Muhammad Umar Nasir¹, Omar Kassem Khalil², Karamath Ateeq³, Bassam SaleemAllah Almogadwy⁴, Muhammad Adnan Khan⁵, Muhammad Hasnain Azam⁶, Khan Muhammad Adnan^7,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3303-3321, 2024, DOI:10.32604/cmc.2024.048035 - 26 March 2024

Abstract Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or pathologic. Various cardiotocography measures infer wrongly and give wrong predictions because of human error. The traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as well. Fetal condition is very important to measure at numerous stages and give proper medications to the fetus for its well-being. In… More >

Jiangang Lu, Ruifeng Zhao^*, Zhiwen Yu, Yue Dai, Kaiwen Zeng

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4453-4469, 2024, DOI:10.32604/cmc.2024.048020 - 26 March 2024

Abstract Time synchronization (TS) is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things (IoT). Multi-clock source time synchronization (MTS) has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales, and the coupling of synchronization latency jitter and pulse phase difference. In this paper, the multi-timescale MTS model is conducted, and the reinforcement learning (RL) and analytic hierarchy process (AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of More >

Haoran Huang, Weiting Chen^*, Zheming Fan

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3665-3681, 2024, DOI:10.32604/cmc.2024.048008 - 26 March 2024

Abstract Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address… More >

Hong Huang¹, Xingxing Zhang^1,*, Ye Lu¹, Ze Li¹, Shaohua Zhou²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3929-3951, 2024, DOI:10.32604/cmc.2024.047918 - 26 March 2024

Abstract While encryption technology safeguards the security of network communications, malicious traffic also uses encryption protocols to obscure its malicious behavior. To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic, we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features, called BERT-based Spatio-Temporal Features Network (BSTFNet). At the packet-level granularity, the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers (BERT)… More >

Mohammad Mehdi Sharifi Nevisi¹, Elnaz Bashir², Diego Martín^3,*, Seyedkian Rezvanjou⁴, Farzaneh Shoushtari⁵, Ehsan Ghafourian²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3971-3991, 2024, DOI:10.32604/cmc.2024.047875 - 26 March 2024

Abstract This paper focuses on wireless-powered communication systems, which are increasingly relevant in the Internet of Things (IoT) due to their ability to extend the operational lifetime of devices with limited energy. The main contribution of the paper is a novel approach to minimize the secrecy outage probability (SOP) in these systems. Minimizing SOP is crucial for maintaining the confidentiality and integrity of data, especially in situations where the transmission of sensitive data is critical. Our proposed method harnesses the power of an improved biogeography-based optimization (IBBO) to effectively train a recurrent neural network (RNN). The… More >

Yilei Wang¹, Xin Sun¹, Guiping Zheng^2,3, Ahmar Rashid⁴, Sami Ullah⁵, Hisham Alasmary⁶, Muhammad Waqas^7,8,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3909-3927, 2024, DOI:10.32604/cmc.2024.047767 - 26 March 2024

Abstract The application of Intelligent Internet of Things (IIoT) in constructing distribution station areas strongly supports platform transformation, upgrade, and intelligent integration. The sensing layer of IIoT comprises the edge convergence layer and the end sensing layer, with the former using intelligent fusion terminals for real-time data collection and processing. However, the influx of multiple low-voltage in the smart grid raises higher demands for the performance, energy efficiency, and response speed of the substation fusion terminals. Simultaneously, it brings significant security risks to the entire distribution substation, posing a major challenge to the smart grid. In… More >

Duy Quang Tran¹, Huy Q. Tran^2,*, Minh Van Nguyen³

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3585-3602, 2024, DOI:10.32604/cmc.2024.047760 - 26 March 2024

Abstract With the advancement of artificial intelligence, traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality. Traffic volume is an influential parameter for planning and operating traffic structures. This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems. A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process. The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal… More >

Guojun Wang^1,2, Qi Liu^3,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4471-4488, 2024, DOI:10.32604/cmc.2024.047669 - 26 March 2024

Abstract With the rapid advancement in exploring perceptual interactions and digital twins, metaverse technology has emerged to transcend the constraints of space-time and reality, facilitating remote AI-based collaboration. In this dynamic metasystem environment, frequent information exchanges necessitate robust security measures, with Authentication and Key Agreement (AKA) serving as the primary line of defense to ensure communication security. However, traditional AKA protocols fall short in meeting the low-latency requirements essential for synchronous interactions within the metaverse. To address this challenge and enable nearly latency-free interactions, a novel low-latency AKA protocol based on chaotic maps is proposed. This… More >

Muhammad Armghan Latif¹, Zohaib Mushtaq², Saad Arif³, Sara Rehman⁴, Muhammad Farrukh Qureshi⁵, Nagwan Abdel Samee⁶, Maali Alabdulhafith^6,*, Yeong Hyeon Gu⁷, Mohammed A. Al-masni⁷

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4225-4241, 2024, DOI:10.32604/cmc.2024.047621 - 26 March 2024

Abstract Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland. Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care. This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques. Sequential forward feature selection, sequential backward feature elimination, and bidirectional feature elimination are investigated in this study. In ensemble learning, random forest, adaptive boosting, and bagging classifiers are employed. The effectiveness of… More >

Youseef Alotaibi¹, B. Rajasekar², R. Jayalakshmi³, Surendran Rajendran^4,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4243-4262, 2024, DOI:10.32604/cmc.2024.047608 - 26 March 2024

Abstract Rapid development in Information Technology (IT) has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle (V2V) transmission. Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data. The communication occurs directly between V2V and Base Station (BS) units such as the Road Side Unit (RSU), named as a Vehicle to Infrastructure (V2I). However, the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time. Therefore, the scheme of an effectual routing protocol for reliable and stable communications… More >