S. Hariprasad^1,*, T. Deepa¹, N. Bharathiraja²

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1425-1440, 2022, DOI:10.32604/iasc.2022.022856 - 25 May 2022

Abstract The Internet of Things (IoT) is a global information and communication technology which aims to connect any type of device to the internet at any time and in any location. Nowadays billions of IoT devices are connected to the world, this leads to easily cause vulnerability to IoT devices. The increasing of users in different IoT-related applications leads to more data attacks is happening in the IoT networks after the fog layer. To detect and reduce the attacks the deep learning model is used. In this article, a hybrid sample selected recurrent neural network-extreme learning… More >

D. Yuvaraj¹, S. Shanmuga Priya^2,*, M. Braveen³, S. Navaneetha Krishnan⁴, S. Nachiyappan⁵, Abolfazl Mehbodniya⁶, A. Mohamed Uvaze Ahamed⁷, M. Sivaram⁸

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1505-1522, 2022, DOI:10.32604/iasc.2022.022151 - 25 May 2022

Abstract Wireless sensor networks are extensively utilized as a communication mechanism in the field of the Internet of Things (IoT). Along with these services, numerous IoT based applications need stabilized transmission or delivery over unbalanced wireless connections. To ensure the stability of data packets delivery, prevailing works exploit diverse geographical routing with multi-hop forwarders in WSNs. Furthermore, critical Denial of Service (DoS) attacks frequently has an impact on these techniques, where an enormous amount of invalid data starts replicating and transmitted to receivers to prevent Wireless Sensor Networks (WSN) communication. In this investigation, a novel adaptive… More >

Huakun Que¹, Guolong Lin², Wenchong Guo¹, Xiaofeng Feng¹, Zetao Jiang¹, Yunfei Cao^2,*, Jinmin Fan², Zhixian Ni³

Energy Engineering, Vol.119, No.4, pp. 1453-1466, 2022, DOI:10.32604/ee.2022.018448 - 23 May 2022

Abstract In order to solve the failure of electricity anti-stealing detection device triggered by the noise mixed in high-frequency electricity stealing signals, a denoising method based on variational mode decomposition (VMD) and wavelet threshold denoising (WTD) was applied to extract the effective high-frequency electricity stealing signals. First, the signal polluted by noise was pre-decomposed using the VMD algorithm, the instantaneous frequency means of each pre-decomposed components was analyzed, so as to determine the optimal K value. The optimal K value was used to decompose the polluted signal into K intrinsic mode components, and the sensitive mode More > Graphic Abstract

Abdulaziz Alhumam^*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1463-1482, 2022, DOI:10.32604/cmc.2022.029473 - 18 May 2022

Abstract The most resource-intensive and laborious part of debugging is finding the exact location of the fault from the more significant number of code snippets. Plenty of machine intelligence models has offered the effective localization of defects. Some models can precisely locate the faulty with more than 95% accuracy, resulting in demand for trustworthy models in fault localization. Confidence and trustworthiness within machine intelligence-based software models can only be achieved via explainable artificial intelligence in Fault Localization (XFL). The current study presents a model for generating counterfactual interpretations for the fault localization model's decisions. Neural system More >

Mohammad Adnan Aladaileh, Mohammed Anbar^*, Iznan H. Hasbullah, Abdullah Ahmed Bahashwan, Shadi Al-Sarawn

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1403-1416, 2022, DOI:10.32604/cmc.2022.029369 - 18 May 2022

Abstract The emergence of a new network architecture, known as Software Defined Networking (SDN), in the last two decades has overcome some drawbacks of traditional networks in terms of performance, scalability, reliability, security, and network management. However, the SDN is vulnerable to security threats that target its controller, such as low-rate Distributed Denial of Service (DDoS) attacks, The low-rate DDoS attack is one of the most prevalent attacks that poses a severe threat to SDN network security because the controller is a vital architecture component. Therefore, there is an urgent need to propose a detection approach… More >

Tahani Alatawi^*, Ahamed Aljuhani

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1067-1086, 2022, DOI:10.32604/cmc.2022.029283 - 18 May 2022

Abstract The rapid development of the Internet of Things (IoT) in the industrial domain has led to the new term the Industrial Internet of Things (IIoT). The IIoT includes several devices, applications, and services that connect the physical and virtual space in order to provide smart, cost-effective, and scalable systems. Although the IIoT has been deployed and integrated into a wide range of industrial control systems, preserving security and privacy of such a technology remains a big challenge. An anomaly-based Intrusion Detection System (IDS) can be an effective security solution for maintaining the confidentiality, integrity, and… More >

Romany F. Mansour^1,*, Sara A. Althubiti², Fayadh Alenezi³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 849-864, 2022, DOI:10.32604/cmc.2022.029265 - 18 May 2022

Abstract Recently, computer vision (CV) based disease diagnosis models have been utilized in various areas of healthcare. At the same time, deep learning (DL) and machine learning (ML) models play a vital role in the healthcare sector for the effectual recognition of diseases using medical imaging tools. This study develops a novel computer vision with optimal machine learning enabled skin lesion detection and classification (CVOML-SLDC) model. The goal of the CVOML-SLDC model is to determine the appropriate class labels for the test dermoscopic images. Primarily, the CVOML-SLDC model derives a gaussian filtering (GF) approach to pre-process More >

Jian Liu¹, Yipeng Du¹, Xiang Wang^1,*, Wuguang Yue², Jim Feng³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1995-2011, 2022, DOI:10.32604/cmc.2022.029073 - 18 May 2022

Abstract Epilepsy is a common neurological disease and severely affects the daily life of patients. The automatic detection and diagnosis system of epilepsy based on electroencephalogram (EEG) is of great significance to help patients with epilepsy return to normal life. With the development of deep learning technology and the increase in the amount of EEG data, the performance of deep learning based automatic detection algorithm for epilepsy EEG has gradually surpassed the traditional hand-crafted approaches. However, the neural architecture design for epilepsy EEG analysis is time-consuming and laborious, and the designed structure is difficult to adapt… More >

Abed Saif Alghawli^*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1961-1976, 2022, DOI:10.32604/cmc.2022.028990 - 18 May 2022

Abstract In the period of Industries 4.0, cyber-physical systems (CPSs) were a major study area. Such systems frequently occur in manufacturing processes and people’s everyday lives, and they communicate intensely among physical elements and lead to inconsistency. Due to the magnitude and importance of the systems they support, the cyber quantum models must function effectively. In this paper, an image-processing-based anomalous mobility detecting approach is suggested that may be added to systems at any time. The expense of glitches, failures or destroyed products is decreased when anomalous activities are detected and unplanned scenarios are avoided. The… More >

Wei Chen¹, Caoyang Chen^1,*, Mi Liu¹, Xuhong Zhou², Haozhi Tan³, Mingliang Zhang⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 767-782, 2022, DOI:10.32604/cmc.2022.028571 - 18 May 2022

Abstract The present paper proposes a detection method for building exterior wall cracks since manual detection methods have high risk and low efficiency. The proposed method is based on Unmanned Aerial Vehicle (UAV) and computer vision technology. First, a crack dataset of 1920 images was established using UAV to collect the images of a residential building exterior wall under different lighting conditions. Second, the average crack detection precisions of different methods including the Single Shot MultiBox Detector, You Only Look Once v3, You Only Look Once v4, Faster Regional Convolutional Neural Network (R-CNN) and Mask R-CNN… More >