Anas W. Abulfaraj^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1137-1156, 2024, DOI:10.32604/cmc.2024.047945

Abstract The utilization of visual attention enhances the performance of image classification tasks. Previous attention-based models have demonstrated notable performance, but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and differences. Neural-Controlled Differential Equations (N-CDE’s) and Neural Ordinary Differential Equations (NODE’s) are extensively utilized within this context. N-CDE’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced clarity. To this end, an attentive neural network has been proposed to generate attention maps, which uses two different types of N-CDE’s, one for adopting hidden layers and the other to generate… More >

Sharaf J. Malebary^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1301-1317, 2024, DOI:10.32604/cmc.2024.047917

Abstract Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates. Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitating the development of more precise and efficient methodologies. To address this formidable challenge, we propose an advanced approach for segmenting brain tumor Magnetic Resonance Imaging (MRI) images that harnesses the formidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methods have displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, marked by irregular shapes, varying sizes, uneven distribution, and limited available… More >

Qiuyang Wang, Zhen Xu^*, Lei Yang

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1613-1630, 2024, DOI:10.32604/cmc.2024.047915

Abstract Wireless sensor networks (WSNs) have the trouble of limited battery power, and wireless charging provides a promising solution to this problem, which is not easily affected by the external environment. In this paper, we study the recharging of sensors in wireless rechargeable sensor networks (WRSNs) by scheduling two mobile chargers (MCs) to collaboratively charge sensors. We first formulate a novel sensor charging scheduling problem with the objective of maximizing the number of surviving sensors, and further propose a collaborative charging scheduling algorithm (CCSA) for WRSNs. In the scheme, the sensors are divided into important sensors and ordinary sensors. Two MCs… More >

Yiwei Liu¹, Yingnan Zhao^1,*, Yi Chen¹, Zheng Hu¹, Min Xia²

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 909-926, 2024, DOI:10.32604/cmc.2024.047901

Abstract Scene text detection is an important task in computer vision. In this paper, we present YOLOv5 Scene Text (YOLOv5ST), an optimized architecture based on YOLOv5 v6.0 tailored for fast scene text detection. Our primary goal is to enhance inference speed without sacrificing significant detection accuracy, thereby enabling robust performance on resource-constrained devices like drones, closed-circuit television cameras, and other embedded systems. To achieve this, we propose key modifications to the network architecture to lighten the original backbone and improve feature aggregation, including replacing standard convolution with depth-wise convolution, adopting the C2 sequence module in place of C3, employing Spatial Pyramid… More >

Emmanuel Oluwatobi Asani^1,2,3, Aderemi Elisha Okeyinka⁴, Sunday Adeola Ajagbe^5,6, Ayodele Ariyo Adebiyi¹, Roseline Oluwaseun Ogundokun^1,2,7,*, Temitope Samson Adekunle⁸, Pragasen Mudali⁵, Matthew Olusegun Adigun⁵

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1581-1597, 2024, DOI:10.32604/cmc.2024.047898

Abstract The study presents the Half Max Insertion Heuristic (HMIH) as a novel approach to solving the Travelling Salesman Problem (TSP). The goal is to outperform existing techniques such as the Farthest Insertion Heuristic (FIH) and Nearest Neighbour Heuristic (NNH). The paper discusses the limitations of current construction tour heuristics, focusing particularly on the significant margin of error in FIH. It then proposes HMIH as an alternative that minimizes the increase in tour distance and includes more nodes. HMIH improves tour quality by starting with an initial tour consisting of a ‘minimum’ polygon and iteratively adding nodes using our novel Half… More >

Muhammad Umar Nasir¹, Omar Kassem Khalil², Karamath Ateeq³, Bassam SaleemAllah Almogadwy⁴, M. A. Khan⁵, Khan Muhammad Adnan^6,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 963-981, 2024, DOI:10.32604/cmc.2024.047874

Abstract Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in the fourth position because of the leading death cause in its premature stages. The cervix which is the lower end of the vagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumor in the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approach uses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer to train the weights with varying neurons… More >

Wuyan Liang¹, Xiaolong Xu^2,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1089-1103, 2024, DOI:10.32604/cmc.2024.047861

Abstract Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual and skeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data, failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility to efficiently process both uniform and disparate input patterns. Thus, in this paper, an attention-enhanced pseudo-3D residual model is proposed to address the GAR problem, called HgaNets. This model comprises two independent components designed for modeling visual RGB (red, green and blue) images and 3D skeletal heatmaps, respectively. More specifically, each component consists of… More >

Yasmine M. Ibrahim^1,2, Reem Essameldin³, Saad M. Darwish^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 243-262, 2024, DOI:10.32604/cmc.2024.047840

Abstract Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due to the complex nature of language used in such platforms. Currently, several methods exist for classifying hate speech, but they still suffer from ambiguity when differentiating between hateful and offensive content and they also lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two Multi-Layer Perceptron (MLPs) for neutrosophic sets classification. During the training process of the MLP, the WOA is employed to explore and determine… More >

Yu Zhou¹, Bosong Lin¹, Siqi Hu², Dandan Yu^3,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 161-182, 2024, DOI:10.32604/cmc.2024.047836

Abstract This paper addresses the problem of predicting population density leveraging cellular station data. As wireless communication devices are commonly used, cellular station data has become integral for estimating population figures and studying their movement, thereby implying significant contributions to urban planning. However, existing research grapples with issues pertinent to preprocessing base station data and the modeling of population prediction. To address this, we propose methodologies for preprocessing cellular station data to eliminate any irregular or redundant data. The preprocessing reveals a distinct cyclical characteristic and high-frequency variation in population shift. Further, we devise a multi-view enhancement model grounded on the… More >

Mohammed Alatiyyah^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 875-907, 2024, DOI:10.32604/cmc.2024.047790

Abstract In the contemporary era of technological advancement, smartphones have become an indispensable part of individuals’ daily lives, exerting a pervasive influence. This paper presents an innovative approach to passenger counting on buses through the analysis of Wi-Fi signals emanating from passengers’ mobile devices. The study seeks to scrutinize the reliability of digital Wi-Fi environments in predicting bus occupancy levels, thereby addressing a crucial aspect of public transportation. The proposed system comprises three crucial elements: Signal capture, data filtration, and the calculation and estimation of passenger numbers. The pivotal findings reveal that the system demonstrates commendable accuracy in estimating passenger counts… More >