Qamar Abbas¹, Khalid Mahmood Malik², Abdul Khader Jilani Saudagar^3,*, Muhammad Badruddin Khan³, Mozaherul Hoque Abul Hasanat³, Abdullah AlTameem³, Mohammed AlKhathami³

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1229-1250, 2022, DOI:10.32604/cmc.2022.024211

Abstract One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima. A novel convergence track based adaptive differential evolution (CTbADE) algorithm is presented in this research paper. The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima. A more diverse population improves the global searching capability and helps to escape from the local optima problem. Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems. An adaptive… More >

Mesfer Al Duhayyim¹, Majdy M. Eltahir², Imène Issaoui³, Fahd N. Al-Wesabi^2,4, Anwer Mustafa Hilal⁵, Fuad Ali Mohammed Al-Yarimi², Manar Ahmed Hamza^5,*, Abu Sarwar Zamani⁵

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 181-195, 2022, DOI:10.32604/cmc.2022.024206

Abstract The grid environment is a dynamic, heterogeneous, and changeable computing system that distributes various services amongst different clients. To attain the benefits of collaborative resource sharing in Grid computing, a novel and proficient grid resource management system (RMS) is essential. Therefore, detection of an appropriate resource for the presented task is a difficult task. Several scientists have presented algorithms for mapping tasks to the resource. Few of them focus on fault tolerance, user fulfillment, and load balancing. With this motivation, this study designs an intelligent grid scheduling scheme using deer hunting optimization algorithm (DHOA), called IGSS-DHOA which schedules in such… More >

Shuguang Wei, Jiaqi Li^*, Zixu Zhao, Dong Yuan

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1035-1052, 2022, DOI:10.32604/cmc.2022.024201

Abstract This paper proposes an artificial neural network for monitoring and detecting the eccentric error of synchronous reluctance motors. Firstly, a 15 kW synchronous reluctance motor is introduced and took as a case study to investigate the effects of eccentric rotor. Then, the equivalent magnetic circuits of the studied motor are analyzed and developed, in cases of dynamic eccentric rotor and static eccentric rotor condition, respectively. After that, the analytical equations of the studied motor are derived, in terms of its air-gap flux density, electromagnetic torque, and electromagnetic force, followed by the electromagnetic finite element analyses. Then, the modal analyses of the… More >

Mehmet Murat Huyut^1,*, Batuhan Kocaoğlu², Ünzile Meram³

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 2093-2107, 2022, DOI:10.32604/cmc.2022.024190

Abstract Regulatory authorities create a lot of legislation that must be followed. These create complex compliance requirements and time-consuming processes to find regulatory non-compliance. While the regulations establish rules in the relevant areas, recommendations and best practices for compliance are not generally mentioned. Best practices are often used to find a solution to this problem. There are numerous governance, management, and security frameworks in Information Technology (IT) area to guide businesses to run their processes at a much more mature level. Best practice maps can used to map another best practice, and users can adapt themselves by the help of this… More >

Arwa A. Mashat, Niayesh Gharaei^*, Aliaa M. Alabdali

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 441-459, 2022, DOI:10.32604/cmc.2022.024131

Abstract Compared with the traditional techniques of forest fires detection, wireless sensor network (WSN) is a very promising green technology in detecting efficiently the wildfires. However, the power constraint of sensor nodes is one of the main design limitations of WSNs, which leads to limited operation time of nodes and late fire detection. In the past years, wireless power transfer (WPT) technology has been known as a proper solution to prolong the operation time of sensor nodes. In WPT-based mechanisms, wireless mobile chargers (WMC) are utilized to recharge the batteries of sensor nodes wirelessly. Likewise, the energy of WMC is provided… More >

Ashit Kumar Dutta¹, Jenyfal Sampson², Sultan Ahmad³, T. Avudaiappan⁴, Kanagaraj Narayanasamy^5,*, Irina V. Pustokhina⁶, Denis A. Pustokhin⁷

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1157-1172, 2022, DOI:10.32604/cmc.2022.024109

Abstract Intelligent Transportation Systems (ITS) have become a vital part in improving human lives and modern economy. It aims at enhancing road safety and environmental quality. There is a tremendous increase observed in the number of vehicles in recent years, owing to increasing population. Each vehicle has its own individual emission rate; however, the issue arises when the emission rate crosses a standard value. Owing to the technological advances made in Artificial Intelligence (AI) techniques, it is easy to leverage it to develop prediction approaches so as to monitor and control air pollution. The current research paper presents Oppositional Shark Shell… More >

Farman Ali¹, Sadia Khan², Arbab Waseem Abbas², Babar Shah³, Tariq Hussain², Dongho Song^4,*, Shaker EI-Sappagh^5,6, Jaiteg Singh⁷

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 73-92, 2022, DOI:10.32604/cmc.2022.024103

Abstract Medical Image Analysis (MIA) is one of the active research areas in computer vision, where brain tumor detection is the most investigated domain among researchers due to its deadly nature. Brain tumor detection in magnetic resonance imaging (MRI) assists radiologists for better analysis about the exact size and location of the tumor. However, the existing systems may not efficiently classify the human brain tumors with significantly higher accuracies. In addition, smart and easily implementable approaches are unavailable in 2D and 3D medical images, which is the main problem in detecting the tumor. In this paper, we investigate various deep learning… More >

Nguyen A. Tuan¹, D. Akila², Souvik Pal³, Bikramjit Sarkar⁴, Thien Khai Tran¹, G. Mothilal Nehru², Dac-Nhuong Le^5,6,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1357-1372, 2022, DOI:10.32604/cmc.2022.024096

Abstract This article presents a new scheme for dynamic data optimization in IoT (Internet of Things)-assisted sensor networks. The various components of IoT assisted cloud platform are discussed. In addition, a new architecture for IoT assisted sensor networks is presented. Further, a model for data optimization in IoT assisted sensor networks is proposed. A novel Membership inducing Dynamic Data Optimization Membership inducing Dynamic Data Optimization (MIDDO) algorithm for IoT assisted sensor network is proposed in this research. The proposed algorithm considers every node data and utilized membership function for the optimized data allocation. The proposed framework is compared with two stage… More >

Soobia Saeed^1,*, Afnizanfaizal Abdullah¹, N. Z. Jhanjhi², Mehmood Naqvi³, Mehedi Masud⁴, Mohammed A. AlZain⁵

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 851-869, 2022, DOI:10.32604/cmc.2022.024085

Abstract Diagnosing data or object detection in medical images is one of the important parts of image segmentation especially those data which is less effective to identify in MRI such as low-grade tumors or cerebral spinal fluid (CSF) leaks in the brain. The aim of the study is to address the problems associated with detecting the low-grade tumor and CSF in brain is difficult in magnetic resonance imaging (MRI) images and another problem also relates to efficiency and less execution time for segmentation of medical images. For tumor and CSF segmentation using trained light field database (LFD) datasets of MRI images.… More >

S. Sudha^*, A. Srinivasan, T. Gayathri Devi

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 597-609, 2022, DOI:10.32604/cmc.2022.024065

Abstract Diabetes is associated with many complications that could lead to death. Diabetic retinopathy, a complication of diabetes, is difficult to diagnose and may lead to vision loss. Visual identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for clinicians. Because clinical testing involves complex procedures and is time-consuming, an automated system would help ophthalmologists to detect DR and administer treatment in a timely manner so that blindness can be avoided. Previous research works have focused on image processing algorithms, or neural networks, or signal processing techniques alone to detect diabetic retinopathy.… More >