Design and Implementation of Human-Computer Interaction Systems Based on Transfer Support Vector Machine and EEG Signal for Depression Patients’ Emotion Recognition

2021 ◽  
Vol 11 (3) ◽  
pp. 948-954
Author(s):  
Xiang Chen ◽  
Lijun Xu ◽  
Ming Cao ◽  
Tinghua Zhang ◽  
Zhongan Shang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Human Computer Interaction ◽  
Transfer Learning ◽  
Field Data ◽  
Interactive Systems ◽  
Classification Model ◽  
Support Vector ◽  
Target Domain ◽  
Emotional Changes ◽  
Computer Interaction

At present, the demand for intelligentization of human-computer interaction systems (HCIS) has become increasingly prominent. Being able to recognize the emotions of users of interactive systems is a distinguishing feature of intelligent interactive systems. The intelligent HCIS can analyze the emotional changes of patients with depression, complete the interaction with the patients in a more appropriate manner, and the recognition results can assist family members or medical personnel to make response measures based on the patient’s emotional changes. Based on this background, this paper proposes a sentiment recognition method based on transfer support vector machines (TSVM) and EEG signals. The ER (ER) results based on this method are applied to HCIS. Such a HCIS is mainly used for the interaction of patients with depression. When a new field related to a certain field appears, if the new field data is relabeled, the sample is expensive, and it is very wasteful to discard all the old field data. The main innovation of this research is that the introduced classification model is TSVM. TSVM is a transfer learning strategy based on SVM. Transfer learning aims to solve related but different target domain problems by using a large amount of labeled source domain data. Therefore, the transfer support vector machine based on the transfer mechanism can use the small labeled data of the target domain and a large amount of old data in the related domain to build a high-quality classification model for the target domain, which can effectively improve the accuracy of classification. Comparing the classification results with other classification models, it can be concluded that TSVM can effectively improve the accuracy of ER in patients with depression. The HCIS based on the classification model has higher accuracy and better stability.

scholarly journals Efficient Classification of Horizontal And Vertical EOG Signals For Human Computer Interaction

2021 ◽  
Author(s):  
Abdel-Gawad A. Abdel-Samei ◽  
Ahmed S.Ali ◽  
Fathi E. Abd El-Samie ◽  
Ayman M.Brisha
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Eye Movements ◽  
Human Computer Interaction ◽  
Nearest Neighbor ◽  
Human Interaction ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Human Eye ◽  
Computer Interaction

Abstract Human-computer interaction (HCI) using Electrooculography (EOG) has been a growing area of research in recent years. The HCI provides communication channels between the human and the external device. Today, EOG is one of the most important biomedical signals for measuring and analyzing the direction of eye movements. The EOG is used to produce both activities in vertical and horizontal directions of human eye movements. In this paper, different human eye movement tasks from vertical and horizontal directions are studied. The dataset of EOG signals were obtained from Electroencephalography (EEG) electrodes from 27 healthy people, 14 males and 13 females. This process resulted from two dipole signals, the vertical-EOG signals and the horizontal-EOG signals. These signals were filtered by band-pass at 0.5–5Hz. A total of 54 datasets from these 27 healthy individuals, each lasting 30 seconds, were given. The Bo-Hjorth parameter was implemented for feature extraction on the preprocessed EOG signals. For classification, Decision Tree (DT), K-Nearest Neighbor (KNN), Ensemble Classifier (EC), Kernel Naive Bayes (KNB) and Support Vector Machine (SVM)) were utilized. The obtained results reveal that the best classifiers on horizontal and vertical signals are the Support Vector Machine (SVM), the Cosine KNN and the Ensemble Subspace Discriminant with having 100% percentage accuracies. Through designing the proposed algorithm for feature extraction, the highest performance of classification can be obtained for rehabilitation purposes and other applications that help the handicapped to take decisions for better life quality, by providing possible human interaction with a computer.

scholarly journals Prediction of functional microexons by transfer learning

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Qi Cheng ◽  
Bo He ◽  
Chengkui Zhao ◽  
Hongyuan Bi ◽  
Duojiao Chen ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Transfer Learning ◽  
Feature Space ◽  
Potential Functions ◽  
Sequence Conservation ◽  
Classification Model ◽  
Support Vector ◽  
Feature Mapping ◽  
Learning Skill ◽  
Reference Knowledge

Abstract Background Microexons are a particular kind of exon of less than 30 nucleotides in length. More than 60% of annotated human microexons were found to have high levels of sequence conservation, suggesting their potential functions. There is thus a need to develop a method for predicting functional microexons. Results Given the lack of a publicly available functional label for microexons, we employed a transfer learning skill called Transfer Component Analysis (TCA) to transfer the knowledge obtained from feature mapping for the prediction of functional microexons. To provide reference knowledge, microindels were chosen because of their similarities to microexons. Then, Support Vector Machine (SVM) was used to train a classification model in the newly built feature space for the functional microindels. With the trained model, functional microexons were predicted. We also built a tool based on this model to predict other functional microexons. We then used this tool to predict a total of 19 functional microexons reported in the literature. This approach successfully predicted 16 out of 19 samples, giving accuracy greater than 80%. Conclusions In this study, we proposed a method for predicting functional microexons and applied it, with the predictive results being largely consistent with records in the literature.

TrCSVM: a novel approach for the classification of melanoma skin cancer using transfer learning

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lokesh Singh ◽  
Rekh Ram Janghel ◽  
Satya Prakash Sahu
Keyword(s):  
Support Vector Machine ◽  
Skin Lesion ◽  
Transfer Learning ◽  
Domain Adaptation ◽  
Training Data ◽  
Support Vector ◽  
Second Phase ◽  
Target Domain ◽  
Content Type

PurposeThe study aims to cope with the problems confronted in the skin lesion datasets with less training data toward the classification of melanoma. The vital, challenging issue is the insufficiency of training data that occurred while classifying the lesions as melanoma and non-melanoma.Design/methodology/approachIn this work, a transfer learning (TL) framework Transfer Constituent Support Vector Machine (TrCSVM) is designed for melanoma classification based on feature-based domain adaptation (FBDA) leveraging the support vector machine (SVM) and Transfer AdaBoost (TrAdaBoost). The working of the framework is twofold: at first, SVM is utilized for domain adaptation for learning much transferrable representation between source and target domain. In the first phase, for homogeneous domain adaptation, it augments features by transforming the data from source and target (different but related) domains in a shared-subspace. In the second phase, for heterogeneous domain adaptation, it leverages knowledge by augmenting features from source to target (different and not related) domains to a shared-subspace. Second, TrAdaBoost is utilized to adjust the weights of wrongly classified data in the newly generated source and target datasets.FindingsThe experimental results empirically prove the superiority of TrCSVM than the state-of-the-art TL methods on less-sized datasets with an accuracy of 98.82%.Originality/valueExperiments are conducted on six skin lesion datasets and performance is compared based on accuracy, precision, sensitivity, and specificity. The effectiveness of TrCSVM is evaluated on ten other datasets towards testing its generalizing behavior. Its performance is also compared with two existing TL frameworks (TrResampling, TrAdaBoost) for the classification of melanoma.

Predicting Hub Genes of Glioblastomas Based on Support Vector Machine Combined with CFS algorithms

2020 ◽  
Vol 15 ◽  
Author(s):  
Chun Qiu ◽  
Sai Li ◽  
Shenghui Yang ◽  
Lin Wang ◽  
Aihui Zeng ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Expression Profiles ◽  
Independent Set ◽  
Classification Model ◽  
Support Vector ◽  
Feature Subset ◽  
Hub Genes ◽  
Effective Prevention ◽  
Key Genes ◽  
Control Samples

Aim: To search the genes related to the mechanisms of the occurrence of glioma and to try to build a prediction model for glioblastomas. Background: The morbidity and mortality of glioblastomas are very high, which seriously endangers human health. At present, the goals of many investigations on gliomas are mainly to understand the cause and mechanism of these tumors at the molecular level and to explore clinical diagnosis and treatment methods. However, there is no effective early diagnosis method for this disease, and there are no effective prevention, diagnosis or treatment measures. Methods: First, the gene expression profiles derived from GEO were downloaded. Then, differentially expressed genes (DEGs) in the disease samples and the control samples were identified. After that, GO and KEGG enrichment analyses of DEGs were performed by DAVID. Furthermore, the correlation-based feature subset (CFS) method was applied to the selection of key DEGs. In addition, the classification model between the glioblastoma samples and the controls was built by an Support Vector Machine (SVM) based on selected key genes. Results and Discussion: Thirty-six DEGs, including 17 upregulated and 19 downregulated genes, were selected as the feature genes to build the classification model between the glioma samples and the control samples by the CFS method. The accuracy of the classification model by using a 10-fold cross-validation test and independent set test was 76.25% and 70.3%, respectively. In addition, PPP2R2B and CYBB can also be found in the top 5 hub genes screened by the protein– protein interaction (PPI) network. Conclusions: This study indicated that the CFS method is a useful tool to identify key genes in glioblastomas. In addition, we also predicted that genes such as PPP2R2B and CYBB might be potential biomarkers for the diagnosis of glioblastomas.

scholarly journals A Support Vector Machine Classification Model for Benzo[c]phenathridine Analogues with Topoisomerase-I Inhibitory Activity

Molecules ◽  
2012 ◽  
Vol 17 (4) ◽  
pp. 4560-4582 ◽  
Author(s):  
Khac-Minh Thai ◽  
Thuy-Quyen Nguyen ◽  
Trieu-Du Ngo ◽  
Thanh-Dao Tran ◽  
Thi-Ngoc-Phuong Huynh
Keyword(s):  
Support Vector Machine ◽  
Inhibitory Activity ◽  
Topoisomerase I ◽  
Classification Model ◽  
Support Vector ◽  
Support Vector Machine Classification
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