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 >

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 >

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 >

Jin Wang^1,2, Guoshu Huang¹, R. Simon Sherratt³, Ding Huang⁴, Jia Ni^4,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4029-4048, 2024, DOI:10.32604/cmc.2024.047468 - 26 March 2024

Abstract With the development of Industry 4.0 and big data technology, the Industrial Internet of Things (IIoT) is hampered by inherent issues such as privacy, security, and fault tolerance, which pose certain challenges to the rapid development of IIoT. Blockchain technology has immutability, decentralization, and autonomy, which can greatly improve the inherent defects of the IIoT. In the traditional blockchain, data is stored in a Merkle tree. As data continues to grow, the scale of proofs used to validate it grows, threatening the efficiency, security, and reliability of blockchain-based IIoT. Accordingly, this paper first analyzes the… More >

Muhammad Saad¹, Muhammad Raheel Bhutta², Jongik Kim^3,*, Tae-Sun Chung¹

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4263-4282, 2024, DOI:10.32604/cmc.2024.047132 - 26 March 2024

Abstract With the increase in IoT (Internet of Things) devices comes an inherent challenge of security. In the world today, privacy is the prime concern of every individual. Preserving one’s privacy and keeping anonymity throughout the system is a desired functionality that does not come without inevitable trade-offs like scalability and increased complexity and is always exceedingly difficult to manage. The challenge is keeping confidentiality and continuing to make the person innominate throughout the system. To address this, we present our proposed architecture where we manage IoT devices using blockchain technology. Our proposed architecture works on… More >

Talal Albalawi, P. Ganeshkumar^*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3511-3528, 2024, DOI:10.32604/cmc.2024.046396 - 26 March 2024

Abstract The Internet of Things (IoT) is a growing technology that allows the sharing of data with other devices across wireless networks. Specifically, IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks. In this framework, a Covariance Linear Learning Embedding Selection (CL2ES) methodology is used at first to extract the features highly associated with the IoT intrusions. Then, the Kernel Distributed Bayes Classifier (KDBC) is created to forecast attacks based on the probability distribution More >

MENNA AL-ADL^1,*, MAGDY M. YOUSSEF¹, AHMED EL-SEBAIE², SHERIF REFAAT³, AFAF EL-SAID⁴

Oncology Research, Vol.32, No.4, pp. 785-797, 2024, DOI:10.32604/or.2024.047021 - 20 March 2024

Abstract Cytochromes P450 (CYPs) play a prominent role in catalyzing phase I xenobiotic biotransformation and account for about 75% of the total metabolism of commercially available drugs, including chemotherapeutics. The gene expression and enzyme activity of CYPs are variable between individuals, which subsequently leads to different patterns of susceptibility to carcinogenesis by genotoxic xenobiotics, as well as differences in the efficacy and toxicity of clinically used drugs. This research aimed to examine the presence of the CYP2B6*9 polymorphism and its possible association with the incidence of B-CLL in Egyptian patients, as well as the clinical outcome after… More > Graphic Abstract

HUAN HU^1,2,*, YUE HUANG¹, FANGZHOU LIU¹, QIAN WANG^1,2, YANZI YAO^3,*

BIOCELL, Vol.48, No.3, pp. 353-362, 2024, DOI:10.32604/biocell.2024.048311 - 15 March 2024

Abstract Increasing life expectancy and an aging population lead to age-related bone diseases like osteoporosis and low bone mass more prevalent. These conditions represent a common, costly and chronic burden, not only for elderly but also to society at large. Consequently, elucidating the pathophysiology and developing effective therapies for these diseases is of paramount importance. Recent advances in research have identified the gut as a novel and promising target for addressing bone disorders, giving rise to the concept of the “gut-bone axis”. An in-depth review of the latest insights into the effects of age-related physiological changes More >

Nazik Alturki¹, Raed Alharthi², Muhammad Umer^3,*, Oumaima Saidani¹, Amal Alshardan¹, Reemah M. Alhebshi⁴, Shtwai Alsubai⁵, Ali Kashif Bashir^6,7,8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3387-3415, 2024, DOI:10.32604/cmes.2023.044700 - 11 March 2024

Abstract The concept of smart houses has grown in prominence in recent years. Major challenges linked to smart homes are identification theft, data safety, automated decision-making for IoT-based devices, and the security of the device itself. Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features. This paper proposes a smart home system based on ensemble learning of random forest (RF) and convolutional neural networks (CNN) for programmed decision-making tasks, such as categorizing gadgets… More >

Ashraf S. Mashaleh^1,2,*, Noor Farizah Binti Ibrahim¹, Mohammad Alauthman³, Mohammad Almseidin⁴, Amjad Gawanmeh⁵

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2245-2267, 2024, DOI:10.32604/cmc.2023.047323 - 27 February 2024

Abstract Increasing Internet of Things (IoT) device connectivity makes botnet attacks more dangerous, carrying catastrophic hazards. As IoT botnets evolve, their dynamic and multifaceted nature hampers conventional detection methods. This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization (PSO) to address the risks associated with IoT botnets. Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically. Fuzzy component settings are optimized using PSO to improve accuracy. The methodology allows for more complex thinking by transitioning from binary to continuous assessment. Instead of expert inputs, PSO data-driven tunes rules and membership More >