Zeshan Faiz¹, Iftikhar Ahmed¹, Dumitru Baleanu^2,3,4, Shumaila Javeed^1,5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.029879

Abstract The purpose of this research work is to investigate the numerical solutions of the fractional dengue transmission model (FDTM) in the presence of Wolbachia using the stochastic-based Levenberg-Marquardt neural network (LM-NN) technique. The fractional dengue transmission model (FDTM) consists of 12 compartments. The human population is divided into four compartments; susceptible humans (), exposed humans (), infectious humans (), and recovered humans (). Wolbachia-infected and Wolbachia-uninfected mosquito population is also divided into four compartments: aquatic (eggs, larvae, pupae), susceptible, exposed, and infectious. We investigated three different cases of vertical transmission probability (), namely when Wolbachia-free mosquitoes persist only (), when… More >

Cong Jin¹, Jinjie Huang^1,2,*, Yuanjian Chen¹, Yuqing Gong¹

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.047489

Abstract In differentiable search architecture search methods, a more efficient search space design can significantly improve the performance of the searched architecture, thus requiring people to carefully define the search space with different complexity according to various operations. Meanwhile rationalizing the search strategies to explore the well-defined search space will further improve the speed and efficiency of architecture search. With this in mind, we propose a faster and more efficient differentiable architecture search method, AllegroNAS. Firstly, we introduce a more efficient search space enriched by the introduction of two redefined convolution modules. Secondly, we utilize a more efficient architectural parameter regularization… More >

Di Zhou¹, Jianxun Zhang^1,*, Chao Li², Yifan Guo¹, Bowen Li¹

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.047236

Abstract Text perception is crucial for understanding the semantics of outdoor scenes, making it a key requirement for building intelligent systems for driver assistance or autonomous driving. Text information in car-mounted videos can assist drivers in making decisions. However, Car-mounted video text images pose challenges such as complex backgrounds, small fonts, and the need for real-time detection. We proposed a robust Car-mounted Video Text Detector (CVTD). It is a lightweight text detection model based on ResNet18 for feature extraction, capable of detecting text in arbitrary shapes. Our model efficiently extracted global text positions through the Coordinate Attention Threshold Activation (CATA) and… More >

Bayi Xu¹, Lei Sun^2,*, Xiuqing Mao², Chengwei Liu³, Zhiyi Ding²

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.046478

Abstract In recent years, frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security. This paper presents a novel intrusion detection system consisting of a data preprocessing stage and a deep learning model for accurately identifying network attacks. We have proposed four deep neural network models, which are constructed using architectures such as Convolutional Neural Networks (CNN), Bi-directional Long Short-Term Memory (BiLSTM), Bidirectional Gate Recurrent Unit (BiGRU), and Attention mechanism. These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the models, we apply various preprocessing… More >

Bowen Yu, Chunli Xie^*

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.046248

Abstract With the advent of Industry 4.0, marked by a surge in intelligent manufacturing, advanced sensors embedded in smart factories now enable extensive data collection on equipment operation. The analysis of such data is pivotal for ensuring production safety, a critical factor in monitoring the health status of manufacturing apparatus. Conventional defect detection techniques, typically limited to specific scenarios, often require manual feature extraction, leading to inefficiencies and limited versatility in the overall process. Our research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization processes. Our proposed approach encompasses a suite… More >

Qinhui Liu, Laizheng Zhu, Zhijie Gao, Jilong Wang, Jiang Li^*

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.046040

Abstract To improve the productivity, the resource utilization and reduce the production cost of flexible job shops, this paper designs an improved two-layer optimization algorithm for the dual-resource scheduling optimization problem of flexible job shop considering workpiece batching. Firstly, a mathematical model is established to minimize the maximum completion time. Secondly, an improved two-layer optimization algorithm is designed: the outer layer algorithm uses an improved PSO (Particle Swarm Optimization) to solve the workpiece batching problem, and the inner layer algorithm uses an improved GA (Genetic Algorithm) to solve the dual-resource scheduling problem. Then, a rescheduling method is designed to solve the… More >

Chengsheng Yuan^1,2, Baojie Cui^1,2, Zhili Zhou³, Xinting Li^4,*, Qingming Jonathan Wu⁵

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.045854

Abstract In recent years, deep learning has been the mainstream technology for fingerprint liveness detection (FLD) tasks because of its remarkable performance. However, recent studies have shown that these deep fake fingerprint detection (DFFD) models are not resistant to attacks by adversarial examples, which are generated by the introduction of subtle perturbations in the fingerprint image, allowing the model to make fake judgments. Most of the existing adversarial example generation methods are based on gradient optimization, which is easy to fall into local optimal, resulting in poor transferability of adversarial attacks. In addition, the perturbation added to the blank area of… More >

Mustafa AlRifaee¹, Sally Almanasra^2,*, Adnan Hnaif³, Ahmad Althunibat³, Mohammad Abdallah³, Thamer Alrawashdeh³

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.043520

Abstract In standard iris recognition systems, a cooperative imaging framework is employed that includes a light source with a near-infrared wavelength to reveal iris texture, look-and-stare constraints, and a close distance requirement to the capture device. When these conditions are relaxed, the system’s performance significantly deteriorates due to segmentation and feature extraction problems. Herein, a novel segmentation algorithm is proposed to correctly detect the pupil and limbus boundaries of iris images captured in unconstrained environments. First, the algorithm scans the whole iris image in the Hue Saturation Value (HSV) color space for local maxima to detect the sclera region. The image… More >

Aysha Naseer¹, Nouf Abdullah Almujally², Saud S. Alotaibi³, Abdulwahab Alazeb⁴, Jeongmin Park^5,*

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.042963

Abstract Object segmentation and recognition is an imperative area of computer vision and machine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their features. The proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks (ANNs). The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label them based on their characteristics. Then, two distinct kinds of features are obtained from the segmented images to help identify the objects… More >

Shulan Lei^1,2, Shujuan Wang¹, Zhuohong Zhu^1,2, Min Lu^1,2, Xinying Li^1,2, Yiming Shen³, Jing Chen^1,2,*

International Journal of Mental Health Promotion, Vol., , DOI:10.32604/ijmhp.2023.045478

Abstract The purpose of this study was to explore the reliability and validity of the Warwick-Edinburgh Mental Well-being Scale (WEMWBS) among Chinese civil servants, thus establishing a useful tool for assessing the mental health of individuals in this occupation. The WEMWBS, Satisfaction with Life Scale (SWLS), and Depression Anxiety and Stress Scale-21 (DASS-21) were administered to a sample of 2,624 civil servants (42.860 ± 9.690 years) in a city located within Shandong Province, China. The findings revealed significant differences between groups with high and low scores on each item of the WEMWBS (t = 48.127–78.308, all p < 0.01). The item-total… More >