International Journal of Innovations in Science & Technology
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Geo-Spatial Dynamics of Snow Cover and Hydro-Meteorological Parameters for Gilgit Balistan, Pakistan
Get PDFSnow cover dynamism is an important component of the UIB’s (Upper Indus Basin) hydrodynamics in the context of snow building up and reduction occurring seasonally. This study incorporates investigation into the dynamics of snow covers in relation to the hydrodynamics of the region. Data acquired through remotely sensed MODIS (Moderate Resolution Imaging Spectro-Radiometer) satellite for the duration of 20 years from 2000-2020, together with additional variables of hydrometeorology was utilized in the assessment of spatial and temporal fluctuation in snow-covered areas of Gilgit Baltistan (GB). The snow cover analysis was done temporally with an evaluation of its relationship with the hydro-meteorological variables through the application of Pearson correlation, Principal Component Analysis (PCA), and basin-wise zonal analysis. The investigation revealed that glacial ice covered an area of 25 to 50% and that the SCA (Snow Covered Area) may expand to 80 to 90% of the region on the amassment of snow in the snowy season. Trends from hydro-meteorological correlativity demonstrate a greatly considerable proportionality of R = 0.78, between the maximal and minimal temperature zones and river drains. However, no noticeable correlativity was found between precipitation and river drains (R = -0.04). For the region of Hunza, a statistically important negative correlativity was observed between the river drains and precipitation i.e., R = –0.83. The minus factor indicates an increase in river drainage with increased melting of snow covers due to high temperatures. This investigation infers a close association of river runoffs of the GB area with its snow cover dynamism. Discharge of rivers is a consequence of melting snow in the basin due to rising temperature and thus it speeds up at the beginning of summers mainly during April and May. Snow and ice start melting from the bottom and then reaches the top areas that have greater upstanding glacial mass
Smart Homes and AI Based Models in Future
Get PDFA new era of architectural ideas is likely to be ushered in by the natural progression of "smart buildings," which necessitates the integration of sensors, rich data, and artificial intelligence (AI) simulation models. Better control, enhanced reliability, and automation are just a few ways in which AI simulation models can make homes more convenient, more comfortable, and more energy efficient. This article discusses the ways in which AI models can be used to enhance the development of smart homes, particularly in the realm of interior design. This case study demonstrates how AI may be integrated into smart homes to enhance the user experience and reduce energy consumption. After that, the essay will delve into the study of current research on the application of AI technology in smart houses, utilizing a wide range of novel concepts such as smart interior design and a Smart Building System Framework based on digital twins (DT). The benefits of employing AI models in smart homes, with an emphasis on living areas, are discussed at length before the paper concludes. The theme\u27s case study is meant to inspire new ways of thinking about how artificial intelligence (AI) might be practically implemented in smart homes to enhance their utility, comfort, and environmental friendliness. The ultimate objective is to maximize the benefits of AI in order to revolutionize domestic life and enhance the quality of human existence. Unanswered questions and promising directions for future AI research in the realm of smart homes are addressed in the article\u27s last section. Smart houses that incorporate AI technology are beneficial to homeowners because they improve security, convenience, and energy economy
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
Classification of Amputee EMG Signals Using Machine Learning Techniques
Get PDFIn the field of prosthetics and assistive technology, the accurate classification of EMG signals from amputees is of paramount importance. These signals provide insights into the intended movements of the user and are essential for designing intuitive and responsive prosthetic devices. This research is primarily centered on the meticulous classification of EMG signals using advanced machine-learning techniques. This research contributes by achieving high accuracy (95.77%, 97.36%, and 95.77%) using SVM, ANN, and CNN, respectively, on EMG signals from 11 amputees in the Ninapro database, offering an innovative approach to improve amputee assistance. We employed SVM, ANN, and CNN algorithms to classify EMG signals from 11 amputees in the Ninapro database, utilizing a robust methodology. This research yielded impressive accuracy rates of 95.77%, 97.36%, and 95.77% for SVM, ANN, and CNN, respectively, demonstrating the effectiveness of machine-learning techniques in amputee EMG signal classification. The discussion highlights the potential implications for improving prosthetic control and rehabilitation. This research presents promising results and highlights the potential of machine learning for advancing amputee assistance, opening new avenues for research and application
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
Design of Mega LEO Constellations for Continuous Coverage over Pakistan: Satellite Communication
No full textSatellite communication was effectively done in Geostationary Earth Orbit (GEO) in the past years. Recently the trend has shifted from GEO to Low Earth Orbit (LEO). The objective of our study is to propose a satellite constellation for Pakistan in LEO that will provide continuous coverage over Pakistan. As LEO is much closer to the earth as compared to other orbits such as GEO and High Earth Orbit (HEO) etc. one can achieve benefits like low latency rate, less fuel consumption, and signal transmission loss. In ongoing research, an attempt has been made to design the satellite constellation in LEO using the software, System Tool Kit (STK) which has 2D and 3D environment modeling. In the designed constellation, the satellites pass over Pakistan and access the target area. To get uninterrupted continuous coverage, the number of satellites per plane and the number of orbits is increased. The orbital inclinations were also adjusted to achieve the objective. One of the important tasks for continuous coverage is the concept of satellite handshaking which means that soon a satellite gets away from the line of sight of the ground station antenna; another satellite comes within the line of sight of that antenna. LEO satellites are more favorable for communication purposes as they provide reliable communication as well as higher bandwidth
AI-Based Stoichiometric Engineering of Zinc Cobaltite
Get PDFThis study investigates the impact of stoichiometric variations and defect engineering on the structural, electrical, and electrochemical properties of zinc cobaltite (Zn1-xCo(1+x)-O4) synthesized via a modified sol-gel method. By systematically varying the Zn: Co ratio, an optimal composition, Zn0.75Co2.25O4, was identified, demonstrating superior performance metrics. SEM images confirmed the morphological changes of spinel phase, with lattice parameter variations correlating to Zn content. EIS analysis revealed that moderate oxygen vacancies significantly enhanced conductivity, with Zn0.75Co2.25O4 exhibiting the highest electrical and electrochemical performance. The optimized material achieved a specific capacity of 290 mAh/g at 1 A g-1 and retained ~90% capacity after 500 cycles, surpassing prior benchmarks. This study provides a detailed understanding of the structure-property-performance relationship, highlighting the potential of defect-engineered zinc cobaltite for advanced energy storage applications
Global Climate Change Adaptation: Mitigating Flooding Impacts in Pakistan
Get PDFClimate change is indeed a wide-reaching problem with noteworthy consequences. The climate in Pakistan has been experiencing a quick-changing pattern, accompanied by an increase in the intensity and frequency of extreme events due to global warming. The capacity of people to adapt to climate change is crucial in reducing its impacts. Over the past decade, the irregular incidents of weather events such as floods, droughts, heat waves, and cyclones have had a significant impact on the economic growth of the country. The main goal of this study is to assess how well the local community is able to adapt to the challenges posed by climate change. The study was specifically conducted in Mianwali district, Punjab province, Pakistan focusing on this specific location allows for a more in-depth analysis of the adaptive capacity of its local community to climate change. A thorough survey of the district was conducted, and the responses of the people were recorded through questionnaires and interviews. People were asked about their views on climate change and the adaptive strategies they are implementing to tackle its effects. The findings unfolded the fact that the limitations of being unaware of environmental issues are not only the lack of education but also the financial constraints are there. The study also explained that the residents of Mianwali who are aware of climate change and flood trends are more concerned about growing and using a variety of crops as a resilience tactic. Although there is a greater number of people who are not even aware of climate change and the association of floods with it, the public also claimed that the local authorities are not providing them with any information in time. So, the results suggest a clear insight for the stakeholders and policymakers to manage flooding and climate change by providing the people with crucial information beforehand and managing the situation by suggesting and implementing multiple adaptive measures
Numerical Analysis of Flow Past Over Square Rods Using Control Rod at Distinct Gap Spacing
Get PDFThe influence of Reynolds number and gap spacing on flow via two detachable square rods with a small control rod in between is examined using two-dimensional numerical simulations. The range of gap spacing is determined by taking Re = 80–200 and g = 0.50–6.0. First, the impact of the computational domain and the accuracy of the grid points are analyzed. Among these are crucial flow modes, fully formed two rows of vortex shedding flows, fully developed regular and irregular vortex shedding flows, consistent flow, and shear layer reattachment. For every combination of (Re, g), the Cdmean of the C1 rod is higher than the Cdmean of the C2 rod. Additionally, push causes Cdmean2 values to be negative between g = 0.50 and 2.0. The value of Cdmean that is larger is 1.3907 (Re, g) = (150, 3.0). Furthermore, for (Re, g) = (200, 3.0) and (200, 1.50), respectively, for C1 and C2, the greatest percentage decrease in Cdmean is 19.3% and 120.3%, respectively
Exploring Learning Patterns: A Review of Clustering in Data-Driven Pedagogy
Get PDFEducational institutes amass and retain extensive amounts of data including records of student attendance, test scores, exam results, and performance statistics. Extracting insights from this data can provide valuable information to educators and policymakers. The rapid expansion of educational data underscores the need for sophisticated algorithms to process such vast quantities of information. This challenge led to the emergence of the field of educational data mining (EDM). Clustering is a popular approach within EDM that can find hidden patterns in data. Numerous studies in EDM have concentrated on applying diverse clustering algorithms to educational attributes. This paper presents a comprehensive literature review focusing on 43 papers spanning between 2013 to 2023 on the use of clustering algorithms and their effectiveness within the realm of EDM. The review indicates that K-means clustering has been utilized extensively in the reviewed literature with 29 of the 43 reviewed papers using K-means clustering in their analysis. It was also uncovered that cluster-based analysis majorly focuses on analyzing student performance in a course or in a degree program closely followed by clustering students based on class of learners. Insights are deduced from the reviewed literature highlighting the focus of current research and potential directions for the future