International Journal of Innovations in Science & Technology
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Remote Sensing Assessment of Small Dam Sites in Swat District, Pakistan: Inferences from Water Resource Scenarios
Get PDFIn a world where water is indispensable, Pakistan grapples with the challenge of ensuring its availability. Freshwater demand from domestic, industrial, and agricultural uses has strained the country\u27s reservoirs. Financial and political barriers have hindered the construction of large dams, making it imperative to seek alternative solutions. However, small dams have the potential to address Pakistan\u27s water security concerns. This study uses advanced technology, engineering expertise, socioeconomic factors, and environmental awareness to find multi-purpose small dam sites in Swat District, Pakistan. Water storage and community and economic development are goals. This study examines criteria using RS and GIS. Dam site selection considers rainfall patterns, slopes, land use, soil types, and drainage density. The study uses Elevation Area Capacity (EAC) curves to view potential reservoirs. The map divides areas into High, Moderate, and Low suitability. This analysis yields some sites where R4 is impressive for its suitability and storage capacity of 358,237 at 2080 m. R1 and R2 are promising with moderate suitability and large storage capacities of 121,346 and 271,964, respectively. These sites are more than numbers on a map they represent local aspirations. Their benefits include electricity, flood protection, irrigation, and drinking water. Small dams are progress catalysts with low maintenance and political support. This study concludes that socioeconomic and environmental factors should be considered when engineering small dams. This small dam can store water and provide essential services to local communities and economies. These multi-purpose small dams advance water security
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
Pragmatic Evidence on Android Malware Analysis Techniques: A Systematic Literature Review
Get PDFA large number of studies including research articles and surveys on android malware detection and analysis techniques have been presented during the last one and a half decades. The authors proposed different systems and frameworks to identify malware from software applications. However, there is no recent and comprehensive systematic literature review on the detection and analysis of android malware methods, systems, and frameworks. We present a systematic review of literature on android malware detection and analysis techniques and tools by following standard guidelines for Systematic Literature Review methodology from 2010 to 2021. We selected 75 most relevant studies out of 3343 published studies. We found that the prominent malicious datasets are Genome (39%) and Drebin (36%) used by different researchers for the detection of malware. The static, dynamic, and hybrid source code analysis methods are applied by android malware detection techniques. We also identified the limitations and future research directions of existing techniques as research gaps for the community. Based on the pragmatic evidence of this research, we have proposed a hybrid analysis-based multiple feature analysis framework. This framework will not only address the limitations of static and dynamic-based approaches, but it also analyzes evolving android malware datasets using deep neural network and machine learning techniques and improve the accuracy of evolving malware samples
How to Combat Against Upcoming Varients of Covid-19
Get PDFNew variant of Covid 19 poses a threat to re-imagine and re-design our cities, which will result in to reduced transportation and a brighter sky. The key objective of the study intends to enlighten how the new variant of COVID-19 may effect cities and their residents socially, economically, psychologically and to suggest measures to combat the effects of a pandemic. Cities are growth engines, and policymakers can help them become more sustainable by creating jobs, reducing poverty, and assisting in the resilience of cities. This is especially relevant for developing countries, which, in comparison to developed ones, are rapidly urbanizing. The researcher carried out a detailed survey in the case study area (Lahore) to gather the facts regarding the impacts of COVID-19. The reliability analysis technique was used to analyze the results. The variables/factors were reliable at the value of 0.8, and 0.7. The analysis shows that residents faced problems in mobility, daily commute, and unavailability of hospitals and health care units. Residents were affected psychologically as well. The most significant impact of the lockdown which proved itself a blessing was the improvement in air quality and the environment of Lahore. The researcher concluded that the epidemic will have a significant impact on Pakistani city administration and governance. Future decisions will determine if post-COVID cities are more environmentally friendly to construct and manage. However, in addition to economic growth, it is vital to address the social and environmental aspects of long-term sustainability
Diastolic Dysfunction Prediction with Symptoms Using Machine Learning Approach
Get PDFCardiac disease is the major cause of deaths all over the world, with 17.9 million deaths annually, as per World Health Organization reports. The purpose of this study is to enable a cardiologist to early predict the patient’s condition before performing the echocardiography test. This study aims to find out whether diastolic function or diastolic dysfunction using symptoms through machine learning. We used the unexplored dataset of diastolic dysfunction disease in this study and checked the symptoms with cardiologist to be enough to predict the disease. For this study, the records of 1285 patients were used, out of which 524 patients had diastolic function and the other 761 patients had diastolic dysfunction. The input parameters considered in this detection include patient age, gender, BP systolic, BP diastolic, BSA, BMI, hypertension, obesity, and Shortness of Breath (SOB). Various machine learning algorithms were used for this detection including Random Forest, J.48, Logistic Regression, and Support Vector Machine algorithms. As a result, with an accuracy of 85.45%, Logistic Regression provided promising results and proved efficient for early prediction of cardiac disease. Other algorithms had an accuracy as follow, J.48 (85.21%), Random Forest (84.94%), and SVM (84.94%). Using a machine learning tool and a patient’s dataset of diastolic dysfunction, we can declare either a patient has cardiac disease or not
Ecological Significance of Floristic Structure and Biological Spectrum of Alpine Floral Biodiversity of Khunjerab National Park Gilgit-Baltistan Pakistan
Get PDFThe current study was conducted in Khunjerab National Park which is situated in the subalpine zone. The study area was thoroughly surveyed to ensure the maximum collection of flowering plants diversity. The work aimed to investigate the ecological significance of floral structure and the biological spectrum of prevailing flowering plants\u27 biodiversity in the study area. For this purpose, we recognized four ecological zones based on altitude in the park namely the subalpine zone (3000m to 3500m), alpine zone (3600m to 4000m), super alpine zone (4100-4500m), and sub naval zone was started from (4600-4800m) altitude. The collected specimens comprised (155) plant species that belong to 97 genera and 36 families. The life forms of the collected species were 72% Hemicryptophyte (H), 13% Therophytes, 10% Chaemephyte, and 5% Phanerophyte. While the habit categories of the flora were analyzed with the help of Theophrastus classification. The breakup of the habit categories shows that the herbs with 137 species held the highest percentage to contribute the flora of the study area was with 88%, followed by shrubs with 14 species which contributed to the flora of the area was 9.03%. Similarly, subshrubs and trees contained the same number of 2 spices. We observed the phenological status of each species, i.e., flowering and fruiting conditions, and of the species that were infrequent.
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Health Implications of Arsenic and Qualitative Deterioration of drinking Water from Underground Water Supply Lines of Lahore, Pakistan
Get PDFThe study is a comparative analysis of water quality among two variant areas of Lahore. There are several problems regarding drinking water facilities. Drinkable water can be contaminated due to various reasons. Thus, the study highlights infrastructural causes (material of pipes and outdated pipes) of water contamination. Wall City and Gulberg are the study areas of this research. Gulberg area is far much better in various terms as compared to the wall city. Under this study, four parameters were selected for water quality pH, Total dissolved solids, E.coli and Arsenic. There were 13 water samples collected from each study area by random sampling. Samples were tested on the latest footing in this field. All results validate the problematic statement and highlight severe health effects. The results of these four parameters were far above the water quality standards declared by World Health Organization. Causes of these severe results include the outdated water pipes that are being laid down for the past several decades, for example Wall City area, etc. Results also depict low values in the Gulberg area which is recently developed as compared to the wall city. The comparative study also attests problem statement of the study.
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Evaluating Artificial Intelligence and Statistical Methods for Electric Load Forecasting
Get PDFElectric Load Forecasting (ELF) is one of the challenges being faced by the Power System industry. With the ever-growing consumer demand, power generating companies struggle to manage and provide an uninterrupted power supply to the users. Over the past few decades, the introduction of smart grids and power deregulation has changed load forecasting dynamics. Most of the current research focuses on short-term load forecasting (STLF), involving an hour to a week’s time forecasting. Various techniques are being used for accurately predicting the electric load. However, gold standards are yet to be defined mainly because of the subject\u27s variety, non-linearity, and un-predictive form. In this study critical review of 25 publications has been carried out to find the most efficient method for ELF. The novelty of this study is that comparative and scientific analyses are carried out to find the most proficient techniques for load forecasting. Also, various parameters are combined for comparison in this study after analyzing published reviews on the subject. Artificial Neural Networks (ANN) and Auto-Regressive Moving Average (ARMA) models outperform other methods basing upon statistical analysis, i.e., Mean Absolute Percentage Error (MAPE) and comparative acceptance, in the research community.
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Assessment and Monitoring of VIIRS-DNB and SQML-L light Pollution in Lahore-Pakistan
Get PDFThe usage of artificial light is excessive and improper. Earth\u27s night picture has changed significantly from space and studies have shown that over-exposure to artificial light in the night can influence animals, the environment and human beings. The purpose of this study was to monitor and measure skylights of Lahore City and temporary light pollution from 2012-2019. The Suite-Day/Night band of the Visible Image Radiometer was used for time changes analysis with GIS and Remote Sensing tools. Indicators were established as a table tool through zonal statistics, and a field survey was also undertaken to measure the Sky-Glow of Lahore with Sky Quality Meter-L. The results suggest that from 2012 to 2019, light pollution rose by 23.43 percent. Results suggest that around 53.99% of Lahore suffered from light pollution. The number of lights in Lahore has increased by 161.82 percent between 2012 and 2019. In the study period, the mean night light and the standard night light deviation were 127.87 and 98.22 percent, respectively. Lahore\u27s night sky was heavily polluted by light. Lahore\u27s average skylight is 17.15 meters above sea level, which means low quality skies at night. This research aims to provide people an insight into light pollution and the causes of local light pollution. Furthermore, this study aims to enhance public attention to light pollution mitigation attempts by governments and politicians.
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Evaluation of Microbial Contamination via Wastewater Collected from Different Oil Industries and its Treatment Using Various Coagulants
Get PDFWastewater from industrial discharged into other water bodies that pose serious risks to human health as well as the environment. The oil and ghee industries are also the main contributors to water pollution along with various other industries. The present study aimed to evaluate microbial load in waste water of oil industries in Lahore and its treatment using chemical and natural coagulants. Water samples were collected from three selected oil and ghee industries in Lahore. Physicochemical properties (Chemical oxygen demand (COD), Biological oxygen demand (BOD), and turbidity) and microbial contamination of water samples were analyzed before and after treatment. It was observed that samples treated with natural coagulants such as orange and banana peel, and date seeds showed a mild reduction in physicochemical parameters. Orange and banana peel coagulants caused a 30% reduction, while date seeds coagulants caused a 60% reduction in physicochemical parameters. A significant decrease in microbial load was noticed by using natural coagulants. However, for the chemical coagulants, it was observed that ferric chloride with alum and Ca+2 cation with bleaching powder caused an extreme reduction in physicochemical indicators and microbial load. While no significant decrease was observed in physicochemical indicators and microbial load when waste water samples were treated with Poly Aluminum chloride (PAC) and alum. It was concluded that chemical coagulants have a better ability to treat waste water as compared to natural coagulants.
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