had a maximum accuracy of 88.39% with a kappa value of 0.845. These high levels of accuracy and kappa coefficient values suggest that these machine techniques hold great potential for geological mapping and mineral exploration. The generated prospectivity maps can assist geologists and mining companies in identifying areas with a high potential for Limestone exploration, thereby reducing exploration costs and time
Quantifying Similarities: Oncology Documents from Google Bard and ChatGPT
Get PDFLarge language models hold immense promise for the future of text generation. Google Bard and ChatGPT, two prominent large language models originating from different research laboratories, have been subjects of various studies since their introduction. Despite numerous perspectives explored in the studies, none has specifically delved into the analysis of the similarity between texts generated by these models within the same category. This study addresses this gap by comparing the document generation capabilities of Google Bard and ChatGPT. The analysis focuses on topic-wise comparable documents related to oncology. In this study, 50 oncology-related documents generated by Google Bard are juxtaposed with equivalent topic-wise documents produced by ChatGPT, utilizing both cosine similarity and Jaccard similarity for comparison. The analysis employed statistical tests including the Kolmogorov-Smirnov test, Shapiro-Wilk test, and the one-sample Wilcoxon signed-rank test. The findings revealed a significant level of resemblance among the documents generated by both models: cosine similarity (mean = 0.66, std. dev. = 0.11, min = 0.23, max = 0.80) and Jaccard similarity (mean = 0.88, std. dev. = 0.06, min = 0.7, max = 1.0). This suggests a probable commonality in their training datasets or sources of oncology-related information. The study also posited that the observed similarity could be attributed to the probabilistic nature of language models and the potential for overfitting during their training processes. This study stands out for offering a unique direction and outcomes that pave the way for further exploration in the domain of large language models
Flood Inundation Modeling and Damage Assessment in Lahore Using Remote Sensing
Get PDFIntroduction:
Ravi River has a great contribution to the glorious history of Lahore City, the second biggest city in Pakistan. But similar to all rivers of Pakistan, River Ravi occasionally experiences extreme floods. During the past 100 years, two extremely high floods created devastation in Lahore City, which caused enormous loss of properties and lives. To save the main metropolitan areas of Lahore in both these floods, the Shahdara Breaching section, on the western bank of the river, was operated. The potential of loss due to floods has increased even more owing to the rise in population, industrialization, and spring of unplanned settlements in the floodplain of the river.
Importance of Study:
This research provides a solution that has the potential for long-term effects in flood management, hygienic improvement of the area, planned urban development around the river, and improvement of sub-surface water quality.
Novelty Statement:
The present study is focused on determining the flood damage assessment using advanced geospatial techniques with HEC-RAS applications.
Materials and Methods:
The reach of the Ravi River is from Shahdara to Balloki. Flood frequency analysis was performed to calculate a flood return period of five and fifty years. Hydraulic modeling of the river on HEC-RAS is used to find river capacity, its Validation, calibration, assessment of hydraulic capacity, flood inundation extent, and depth analysis.
Results:
It is concluded that a flood of 3643.97 cumecs magnitude corresponds to 5 years return period and 7406.699 cumecs magnitude corresponds to 50 years return period. If the same phenomena occur in a repeating manner, then the built-up settlement near Ravi can meet alarming threats. According to the maximum likelihood classification, the damage assessment was mapped wherein the results show that the buildup area was 15657 acres, the water body was 7059.246 acres, the cultivated area was 38395.3 acres, and uncultivated 59464.51 acres were affected.
Conclusion:
The solution can also address the problems arising due to changes in river course and depletion of natural habitat.
Recommendations:
However, along the Lahore City, the required width is not available. In this condition, an engineering solution is mandatory to pass the flood. Channelization can be proposed to create the width of the river. The reclaimed land should be used for high-quality urban development to increase revenues. For the sake of channel stability, a detailed sediment study should be done
Lossy Image Compression Unveiled: A Comprehensive Evaluation of DCT, Wavelet Transform, and Vector Quantization
Get PDFThe increasing demand for efficient image storage and transmission has driven extensive research into lossy image compression algorithms. This paper presents a comprehensive comparative analysis of three prominent lossy image compression techniques: Discrete Cosine Transform (DCT), Wavelet Transform, and Vector Quantization (VQ). Employing a diverse dataset and assessing their performance through key metrics, including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Squared Error (MSE), Bitrate, and Computational Complexity, we meticulously evaluated these techniques across dimensions of image quality, compression efficiency, and computational demands. DCT emerges as a standout performer in preserving image quality, closely followed by Wavelet Transform. While Vector Quantization demonstrates efficiency in compression, its limitations become apparent in the realm of image quality preservation. The comparative analysis unequivocally positions DCT as the optimal choice for applications prioritizing image quality. This preference is substantiated by its remarkable PSNR and SSIM scores. Despite DCT not being the most computationally efficient, its ability to strike a crucial balance between compression efficiency and image quality renders it a well-rounded and effective solution. In conclusion, this research provides valuable insights into the comparative performance of DCT, Wavelet Transform, and VQ in the context of lossy image compression. The findings underscore DCT\u27s superiority in image quality preservation, offering practical guidance for decision-makers in the field. The paper contributes to informed choices based on specific application requirements and emphasizes the pivotal role of DCT as a well-rounded and effective solution
Utilizing Machine Learning for Detecting Cyber Bullying in Social Media
Get PDFThe widespread dominance of the Internet and Electronic Media has made Social Media platforms a primary mode of communication. Unfortunately, these platforms have also become breeding grounds for harmful behavior, notably "Cyber Bullying," which involves using technology to inflict disrespect and harm on others. Despite various efforts by researchers to address this issue, the detection of such behavior remains crucial in combating this menace. This study aims to emphasize an effective approach for detecting cyberbullying on Social Media platforms. The findings indicate that the SVM (Support Vector Machine) classifier outperforms other classifiers in this context. We acquired tweet data from Twitter and used significant machine learning techniques to classify and forecast whether tweets are "offensive" or "non-offensive" and after that, using the Support Vector Machine\u27s Algorithm, a machine learning-model is prepared to detect Cyber Bullying on Social Media Platform. This research provide promising results
Rock Fall Hazard and Risk Assessment using GIS Along Jaglot-Skardu Road, Pakistan
Get PDFRockfall is one of the major hazards all around the world. In Northern Pakistan, Jaglot-Skardu. The road is the main highway that connects KKH and Skardu, located in Gilgit Baltistan. The Area is very unique to study rock falls because of the variety of changes in the geological, seismological, and atmospheric conditions. The hazard and risk mapping of rock falls includes preparation of rock fall inventory map, susceptibility map and spatial analysis of Rock fall with its conditioning factors using GIS and Remote Sensing. The inventory map includes record of past rock falls along the road and prepared on hill shade map of the area using ArcGIS 10.4. Susceptibility maps of the area is generated using Weighted overlay technique. In Weighted overlay technique we use multi influencing factors of the rock falls as such as aspect, geology, slope, elevation, faults, curvature, Topographic wetness index, streams and road. Each map unit is than reclassified and assigned weight in weighted overlay to generate susceptibility map of area. In inventory map, almost 200 rock falls are marked and delineated on hill shade map of the area. The results of susceptibility show four zones i.e., low, moderate, high and very high hazard zones.The spatial analysis of rock falls showed that fault and geology is the main factor that are triggering the rock falls in the area. The areas which are present low and moderate susceptible zones are somehow safe and suitable for future planning and development and areas present in high to very high susceptible zones, large scale geotechnical investigations are required before any development and construction
Unveiling Inefficiencies in Open-Source Code Using Multistage Analysis with Software Metrics
Get PDFSoftware development is challenging due to its technical complexity and time-consuming nature. To overcome these difficulties, various technical solutions have been introduced. In commercial software development, code repositories serve as valuable resources, reducing the time and cost involved in the process. The utilization of pre-developed open code repositories has proven to reduce development time. However, ample amount of work has not determined whether these repositories are testable, maintainable, free of dead code, and have a concise implementation of equivalent algorithms. The objective of this article is to address this gap by thoroughly analyzing the complexity and maintainability of code repositories, determining the impact of removing dead code on size, complexity, and maintainability. For this study, a total of 200 Python open-source code were analyzed using RADON, a widely-used metric tool for assessing cyclomatic complexity, size, volume, and maintainability. The identification of dead code within the repositories was accomplished using Vulture, supplemented by expert evaluation. It has been revealed that the majority of the examined code included dead code, and the removal of this code led to a significant reduction in cyclomatic complexity, volume, and size, while improving code maintainability, as observed by the Mann Whitney U test. The study concludes that the blind use of open-source code is not safe. It strongly recommends the community to thoroughly explore and examine such code from different perspectives before actual implementation. The novelty of this study lies in the use of multiple software metrics in a multi-stage analysis to examine the impact of removing dead code on program complexity, size, and maintainability