Rui Wen^1,*, Jie Zhou², Zhongliang Shen¹, Xiaorui Zhang^2,3,4, Sunil Kumar Jha⁵

Computer Systems Science and Engineering, Vol.47, No.1, pp. 187-204, 2023, DOI:10.32604/csse.2023.037889

Abstract Convolution Neural Networks (CNN) can quickly diagnose COVID-19 patients by analyzing computed tomography (CT) images of the lung, thereby effectively preventing the spread of COVID-19. However, the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 population. Which reduces the model’s classification sensitivity, resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 people. To solve the problem, this paper first attempts to apply triplet loss and center loss to the field of COVID-19 image… More >

Yan Zeng^1,2,3, Siyuan Teng¹, Tian Xiang^4,*, Jilin Zhang^1,2,3, Yuankai Mu⁵, Yongjian Ren^1,2,3,*, Jian Wan^1,2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 1047-1064, 2023, DOI:10.32604/cmes.2023.027226

Abstract Federated learning is a distributed machine learning method that can solve the increasingly serious problem of data islands and user data privacy, as it allows training data to be kept locally and not shared with other users. It trains a global model by aggregating locally-computed models of clients rather than their raw data. However, the divergence of local models caused by data heterogeneity of different clients may lead to slow convergence of the global model. For this problem, we focus on the client selection with federated learning, which can affect the convergence performance of the global model with the selected… More > Graphic Abstract

Anoud A. Mahmoud^1,*, Hanaa A. Sayed^2,3, Sara S. Mohamed¹

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4535-4552, 2023, DOI:10.32604/cmc.2023.037087

Abstract Computed Tomography (CT) images have been extensively employed in disease diagnosis and treatment, causing a huge concern over the dose of radiation to which patients are exposed. Increasing the radiation dose to get a better image may lead to the development of genetic disorders and cancer in the patients; on the other hand, decreasing it by using a Low-Dose CT (LDCT) image may cause more noise and increased artifacts, which can compromise the diagnosis. So, image reconstruction from LDCT image data is necessary to improve radiologists’ judgment and confidence. This study proposed three novel models for denoising LDCT images based… More >

Qunbiao Wu¹, Zhen Wang^1,*, Haifeng Fang¹, Junji Chen¹, Xinfeng Wan²

Computer Systems Science and Engineering, Vol.46, No.1, pp. 961-979, 2023, DOI:10.32604/csse.2023.036239

Abstract For surface defects in electronic water pump shells, the manual detection efficiency is low, prone to misdetection and leak detection, and encounters problems, such as uncertainty. To improve the speed and accuracy of surface defect detection, a lightweight detection method based on an improved YOLOv5s method is proposed to replace the traditional manual detection methods. In this method, the MobileNetV3 module replaces the backbone network of YOLOv5s, depth-separable convolution is introduced, the parameters and calculations are reduced, and CIoU_Loss is used as the loss function of the boundary box regression to improve its detection accuracy. A dataset of electronic pump… More >

Ying Ma¹, Jiaqi Zhang^2,3,4, Pengyu Liu^1,2,3,4,*, Zhihao Wei⁵, Lingfei Zhang¹, Xiaowei Jia⁶

Journal of New Media, Vol.4, No.4, pp. 165-177, 2022, DOI:10.32604/jnm.2022.026684

Abstract Plateau forest plays an important role in the high-altitude ecosystem, and contributes to the global carbon cycle. Plateau forest monitoring request in-suit data from field investigation. With recent development of the remote sensing technic, large-scale satellite data become available for surface monitoring. Due to the various information contained in the remote sensing data, obtain accurate plateau forest segmentation from the remote sensing imagery still remain challenges. Recent developed deep learning (DL) models such as deep convolutional neural network (CNN) has been widely used in image processing tasks, and shows possibility for remote sensing segmentation. However, due to the unique characteristics… More >

Sunil Kumar¹, Manisha Jailia¹, Sudeep Varshney², Nitish Pathak³, Shabana Urooj^4,*, Nouf Abd Elmunim⁴

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3561-3577, 2023, DOI:10.32604/cmc.2023.029999

Abstract Vehicle detection is still challenging for intelligent transportation systems (ITS) to achieve satisfactory performance. The existing methods based on one stage and two-stage have intrinsic weakness in obtaining high vehicle detection performance. Due to advancements in detection technology, deep learning-based methods for vehicle detection have become more popular because of their higher detection accuracy and speed than the existing algorithms. This paper presents a robust vehicle detection technique based on Improved You Look Only Once (RVD-YOLOv5) to enhance vehicle detection accuracy. The proposed method works in three phases; in the first phase, the K-means algorithm performs data clustering on datasets… More >

Mohamed Hassan Essai Ali^1,*, Fahad Alraddady², Mo’ath Y. Al-Thunaibat³, Shaima Elnazer²

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 755-778, 2023, DOI:10.32604/cmes.2022.022246

Abstract For a 5G wireless communication system, a convolutional deep neural network (CNN) is employed to synthesize a robust channel state estimator (CSE). The proposed CSE extracts channel information from transmit-and-receive pairs through offline training to estimate the channel state information. Also, it utilizes pilots to offer more helpful information about the communication channel. The proposed CNN-CSE performance is compared with previously published results for Bidirectional/long short-term memory (BiLSTM/LSTM) NNs-based CSEs. The CNN-CSE achieves outstanding performance using sufficient pilots only and loses its functionality at limited pilots compared with BiLSTM and LSTM-based estimators. Using three different loss function-based classification layers and… More >

Hong’an Li¹, Min Zhang^1,*, Dufeng Chen², Jing Zhang¹, Meng Yang³, Zhanli Li¹

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 779-794, 2023, DOI:10.32604/cmes.2022.022369

Abstract Computer-aided diagnosis based on image color rendering promotes medical image analysis and doctor-patient communication by highlighting important information of medical diagnosis. To overcome the limitations of the color rendering method based on deep learning, such as poor model stability, poor rendering quality, fuzzy boundaries and crossed color boundaries, we propose a novel hinge-cross-entropy generative adversarial network (HCEGAN). The self-attention mechanism was added and improved to focus on the important information of the image. And the hinge-cross-entropy loss function was used to stabilize the training process of GAN models. In this study, we implement the HCEGAN model for image color rendering… More > Graphic Abstract

Mansour F. Yassen^1,2,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1691-1706, 2022, DOI:10.32604/cmc.2022.029701

Abstract Quantifying the tool–tissue interaction forces in surgery can be utilized in the training of inexperienced surgeons, assist them better use surgical tools and avoid applying excessive pressures. The voltages read from strain gauges are used to approximate the unknown values of implemented forces. To this objective, the force-voltage connection must be quantified in order to evaluate the interaction forces during surgery. The progress of appropriate statistical learning approaches to describe the link between the genuine force applied on the tissue and numerous outputs obtained from sensors installed on surgical equipment is a key problem. In this study, different probabilistic approaches… More >

Walid Aydi^1,3,*, Fuad S. Alduais^2,4

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 4033-4050, 2022, DOI:10.32604/cmc.2022.023119

Abstract The Weibull distribution is regarded as among the finest in the family of failure distributions. One of the most commonly used parameters of the Weibull distribution (WD) is the ordinary least squares (OLS) technique, which is useful in reliability and lifetime modeling. In this study, we propose an approach based on the ordinary least squares and the multilayer perceptron (MLP) neural network called the OLSMLP that is based on the resilience of the OLS method. The MLP solves the problem of heteroscedasticity that distorts the estimation of the parameters of the WD due to the presence of outliers, and eases… More >