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- 저자 : Fath U Min Ullah (검색결과 6 건)
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머신 러닝 기법을 활용한 무인 항공기 기반 재난 영상 분류
Altaf Hussain,Samee Ullah Khan,Fath U Min Ullah,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2021 한국차세대컴퓨팅학회 학술대회 Vol.2021 No.05
Recently due to natural disasters, the world is facing huge ecological, social, economic, and loss of precious lives. Traditionally during natural disasters, emergency response teams are physically visiting different areas to inspect and stop their further damages. Therefore, the existing monitoring system is facing issues such as human accessibility and unable to analyze disaster in real-time. To address these issues, we propose a machine learning inspired framework for automatically recognized disaster scenes that contains three main steps. In the first step preprocessing is applied for condense and normalize the image dimension. Next, histogram of oriented gradient (HOG) descriptor is utilize to extract discriminative features and extracted features are classified through SVM. Finally in testing step, in case of disaster scenes our system trigger notification to nearby disaster management centers to take an appropriate action. We provide comprehensive experiments on various machine learning approaches among them we obtain 64% accuracy on HOG with SVM.
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Sajjad, Muhammad,Nasir, Mansoor,Ullah, Fath U Min,Muhammad, Khan,Sangaiah, Arun Kumar,Baik, Sung Wook Elsevier science 2019 Information sciences Vol.479 No.-
<P><B>Abstract</B></P> <P>Facial expression recognition is an active research area for which the research community has presented a number of approaches due to its diverse applicability in different real-world situations such as real-time suspicious activity recognition for smart security, monitoring, marketing, and group sentiment analysis. However, developing a robust application with high accuracy is still a challenging task mainly due to the inherent problems related to human emotions, lack of sufficient data, and computational complexity. In this paper, we propose a novel, cost-effective, and energy-efficient framework designed for suspicious activity recognition based on facial expression analysis for smart security in law-enforcement services. The Raspberry Pi camera captures the video stream and detects faces using the Viola Jones algorithm. The face region is pre-processed using Gabor filter and median filter prior to feature extraction. Oriented FAST and Rotated BRIEF (ORB) features are then extracted and the support vector machine (SVM) classifier is trained, which predicts the known emotions (Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise). Based on the collective emotions of the faces, we predict the sentiment behind the scene. Using this approach, we predict if a certain situation is hostile and can prevent it prior to its occurrence. The system is tested on three publically available datasets: Cohen Kande (CK+), MMI, and JAFEE. A detailed comparative analysis based on SURF, SIFT, and ORB is also presented. Experimental results verify the efficiency and effectiveness of the proposed system in accurate recognition of suspicious activity compared to state-of-the-art methods and validate its superiority for enhancing security in law enforcement services.</P>
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드론을 통한 산불 감지를 위한 효율적인 CNN 아키텍처
Hikmat Yar,Noman Khan,Fath U Min Ullah,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2021 한국차세대컴퓨팅학회 학술대회 Vol.2021 No.05
Forest fire is one of the most dangerous disasters worldwide, due to which its management is a key concern of the research community to prevent social, ecological, and economic damages. Wildfires are extremely catastrophic disasters that lead to the destruction of forests, human assets, reduction of soil fertility and cause global warming. To overcome such kind of losses early fire detection and quick response is the key concern of research community. Therefore, in this paper, we propose a lightweight convolution neural network (CNN) method to efficiently detect the forest fire for unmanned aerial vehicles (UAVs) or drones. For the experimental evaluations, we develop an aerial images dataset from YouTube, movies, and google images. The results of the proposed architecture reveal its good performance in terms of 96% accuracy.
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Towards Autonomous Grid : Solar, Wind, and Weather Data for Renewable Energy Production
Sang ll Yoon,Noman Khan,Samee Ullah Khan,Fath U Min Ullah,Su Min Lee,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2022 한국차세대컴퓨팅학회 학술대회 Vol.2022 No.10
Nowadays, energy management and its optimization using smart devices are getting more attention due to their significant applications. Moreover, the applications used in these devices play a key role in developing smart cities that is only the way to solve urban problems. The potential of renewable energy sources like solar and wind power has been integrated in the smart grids to overcome the lack of supply via conventional fossil fuels and their environmental disputes that reduce operational cost. This review paper describes the significance of renewable power data that directly assists all the functions in smart cities such as the evolution of microgrids, renewable resources, energy forecasting, and power storage technologies. Furthermore, solar and wind power plants’ data with weather information as an additional cue is collected from different companies in South Korea. We aim to assist the researchers to develop artificial intelligence (AI)-based algorithms for power forecasting and establish its efficient management between suppliers and consumers.
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효율적인 크로스 플랫폼 활용을 위한 서로 다른 기관의 DB 연결 방법에 대한 연구
권찬민,김민제,Ijaz Ul Haq,Fath U Min Ullah,Umair Haroon,이미영,백성욱 한국차세대컴퓨팅학회 2021 한국차세대컴퓨팅학회 학술대회 Vol.2021 No.05
오늘날 빅데이터 분석, 사물인터넷(IoT) 등 여러 ICT 기술이 발전하면서, 전 세계적으로 빅데이터 중심의 경제 및 사회적 가치 창출을 위한 빅데이터 분석 기술 연구가 진행되고 있다. 그러나 최근 들어 기업 및 기관에서 다중 플랫폼으로부터 분석하기 위한 방법이 필요하기 시작하면서 크로스 플랫폼 관련 연구가 주목받기 시작하였다. 본 논문에서는 플랫폼 연계를 위하여 서로 다른 기관의 데이터를 묶기 위한 방법과 쿼리 입력을 통해 서로 다른 플랫폼의 DB가 가상으로 릴레이션 되어 결과물을 도출하는 크로스 플랫폼 정보 연결 방법에 대하여 제안하고 해당 방법을 임의로 구축한 시스템을 통해 확인할 수 있는 분석 결과를 유즈케이스(Use Case)를 통해 소개한다.
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PV-ANet: Attention-Based Network for Short-term Photovoltaic Power Forecasting
Muhammad Munsif,Habib Khan,Zulfiqar Ahmad Khan,Altaf Hussain,Fath U Min Ullah,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2022 한국차세대컴퓨팅학회 학술대회 Vol.2022 No.10
Nowadays, renewable energy resources such as Photovoltaic (PV) is one of the convenient ways to integrate it into the distributed grid to fulfill the huge energy demands without burning costly and pollutant fossil fuels. Researchers have been contributing from various aspects to develop accurate PV-power forecasting methods however further improvements are needed for an effective power management system. Therefore, in this work, we propose an attention-based deep learning (DL) model (PV-ANet) for short-term PV-power forecasting. The proposed system mainly consists of three modules. First, data from an actual PV power plant is acquired and preprocessed to remove outliers and normalized for efficient processing. Next, the PV-ANet model is developed, which is consisting of an encoder and decoder modules. The encoder encodes the input attributes via stack conventional and attention layer. While the decoder part contains the normalization and series of the dense layers to expends the encoded features into optimal features and generate one hour ahead forecast. Finally, the proposed model is evaluated via standard error metrics including MSE, MAE, and RMSE and achieved the lowest errors rates compared to state-of-the-art methods.
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