Cmes-computer Modeling in Engineering & Sciences最新文献

ThyroidNet: A Deep Learning Network for Localization and Classification of Thyroid Nodules. 甲状腺网络：用于甲状腺结节定位和分类的深度学习网络

IF 2.4 4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2023-12-30 DOI: 10.32604/cmes.2023.031229

Lu Chen, Huaqiang Chen, Zhikai Pan, Sheng Xu, Guangsheng Lai, Shuwen Chen, Shuihua Wang, Xiaodong Gu, Yudong Zhang

{"title":"ThyroidNet: A Deep Learning Network for Localization and Classification of Thyroid Nodules.","authors":"Lu Chen, Huaqiang Chen, Zhikai Pan, Sheng Xu, Guangsheng Lai, Shuwen Chen, Shuihua Wang, Xiaodong Gu, Yudong Zhang","doi":"10.32604/cmes.2023.031229","DOIUrl":"https://doi.org/10.32604/cmes.2023.031229","url":null,"abstract":"Aim: This study aims to establish an artificial intelligence model, ThyroidNet, to diagnose thyroid nodules using deep learning techniques accurately.Methods: A novel method, ThyroidNet, is introduced and evaluated based on deep learning for the localization and classification of thyroid nodules. First, we propose the multitask TransUnet, which combines the TransUnet encoder and decoder with multitask learning. Second, we propose the DualLoss function, tailored to the thyroid nodule localization and classification tasks. It balances the learning of the localization and classification tasks to help improve the model's generalization ability. Third, we introduce strategies for augmenting the data. Finally, we submit a novel deep learning model, ThyroidNet, to accurately detect thyroid nodules.Results: ThyroidNet was evaluated on private datasets and was comparable to other existing methods, including U-Net and TransUnet. Experimental results show that ThyroidNet outperformed these methods in localizing and classifying thyroid nodules. It achieved improved accuracy of 3.9% and 1.5%, respectively.Conclusion: ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis tasks. Future research directions include optimization of the model structure, expansion of the dataset size, reduction of computational complexity and memory requirements, and exploration of additional applications of ThyroidNet in medical image analysis.","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7615790/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140848498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey on Artificial Intelligence in Posture Recognition. 人工智能在姿态识别中的研究进展。

IF 2.4 4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2023-04-23 DOI: 10.32604/cmes.2023.027676

Xiaoyan Jiang, Zuojin Hu, Shuihua Wang, Yudong Zhang

{"title":"A Survey on Artificial Intelligence in Posture Recognition.","authors":"Xiaoyan Jiang, Zuojin Hu, Shuihua Wang, Yudong Zhang","doi":"10.32604/cmes.2023.027676","DOIUrl":"https://doi.org/10.32604/cmes.2023.027676","url":null,"abstract":"Over the years, the continuous development of new technology has promoted research in the field of posture recognition and also made the application field of posture recognition have been greatly expanded. The purpose of this paper is to introduce the latest methods of posture recognition and review the various techniques and algorithms of posture recognition in recent years, such as scale-invariant feature transform, histogram of oriented gradients, support vector machine (SVM), Gaussian mixture model, dynamic time warping, hidden Markov model (HMM), lightweight network, convolutional neural network (CNN). We also investigate improved methods of CNN, such as stacked hourglass networks, multi-stage pose estimation networks, convolutional pose machines, and high-resolution nets. The general process and datasets of posture recognition are analyzed and summarized, and several improved CNN methods and three main recognition techniques are compared. In addition, the applications of advanced neural networks in posture recognition, such as transfer learning, ensemble learning, graph neural networks, and explainable deep neural networks, are introduced. It was found that CNN has achieved great success in posture recognition and is favored by researchers. Still, a more in-depth research is needed in feature extraction, information fusion, and other aspects. Among classification methods, HMM and SVM are the most widely used, and lightweight network gradually attracts the attention of researchers. In addition, due to the lack of 3D benchmark data sets, data generation is a critical research direction.","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7614502/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9424115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Survey of Convolutional Neural Network in Breast Cancer. 卷积神经网络在癌症中的应用研究。

IF 2.2 4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2023-03-09 DOI: 10.32604/cmes.2023.025484

Ziquan Zhu, Shui-Hua Wang, Yu-Dong Zhang

{"title":"A Survey of Convolutional Neural Network in Breast Cancer.","authors":"Ziquan Zhu, Shui-Hua Wang, Yu-Dong Zhang","doi":"10.32604/cmes.2023.025484","DOIUrl":"10.32604/cmes.2023.025484","url":null,"abstract":"Problems: For people all over the world, cancer is one of the most feared diseases. Cancer is one of the major obstacles to improving life expectancy in countries around the world and one of the biggest causes of death before the age of 70 in 112 countries. Among all kinds of cancers, breast cancer is the most common cancer for women. The data showed that female breast cancer had become one of the most common cancers.Aims: A large number of clinical trials have proved that if breast cancer is diagnosed at an early stage, it could give patients more treatment options and improve the treatment effect and survival ability. Based on this situation, there are many diagnostic methods for breast cancer, such as computer-aided diagnosis (CAD).Methods: We complete a comprehensive review of the diagnosis of breast cancer based on the convolutional neural network (CNN) after reviewing a sea of recent papers. Firstly, we introduce several different imaging modalities. The structure of CNN is given in the second part. After that, we introduce some public breast cancer data sets. Then, we divide the diagnosis of breast cancer into three different tasks: 1. classification; 2. detection; 3. segmentation.Conclusion: Although this diagnosis with CNN has achieved great success, there are still some limitations. (i) There are too few good data sets. A good public breast cancer dataset needs to involve many aspects, such as professional medical knowledge, privacy issues, financial issues, dataset size, and so on. (ii) When the data set is too large, the CNN-based model needs a sea of computation and time to complete the diagnosis. (iii) It is easy to cause overfitting when using small data sets.","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7614504/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10644432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Improved Hyperplane Assisted Multiobjective Optimization for Distributed Hybrid Flow Shop Scheduling Problem in Glass Manufacturing Systems 玻璃制造系统中分布式混合流水车间调度问题的改进超平面辅助多目标优化

4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2023-01-01 DOI: 10.32604/cmes.2022.020307

Yadian Geng, Junqing Li

{"title":"An Improved Hyperplane Assisted Multiobjective Optimization for Distributed Hybrid Flow Shop Scheduling Problem in Glass Manufacturing Systems","authors":"Yadian Geng, Junqing Li","doi":"10.32604/cmes.2022.020307","DOIUrl":"https://doi.org/10.32604/cmes.2022.020307","url":null,"abstract":"To solve the distributed hybrid flow shop scheduling problem (DHFS) in raw glass manufacturing systems, we investigated an improved hyperplane assisted evolutionary algorithm (IhpaEA). Two objectives are simultaneously considered, namely, the maximum completion time and the total energy consumptions. Firstly, each solution is encoded by a three-dimensional vector, i.e., factory assignment, scheduling, and machine assignment. Subsequently, an efficient initialization strategy embeds two heuristics are developed, which can increase the diversity of the population. Then, to improve the global search abilities, a Pareto-based crossover operator is designed to take more advantage of non-dominated solutions. Furthermore, a local search heuristic based on three parts encoding is embedded to enhance the searching performance. To enhance the local search abilities, the cooperation of the search operator is designed to obtain better non-dominated solutions. Finally, the experimental results demonstrate that the proposed algorithm is more efficient than the other three state-of-the-art algorithms. The results show that the Pareto optimal solution set obtained by the improved algorithm is superior to that of the traditional multiobjective algorithm in terms of diversity and convergence of the solution.","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136297050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Exploring the Latest Applications of OpenAI and ChatGPT: An In-Depth Survey 探索OpenAI和ChatGPT的最新应用:深度调查

4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2023-01-01 DOI: 10.32604/cmes.2023.030649

Hong Zhang, Haijian Shao

引用次数: 0

A Novel SE-CNN Attention Architecture for sEMG-Based Hand Gesture Recognition 基于表面肌电信号的手势识别的SE-CNN注意力结构

4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2023-01-01 DOI: 10.32604/cmes.2022.020035

Zhengyuan Xu, Junxiao Yu, Wentao Xiang, Songsheng Zhu, Mubashir Hussain, Bin Liu, Jianqing Li

{"title":"A Novel SE-CNN Attention Architecture for sEMG-Based Hand Gesture Recognition","authors":"Zhengyuan Xu, Junxiao Yu, Wentao Xiang, Songsheng Zhu, Mubashir Hussain, Bin Liu, Jianqing Li","doi":"10.32604/cmes.2022.020035","DOIUrl":"https://doi.org/10.32604/cmes.2022.020035","url":null,"abstract":"In this article, to reduce the complexity and improve the generalization ability of current gesture recognition systems, we propose a novel SE-CNN attention architecture for sEMG-based hand gesture recognition. The proposed algorithm introduces a temporal squeeze-and-excite block into a simple CNN architecture and then utilizes it to recalibrate the weights of the feature outputs from the convolutional layer. By enhancing important features while suppressing useless ones, the model realizes gesture recognition efficiently. The last procedure of the proposed algorithm is utilizing a simple attention mechanism to enhance the learned representations of sEMG signals to perform multi-channel sEMG-based gesture recognition tasks. To evaluate the effectiveness and accuracy of the proposed algorithm, we conduct experiments involving multi-gesture datasets Ninapro DB4 and Ninapro DB5 for both inter-session validation and subject-wise cross-validation. After a series of comparisons with the previous models, the proposed algorithm effectively increases the robustness with improved gesture recognition performance and generalization ability.","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135182952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Introduction to the Special Issue on Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications 解决复杂工程问题的计算智能系统特刊导论:原理与应用

4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2023-01-01 DOI: 10.32604/cmes.2023.031701

Danial Jahed Armaghani, Ahmed Salih Mohammed, Ramesh Murlidhar Bhatawdekar, Pouyan Fakharian, Ashutosh Kainthola, Wael Imad Mahmood

引用次数: 0

An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate 基于广义对立学习的随机森林模型全局和谐搜索优化系统预测掘进率

4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2023-01-01 DOI: 10.32604/cmes.2023.029938

Yingui Qiu, Shuai Huang, Danial Jahed Armaghani, Biswajeet Pradhan, Annan Zhou, Jian Zhou

引用次数: 0

Analytical Models of Concrete Fatigue: A State-of-the-Art Review 混凝土疲劳分析模型的研究进展

4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2023-01-01 DOI: 10.32604/cmes.2022.020160

Xiaoli Wei, D. A. Makhloof, Xiaodan Ren

引用次数: 3

Experimental and Numerical Investigation on High-Pressure Centrifugal Pumps: Ultimate Pressure Formulation, Fatigue Life Assessment and Topological Optimization of Discharge Section 高压离心泵的实验与数值研究:极限压力公式、疲劳寿命评估及排气段拓扑优化

4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2023-01-01 DOI: 10.32604/cmes.2023.030777

Abdourahamane Salifou Adam, Hatem Mrad, Haykel Marouani, Yasser Fouad

{"title":"Experimental and Numerical Investigation on High-Pressure Centrifugal Pumps: Ultimate Pressure Formulation, Fatigue Life Assessment and Topological Optimization of Discharge Section","authors":"Abdourahamane Salifou Adam, Hatem Mrad, Haykel Marouani, Yasser Fouad","doi":"10.32604/cmes.2023.030777","DOIUrl":"https://doi.org/10.32604/cmes.2023.030777","url":null,"abstract":"A high percentage of failure in pump elements originates from fatigue. This study focuses on the discharge section behavior, made of ductile iron, under dynamic load. An experimental protocol is established to collect the strain under pressurization and depressurization tests at specific locations. These experimental results are used to formulate the ultimate pressure expression function of the strain and the lateral surface of the discharge section and to validate finite element modeling. Fe-Safe is then used to assess the fatigue life cycle using different types of fatigue criteria (Coffin-Manson, Morrow, Goodman, and Soderberg). When the pressure is under 3000 PSI, pumps have an unlimited service life of 107 cycles, regardless of the criterion. However, for a pressure of 3555 PSI, only the Morrow criterion denotes a significant decrease in fatigue life cycles, as it considers the average stress. The topological optimization is then applied to the most critical pump model (with the lowest fatigue life cycle) to increase its fatigue life. Using the solid isotropic material with a penalization approach, the Abaqus Topology Optimization Module is employed. The goal is to reduce the strain energy density while keeping the volume within bounds. According to the findings, a 5% volume reduction causes the strain energy density to decrease from 1.06 to 0.66 106 J/m3. According to Morrow, the fatigue life cycle at 3,555 PSI is 782,425 longer than the initial 309,742 cycles.","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135894277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0