Wei Fang^1,2,*, Liang Shen¹, Victor S. Sheng³, Qiongying Xue¹

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4867-4877, 2022, DOI:10.32604/cmc.2022.027197 - 21 April 2022

Abstract Precipitation nowcasting is of great significance for severe convective weather warnings. Radar echo extrapolation is a commonly used precipitation nowcasting method. However, the traditional radar echo extrapolation methods are encountered with the dilemma of low prediction accuracy and extrapolation ambiguity. The reason is that those methods cannot retain important long-term information and fail to capture short-term motion information from the long-range data stream. In order to solve the above problems, we select the spatiotemporal long short-term memory (ST-LSTM) as the recurrent unit of the model and integrate the 3D convolution operation in it to strengthen… More >

Yingnan Zhao^1,*, 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 - 20 April 2022

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 >

Weiwei Cai^1,2, Yaping Song¹, Huan Duan¹, Zhenwei Xia¹, Zhanguo Wei^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1539-1555, 2022, DOI:10.32604/cmes.2022.019785 - 19 April 2022

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 >

Rongrong Ni¹, Xiaofeng Liu^1,*, 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 - 15 April 2022

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 >

Zi Ye¹, Yogan Jaya Kumar², Goh Ong Sing², Fengyan Song³, Xianda Ni^4,*

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1197-1215, 2022, DOI:10.32604/iasc.2022.023555 - 08 February 2022

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 >

Yuhua Li¹, Zhiqiang He^1,2, Junxia Ma^1,*, Zhifeng Zhang^1,*, 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 - 24 January 2022

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 >

Riad Alharbey¹, Ameen Banjar¹, Yahia Said^2,3,*, Mohamed Atri⁴, Abdulrahman Alshdadi¹, Mohamed Abid⁵

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6275-6291, 2022, DOI:10.32604/cmc.2022.024272 - 14 January 2022

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 >

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 - 14 January 2022

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 >

Mohd Sadiq¹, Mohd Tauheed Khan^2,*, Sarfaraz Masood³

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5183-5200, 2022, DOI:10.32604/cmc.2022.020531 - 14 January 2022

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 >

Fayadh Alenezi^*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3425-3443, 2022, DOI:10.32604/cmc.2022.023339 - 07 December 2021

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 >