Muhammad Asif¹, Ishfaq Ali¹, Shahbaz Ahmad¹, Azeem Irshad², Akber Abid Gardezi³, Fawaz Alassery⁴, Habib Hamam⁵, Muhammad Shafiq^6,*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3985-3999, 2022, DOI:10.32604/cmc.2022.023010

Abstract Industrial automation or assembly automation is a strictly monitored environment, in which changes occur at a good speed. There are many types of entities in the focusing environment, and the data generated by these devices is huge. In addition, because the robustness is achieved by sensing redundant data, the data becomes larger. The data generating device, whether it is a sensing device or a physical device, streams the data to a higher-level deception device for calculation, so that it can be driven and configured according to the updated conditions. With the emergence of the Industry 4.0 concept that includes a… More >

Iqra Aitbar¹, Nosherwan Shoaib^1,*, Akram Alomainy², Abdul Quddious³, Symeon Nikolaou⁴, Muhammad Ali Imran⁵, Qammer H. Abbasi⁵

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3227-3241, 2022, DOI:10.32604/cmc.2022.023008

Abstract In this paper, a compact, efficient and easy to fabricate wearable antenna integrated with Artificial Magnetic Conductor (AMC) is presented. Addition of slots and bevels/cuts in the rectangular monopole patch antenna yield a wide bandwidth along with band notches. The proposed antenna is backed with an AMC metasurface that changes the bidirectional radiation pattern to a unidirectional, thus, considerably reducing the Specific Absorption Ratio (SAR). The demonstrated antenna has a good coverage radiating away from the body and presents reduced radiation towards the body with a front-to-back ratio of 13 dB and maximum gain of 3.54 dB. The proposed design… More >

Supreet Singh^1,2, Nitin Mittal¹, Urvinder Singh², Rohit Salgotra², Atef Zaguia³, Dilbag Singh^4,*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3445-3462, 2022, DOI:10.32604/cmc.2022.023004

Abstract This paper provides a new optimization algorithm named as tunicate swarm naked mole-rat algorithm (TSNMRA) which uses hybridization concept of tunicate swarm algorithm (TSA) and naked mole-rat algorithm (NMRA). This newly developed algorithm uses the characteristics of both algorithms (TSA and NMRA) and enhance the exploration abilities of NMRA. Apart from the hybridization concept, important parameter of NMRA such as mating factor is made to be self-adaptive with the help of simulated annealing mutation operator and there is no need to define its value manually. For evaluating the working capabilities of proposed TSNMRA, it is tested for 100-digit challenge (CEC… More >

Sohail Abbas¹, Zahid Raza², Nida Siddiqui², Faheem Khan³, Taegkeun Whangbo^3,*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2683-2695, 2022, DOI:10.32604/cmc.2022.023003

Abstract Wireless Sensor Network (WSN) is considered to be one of the fundamental technologies employed in the Internet of things (IoT); hence, enabling diverse applications for carrying out real-time observations. Robot navigation in such networks was the main motivation for the introduction of the concept of landmarks. A robot can identify its own location by sending signals to obtain the distances between itself and the landmarks. Considering networks to be a type of graph, this concept was redefined as metric dimension of a graph which is the minimum number of nodes needed to identify all the nodes of the graph. This… More >

Faizan Rasheed¹, Kok-Lim Alvin Yau², Rafidah Md Noor³, Yung-Wey Chong^4,*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2225-2247, 2022, DOI:10.32604/cmc.2022.022952

Abstract This paper investigates the use of multi-agent deep Q-network (MADQN) to address the curse of dimensionality issue occurred in the traditional multi-agent reinforcement learning (MARL) approach. The proposed MADQN is applied to traffic light controllers at multiple intersections with busy traffic and traffic disruptions, particularly rainfall. MADQN is based on deep Q-network (DQN), which is an integration of the traditional reinforcement learning (RL) and the newly emerging deep learning (DL) approaches. MADQN enables traffic light controllers to learn, exchange knowledge with neighboring agents, and select optimal joint actions in a collaborative manner. A case study based on a real traffic… More >

Shabir Ahmad, Faheem Khan, Taeg Keun Whangbo^*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2653-2666, 2022, DOI:10.32604/cmc.2022.022821

Abstract Serious games have recently enticed many researchers due to their wide range of capabilities. A serious game is a mean of gaming for a serious job such as healthcare, education, and entertainment purposes. With the advancement in the Internet of Things, new research directions are paving the way in serious games. However, the internet connectivity of players in Internet-of-things-enabled serious games is a matter of concern and has been worth investigating. Different studies on topologies, frameworks, and architecture of communication technologies are conducted to integrate them with serious games on machine and network levels. However, the Internet of things, whose… More >

Abhishek Kumar^1,5, Kamred Udham Singh², Visvam Devadoss Ambeth Kumar³, Tapan Kant⁴, Abdul Khader Jilani Saudagar^5,*, Abdullah Al Tameem⁵, Mohammed Al Khathami⁵, Muhammad Badruddin Khan⁵, Mozaherul Hoque Abul Hasanat⁵, Khalid Mahmood Malik⁶

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3107-3125, 2022, DOI:10.32604/cmc.2022.022817

Abstract Computed Tomography (CT) scan and Magnetic Resonance Imaging (MRI) technologies are widely used in medical field. Within the last few months, due to the increased use of CT scans, millions of patients have had their CT scans done. So, as a result, images showing the Corona Virus for diagnostic purposes were digitally transmitted over the internet. The major problem for the world health care system is a multitude of attacks that affect copyright protection and other ethical issues as images are transmitted over the internet. As a result, it is important to apply a robust and secure watermarking technique to… More >

Fan Yang¹, Jie Xu^1,*, Haoliang Wei¹, Meng Ye², Mingzhu Xu¹, Qiuru Fu¹, Lingfei Ren³, Zhengwen Huang⁴

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2963-2980, 2022, DOI:10.32604/cmc.2022.022810

Abstract Zanthoxylum bungeanum Maxim, generally called prickly ash, is widely grown in China. Zanthoxylum rust is the main disease affecting the growth and quality of Zanthoxylum. Traditional method for recognizing the degree of infection of Zanthoxylum rust mainly rely on manual experience. Due to the complex colors and shapes of rust areas, the accuracy of manual recognition is low and difficult to be quantified. In recent years, the application of artificial intelligence technology in the agricultural field has gradually increased. In this paper, based on the DeepLabV2 model, we proposed a Zanthoxylum rust image segmentation model based on the FASPP module… More >

Erkan Erdemir^1,*, Adem Alpaslan Altun²

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2923-2941, 2022, DOI:10.32604/cmc.2022.022797

Abstract Metaheuristic algorithms are one of the methods used to solve optimization problems and find global or close to optimal solutions at a reasonable computational cost. As with other types of algorithms, in metaheuristic algorithms, one of the methods used to improve performance and achieve results closer to the target result is the hybridization of algorithms. In this study, a hybrid algorithm (HSSJAYA) consisting of salp swarm algorithm (SSA) and jaya algorithm (JAYA) is designed. The speed of achieving the global optimum of SSA, its simplicity, easy hybridization and JAYA's success in achieving the best solution have given us the idea… More >

Vatsala Anand¹, Sheifali Gupta¹, Deepika Koundal^2,*, Shubham Mahajan³, Amit Kant Pandit³, Atef Zaguia⁴

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3145-3160, 2022, DOI:10.32604/cmc.2022.022788

Abstract Biomedical image analysis has been exploited considerably by recent technology involvements, carrying about a pattern shift towards ‘automation’ and ‘error free diagnosis’ classification methods with markedly improved accurate diagnosis productivity and cost effectiveness. This paper proposes an automated deep learning model to diagnose skin disease at an early stage by using Dermoscopy images. The proposed model has four convolutional layers, two maxpool layers, one fully connected layer and three dense layers. All the convolutional layers are using the kernel size of 3 * 3 whereas the maxpool layer is using the kernel size of 2 * 2. The dermoscopy images… More >