Muhammad Hameed Siddiqi^1,*, Helal Alshammari¹, Amjad Ali², Madallah Alruwaili¹, Yousef Alhwaiti¹, Saad Alanazi¹, M. M. Kamruzzaman¹

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 611-634, 2022, DOI:10.32604/cmc.2022.024760

Abstract Human activity recognition (HAR) can play a vital role in the monitoring of human activities, particularly for healthcare conscious individuals. The accuracy of HAR systems is completely reliant on the extraction of prominent features. Existing methods find it very challenging to extract optimal features due to the dynamic nature of activities, thereby reducing recognition performance. In this paper, we propose a robust feature extraction method for HAR systems based on template matching. Essentially, in this method, we want to associate a template of an activity frame or sub-frame comprising the corresponding silhouette. In this regard,… More >

Fatemeh Ahmadi Zeidabadi¹, Mohammad Dehghani², Pavel Trojovský^2,*, Štěpán Hubálovský³, Victor Leiva⁴, Gaurav Dhiman⁵

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 399-416, 2022, DOI:10.32604/cmc.2022.024736

Abstract Finding a suitable solution to an optimization problem designed in science is a major challenge. Therefore, these must be addressed utilizing proper approaches. Based on a random search space, optimization algorithms can find acceptable solutions to problems. Archery Algorithm (AA) is a new stochastic approach for addressing optimization problems that is discussed in this study. The fundamental idea of developing the suggested AA is to imitate the archer's shooting behavior toward the target panel. The proposed algorithm updates the location of each member of the population in each dimension of the search space by a… More >

Suleiman A. Babale¹, Muhammad K. Ishfaq^2,*, Ali Raza², Jamal Nasir³, Ahmad Fayyaz³, Umer Ijaz²

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 925-937, 2022, DOI:10.32604/cmc.2022.024711

Abstract This paper presents the design and implementation of a miniaturized beam steering network that produces broadside beams when it is fed with a compact antenna array. Butler Matrix (BM) was used as the beam steering network. It was completely built from a miniaturized 3 dB hybrid-couplers in planar microstrip technology. It was configured by feeding the BM with a Planar Inverted-E Antenna (PIEA) array separated at 0.3 λ as against the 0.5 λ separation. This makes the BM produce a major radiation pattern at the broadside. Apart from the miniaturization, no modification was made from the More >

Manar Ahmed Hamza^1,*, Abdelzahir Abdelmaboud², Souad Larabi-Marie-Sainte³, Haya Mesfer Alshahrani⁴, Mesfer Al Duhayyim⁵, Hamza Awad Ibrahim⁶, Mohammed Rizwanullah¹, Ishfaq Yaseen¹

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1951-1965, 2022, DOI:10.32604/cmc.2022.024692

Abstract Generally, software testing is considered as a proficient technique to achieve improvement in quality and reliability of the software. But, the quality of test cases has a considerable influence on fault revealing capability of software testing activity. Test Case Prioritization (TCP) remains a challenging issue since prioritizing test cases is unsatisfactory in terms of Average Percentage of Faults Detected (APFD) and time spent upon execution results. TCP is mainly intended to design a collection of test cases that can accomplish early optimization using preferred characteristics. The studies conducted earlier focused on prioritizing the available test… More >

Ahmed S. Almasoud¹, Abdelzahir Abdelmaboud², Taiseer Abdalla Elfadil Eisa³, Mesfer Al Duhayyim⁴, Asma Abbas Hassan Elnour⁵, Manar Ahmed Hamza^6,*, Abdelwahed Motwakel⁶, Abu Sarwar Zamani⁶

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1391-1407, 2022, DOI:10.32604/cmc.2022.024618

Abstract In agriculture, rice plant disease diagnosis has become a challenging issue, and early identification of this disease can avoid huge loss incurred from less crop productivity. Some of the recently-developed computer vision and Deep Learning (DL) approaches can be commonly employed in designing effective models for rice plant disease detection and classification processes. With this motivation, the current research work devises an Efficient Deep Learning based Fusion Model for Rice Plant Disease (EDLFM-RPD) detection and classification. The aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in… More >

Ahmed S. Almasoud¹, Taiseer Abdalla Elfadil Eisa², Fahd N. Al-Wesabi^3,4, Abubakar Elsafi⁵, Mesfer Al Duhayyim⁶, Ishfaq Yaseen⁷, Manar Ahmed Hamza^7,*, Abdelwahed Motwakel⁷

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 871-886, 2022, DOI:10.32604/cmc.2022.024596

Abstract Early detection of Parkinson's Disease (PD) using the PD patients’ voice changes would avoid the intervention before the identification of physical symptoms. Various machine learning algorithms were developed to detect PD detection. Nevertheless, these ML methods are lack in generalization and reduced classification performance due to subject overlap. To overcome these issues, this proposed work apply graph long short term memory (GLSTM) model to classify the dynamic features of the PD patient speech signal. The proposed classification model has been further improved by implementing the recurrent neural network (RNN) in batch normalization layer of GLSTM… More >

Xiaorui Zhang^1,2,3,*, Jie Zhou², Wei Sun^3,4, Sunil Kumar Jha⁵

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1123-1137, 2022, DOI:10.32604/cmc.2022.024589

Abstract The key to preventing the COVID-19 is to diagnose patients quickly and accurately. Studies have shown that using Convolutional Neural Networks (CNN) to analyze chest Computed Tomography (CT) images is helpful for timely COVID-19 diagnosis. However, personal privacy issues, public chest CT data sets are relatively few, which has limited CNN's application to COVID-19 diagnosis. Also, many CNNs have complex structures and massive parameters. Even if equipped with the dedicated Graphics Processing Unit (GPU) for acceleration, it still takes a long time, which is not conductive to widespread application. To solve above problems, this paper… More >

Souad Larabi-Marie-Sainte¹, Taiseer Abdalla Elfadil Eisa², Fahd N. Al-Wesabi^3,4, Amani Abdulrahman Albraikan⁵, Manar Ahmed Hamza^6,*, Abdelwahed Motwakel⁶, Ishfaq Yaseen⁶, Mesfer Al Duhayyim⁷

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1293-1308, 2022, DOI:10.32604/cmc.2022.024585

Abstract Snake Robots (SR) have been successfully deployed and proved to attain bio-inspired solutions owing to its capability to move in harsh environments, a characteristic not found in other kinds of robots (like wheeled or legged robots). Underwater Snake Robots (USR) establish a bioinspired solution in the domain of underwater robotics. It is a key challenge to increase the motion efficiency in underwater robots, with respect to forwarding speed, by enhancing the locomotion method. At the same time, energy efficiency is also considered as a crucial issue for long-term automation of the systems. In this aspect,… More >

Noman Shabbir¹, Lauri Kütt¹, Muhammad Jawad², Oleksandr Husev¹, Ateeq Ur Rehman³, Akber Abid Gardezi⁴, Muhammad Shafiq⁵, Jin-Ghoo Choi^5,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1017-1033, 2022, DOI:10.32604/cmc.2022.024576

Abstract Wind energy is featured by instability due to a number of factors, such as weather, season, time of the day, climatic area and so on. Furthermore, instability in the generation of wind energy brings new challenges to electric power grids, such as reliability, flexibility, and power quality. This transition requires a plethora of advanced techniques for accurate forecasting of wind energy. In this context, wind energy forecasting is closely tied to machine learning (ML) and deep learning (DL) as emerging technologies to create an intelligent energy management paradigm. This article attempts to address the short-term… More >

Batyrkhan Omarov^1,2,3,4,*, Sergazy Narynov¹, Zhandos Zhumanov^1,2, Aidana Gumar^1,5, Mariyam Khassanova^1,5

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 315-331, 2022, DOI:10.32604/cmc.2022.024566

Abstract In this paper, we propose a skeleton-based method to identify violence and aggressive behavior. The approach does not necessitate high-processing equipment and it can be quickly implemented. Our approach consists of two phases: feature extraction from image sequences to assess a human posture, followed by activity classification applying a neural network to identify whether the frames include aggressive situations and violence. A video violence dataset of 400 min comprising a single person's activities and 20 h of video data including physical violence and aggressive acts, and 13 classifications for distinguishing aggressor and victim behavior were More >