Huaxin Zhou^1,2, Ziying Fang³, Yilin Wang⁴, Mengjun Tong^1,2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 175-194, 2023, DOI:10.32604/cmc.2023.037342

Abstract Plant diseases have become a challenging threat in the agricultural field. Various learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases early. However, deep learning entails extensive data for training, and it may be challenging to collect plant datasets. Even though plant datasets can be collected, they may be uneven in quantity. As a result, the problem of classification model overfitting arises. This study targets this issue and proposes an auxiliary classifier GAN (small-ACGAN) model based on a small number of datasets to extend the available data. First, after comparing various attention… More >

Louay Abd Al-Azez Mahdi, Mohammed A. Fayad, Miqdam T. Chaichan^*

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.9, pp. 2201-2214, 2023, DOI:10.32604/fdmp.2023.027166

Abstract A theoretical study based on the Penalty factor (PF) method by Cavallini et al. is conducted to show that the pressure drop occurring in a wire-on-tube heat exchanger can be converted into a temperature difference for two types of refrigerants R-134a and R-600a typically used for charging refrigerators and freezers. The following conditions are considered: stratified or stratified-wavy flow condensation occurring inside the smooth tube of a wire-on-tube condenser with diameter 3.25, 4.83, and 6.299 mm, condensation temperatures 35°C, 45°C, and 54.4°C and cover refrigerant mass flow rate spanning the interval from 1 to 7 kg/hr. The results show that the… More > Graphic Abstract

Wenyuan Wang^1,#, Shuili Luo^1,#, Everett L. Worthington Jr², Haijiang Li^1,3,*

International Journal of Mental Health Promotion, Vol.25, No.4, pp. 539-550, 2023, DOI:10.32604/ijmhp.2023.025634

Abstract Previous studies have found that self-control training was effective in improving an individual’s self-control, which plays an important role in inhibiting negative emotions. However, it is unclear whether self-control training can facilitate refraining from retaliation. This study randomly assigned participants (N = 55) to a training condition (building self-control by avoiding sweets) or a control condition. Before and after training, participants completed the Transgression-Related Interpersonal Motivations Inventory-18 (TRIM-18) and a modified Taylor aggression task once each. Participants in the training condition inflicted more low-intensity penalties on the previous offender compared to control participants. Participants in the training condition reported lower revenge scores… More >

Anubha¹, Ravneet Preet Singh Bedi², Arfat Ahmad Khan^3,*, Mohd Anul Haq⁴, Ahmad Alhussen⁵, Zamil S. Alzamil⁴

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1331-1351, 2023, DOI:10.32604/cmc.2023.033181

Abstract Mobile adhoc networks have grown in prominence in recent years, and they are now utilized in a broader range of applications. The main challenges are related to routing techniques that are generally employed in them. Mobile Adhoc system management, on the other hand, requires further testing and improvements in terms of security. Traditional routing protocols, such as Adhoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR), employ the hop count to calculate the distance between two nodes. The main aim of this research work is to determine the optimum method for sending packets while also extending life time of… More >

Wang Ning^1,*, Siliang Chen^2,*, Fu Qiang², Haitao Tang², Shen Jie²

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5951-5965, 2023, DOI:10.32604/cmc.2023.035558

Abstract With the popularity of online payment, how to perform credit card fraud detection more accurately has also become a hot issue. And with the emergence of the adaptive boosting algorithm (Adaboost), credit card fraud detection has started to use this method in large numbers, but the traditional Adaboost is prone to overfitting in the presence of noisy samples. Therefore, in order to alleviate this phenomenon, this paper proposes a new idea: using the number of consecutive sample misclassifications to determine the noisy samples, while constructing a penalty factor to reconstruct the sample weight assignment. Firstly, the theoretical analysis shows that… More >

Xiaojun Zhu^1,2,3, Heming Huang^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2155-2172, 2023, DOI:10.32604/cmes.2023.021453

Abstract Recently, speech enhancement methods based on Generative Adversarial Networks have achieved good performance in time-domain noisy signals. However, the training of Generative Adversarial Networks has such problems as convergence difficulty, model collapse, etc. In this work, an end-to-end speech enhancement model based on Wasserstein Generative Adversarial Networks is proposed, and some improvements have been made in order to get faster convergence speed and better generated speech quality. Specifically, in the generator coding part, each convolution layer adopts different convolution kernel sizes to conduct convolution operations for obtaining speech coding information from multiple scales; a gated linear unit is introduced to… More >

Heng Cheng¹, Zebin Xing¹, Miaojuan Peng^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 945-964, 2022, DOI:10.32604/cmes.2022.020755

Abstract In this paper, we considered the improved element-free Galerkin (IEFG) method for solving 2D anisotropic steady-state heat conduction problems. The improved moving least-squares (IMLS) approximation is used to establish the trial function, and the penalty method is applied to enforce the boundary conditions, thus the final discretized equations of the IEFG method for anisotropic steady-state heat conduction problems can be obtained by combining with the corresponding Galerkin weak form. The influences of node distribution, weight functions, scale parameters and penalty factors on the computational accuracy of the IEFG method are analyzed respectively, and these numerical solutions show that less computational… More >

Sami Ullah Khan¹, Babar Nazir¹, Muhammad Hanif^2,*, Akhtar Ali³, Sardar Alam¹, Usman Habib⁴

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4199-4220, 2022, DOI:10.32604/cmc.2022.020852

Abstract The cloud service level agreement (SLA) manage the relationship between service providers and consumers in cloud computing. SLA is an integral and critical part of modern era IT vendors and communication contracts. Due to low cost and flexibility more and more consumers delegate their tasks to cloud providers, the SLA emerges as a key aspect between the consumers and providers. Continuous monitoring of Quality of Service (QoS) attributes is required to implement SLAs because of the complex nature of cloud communication. Many other factors, such as user reliability, satisfaction, and penalty on violations are also taken into account. Currently, there… More >

Chunjing Xiao^1,∗, Yongwei Qiao², Kewen Xia¹, Yuxiang Zhang³

Computer Systems Science and Engineering, Vol.33, No.2, pp. 95-103, 2018, DOI:10.32604/csse.2018.33.095

Abstract Tourist routes recommendation is a way to improve the tourist experience and the efficiency of tourism companies. Session-based methods divide all users’ interaction histories into the same number sessions with fixed time window and treat the user preference as time sequences. There have few or even no interaction in some sessions for some users because of the high sparsity and temporal characteristics of tourist data. That lead to many session-based methods can not be applied to routes recommendation due to aggravate the sparsity. In order to better adapt and apply the characteristics of tourism data and alleviate the sparsity, a… More >

Juan Li

Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 35-48, 2019, DOI:10.31209/2018.100000003

Abstract K-Nearest Neighbor algorithm (KNN) is a simple and mature classification method. However there are susceptible factors influencing the classification performance, such as k value determination, the overlarge search space, unbalanced and multi-class patterns, etc. To deal with the above problems, a new classification algorithm that absorbs tree structure, tree pruning and adaptive k value method was proposed. The proposed algorithm can overcome the shortcoming of KNN, improve the performance of multi-class and unbalanced classification, reduce the scale of dataset maintaining the comparable classification accuracy. The simulations are conducted and the proposed algorithm is compared with several existing algorithms. The results… More >