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- 저자 : Hikmat Yar (검색결과 4 건)
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데이터 주석 및 모델 성능 향상을 위한 능동적 학습 접근
Hikmat Yar,Samee Ullah Khan,Tanveer Hussain,Min Je Kim,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2022 한국차세대컴퓨팅학회 학술대회 Vol.2022 No.05
Deep learning models achieved a lot of success due to the availability of labeled training data. In contrast, labeling a huge amount of data by a human is a time-consuming and expensive solution. Active Learning (AL) efficiently addresses the issue of labeled data collection at a low cost by picking the most useful samples from a large number of unlabeled datasets However, current AL techniques largely depend on regular human involvement to annotate the most uncertain/informative samples in the collection. Therefore, a novel AL-based framework is proposed comprised of proxy and active models to reduce the manual labeling costs. In the proxy model, VGG-16 is trained on chunks of labeled data that later act as an annotator decision. On the other hand, in the active model, unlabeled is passed to Inception-V3 using the sampling strategy. The uncorrected predicted samples are then forwarded to the proxy model for annotation and considered those data have a high confidence score. The empirical results verify that our proposed model is the best in terms of annotation and accuracy.
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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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Samee Ullah Khan,Hikmat Yar,Habib Khan,Sumin Lee,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2023 한국차세대컴퓨팅학회 학술대회 Vol.2023 No.06
Fire detection is a significant attempt for preserving public safety in complex surveillance environments. Although advances in deep learning for fire detection, the task remains challenging due to the natural irregularity in fire images, including differences in lighting conditions, occlusions, and background complexity. To address these challenges, we present a novel framework for fire detection named fire channel attention network (FCAN), which is capable of differentiating challenging fire scenes. Our approach is motivated by the need to enhance the accuracy of fire detection by selectively emphasizing the most informative channels of the input image through a channel attention (CA). Furthermore, our model captures the salient features from the input image and suppresses the irrelevant ones, thereby overcoming the aforementioned challenges of fire detection. The FCAN is evaluated on two benchmark datasets and surpassed existing methods in terms of accuracy and F1 score. The proposed model demonstrates the effectiveness of fire detection, highlighting its potential for practical applications in fire safety and prevention.
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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.
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