Journal of Mechanics in Medicine and Biology最新文献_第2页

A mechano-biological study comparing external fixation using monocortical and bicortical pins in tibial diaphyseal fracture models: A finite element analysis 一项力学生物学研究比较了胫骨骨干骨折模型中使用单皮质和双皮质针的外固定:有限元分析

4区医学

Journal of Mechanics in Medicine and Biology Pub Date : 2023-11-03 DOI: 10.1142/s0219519423501014

Targol Bayat, Yousof Mohandes, Mohammad Tahami, Masoud Tahani

引用次数: 0

Deep Learning-Based Feature Fusion and Transfer Learning for Approximating Pic Value Of COVID-19 Medicine Using Drug Discovery Data 基于深度学习的特征融合和迁移学习，利用药物发现数据逼近COVID-19药物Pic值

4区医学

Journal of Mechanics in Medicine and Biology Pub Date : 2023-11-03 DOI: 10.1142/s0219519423501002

Amol Dattatray Dhaygude, Mehadi Hasan, M Vijay, Chittibabu Ravela

引用次数: 0

Domain-adaptive TSK fuzzy system based on multisource data fusion for epileptic EEG signal classification 基于多源数据融合的域自适应TSK模糊系统在癫痫脑电信号分类中的应用

4区医学

Journal of Mechanics in Medicine and Biology Pub Date : 2023-10-21 DOI: 10.1142/s0219519423400900

Zaihe Cheng, Guohua Zhou

{"title":"Domain-adaptive TSK fuzzy system based on multisource data fusion for epileptic EEG signal classification","authors":"Zaihe Cheng, Guohua Zhou","doi":"10.1142/s0219519423400900","DOIUrl":"https://doi.org/10.1142/s0219519423400900","url":null,"abstract":"In recent years, machine learning methods based on epileptic signals have shown good results with brain-computer interfaces (BCIs). With the continuous expansion of their applications, the demand for labeled epileptic signals is increasing. For a large number of data-driven models, such signals are not suitable, as they extend the calibration cycle. Therefore, a new domain-adaptive TSK fuzzy system model based on multisource data fusion (DA-TSK) is proposed. The purpose of DA-TSK is to maintain high classification performance when the amount of labeled data is insufficient. The DA-TSK model not only has a strong learning ability to learn characteristic information from EEG data but is also interpretable, which aids in the understanding of the analytic process of the model for medical purposes. In particular, this model can make full use of a small amount of labeled EEG data in the source domain and target domain through domain adaptation. Therefore, the DA-TSK model can reduce data dependence to a certain extent and improve the generalization performance of the target classifier. Experiments are performed to evaluate the effectiveness of the DA-TSK model on public EEG datasets based on epileptic signals. The DA-TSK model can obtain satisfactory accuracy when the labeled data are insufficient in the target domain.","PeriodicalId":50135,"journal":{"name":"Journal of Mechanics in Medicine and Biology","volume":"13 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135463432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A New Intelligent Model Based on Improved Inception-V3 for Oral Cancer and Cyst Classification 基于改进Inception-V3的口腔癌和囊肿分类智能新模型

4区医学

Journal of Mechanics in Medicine and Biology Pub Date : 2023-10-21 DOI: 10.1142/s0219519423400985

Suxian Xiang, Yun He, Chenxi Huang, Ziyi Guo, Siming Lin, Jin Zhu

引用次数: 0

Analysis of traditional chinese medicine prescriptions for cerebral stroke-related diseases 脑卒中相关疾病的中药处方分析

4区医学

Journal of Mechanics in Medicine and Biology Pub Date : 2023-10-21 DOI: 10.1142/s0219519423400924

Zeguo Shao, Tingting Huang, Yanlin Jiang, Chaoyue Han, Jiankun Chen, WenHao Ju, Li Wang

{"title":"Analysis of traditional chinese medicine prescriptions for cerebral stroke-related diseases","authors":"Zeguo Shao, Tingting Huang, Yanlin Jiang, Chaoyue Han, Jiankun Chen, WenHao Ju, Li Wang","doi":"10.1142/s0219519423400924","DOIUrl":"https://doi.org/10.1142/s0219519423400924","url":null,"abstract":"Cerebral stroke, a type of cerebrovascular disease, has become the second leading cause of death globally. It is closely related to many diseases, including hypertension, diabetes, senile dementia, and so forth. As traditional Chinese medicine formulas are increasingly used to treat stroke and its associated diseases, people have begun to use machine learning methods to analyze Chinese medicine prescriptions and summarize their laws. In this study, we collected the data from classic Chinese formulations. Using the Jaccard similarity coefficient method, we calculated the similarity between different prescriptions. We then employed average linkage clustering, categorizing medicine prescriptions for chronic diseases such as diabetes, hypertension, and coronary heart disease into 12 groups. Some of these included “Heart Failure and Warm Kidney Soup”, “Sinus Chamber Junction Syndrome, Quadruple One Depression, Yi Qi, Activating Blood and Feeding Heart Soup”, “Nerves One, Depression One, Tian Ma Hook and Vine Drink”, and “Bawei Antihypertensive Decoction, Anemia Decoction, Hypotension Decoction, Myocardial Live Drink”. We observed that similar prescriptions had more meaningful mutual references. Subsequently, a correlation algorithm was used to analyze the “indications” and “prescription composition”, revealing 11 effective correlation rules. Among these, palpitations were strongly correlated with Astragalus membranaceus, Angelica sinensis, and cassia twig; weakness with Salvia miltiorrhiza, A. membranaceus, and A. sinensis; headaches with Ligusticum wallichii; and vertigo with A. membranaceus. These findings provided a theoretical reference for using traditional Chinese medicine in treating cerebral stroke and associated illnesses.","PeriodicalId":50135,"journal":{"name":"Journal of Mechanics in Medicine and Biology","volume":"37 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135463435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel deep learning method for brain tumor segmentation in magnetic resonance images based on residual units and modified U-net model 基于残差单元和改进U-net模型的脑肿瘤磁共振图像深度学习分割新方法

4区医学

Journal of Mechanics in Medicine and Biology Pub Date : 2023-10-21 DOI: 10.1142/s0219519423400882

Yuxuan Chen, Yunyi Chen, Jian Chen, Chenxi Huang, Bin Wang, Xu Cui

{"title":"A novel deep learning method for brain tumor segmentation in magnetic resonance images based on residual units and modified U-net model","authors":"Yuxuan Chen, Yunyi Chen, Jian Chen, Chenxi Huang, Bin Wang, Xu Cui","doi":"10.1142/s0219519423400882","DOIUrl":"https://doi.org/10.1142/s0219519423400882","url":null,"abstract":"Brain tumors are among the most deadly forms of cancer, as the brain is a crucial organ for human activity. Early detection and treatment are key to recovery. An expert’s final decision on tumor diagnosis mainly depends on the evaluation of Magnetic Resonance Imaging (MRI) images. However, the traditional manual assessment process is time-consuming, error-prone, and relies on the experience and knowledge of doctors, along with other unstable factors. An automated brain tumor detection system can assist radiologists and internal medicine experts in detecting and diagnosing brain tumors. This study proposes a novel deep learning model that combines residual units with a modified U-Net framework for brain tumor segmentation tasks in brain MR images. In this study, the U-Net-based framework is implemented with a stack of neural units and residual units and uses Leaky Rectified Linear Unit (LReLU) as the model’s activation function. First, neural units are added before the first layer of downsampling and upsampling to enhance feature propagation and reuse. Then, the stacking of residual blocks is applied to achieve deep semantic information extraction for downsampling and pixel classification for upsampling. Finally, a single-layer convolution outputs the predicted segmented images. The experimental results show that the segmentation Dice Similarity Coefficient of this model is 90.79%, and the model demonstrates better segmentation accuracy than other research models.","PeriodicalId":50135,"journal":{"name":"Journal of Mechanics in Medicine and Biology","volume":"129 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135463434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A deep learning framework-based exercise assessment for rehabilitation of chronic obstructive pulmonary disease 基于深度学习框架的慢性阻塞性肺疾病康复运动评估

4区医学

Journal of Mechanics in Medicine and Biology Pub Date : 2023-10-21 DOI: 10.1142/s0219519423400894

Lei Cao, Zhiheng Xie, Tianyu Liu, Zijian Wang, Chunjiang Fan

引用次数: 0

ERRATUM — RESEARCH ON 3D RECONSTRUCTION METHOD AND APPLICATION OF FOOD IN STROKE PATIENTS BASED ON RGB-D IMAGE 勘误 - 基于RGB-D图像的脑卒中患者食物三维重建方法及应用研究

IF 0.8 4区医学

Journal of Mechanics in Medicine and Biology Pub Date : 2023-10-19 DOI: 10.1142/s0219519423920021

Chendi Yuan, Jian-Kun Chen, Fei Wang, Jing-Jie Ouyang, Tao Jing, Xue-Fei Wang, Bo Yang, Zeguo Shao

引用次数: 0

CORRIGENDUM — FRACTOGRAPHIC ANALYSIS OF LITHIUM DISILICATE CERAMICS AND MONOLITHIC ZIRCONIA CERAMICS 勘误-二硅酸锂陶瓷和单片氧化锆陶瓷的断口分析

4区医学

Journal of Mechanics in Medicine and Biology Pub Date : 2023-10-19 DOI: 10.1142/s021951942392001x

SHANYU ZHOU, YUEHUA YOU

{"title":"CORRIGENDUM — FRACTOGRAPHIC ANALYSIS OF LITHIUM DISILICATE CERAMICS AND MONOLITHIC ZIRCONIA CERAMICS","authors":"SHANYU ZHOU, YUEHUA YOU","doi":"10.1142/s021951942392001x","DOIUrl":"https://doi.org/10.1142/s021951942392001x","url":null,"abstract":"Journal of Mechanics in Medicine and BiologyOnline Ready No AccessCORRIGENDUM — FRACTOGRAPHIC ANALYSIS OF LITHIUM DISILICATE CERAMICS AND MONOLITHIC ZIRCONIA CERAMICSis erratum ofFRACTOGRAPHIC ANALYSIS OF LITHIUM DISILICATE CERAMICS AND MONOLITHIC ZIRCONIA CERAMICSSHANYU ZHOU and YUEHUA YOUSHANYU ZHOUDepartment of Stomatology, Affiliated Longhua People’s Hospital, Southern Medical University, Shenzhen, Guangdong 518109, P. R. China and YUEHUA YOUDepartment of Stomatology, Affiliated Longhua People’s Hospital, Southern Medical University, Shenzhen, Guangdong 518109, P. R. Chinahttps://doi.org/10.1142/S021951942392001XCited by:0 (Source: Crossref) Next AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail View articleJournal of Mechanics in Medicine and Biology Vol. 22, No. 3 (2022) 2240012 (13 pages) https://doi.org/10.1142/S0219519422400127 FiguresReferencesRelatedDetailsRelated articlesFRACTOGRAPHIC ANALYSIS OF LITHIUM DISILICATE CERAMICS AND MONOLITHIC ZIRCONIA CERAMICS29 Mar 2022Journal of Mechanics in Medicine and Biology Recommended Online Ready Metrics History Published: 19 October 2023 PDF download","PeriodicalId":50135,"journal":{"name":"Journal of Mechanics in Medicine and Biology","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135730911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A sentiment analysis model for electroencephalogram signals of students in universities using a convolutional neural network and support vector machine models 基于卷积神经网络和支持向量机模型的大学生脑电图信号情感分析模型

4区医学

Journal of Mechanics in Medicine and Biology Pub Date : 2023-10-13 DOI: 10.1142/s0219519423400869

Xuezhi Fan, Jie Zhang, Mengting Yang

{"title":"A sentiment analysis model for electroencephalogram signals of students in universities using a convolutional neural network and support vector machine models","authors":"Xuezhi Fan, Jie Zhang, Mengting Yang","doi":"10.1142/s0219519423400869","DOIUrl":"https://doi.org/10.1142/s0219519423400869","url":null,"abstract":"Sentiment analysis in teaching evaluation has significant implications. By analyzing students’ sentiments toward instructors, educational institutions can gain valuable insights into teaching effectiveness. These data can guide curriculum development, instructional improvements, and faculty training initiatives. Positive sentiment indicates effective teaching methods, engagement, and student satisfaction; negative sentiment flags areas that need attention. Sentiment analysis can help identify patterns, trends, and outliers, aiding in targeted interventions and personalized support. It also enables comparisons across different courses, instructors, and departments. However, it is crucial to ensure the accuracy and fairness of sentiment analysis algorithms, considering potential biases and the contextual nature of the feedback. This study proposes a sentiment classification model CNN–SVM that combines a convolutional neural network (CNN) and a support vector machine (SVM). Taking students majoring in art in comprehensive colleges and universities as the research object, by collecting the electroencephalogram (EEG) signals of students during teaching evaluation. CNN–SVM is used as the emotional analysis model to obtain the emotional analysis of teaching evaluation results. EEG is a typical physiological signal, and data based on this signal can more truly reflect student emotions. The adaptive CNN feature extraction function and the super generalization classification performance of SVM can reduce the individual differences and data noise between data, thereby improving sentiment classification performance. The experimental results demonstrate that using technology to analyze sentiment can assist educational institutions in more properly comprehending the feedback and opinions of students on instruction. With regard to sentiment analysis, the CNN–SVM method that is derived to produce the fusion algorithm has solid performance.","PeriodicalId":50135,"journal":{"name":"Journal of Mechanics in Medicine and Biology","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135804723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0