Kasidit Kokkhunthod¹, Khomdet Phapatanaburi², Wongsathon Pathonsuwan¹, Talit Jumphoo¹, Patikorn Anchuen³, Porntip Nimkuntod⁴, Monthippa Uthansakul¹, Peerapong Uthansakul^1,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1775-1794, 2024, DOI:10.32604/cmc.2024.049276

Abstract Monitoring blood pressure is a critical aspect of safeguarding an individual’s health, as early detection of abnormal blood pressure levels facilitates timely medical intervention, ultimately leading to a reduction in mortality rates associated with cardiovascular diseases. Consequently, the development of a robust and continuous blood pressure monitoring system holds paramount significance. In the context of this research paper, we introduce an innovative deep learning regression model that harnesses phonocardiogram (PCG) data to achieve precise blood pressure estimation. Our novel approach incorporates a convolutional neural network (CNN)-based regression model, which not only enhances its adaptability to spatial variations but also empowers… More >

Ahmed Alhussen¹, Arshiya S. Ansari^2,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1903-1923, 2024, DOI:10.32604/cmc.2024.049260

Abstract Traffic in today’s cities is a serious problem that increases travel times, negatively affects the environment, and drains financial resources. This study presents an Artificial Intelligence (AI) augmented Mobile Ad Hoc Networks (MANETs) based real-time prediction paradigm for urban traffic challenges. MANETs are wireless networks that are based on mobile devices and may self-organize. The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts. This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network (CSFPNN) technique to assess real-time data acquired from various sources within… More >

Chuandong Qin^1,2, Yu Cao^1,*, Liqun Meng¹

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1975-1994, 2024, DOI:10.32604/cmc.2024.049228

Abstract Brain tumors come in various types, each with distinct characteristics and treatment approaches, making manual detection a time-consuming and potentially ambiguous process. Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes. Machine learning models have become key players in automating brain tumor detection. Gradient descent methods are the mainstream algorithms for solving machine learning models. In this paper, we propose a novel distributed proximal stochastic gradient descent approach to solve the L-Smooth Support Vector Machine (SVM) classifier for brain tumor detection. Firstly, the smooth hinge loss is introduced to be used… More >

Amal Hkiri^1,*, Hamzah Faraj², Omar Ben Bahri², Mouna Karmani¹, Sami Alqurashi², Mohsen Machhout¹

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1835-1854, 2024, DOI:10.32604/cmc.2024.049207

Abstract The Internet of Things (IoT) has witnessed a significant surge in adoption, particularly through the utilization of Wireless Sensor Networks (WSNs), which comprise small internet-connected devices. These deployments span various environments and offer a multitude of benefits. However, the widespread use of battery-powered devices introduces challenges due to their limited hardware resources and communication capabilities. In response to this, the Internet Engineering Task Force (IETF) has developed the IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) to address the unique requirements of such networks. Recognizing the critical role of RPL in maintaining high performance, this paper proposes a novel… More >

Hao Sun^*, Xiubo Chen, Kaiguo Yuan

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2361-2373, 2024, DOI:10.32604/cmc.2024.049159

Abstract Federated learning ensures data privacy and security by sharing models among multiple computing nodes instead of plaintext data. However, there is still a potential risk of privacy leakage, for example, attackers can obtain the original data through model inference attacks. Therefore, safeguarding the privacy of model parameters becomes crucial. One proposed solution involves incorporating homomorphic encryption algorithms into the federated learning process. However, the existing federated learning privacy protection scheme based on homomorphic encryption will greatly reduce the efficiency and robustness when there are performance differences between parties or abnormal nodes. To solve the above problems, this paper proposes a… More >

Muhammad Zaman Ali¹, Omer Riaz¹, Hafiz Muhammad Hasnain², Waqas Sharif², Tenvir Ali², Gyu Sang Choi^3,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2923-2943, 2024, DOI:10.32604/cmc.2024.049139

Abstract In today’s rapidly evolving landscape of communication technologies, ensuring the secure delivery of sensitive data has become an essential priority. To overcome these difficulties, different steganography and data encryption methods have been proposed by researchers to secure communications. Most of the proposed steganography techniques achieve higher embedding capacities without compromising visual imperceptibility using LSB substitution. In this work, we have an approach that utilizes a combination of Most Significant Bit (MSB) matching and Least Significant Bit (LSB) substitution. The proposed algorithm divides confidential messages into pairs of bits and connects them with the MSBs of individual pixels using pair matching,… More >

Danyang Zheng, Jinchen Xv, Feng Yue, Qiming Du, Zhiheng Wang, Zheng Shan^*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1957-1974, 2024, DOI:10.32604/cmc.2024.048956

Abstract As cloud quantum computing gains broader acceptance, a growing quantity of researchers are directing their focus towards this domain. Nevertheless, the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity, which in turn hampers users from achieving optimal satisfaction. Therefore, cloud quantum computing service providers require a unified analysis and scheduling framework for their quantum resources and user jobs to meet the ever-growing usage demands. This paper introduces a new multi-programming scheduling framework for quantum computing in a cloud environment. The framework addresses the issue of limited quantum computing resources in cloud environments and ensures… More >

Mingze Li, Diwen Zheng, Shuhua Lu^*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2105-2122, 2024, DOI:10.32604/cmc.2024.048928

Abstract Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis, achieving tremendous success recently with the development of deep learning. However, there have been still many challenges including crowd multi-scale variations and high network complexity, etc. To tackle these issues, a lightweight Res-connection multi-branch network (LRMBNet) for highly accurate crowd counting and localization is proposed. Specifically, using improved ShuffleNet V2 as the backbone, a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters. A light multi-branch structure with different expansion rate convolutions is demonstrated to extract… More >

Saeed Seraj¹, Ali Safaa Sadiq^1,*, Omprakash Kaiwartya¹, Mohammad Aljaidi², Alexandros Konios¹, Mohammed Ali³, Mohammed Abazeed³

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2123-2149, 2024, DOI:10.32604/cmc.2024.048914

Abstract Mobile banking security has witnessed significant R&D attention from both financial institutions and academia. This is due to the growing number of mobile baking applications and their reachability and usefulness to society. However, these applications are also attractive prey for cybercriminals, who use a variety of malware to steal personal banking information. Related literature in mobile banking security requires many permissions that are not necessary for the application’s intended security functionality. In this context, this paper presents a novel efficient permission identification approach for securing mobile banking (MoBShield) to detect and prevent malware. A permission-based dataset is generated for mobile… More >

Shuai Zhou¹, Dazhi Wang^1,*, Yongliang Ni², Keling Song², Yanming Li²

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2187-2207, 2024, DOI:10.32604/cmc.2024.048859

Abstract In the process of identifying parameters for a permanent magnet synchronous motor, the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration, resulting in low parameter accuracy. This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function. This approach addresses the topic of particle swarm optimization in parameter identification from two perspectives. Firstly, the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness function, making the extreme point… More >