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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.
Seasonal distribution and species composition of daytime biting mosquitoes
Waseem Akram,Faisal HAFEEZ,Unsar Naeem ULLAH,김연국,Aftab HUSSAIN,이종진 한국곤충학회 2009 Entomological Research Vol.39 No.2
Adults and immatures of Aedes mosquito populations were collected at temperatures between 40 and 44°C (summer), while larvae were collected at 0°C (winter). Major mosquito activities were observed from February to mid-December at various collection sites that yielded high populations of Aedes spp. from May to September, and high populations of Culex spp. and Anopheles spp. from March to September. In June to July, mosquito activity was suspended because the relative humidity was high (70%); a result of the monsoon rains. In August, with temperature ranging from 38 to 42°C, the populations of Culex, Anopheles and Aedes began to increase (36.8, 32.1 and 26.3%, respectively). Population estimates (through standard prototype Centers for Disease Control (CDC) and Biogents (BG)-sentinel) and species composition of Aedes in forest habitats indicated a rapid increase in the populations of Ae. albopictus (52.3%), Ae. aegypti (19.1%) and Ae. vittatus (28.5%) following the rainy season in July. Areas positive for Ae. albopictus had identical population levels and distribution ranges of Ae. vittatus, however, there were no Ae. aegypti in Ae. albopictus areas from August to September. The population level, seasonal distribution, habitat and areas of adult activity marked by global positioning system (GPS) coordinates are being used for reference and for species composition data of Anopheles spp. (2), Culex spp. (10) and Aedes spp. (5) in addition to associated temperature, relative humidity and physico-chemical factors of larval habitat. Global meteorological changes have caused an expansion of the active period, leading to the mosquito's possibility of being a vector of disease increasing, resulting in the spread of dengue fever.
Noman Khan,Waseem Ullah,Zulfiqar Ahmad Khan,Adnan Hussain,Min Je Kim,Sang Il Yoon,Sung Wook Baik 한국차세대컴퓨팅학회 2023 한국차세대컴퓨팅학회 학술대회 Vol.2023 No.12
Sustainable power systems should include solar energy generation. However, for effective grid management and the integration of renewable energy sources, accurate solar power generation predictions are essential. Therefore, this study compares the prediction of solar power forecasting in Italy and Bulgaria. These are two countries that have alike latitudes but different populations and solar energy production. The historical solar power generation and meteorological data from these countries are preprocessed and then used to apply four different deep learning models including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The results are analyzed to gain insights into how the proximity of geographical locations and the quality and quantity of data impact the precision of prediction algorithms.
Dataset Standardization for Effective Solar Power Forecasting : A Comprehensive Analysis
Zulfiqar Ahmad Khan,Waseem Ullah,Hikmat Yar,Noman Khan,Min Je Kim,Sung Wook Baik 한국차세대컴퓨팅학회 2023 한국차세대컴퓨팅학회 학술대회 Vol.2023 No.12
This paper introduces a comprehensive approach to dataset standardization aimed at enhancing the effectiveness and reliability of solar power forecasting models. Leveraging multiple datasets, this study incorporates additional attributes such as atmospheric pressure and sunshine duration. These enrichments bridge critical gaps in meteorological and environmental data, facilitating more robust and precise solar power forecasting. The paper underscores the significance of these attributes, furnishes detailed equations for their computation, and presents the outcomes of their integration. It underscores their pivotal role in enabling solar energy stakeholders to make informed decisions and optimize energy production effectively.
Dual Modality-based Animals Species Recognition using Deep learning Techniques
Min Je Kim,Tanveer Hussain,Waseem Ullah,Hikmat Yar,Mi Young Lee,Muhammad Sajjad,Sung Wook Baik 한국차세대컴퓨팅학회 2022 한국차세대컴퓨팅학회 학술대회 Vol.2022 No.10
The analysis, recognition and perception of behavior has usually been a crucial task for researchers. The goal of this paper is to address the problem to recognize animal species, which has numerous applications in zoology, ecology, biology, and entertainment. Researchers used different machine learning approach for animal species recognition, however the researchers mostly used image data for this purpose and ignore the importance of audio data. In this work, our focus is to process multi modality (image and voice) dataset for animal species recognition. We proposed two different networks for animals’ audio and visual representation to recongize animals’ species. First network for animals’ audios classification that extract MFCC features, and these features is passed from four VGG style blocks while the second network extract visual features from images to classify according to their species. The experimental results demonstrated the effectiveness of the proposed model of achieved better performance in terms of classification accuracies.
이상행동 및 행동 인식 모델 학습 및 테스트를 위한 시스템 UI 설계에 대한 연구
이수민,권찬민,Tanveer Hussain,Samee Ullah Khan,Waseem Ullah,Noman Khan,Zulfiqar Ahmad Khan,이미영,백성욱 한국차세대컴퓨팅학회 2021 한국차세대컴퓨팅학회 학술대회 Vol.2021 No.05
인공지능을 활용한 사업이 활발히 진행되면서 범죄 예방 및 안전분야와 관련하여 이상행동 및 행동 인식에 대한 연구와 관심이 높아지고 있다. 하지만 딥러닝 등 인공지능 모델을 생성하는 것은 전문 지식이 없는 경우 많은 어려움이 따른다. 본 논문에서는 사용자가 편리하게 딥러닝 모델을 생성할 수 있도록 데이터셋을 제공하고 이상행동 및 행동 인식 기술을 API화하여 인터페이스에서 호출하는 방식을 사용하는 사용자 친화적인 모델 학습 및 테스트를 위한 시스템 UI를 제안하였다. 본 논문에서 제안한 시스템은 딥러닝에 대한 사전 지식이 없는 사용자가 편리하게 딥러닝 모델을 생성할 수 있을 것으로 기대된다.
Zeeshan AHMED,Zain SHAKOOR,Mubashir Ali KHAN,Waseem ULLAH 한국유통과학회 2021 The Journal of Asian Finance, Economics and Busine Vol.8 No.5
The study aims to examine the role of financial risk management in predicting the financial performance of commercial banks in Pakistan over the period of 2006–2017. For this purpose, risk management is measured through credit risk, interest rate risk, and liquidity risk, while financial performance is measured through ROA, ROE, and ROI. For this purpose, the dynamic panel model and two step GMM panel estimators are used to test the hypothesis empirically. The annual secondary data has been taken from the published financial reports of commercial banks of Pakistan. The results show that financial risk management significantly decreases the financial performance of commercial banks in Pakistan. Overall, the results are conclusive across the alternative measures of financial risk management in predicting the financial performance of the banking sector in Pakistan. The study suggested that managers should adopt risk management and risk hedging strategies to manage commercial banks’ financial risks in Pakistan. They should hold extra cash while using the trade credit facilities. Previous studies mostly used a static model, but this study used a dynamic panel model. This study is among the first that focused on the various factors affecting the banks’ performance in Pakistan.
Habib Khan,Zulfiqar Ahmad Khan,Waseem Ullah,Min Jee Kim,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2023 한국차세대컴퓨팅학회 학술대회 Vol.2023 No.06
Accurate detection of small targets in aerial images is crucial but challenging due to the limited computational resources of UAVs. This paper presents an efficient approach based on YOLO-V5S for detecting and classifying distant vehicles in aerial scenes. Extensive ablation study is conducted to find the optimal YOLO architecture. The proposed method is efficient and effective, making it applicable for real-time deployment. A dataset of 1000 annotated images are developed to validate the proposed method's effectiveness. The proposed network outperforms existing state-of-the-art methods in accuracy, speed, and resource efficiency, making it a promising solution for aerial vision-based applications.
Facemask Detection in Real-World Environment with a Diversified Facemask Dataset
Khan Abbas,Min Je Kim,Ullah Waseem,Yar Hikmat,Hussain Altaf,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2021 한국차세대컴퓨팅학회 학술대회 Vol.2021 No.11
Covid-19 has been substantially impacting all major sectors of life since its outbreak in the early 2020. Owing to the sheer contagiousness and rapid transmission, the World Health Organization (WHO) issued stringent precautionary measures such as wearing facemask and keeping social distance to curb the spread of the pandemic. To enforce these precautionary measures, governments and multifarious private sectors across the world leveraged Deep Learning (DL) especially Computer Vision (CV). In this regard, the CV research community has paid greater focus on social distancing and facemask detection tools. DL undoubtedly exhibits better performance on large amount of properly annotated data. Therefore, this work focuses on the development of a large-scale and diversified facemask detection dataset that contains images of faces with masks and without masks under different lightning conditions and varying angles. The remarkable training and testing performance achieved by YOLOv4 on real-life test videos and movies, attests the diversity of the dataset samples.