Mahmoud Ragab^1,2,3,*, Ashwag Albukhari^2,4

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5577-5591, 2022, DOI:10.32604/cmc.2022.026715

Abstract Colorectal cancer is one of the most commonly diagnosed cancers and it develops in the colon region of large intestine. The histopathologist generally investigates the colon biopsy at the time of colonoscopy or surgery. Early detection of colorectal cancer is helpful to maintain the concept of accumulating cancer cells. In medical practices, histopathological investigation of tissue specimens generally takes place in a conventional way, whereas automated tools that use Artificial Intelligence (AI) techniques can produce effective results in disease detection performance. In this background, the current study presents an Automated AI-empowered Colorectal Cancer Detection and Classification (AAI-CCDC) technique. The proposed… More >

Esam A. AlQaralleh¹, Fahad Aldhaban², Halah Nasseif², Malek Z. Alksasbeh³, Bassam A. Y. Alqaralleh^2,*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5593-5605, 2022, DOI:10.32604/cmc.2022.026666

Abstract Real-time video surveillance system is commonly employed to aid security professionals in preventing crimes. The use of deep learning (DL) technologies has transformed real-time video surveillance into smart video surveillance systems that automate human behavior classification. The recognition of events in the surveillance videos is considered a hot research topic in the field of computer science and it is gaining significant attention. Human action recognition (HAR) is treated as a crucial issue in several applications areas and smart video surveillance to improve the security level. The advancements of the DL models help to accomplish improved recognition performance. In this view,… More >

Yangjae Lee¹, Sung Hoon Baek², Jung Taek Seo³, Ki-Woong Park^1,*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4911-4929, 2022, DOI:10.32604/cmc.2022.026657

Abstract One of the latest technologies enabling remote control, operational efficiency upgrades, and real-time big-data monitoring in an industrial control system (ICS) is the IIoT-Cloud ICS, which integrates the Industrial Internet of Things (IIoT) and the cloud into the ICS. Although an ICS benefits from the application of IIoT and the cloud in terms of cost reduction, efficiency improvement, and real-time monitoring, the application of this technology to an ICS poses an unprecedented security risk by exposing its terminal devices to the outside world. An adversary can collect information regarding senders, recipients, and prime-time slots through traffic analysis and use it… More >

Gang Li¹, Haoyang Sun¹, Zhenbing Li¹, Peiqi Wu¹, Daniele Inserra^1,*, Jian Su², Xiaochuan Fang³, Guangjun Wen¹

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4931-4944, 2022, DOI:10.32604/cmc.2022.026654

Abstract

In this paper, a dynamic multi-ary query tree (DMQT) anti-collision protocol for Radio Frequency Identification (RFID) systems is proposed for large scale passive RFID tag identification. The proposed DMQT protocol is based on an iterative process between the reader and tags which identifies the position of collision bits through map commands and dynamically encodes them to optimize slots allocation through query commands. In this way, the DMQT completely eliminates empty slots and greatly reduces collision slots, which in turn reduces the identification time and energy costs. In addition and differently to other known protocols, the DMQT does not need to… More >

Junwon Kim¹, Jiho Shin², Ki-Woong Park³, Jung Taek Seo^4,*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5377-5394, 2022, DOI:10.32604/cmc.2022.026619

Abstract Industrial Control System (ICS), which is based on Industrial IoT (IIoT), has an intelligent mobile environment that supports various mobility, but there is a limit to relying only on the physical security of the ICS environment. Due to various threat factors that can disrupt the workflow of the IIoT, machine learning-based anomaly detection technologies are being presented; it is also essential to study for increasing detection performance to minimize model errors for promoting stable ICS operation. In this paper, we established the requirements for improving the anomaly detection performance in the IIoT-based ICS environment by analyzing the related cases. After… More >

Mahmoud Ragab^1,2,3,*, Abdullah Addas⁴

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4323-4337, 2022, DOI:10.32604/cmc.2022.026602

Abstract Due to the advanced development in the multimedia-on-demand traffic in different forms of audio, video, and images, has extremely moved on the vision of the Internet of Things (IoT) from scalar to Internet of Multimedia Things (IoMT). Since Unmanned Aerial Vehicles (UAVs) generates a massive quantity of the multimedia data, it becomes a part of IoMT, which are commonly employed in diverse application areas, especially for capturing remote sensing (RS) images. At the same time, the interpretation of the captured RS image also plays a crucial issue, which can be addressed by the multi-label classification and Computational Linguistics based image… More >

Ye-Eun Kim¹, Min-Gyu Kim², Hwankuk Kim^2,*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4467-4488, 2022, DOI:10.32604/cmc.2022.026581

Abstract As Internet of Things (IoT) devices with security issues are connected to 5G mobile networks, the importance of IoT Botnet detection research in mobile network environments is increasing. However, the existing research focused on AI-based IoT Botnet detection research in wired network environments. In addition, the existing research related to IoT Botnet detection in ML-based mobile network environments have been conducted up to 4G. Therefore, this paper conducts a study on ML-based IoT Botnet traffic detection in the 5G core network. The binary and multiclass classification was performed to compare simple normal/malicious detection and normal/three-type IoT Botnet malware detection. In… More >

Bassam A. Y. Alqaralleh^1,*, Fahad Aldhaban¹, Esam A. AlQarallehs², Ahmad H. Al-Omari³

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4691-4707, 2022, DOI:10.32604/cmc.2022.026556

Abstract Cyber-attacks on cyber-physical systems (CPSs) resulted to sensing and actuation misbehavior, severe damage to physical object, and safety risk. Machine learning (ML) models have been presented to hinder cyberattacks on the CPS environment; however, the non-existence of labelled data from new attacks makes their detection quite interesting. Intrusion Detection System (IDS) is a commonly utilized to detect and classify the existence of intrusions in the CPS environment, which acts as an important part in secure CPS environment. Latest developments in deep learning (DL) and explainable artificial intelligence (XAI) stimulate new IDSs to manage cyberattacks with minimum complexity and high sophistication.… More >

Songbin Li^1,*, Peng Liu¹, Qiandong Yan¹, Ruiling Qian²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4999-5013, 2022, DOI:10.32604/cmc.2022.026498

Abstract Recent convolutional neural networks (CNNs) based deep learning has significantly promoted fire detection. Existing fire detection methods can efficiently recognize and locate the fire. However, the accurate flame boundary and shape information is hard to obtain by them, which makes it difficult to conduct automated fire region analysis, prediction, and early warning. To this end, we propose a fire semantic segmentation method based on Global Position Guidance (GPG) and Multi-path explicit Edge information Interaction (MEI). Specifically, to solve the problem of local segmentation errors in low-level feature space, a top-down global position guidance module is used to restrain the offset… More >

Dongho Lee, Geochang Jeon, Sunjun Lee, Haehyun Cho^*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5909-5923, 2022, DOI:10.32604/cmc.2022.026395

Abstract The smart phone market is continuously increasing and there are more than 6 billion of smart phone users worldwide with the aid of the 5G technology. Among them Android occupies 87% of the market share. Naturally, the widespread Android smartphones has drawn the attention of the attackers who implement and spread malware. Consequently, currently the number of malware targeting Android mobile phones is ever increasing. Therefore, it is a critical task to find and detect malicious behaviors of malware in a timely manner. However, unfortunately, attackers use a variety of obfuscation techniques for malware to evade or delay detection. When… More >