Yunbo Rao^1,2, Hongyu Mu¹, Zeyu Yang¹, Weibin Zheng¹, Faxin Wang¹, Jiansu Pu¹, Shaoning Zeng²

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 1039-1054, 2022, DOI:10.32604/cmes.2022.020331 - 27 June 2022

Abstract Multi-scale object detection is a research hotspot, and it has critical applications in many secure systems. Although the object detection algorithms have constantly been progressing recently, how to perform highly accurate and reliable multi-class object detection is still a challenging task due to the influence of many factors, such as the deformation and occlusion of the object in the actual scene. The more interference factors, the more complicated the semantic information, so we need a deeper network to extract deep information. However, deep neural networks often suffer from network degradation. To prevent the occurrence of… More >

Qiang Zhang¹, Ziyu Pei¹, Rong Guo¹, Haojun Zhang², Wanru Kong², Jie Lu³, Xueyan Liu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 845-863, 2022, DOI:10.32604/cmes.2022.019085 - 27 June 2022

Abstract Personal protective equipment (PPE) donning detection for medical staff is a key link of medical operation safety guarantee and is of great significance to combat COVID-19. However, the lack of dedicated datasets makes the scarce research on intelligence monitoring of workers’ PPE use in the field of healthcare. In this paper, we construct a dress codes dataset for medical staff under the epidemic. And based on this, we propose a PPE donning automatic detection approach using deep learning. With the participation of health care personnel, we organize 6 volunteers dressed in different combinations of PPE… More >

Aiming Wu¹, Shanshan Tu^1,*, Muhammad Wagas^1,2,3, Yongjie Yang¹, Yihe Zhang¹, Xuetao Bai¹

Journal of Information Hiding and Privacy Protection, Vol.4, No.1, pp. 25-39, 2022, DOI:10.32604/jihpp.2022.029922 - 17 June 2022

Abstract With the rapid development of the Internet of Things (IoT), all kinds of data are increasing exponentially. Data storage and computing on cloud servers are increasingly restricted by hardware. This has prompted the development of fog computing. Fog computing is to place the calculation and storage of data at the edge of the network, so that the entire Internet of Things system can run more efficiently. The main function of fog computing is to reduce the burden of cloud servers. By placing fog nodes in the IoT network, the data in the IoT devices can… More >

Ming Wan¹, Quanliang Li¹, Jiangyuan Yao^2,*, Yan Song³, Yang Liu⁴, Yuxin Wan⁵

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4033-4049, 2022, DOI:10.32604/cmc.2022.030895 - 16 June 2022

Abstract Anomaly detection is becoming increasingly significant in industrial cyber security, and different machine-learning algorithms have been generally acknowledged as various effective intrusion detection engines to successfully identify cyber attacks. However, different machine-learning algorithms may exhibit their own detection effects even if they analyze the same feature samples. As a sequence, after developing one feature generation approach, the most effective and applicable detection engines should be desperately selected by comparing distinct properties of each machine-learning algorithm. Based on process control features generated by directed function transition diagrams, this paper introduces five different machine-learning algorithms as alternative… More >

Belqasem Aljafari¹, Eydhah Almatrafi^2,3,4, Sudhakar Babu Thanikanti⁵, Sara A. Ibrahim⁶, Mohamed A. Enany^6,*, Marwa M. Ahmed⁷

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3595-3611, 2022, DOI:10.32604/cmc.2022.030733 - 16 June 2022

Abstract This paper presents a novel Simulink models with an evaluation study of more widely used On-Line Maximum Power Point tracking (MPPT) techniques for Photo-Voltaic based Battery Storage Systems (PV-BSS). To have a full comparative study in terms of the dynamic response, battery state of charge (SOC), and oscillations around the Maximum Power Point (MPP) of the PV-BSS to variations in climate conditions, these techniques are simulated in Matlab/Simulink. The introduced methodologies are classified into two types; the first type is conventional hill-climbing techniques which are based on instantaneous PV data measurements such as Perturb &… More >

Areej A. Malibari¹, Marwa Obayya², Mohamed K. Nour³, Amal S. Mehanna⁴, Manar Ahmed Hamza^5,*, Abu Sarwar Zamani⁵, Ishfaq Yaseen⁵, Abdelwahed Motwakel⁵

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4123-4138, 2022, DOI:10.32604/cmc.2022.030492 - 16 June 2022

Abstract With the rapid increase of new cases with an increased mortality rate, cancer is considered the second and most deadly disease globally. Breast cancer is the most widely affected cancer worldwide, with an increased death rate percentage. Due to radiologists’ processing of mammogram images, many computer-aided diagnoses have been developed to detect breast cancer. Early detection of breast cancer will reduce the death rate worldwide. The early diagnosis of breast cancer using the developed computer-aided diagnosis (CAD) systems still needed to be enhanced by incorporating innovative deep learning technologies to improve the accuracy and sensitivity… More >

Abeer Abdulaziz AlArfaj, Hanan Ahmed Hosni Mahmoud^*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4231-4244, 2022, DOI:10.32604/cmc.2022.030090 - 16 June 2022

Abstract In recent research, deep learning algorithms have presented effective representation learning models for natural languages. The deep learning-based models create better data representation than classical models. They are capable of automated extraction of distributed representation of texts. In this research, we introduce a new tree Extractive text summarization that is characterized by fitting the text structure representation in knowledge base training module, and also addresses memory issues that were not addresses before. The proposed model employs a tree structured mechanism to generate the phrase and text embedding. The proposed architecture mimics the tree configuration of… More >

Dmytro Kushnir^*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3649-3665, 2022, DOI:10.32604/cmc.2022.030016 - 16 June 2022

Abstract A literature analysis has shown that object search, recognition, and tracking systems are becoming increasingly popular. However, such systems do not achieve high practical results in analyzing small moving living objects ranging from 8 to 14 mm. This article examines methods and tools for recognizing and tracking the class of small moving objects, such as ants. To fulfill those aims, a customized You Only Look Once Ants Recognition (YOLO_AR) Convolutional Neural Network (CNN) has been trained to recognize Messor Structor ants in the laboratory using the LabelImg object marker tool. The proposed model is an… More >

Muhammad Shahzad Haroon^*, Husnain Mansoor Ali

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3513-3527, 2022, DOI:10.32604/cmc.2022.029858 - 16 June 2022

Abstract Intrusion detection system plays an important role in defending networks from security breaches. End-to-end machine learning-based intrusion detection systems are being used to achieve high detection accuracy. However, in case of adversarial attacks, that cause misclassification by introducing imperceptible perturbation on input samples, performance of machine learning-based intrusion detection systems is greatly affected. Though such problems have widely been discussed in image processing domain, very few studies have investigated network intrusion detection systems and proposed corresponding defence. In this paper, we attempt to fill this gap by using adversarial attacks on standard intrusion detection datasets… More >

Hassen Mestiri^1,2,3,*, Imen Barraj^1,4,5, Mohsen Machhout³

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4051-4066, 2022, DOI:10.32604/cmc.2022.029794 - 16 June 2022

Abstract Robustness of the electronic cryptographic devices against fault injection attacks is a great concern to ensure security. Due to significant resource constraints, these devices are limited in their capabilities. The increasing complexity of cryptographic devices necessitates the development of a fast simulation environment capable of performing security tests against fault injection attacks. SystemC is a good choice for Electronic System Level (ESL) modeling since it enables models to run at a faster rate. To enable fault injection and detection inside a SystemC cryptographic model, however, the model’s source code must be updated. Without altering the… More >