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  • Open Access

    ARTICLE

    A Novel Feature Aggregation Approach for Image Retrieval Using Local and Global Features

    Yuhua Li1, Zhiqiang He1,2, Junxia Ma1,*, Zhifeng Zhang1,*, Wangwei Zhang1, Prasenjit Chatterjee3, Dragan Pamucar4

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 239-262, 2022, DOI:10.32604/cmes.2022.016287

    Abstract The current deep convolution features based on retrieval methods cannot fully use the characteristics of the salient image regions. Also, they cannot effectively suppress the background noises, so it is a challenging task to retrieve objects in cluttered scenarios. To solve the problem, we propose a new image retrieval method that employs a novel feature aggregation approach with an attention mechanism and utilizes a combination of local and global features. The method first extracts global and local features of the input image and then selects keypoints from local features by using the attention mechanism. After that, the feature aggregation mechanism… More >

  • Open Access

    ARTICLE

    Human Faces Detection and Tracking for Crowd Management in Hajj and Umrah

    Riad Alharbey1, Ameen Banjar1, Yahia Said2,3,*, Mohamed Atri4, Abdulrahman Alshdadi1, Mohamed Abid5

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6275-6291, 2022, DOI:10.32604/cmc.2022.024272

    Abstract Hajj and Umrah are two main religious duties for Muslims. To help faithfuls to perform their religious duties comfortably in overcrowded areas, a crowd management system is a must to control the entering and exiting for each place. Since the number of people is very high, an intelligent crowd management system can be developed to reduce human effort and accelerate the management process. In this work, we propose a crowd management process based on detecting, tracking, and counting human faces using Artificial Intelligence techniques. Human detection and counting will be performed to calculate the number of existing visitors and face… More >

  • Open Access

    ARTICLE

    Deep Learning-based Wireless Signal Classification in the IoT Environment

    Hyeji Roh, Sheungmin Oh, Hajun Song, Jinseo Han, Sangsoon Lim*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5717-5732, 2022, DOI:10.32604/cmc.2022.024135

    Abstract With the development of the Internet of Things (IoT), diverse wireless devices are increasing rapidly. Those devices have different wireless interfaces that generate incompatible wireless signals. Each signal has its own physical characteristics with signal modulation and demodulation scheme. When there exist different wireless devices, they can suffer from severe Cross-Technology Interferences (CTI). To reduce the communication overhead due to the CTI in the real IoT environment, a central coordinator can be able to detect and identify wireless signals existing in the same communication areas. This paper investigates how to classify various radio signals using Convolutional Neural Networks (CNN), Long… More >

  • Open Access

    ARTICLE

    Attention-Based Deep Learning Model for Early Detection of Parkinson's Disease

    Mohd Sadiq1, Mohd Tauheed Khan2,*, Sarfaraz Masood3

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5183-5200, 2022, DOI:10.32604/cmc.2022.020531

    Abstract Parkinson's disease (PD), classified under the category of a neurological syndrome, affects the brain of a person which leads to the motor and non-motor symptoms. Among motor symptoms, one of the major disabling symptom is Freezing of Gait (FoG) that affects the daily standard of living of PD patients. Available treatments target to improve the symptoms of PD. Detection of PD at the early stages is an arduous task due to being indistinguishable from a healthy individual. This work proposed a novel attention-based model for the detection of FoG events and PD, and measuring the intensity of PD on the… More >

  • Open Access

    ARTICLE

    Image Dehazing Based on Pixel Guided CNN with PAM via Graph Cut

    Fayadh Alenezi*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3425-3443, 2022, DOI:10.32604/cmc.2022.023339

    Abstract Image dehazing is still an open research topic that has been undergoing a lot of development, especially with the renewed interest in machine learning-based methods. A major challenge of the existing dehazing methods is the estimation of transmittance, which is the key element of haze-affected imaging models. Conventional methods are based on a set of assumptions that reduce the solution search space. However, the multiplication of these assumptions tends to restrict the solutions to particular cases that cannot account for the reality of the observed image. In this paper we reduce the number of simplified hypotheses in order to attain… More >

  • Open Access

    ARTICLE

    Attention-Based Bi-LSTM Model for Arabic Depression Classification

    Abdulqader M. Almars*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3091-3106, 2022, DOI:10.32604/cmc.2022.022609

    Abstract Depression is a common mental health issue that affects a large percentage of people all around the world. Usually, people who suffer from this mood disorder have issues such as low concentration, dementia, mood swings, and even suicide. A social media platform like Twitter allows people to communicate as well as share photos and videos that reflect their moods. Therefore, the analysis of social media content provides insight into individual moods, including depression. Several studies have been conducted on depression detection in English and less in Arabic. The detection of depression from Arabic social media lags behind due the complexity… More >

  • Open Access

    ARTICLE

    A Prediction Method of Fracture Toughness of Nickel-Based Superalloys

    Yabin Xu1,*, Lulu Cui1, Xiaowei Xu2

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 121-132, 2022, DOI:10.32604/csse.2022.022758

    Abstract Fracture toughness plays a vital role in damage tolerance design of materials and assessment of structural integrity. To solve these problems of complexity, time-consuming, and low accuracy in obtaining the fracture toughness value of nickel-based superalloys through experiments. A combination prediction model is proposed based on the principle of materials genome engineering, the fracture toughness values of nickel-based superalloys at different temperatures, and different compositions can be predicted based on the existing experimental data. First, to solve the problem of insufficient feature extraction based on manual experience, the Deep Belief Network (DBN) is used to extract features, and an attention… More >

  • Open Access

    ARTICLE

    WMA: A Multi-Scale Self-Attention Feature Extraction Network Based on Weight Sharing for VQA

    Yue Li, Jin Liu*, Shengjie Shang

    Journal on Big Data, Vol.3, No.3, pp. 111-118, 2021, DOI:10.32604/jbd.2021.017169

    Abstract Visual Question Answering (VQA) has attracted extensive research focus and has become a hot topic in deep learning recently. The development of computer vision and natural language processing technology has contributed to the advancement of this research area. Key solutions to improve the performance of VQA system exist in feature extraction, multimodal fusion, and answer prediction modules. There exists an unsolved issue in the popular VQA image feature extraction module that extracts the fine-grained features from objects of different scale difficultly. In this paper, a novel feature extraction network that combines multi-scale convolution and self-attention branches to solve the above… More >

  • Open Access

    ARTICLE

    Effective Video Summarization Approach Based on Visual Attention

    Hilal Ahmad1, Habib Ullah Khan2, Sikandar Ali3,*, Syed Ijaz Ur Rahman1, Fazli Wahid3, Hizbullah Khattak4

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1427-1442, 2022, DOI:10.32604/cmc.2022.021158

    Abstract Video summarization is applied to reduce redundancy and develop a concise representation of key frames in the video, more recently, video summaries have been used through visual attention modeling. In these schemes, the frames that stand out visually are extracted as key frames based on human attention modeling theories. The schemes for modeling visual attention have proven to be effective for video summaries. Nevertheless, the high cost of computing in such techniques restricts their usability in everyday situations. In this context, we propose a method based on KFE (key frame extraction) technique, which is recommended based on an efficient and… More >

  • Open Access

    ARTICLE

    The Acute Effects of Aerobic Dance Exercise with and without Face Mask Use on Attention, Perceived Exertion and Mood States

    Maamer Slimani1,2,*, Nicola Bragazzi3, Amri Hammami2, Hela Znazen4, Qian Yu5,6, Zhaowei Kong6, Liye Zou5

    International Journal of Mental Health Promotion, Vol.23, No.4, pp. 513-520, 2021, DOI:10.32604/IJMHP.2021.017639

    Abstract The present study aimed to determine the effect of wearing a face mask during aerobic dance exercise on cognitive function, more specifically on attention, as well as on perceived exertion and mood states. Thirteen healthy college students (9 males and 4 females: mean age = 17.5 years, height = 1.72 m, weight = 71.00 kg) volunteered to participate in this study. They were randomized to perform aerobic dance exercise while wearing a cloth face mask or no mask or a control condition (sitting on a comfortable chair and reading information about the health benefits of aerobic dance exercise) on three separate occasions (with at least one week of… More >

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