Zakarya Farea Shaaf^1,*, Muhammad Mahadi Abdul Jamil¹, Radzi Ambar¹, Ahmed Abdu Alattab^2,3, Anwar Ali Yahya^3,4, Yousef Asiri⁴

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1819-1835, 2023, DOI:10.32604/cmc.2023.031900

Abstract The automatic localization of the left ventricle (LV) in short-axis magnetic resonance (MR) images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest (ROI). The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or registration. Nevertheless, this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different slices. Thus, this study proposed a region-based convolutional network (Faster R-CNN) for the LV localization from… More >

Thongchai Botmart¹, Qusain Hiader², Zulqurnain Sabir³, Muhammad Asif Zahoor Raja⁴, Wajaree Weera^1,*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 559-573, 2023, DOI:10.32604/cmc.2023.031871

Abstract The goal of this research is to introduce the simulation studies of the vector-host disease nonlinear system (VHDNS) along with the numerical treatment of artificial neural networks (ANNs) techniques supported by Levenberg-Marquardt backpropagation (LMQBP), known as ANNs-LMQBP. This mechanism is physically appropriate, where the number of infected people is increasing along with the limited health services. Furthermore, the biological effects have fading memories and exhibit transition behavior. Initially, the model is developed by considering the two and three categories for the humans and the vector species. The VHDNS is constructed with five classes, susceptible humans , infected humans , recovered… More >

Sandeep Kumar¹, Muhammad Badruddin Khan², Mozaherul Hoque Abul Hasanat², Abdul Khader Jilani Saudagar^2,*, Abdullah AlTameem², Mohammed AlKhathami²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 897-914, 2023, DOI:10.32604/cmc.2023.031867

Abstract Social media, like Twitter, is a data repository, and people exchange views on global issues like the COVID-19 pandemic. Social media has been shown to influence the low acceptance of vaccines. This work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individual’s sensitivities and feelings that lead to achievement. This work proposes a method to analyze the opinion of an individual’s tweet about the COVID-19 vaccines. This paper introduces a sigmoidal particle swarm optimization (SPSO) algorithm. First, the performance of SPSO is measured on a set of 12 benchmark problems, and later it is deployed… More >

Hanadi AlZaabi¹, Khaled Shaalan¹, Taher M. Ghazal^2,3,*, Muhammad A. Khan^4,5, Sagheer Abbas⁶, Beenu Mago⁷, Mohsen A. A. Tomh⁶, Munir Ahmad⁶

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2261-2278, 2023, DOI:10.32604/cmc.2023.031834

Abstract Energy is essential to practically all exercises and is imperative for the development of personal satisfaction. So, valuable energy has been in great demand for many years, especially for using smart homes and structures, as individuals quickly improve their way of life depending on current innovations. However, there is a shortage of energy, as the energy required is higher than that produced. Many new plans are being designed to meet the consumer’s energy requirements. In many regions, energy utilization in the housing area is 30%–40%. The growth of smart homes has raised the requirement for intelligence in applications such as… More >

Xiaojuan Wang¹, Zikui Lu^1,*, Siyuan Sun², Jingyue Wang¹, Luona Song³, Merveille Nicolas⁴

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2055-2071, 2023, DOI:10.32604/cmc.2023.031750

Abstract With the development of the Industrial Internet of Things (IIoT), end devices (EDs) are equipped with more functions to capture information. Therefore, a large amount of data is generated at the edge of the network and needs to be processed. However, no matter whether these computing tasks are offloaded to traditional central clusters or mobile edge computing (MEC) devices, the data is short of security and may be changed during transmission. In view of this challenge, this paper proposes a trusted task offloading optimization scheme that can offer low latency and high bandwidth services for IIoT with data security. Blockchain… More >

Poonam^*, Suman Sangwan

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1783-1799, 2023, DOI:10.32604/cmc.2023.031729

Abstract This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan. The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly. It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function. It works for 3-tier architecture, including cloudlet and public cloud. As cloudlets have limited resources, fuzzy logic is used for cloudlet selection using capacity and waiting time as input. Fuzzy provides human-like decisions without using any mathematical model. Firefly is a powerful meta-heuristic optimization technique to balance diversification… More >

Aqsa Aqdus¹, Rashid Amin^1,*, Sadia Ramzan¹, Sultan S. Alshamrani², Abdullah Alshehri³, El-Sayed M. El-kenawy⁴

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1413-1435, 2023, DOI:10.32604/cmc.2023.031719

Abstract The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of data. The traffic control and data forwarding functions are decoupled in software-defined networking (SDN) and allow the network to be programmable. Each switch in SDN keeps track of forwarding information in a flow table. The SDN switches must search the flow table for the flow rules that match the packets to handle the incoming packets. Due to the obvious vast quantity of data in data centres, the capacity of the flow table restricts the data plane’s forwarding capabilities.… More >

Azuwa Ali¹, Mohd Najib Mohd Yasin^2,*, Ismahayati Adam², Ali H. Rambe³, Mohd Haizal Jamaludin⁴, Hasliza A Rahim², Corhan Cengiz⁵, Mohd Ibrahim Shapiai Abd. Razak⁶

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 313-325, 2023, DOI:10.32604/cmc.2023.031706

Abstract This paper proposes a new dielectric resonator antenna (DRA) design that can generate circularly polarized (CP) triple-band signals. A triple-band CP DRA antenna fed by a probe feed system is achieved with metal strips structure on side of DRA structure. The design start with conventional rectangular DRA with F shaped metal strips on DRA structure alongside the feed. Then, the F metal strip is enhanced by extending the length of the metal strip to obtain wider impedance bandwidth. Further improvement on the antenna performance is observed by improvised the conventional DRA structure. The method of removing part of DRA bottom… More >

Rehana Ghulam¹, Sammar Fatima¹, Tariq Ali¹, Nazir Ahmad Zafar¹, Abdullah A. Asiri², Hassan A. Alshamrani^2,*, Samar M. Alqhtani³, Khlood M. Mehdar⁴

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1333-1349, 2023, DOI:10.32604/cmc.2023.031695

Abstract Human brain consists of millions of cells to control the overall structure of the human body. When these cells start behaving abnormally, then brain tumors occurred. Precise and initial stage brain tumor detection has always been an issue in the field of medicines for medical experts. To handle this issue, various deep learning techniques for brain tumor detection and segmentation techniques have been developed, which worked on different datasets to obtain fruitful results, but the problem still exists for the initial stage of detection of brain tumors to save human lives. For this purpose, we proposed a novel U-Net-based Convolutional… More >

Khlood Shinan^1,2, Khalid Alsubhi², M. Usman Ashraf^3,*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 693-714, 2023, DOI:10.32604/cmc.2023.031641

Abstract The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the Internet. Bot detection using machine learning (ML) with flow-based features has been extensively studied in the literature. Existing flow-based detection methods involve significant computational overhead that does not completely capture network communication patterns that might reveal other features of malicious hosts. Recently, Graph-Based Bot Detection methods using ML have gained attention to overcome these limitations, as graphs provide a real representation of network communications. The purpose of this study is to build a botnet… More >