Pub Date : 2023-11-03DOI: 10.1142/s0219519423501014
Targol Bayat, Yousof Mohandes, Mohammad Tahami, Masoud Tahani
{"title":"A mechano-biological study comparing external fixation using monocortical and bicortical pins in tibial diaphyseal fracture models: A finite element analysis","authors":"Targol Bayat, Yousof Mohandes, Mohammad Tahami, Masoud Tahani","doi":"10.1142/s0219519423501014","DOIUrl":"https://doi.org/10.1142/s0219519423501014","url":null,"abstract":"","PeriodicalId":50135,"journal":{"name":"Journal of Mechanics in Medicine and Biology","volume":"23 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135875077","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}
Pub Date : 2023-11-03DOI: 10.1142/s0219519423501002
Amol Dattatray Dhaygude, Mehadi Hasan, M Vijay, Chittibabu Ravela
{"title":"Deep Learning-Based Feature Fusion and Transfer Learning for Approximating Pic Value Of COVID-19 Medicine Using Drug Discovery Data","authors":"Amol Dattatray Dhaygude, Mehadi Hasan, M Vijay, Chittibabu Ravela","doi":"10.1142/s0219519423501002","DOIUrl":"https://doi.org/10.1142/s0219519423501002","url":null,"abstract":"","PeriodicalId":50135,"journal":{"name":"Journal of Mechanics in Medicine and Biology","volume":"24 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135875287","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}
Pub Date : 2023-10-21DOI: 10.1142/s0219519423400900
Zaihe Cheng, Guohua Zhou
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.
{"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}
Oral cancer, which is also called mouth cancer, is cancer of the lining of the mouth, lips, or upper throat that has appeared in more than 355,000 people worldwide and caused more than 177,000 deaths, so it is essential to diagnose it as early as possible. Computed tomography (CT) scan is conducive to oral cancer diagnosis, but classifying oral CT images to cancer and cyst manually is difficult and time-consuming. A novel intelligent model based on improved Inception-v3 for classifying oral cancer and cyst CT images automatically is proposed in this paper. We replace the conventional convolution block in Inception-v3 with the Inverted Bottleneck Block and introduce Squeeze-and-Excitation Block (SEB) and Convolutional Block Attention Block (CBAB). The proposed model in this paper is trained on a dataset consisting of CT images of two classes (oral cancer and cyst), and the proposed model achieves 84.053% accuracy, 82.364% sensitivity, 84.508% specificity for oral cancer classification and outperforms other common models in classifying oral CT images.
{"title":"A New Intelligent Model Based on Improved Inception-V3 for Oral Cancer and Cyst Classification","authors":"Suxian Xiang, Yun He, Chenxi Huang, Ziyi Guo, Siming Lin, Jin Zhu","doi":"10.1142/s0219519423400985","DOIUrl":"https://doi.org/10.1142/s0219519423400985","url":null,"abstract":"Oral cancer, which is also called mouth cancer, is cancer of the lining of the mouth, lips, or upper throat that has appeared in more than 355,000 people worldwide and caused more than 177,000 deaths, so it is essential to diagnose it as early as possible. Computed tomography (CT) scan is conducive to oral cancer diagnosis, but classifying oral CT images to cancer and cyst manually is difficult and time-consuming. A novel intelligent model based on improved Inception-v3 for classifying oral cancer and cyst CT images automatically is proposed in this paper. We replace the conventional convolution block in Inception-v3 with the Inverted Bottleneck Block and introduce Squeeze-and-Excitation Block (SEB) and Convolutional Block Attention Block (CBAB). The proposed model in this paper is trained on a dataset consisting of CT images of two classes (oral cancer and cyst), and the proposed model achieves 84.053% accuracy, 82.364% sensitivity, 84.508% specificity for oral cancer classification and outperforms other common models in classifying oral CT images.","PeriodicalId":50135,"journal":{"name":"Journal of Mechanics in Medicine and Biology","volume":"3 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":"135463439","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}
Pub Date : 2023-10-21DOI: 10.1142/s0219519423400924
Zeguo Shao, Tingting Huang, Yanlin Jiang, Chaoyue Han, Jiankun Chen, WenHao Ju, Li Wang
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.
{"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}
Pub Date : 2023-10-21DOI: 10.1142/s0219519423400882
Yuxuan Chen, Yunyi Chen, Jian Chen, Chenxi Huang, Bin Wang, Xu Cui
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.
脑瘤是最致命的癌症之一，因为大脑是人类活动的重要器官。早期发现和治疗是康复的关键。专家对肿瘤诊断的最终决定主要取决于对磁共振成像(MRI)图像的评价。然而，传统的人工评估过程耗时长，容易出错，并且依赖于医生的经验和知识，以及其他不稳定因素。一种自动化的脑肿瘤检测系统可以帮助放射科医生和内科专家检测和诊断脑肿瘤。本研究提出了一种新的深度学习模型，该模型将残差单元与改进的U-Net框架相结合，用于脑MR图像中的脑肿瘤分割任务。在本研究中，基于u - net的框架由神经单元和残差单元堆栈实现，并使用Leaky Rectified Linear Unit (LReLU)作为模型的激活函数。首先，在下采样和上采样的第一层之前加入神经单元，增强特征的传播和重用;然后，利用残差块叠加实现下采样的深度语义信息提取和上采样的像素分类。最后，单层卷积输出预测的分割图像。实验结果表明，该模型的分割骰子相似系数为90.79%，分割精度优于其他研究模型。
{"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}
Pub Date : 2023-10-21DOI: 10.1142/s0219519423400894
Lei Cao, Zhiheng Xie, Tianyu Liu, Zijian Wang, Chunjiang Fan
Chronic obstructive pulmonary disease (COPD), which has a high prevalence and mortality rate, is an irreversible condition marked by airflow restriction with different degrees of reversible damage. Notably, there is no cure for COPD, whose treatment primarily relies on rehabilitation exercises to improve airflow limitation. In this paper, a vision-based rehabilitation exercise efficacy prediction system is proposed to assess the efficacy of rehabilitation training for COPD patients. A camera was utilized to capture rehabilitation training videos of COPD patients, and we also collected various physical indicators. In addition, we used clustering algorithm to divide patients with different rehabilitation effects for subsequent progression analysis. Our model achieved a classification of rehabilitation progress accuracy of 90.6%, making it possible to effectively obtain favorable rehabilitation training results without physician supervision. It was meaningful for helping COPD patients get effective feedback when training alone.
{"title":"A deep learning framework-based exercise assessment for rehabilitation of chronic obstructive pulmonary disease","authors":"Lei Cao, Zhiheng Xie, Tianyu Liu, Zijian Wang, Chunjiang Fan","doi":"10.1142/s0219519423400894","DOIUrl":"https://doi.org/10.1142/s0219519423400894","url":null,"abstract":"Chronic obstructive pulmonary disease (COPD), which has a high prevalence and mortality rate, is an irreversible condition marked by airflow restriction with different degrees of reversible damage. Notably, there is no cure for COPD, whose treatment primarily relies on rehabilitation exercises to improve airflow limitation. In this paper, a vision-based rehabilitation exercise efficacy prediction system is proposed to assess the efficacy of rehabilitation training for COPD patients. A camera was utilized to capture rehabilitation training videos of COPD patients, and we also collected various physical indicators. In addition, we used clustering algorithm to divide patients with different rehabilitation effects for subsequent progression analysis. Our model achieved a classification of rehabilitation progress accuracy of 90.6%, making it possible to effectively obtain favorable rehabilitation training results without physician supervision. It was meaningful for helping COPD patients get effective feedback when training alone.","PeriodicalId":50135,"journal":{"name":"Journal of Mechanics in Medicine and Biology","volume":"19 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135463433","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}
Pub Date : 2023-10-19DOI: 10.1142/s0219519423920021
Chendi Yuan, Jian-Kun Chen, Fei Wang, Jing-Jie Ouyang, Tao Jing, Xue-Fei Wang, Bo Yang, Zeguo Shao
{"title":"ERRATUM — RESEARCH ON 3D RECONSTRUCTION METHOD AND APPLICATION OF FOOD IN STROKE PATIENTS BASED ON RGB-D IMAGE","authors":"Chendi Yuan, Jian-Kun Chen, Fei Wang, Jing-Jie Ouyang, Tao Jing, Xue-Fei Wang, Bo Yang, Zeguo Shao","doi":"10.1142/s0219519423920021","DOIUrl":"https://doi.org/10.1142/s0219519423920021","url":null,"abstract":"","PeriodicalId":50135,"journal":{"name":"Journal of Mechanics in Medicine and Biology","volume":"38 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139316595","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}
Pub Date : 2023-10-19DOI: 10.1142/s021951942392001x
SHANYU ZHOU, YUEHUA YOU
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
{"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}
Pub Date : 2023-10-13DOI: 10.1142/s0219519423400869
Xuezhi Fan, Jie Zhang, Mengting Yang
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.
{"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}
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