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  • Open Access

    ARTICLE

    Spatio-Temporal Wind Speed Prediction Based on Variational Mode Decomposition

    Yingnan Zhao1,*, Guanlan Ji1, Fei Chen1, Peiyuan Ji1, Yi Cao2

    Computer Systems Science and Engineering, Vol.43, No.2, pp. 719-735, 2022, DOI:10.32604/csse.2022.027288

    Abstract Improving short-term wind speed prediction accuracy and stability remains a challenge for wind forecasting researchers. This paper proposes a new variational mode decomposition (VMD)-attention-based spatio-temporal network (VASTN) method that takes advantage of both temporal and spatial correlations of wind speed. First, VASTN is a hybrid wind speed prediction model that combines VMD, squeeze-and-excitation network (SENet), and attention mechanism (AM)-based bidirectional long short-term memory (BiLSTM). VASTN initially employs VMD to decompose the wind speed matrix into a series of intrinsic mode functions (IMF). Then, to extract the spatial features at the bottom of the model, each More >

  • Open Access

    ARTICLE

    Multi-Feature Fusion-Guided Multiscale Bidirectional Attention Networks for Logistics Pallet Segmentation

    Weiwei Cai1,2, Yaping Song1, Huan Duan1, Zhenwei Xia1, Zhanguo Wei1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1539-1555, 2022, DOI:10.32604/cmes.2022.019785

    Abstract In the smart logistics industry, unmanned forklifts that intelligently identify logistics pallets can improve work efficiency in warehousing and transportation and are better than traditional manual forklifts driven by humans. Therefore, they play a critical role in smart warehousing, and semantics segmentation is an effective method to realize the intelligent identification of logistics pallets. However, most current recognition algorithms are ineffective due to the diverse types of pallets, their complex shapes, frequent blockades in production environments, and changing lighting conditions. This paper proposes a novel multi-feature fusion-guided multiscale bidirectional attention (MFMBA) neural network for logistics… More >

  • Open Access

    ARTICLE

    Negative Emotions Sensitive Humanoid Robot with Attention-Enhanced Facial Expression Recognition Network

    Rongrong Ni1, Xiaofeng Liu1,*, Yizhou Chen1, Xu Zhou1, Huili Cai1, Loo Chu Kiong2

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 149-164, 2022, DOI:10.32604/iasc.2022.026813

    Abstract Lonely older adults and persons restricted in movements are apt to cause negative emotions, which is harmful to their mental health. A humanoid robot with audiovisual interactions is presented, which can correspondingly output positive facial expressions to relieve human's negative facial expressions. The negative emotions are identified through an attention-enhanced facial expression recognition (FER) network. The network is firstly trained on MMEW macro-and micro-expression databases to discover expression-related features. Then, macro-expression recognition tasks are performed by fine-tuning the trained models on several benchmarking FER databases, including CK+ and Oulu-CASIA. A transformer network is introduced to More >

  • Open Access

    ARTICLE

    Classification of Echocardiographic Standard Views Using a Hybrid Attention-based Approach

    Zi Ye1, Yogan Jaya Kumar2, Goh Ong Sing2, Fengyan Song3, Xianda Ni4,*

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1197-1215, 2022, DOI:10.32604/iasc.2022.023555

    Abstract The determination of the probe viewpoint forms an essential step in automatic echocardiographic image analysis. However, classifying echocardiograms at the video level is complicated, and previous observations concluded that the most significant challenge lies in distinguishing among the various adjacent views. To this end, we propose an ECHO-Attention architecture consisting of two parts. We first design an ECHO-ACTION block, which efficiently encodes Spatio-temporal features, channel-wise features, and motion features. Then, we can insert this block into existing ResNet architectures, combined with a self-attention module to ensure its task-related focus, to form an effective ECHO-Attention network. More >

  • Open Access

    ARTICLE

    Research of Insect Recognition Based on Improved YOLOv5

    Zhong Yuan1, Wei Fang1,2,*, Yongming Zhao3,*, Victor S. Sheng4

    Journal on Artificial Intelligence, Vol.3, No.4, pp. 145-152, 2021, DOI:10.32604/jai.2021.026902

    Abstract Insects play an important role in the natural ecology, it is of great significance for ecology to research on insects. Nowadays, the invasion of alien species has brought serious troubles and a lot of losses to local life. However, there is still much room for improvement in the accuracy of insect recognition to effectively prevent the invasion of alien species. As the latest target detection algorithm, YOLOv5 has been used in various scene detection tasks, because of its powerful recognition capabilities and extremely high accuracy. As the problem of imbalance of feature maps at different… More >

  • 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… 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… 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… 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… 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… More >

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