Sofiene Haboubi^1,*, Tawfik Guesmi², Badr M Alshammari², Khalid Alqunun², Ahmed S Alshammari², Haitham Alsaif², Hamid Amiri¹

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5385-5397, 2022, DOI:10.32604/cmc.2022.029198 - 28 July 2022

Abstract Handwriting recognition is a challenge that interests many researchers around the world. As an exception, handwritten Arabic script has many objectives that remain to be overcome, given its complex form, their number of forms which exceeds 100 and its cursive nature. Over the past few years, good results have been obtained, but with a high cost of memory and execution time. In this paper we propose to improve the capacity of bidirectional gated recurrent unit (BGRU) to recognize Arabic text. The advantages of using BGRUs is the execution time compared to other methods that can More >

Ng Lai Yee¹, Mohd Asyraf Zulkifley^2,*, Adhi Harmoko Saputro³, Siti Raihanah Abdani⁴

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5331-5348, 2022, DOI:10.32604/cmc.2022.028801 - 28 July 2022

Abstract Micro-expression is manifested through subtle and brief facial movements that relay the genuine person’s hidden emotion. In a sequence of videos, there is a frame that captures the maximum facial differences, which is called the apex frame. Therefore, apex frame spotting is a crucial sub-module in a micro-expression recognition system. However, this spotting task is very challenging due to the characteristics of micro-expression that occurs in a short duration with low-intensity muscle movements. Moreover, most of the existing automated works face difficulties in differentiating micro-expressions from other facial movements. Therefore, this paper presents a deep… More >

Yong-Woon Kim¹, Yung-Cheol Byun^2,*, Dong Seog Han³, Dalia Dominic¹, Sibu Cyriac¹

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4743-4762, 2022, DOI:10.32604/cmc.2022.028632 - 28 July 2022

Abstract A wide range of camera apps and online video conferencing services support the feature of changing the background in real-time for aesthetic, privacy, and security reasons. Numerous studies show that the Deep-Learning (DL) is a suitable option for human segmentation, and the ensemble of multiple DL-based segmentation models can improve the segmentation result. However, these approaches are not as effective when directly applied to the image segmentation in a video. This paper proposes an Adaptive N-Frames Ensemble (AFE) approach for high-movement human segmentation in a video using an ensemble of multiple DL models. In contrast… More >

Guanghao Jin¹, Chunmei Pei¹, Na Zhao¹, Hengguang Li², Qingzeng Song³, Jing Yu^1,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5185-5196, 2022, DOI:10.32604/cmc.2022.027307 - 28 July 2022

Abstract When utilizing the deep learning models in some real applications, the distribution of the labels in the environment can be used to increase the accuracy. Generally, to compute this distribution, there should be the validation set that is labeled by the ground truths. On the other side, the dependency of ground truths limits the utilization of the distribution in various environments. In this paper, we carried out a novel system for the deep learning-based classification to solve this problem. Firstly, our system only uses one validation set with ground truths to compute some hyper parameters,… More >

Akbar Khan¹, Kushsairy Abdul Kadir^1,*, Jawad Ali Shah², Waleed Albattah³, Muhammad Saeed⁴, Haidawati Nasir⁵, Megat Norulazmi Megat Mohamed Noor⁵, Muhammad Haris Kaka Khel¹

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5825-5844, 2022, DOI:10.32604/cmc.2022.027077 - 28 July 2022

Abstract With the rapid progress of deep convolutional neural networks, several applications of crowd counting have been proposed and explored in the literature. In congested scene monitoring, a variety of crowd density estimating approaches has been developed. The understanding of highly congested scenes for crowd counting during Muslim gatherings of Hajj and Umrah is a challenging task, as a large number of individuals stand nearby and, it is hard for detection techniques to recognize them, as the crowd can vary from low density to high density. To deal with such highly congested scenes, we have proposed… More >

Thavavel Vaiyapuri¹, S. Srinivasan², Mohamed Yacin Sikkandar³, T. S. Balaji^4,5, Seifedine Kadry⁶, Maytham N. Meqdad⁷, Yunyoung Nam^8,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5543-5557, 2022, DOI:10.32604/cmc.2022.023919 - 28 July 2022

Abstract In past decades, retinal diseases have become more common and affect people of all age grounds over the globe. For examining retinal eye disease, an artificial intelligence (AI) based multilabel classification model is needed for automated diagnosis. To analyze the retinal malady, the system proposes a multiclass and multi-label arrangement method. Therefore, the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge, which tends to be time-consuming, vulnerable generalization ability, and unfeasible in massive datasets. Therefore, the automated diagnosis of multi-retinal diseases becomes essential, which can be solved… More >

Majid Hussain^1,2,*, Tayab Din Memon^3,4, Imtiaz Hussain⁵, Zubair Ahmed Memon³, Dileep Kumar²

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.2, pp. 435-470, 2022, DOI:10.32604/cmes.2022.020583 - 21 July 2022

Abstract Owing to the 4.0 industrial revolution condition monitoring maintenance is widely accepted as a useful approach to avoiding plant disturbances and shutdown. Recently, Motor Current Signature Analysis (MCSA) is widely reported as a condition monitoring technique in the detection and identification of individual and multiple Induction Motor (IM) faults. However, checking the fault detection and classification with deep learning models and its comparison among themselves or conventional approaches is rarely reported in the literature. Therefore, in this work, we present the detection and identification of induction motor faults with MCSA and three Deep Learning (DL)… More >

Somenath Bera¹, Vimal K. Shrivastava², Suresh Chandra Satapathy^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.2, pp. 219-250, 2022, DOI:10.32604/cmes.2022.020601 - 21 July 2022

Abstract Hyperspectral image (HSI) classification has been one of the most important tasks in the remote sensing community over the last few decades. Due to the presence of highly correlated bands and limited training samples in HSI, discriminative feature extraction was challenging for traditional machine learning methods. Recently, deep learning based methods have been recognized as powerful feature extraction tool and have drawn a significant amount of attention in HSI classification. Among various deep learning models, convolutional neural networks (CNNs) have shown huge success and offered great potential to yield high performance in HSI classification. Motivated… More >

Yunlei Zhang¹, Ruifeng Cao¹, Danhuang Dong², Sha Peng^3,*, Ruoyun Du³, Xiaomin Xu³

Energy Engineering, Vol.119, No.5, pp. 1829-1841, 2022, DOI:10.32604/ee.2022.020118 - 21 July 2022

Abstract In the electricity market, fluctuations in real-time prices are unstable, and changes in short-term load are determined by many factors. By studying the timing of charging and discharging, as well as the economic benefits of energy storage in the process of participating in the power market, this paper takes energy storage scheduling as merely one factor affecting short-term power load, which affects short-term load time series along with time-of-use price, holidays, and temperature. A deep learning network is used to predict the short-term load, a convolutional neural network (CNN) is used to extract the features, More >

Siddiqui Muhammad Yasir¹, Hyunsik Ahn^2,*

Journal on Artificial Intelligence, Vol.4, No.2, pp. 99-114, 2022, DOI:10.32604/jai.2022.031235 - 18 July 2022

Abstract The process of segmenting point cloud data into several homogeneous areas with points in the same region having the same attributes is known as 3D segmentation. Segmentation is challenging with point cloud data due to substantial redundancy, fluctuating sample density and lack of apparent organization. The research area has a wide range of robotics applications, including intelligent vehicles, autonomous mapping and navigation. A number of researchers have introduced various methodologies and algorithms. Deep learning has been successfully used to a spectrum of 2D vision domains as a prevailing A.I. methods. However, due to the specific… More >