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- 저자 : Waseemullah, Waseemullah (검색결과 4 건)
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Survey of AI‑Empowered Methods for Detecting Electricity Theft in Smart Grids
Waseem Ullah,Altaf Hussain,Muhammad Munsif,Habib Khan,Min Je Kim,Su Min Lee,Myoung Ho Seong,Sung Wook Baik 한국차세대컴퓨팅학회 2023 한국차세대컴퓨팅학회 학술대회 Vol.2023 No.12
This survey explores electricity theft detection in smart grids, where traditional power systems meet modern technology. Smart grids, designed for efficient energy management and continuous integration of renewables, face a pressing challenge electricity theft, costing utility companies over $96 billion annually. The survey traces the evolution from conventional to smart grids, emphasizing their core components. It underscores the economic impact of theft, driving researchers to explore Artificial Intelligence (AI) and Deep Learning (DL) techniques for detection. A comprehensive literature review reveals various approaches, with a focus on DL's growing influence. Public datasets are explored as invaluable resources, and methods for theft detection, including advanced AI and DL, are dissected. Performance metrics like accuracy and precision are discussed, and challenges, including imbalanced data and privacy concerns, are highlighted. In conclusion, the survey emphasizes the need for diverse AI and DL approaches, data sources, and features to create robust theft detection systems for smart grids, ensuring their secure and efficient operation.
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Animated Game-Based Learning of Data Structures In Professional Education
Waseemullah, Waseemullah,Kazi, Abdul Karim,Hyder, Muhammad Faraz,Basit, Faraz Abdul International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.9
Teaching and learning are one of the major issues during this pandemic (COVID-19). Since the pandemic started, there are many changes in teaching and learning styles as everything related to studies started online. Game-Based Learning has got remarkable importance in the educational system and pedagogy as an effective way of increasing student inspiration and engagement. In this field, most of the work has been carried out in digital games. This research uses an Animated Game-Based Learning design in enhancing student engagement and perception of learning. In teaching Computer Science (CS) concepts in higher education, to enhance the pedagogy activities in CS concepts, more specifically the concepts of "Data Structures (DS)" i.e., Array, Stack, and Queue concepts are focused. This study aims to observe the difference in students' learning with the use of different learning methods i.e., the traditional learning (TL) method and the Animated Game-Based Learning (AGBL) Method. The experimental results show that learning DS concepts has been improved by the AGBL method as compared to the TL method.
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Analysis of 3D Building Construction Applications in Augmented Reality
Khan, Humera Mehfooz,Waseemullah, Waseemullah,Bhutto, Muhammad Aslam,Khan, Shariq Mahmood,Baig, Mirza Adnan International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.10
Construction industry is considered as one of the oldest industries in the world since human came into being and the need of their own space is realized. All this led to make the world a space of many beautiful constructive ventures. As per the requirements of today's world, every industry is recognizing the need for use and adoption of modern as well as innovative technologies due to their benefits and timely production. Now construction industry has also started adopting the use of modern and innovative technologies during their projects but still the rate of adoption is so slow. From design to completion, construction projects take a lot to manage for which technology based solutions have continuously been proposed. These include Computer Aided Design (CAD), building information modeling (BIM) and cloud computing have been proved to be much successful until now. The construction projects are high budgeted, and direly require timely and successful completion with quality, resource and other constraints. So, the researchers observe the need of more clear and technology based communication between the construction projects and its constructors and other stakeholders is required before and during the construction to take timely precautions for expected issues. This study has analyzed the use of Augmented Reality (AR) technology adopting GammaAR, and ARki applications in construction industry. It has been found that both applications are light-weighted, upgradable, provide offline availability and collaborative environment as well as fulfil most of the requirements of the construction industry except the cost. These applications also support different screen size for better visualization and deep understanding. Both applications are analyzed, based on construction's application requirements, usability of AR and ratings of applications user collected from application's platform. The purpose of this research is to provide a detail insight of construction applications which are using AR to facilitate both the future developers and consumers.
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An Application of Machine Learning in Retail for Demand Forecasting
Muhammad Umer Farooq,Mustafa Latif,Waseemullah,Mirza Adnan Baig,Muhammad Ali Akhtar,Nuzhat Sana International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.9
Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.
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