scholarly journals Identification of Food/Nonfood Visual Stimuli from Event-Related Brain Potentials

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Selen Güney ◽  
Sema Arslan ◽  
Adil Deniz Duru ◽  
Dilek Göksel Duru
Keyword(s):  
Nearest Neighbor ◽  
Sampling Rate ◽  
Motor Task ◽  
Support Vector ◽  
Brain Potentials ◽  
Linear Discriminant ◽  
Frequency Space ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Electrophysiological Responses

Although food consumption is one of the most basic human behaviors, the factors underlying nutritional preferences are not yet clear. The use of classification algorithms can clarify the understanding of these factors. This study was aimed at measuring electrophysiological responses to food/nonfood stimuli and applying classification techniques to discriminate the responses using a single-sweep dataset. Twenty-one right-handed male athletes with body mass index (BMI) levels between 18.5% and 25% (mean age: 21.05 ± 2.5 ) participated in this study voluntarily. The participants were asked to focus on the food and nonfood images that were randomly presented on the monitor without performing any motor task, and EEG data have been collected using a 16-channel amplifier with a sampling rate of 1024 Hz. The SensoMotoric Instruments (SMI) iView XTM RED eye tracking technology was used simultaneously with the EEG to measure the participants’ attention to the presented stimuli. Three datasets were generated using the amplitude, time-frequency decomposition, and time-frequency connectivity metrics of P300 and LPP components to separate food and nonfood stimuli. We have implemented k -nearest neighbor (kNN), support vector machine (SVM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Bayesian classifier, decision tree (DT), and Multilayer Perceptron (MLP) classifiers on these datasets. Finally, the response to food-related stimuli in the hunger state is discriminated from nonfood with an accuracy value close to 78% for each dataset. The results obtained in this study motivate us to employ classifier algorithms using the features obtained from single-trial measurements in amplitude and time-frequency space instead of applying more complex ones like connectivity metrics.

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

2019 ◽  
Vol 20 (5) ◽  
pp. 488-500 ◽  
Author(s):  
Yan Hu ◽  
Yi Lu ◽  
Shuo Wang ◽  
Mengying Zhang ◽  
Xiaosheng Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Anticancer Drugs ◽  
Nearest Neighbor ◽  
Cost Effective ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Activity Prediction ◽  
Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world&#039;s highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. </P><P> Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. </P><P> Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. </P><P> Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

IMPROVEMENT OF THE PERFORMANCE OF FINGERPRINT VERIFICATION USING A COMBINATORIAL APPROACH

2018 ◽  
Vol 30 (03) ◽  
pp. 1850019
Author(s):  
Fatemeh Alimardani ◽  
Reza Boostani
Keyword(s):  
Nearest Neighbor ◽  
State Of The Art ◽  
Recognition Rate ◽  
Acceptance Rate ◽  
Feature Reduction ◽  
Support Vector ◽  
Fingerprint Verification ◽  
Linear Discriminant ◽  
Comparative Results ◽  
Verification Systems

Fingerprint verification systems have attracted much attention in secure organizations; however, conventional methods still suffer from unconvincing recognition rate for noisy fingerprint images. To design a robust verification system, in this paper, wavelet and contourlet transforms (CTS) were suggested as efficient feature extraction techniques to elicit a coverall set of descriptive features to characterize fingerprint images. Contourlet coefficients capture the smooth contours of fingerprints while wavelet coefficients reveal its rough details. Due to the high dimensionality of the elicited features, across group variance (AGV), greedy overall relevancy (GOR) and Davis–Bouldin fast feature reduction (DB-FFR) methods were adopted to remove the redundant features. These features were applied to three different classifiers including Boosting Direct Linear Discriminant Analysis (BDLDA), Support Vector Machine (SVM) and Modified Nearest Neighbor (MNN). The proposed method along with state-of-the-art methods were evaluated, over the FVC2004 dataset, in terms of genuine acceptance rate (GAR), false acceptance rate (FAR) and equal error rate (EER). The features selected by AGV were the most significant ones and provided 95.12% GAR. Applying the selected features, by the GOR method, to the modified nearest neighbor, resulted in average EER of [Formula: see text]%, which outperformed the compared methods. The comparative results imply the statistical superiority ([Formula: see text]) of the proposed approach compared to the counterparts.

scholarly journals Discrimination between Alternative Herbal Medicines from Different Categories with the Electronic Nose

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2936 ◽  
Author(s):  
Xianghao Zhan ◽  
Xiaoqing Guan ◽  
Rumeng Wu ◽  
Zhan Wang ◽  
You Wang ◽  
...  
Keyword(s):  
Herbal Medicine ◽  
Electronic Nose ◽  
Nearest Neighbor ◽  
Herbal Medicines ◽  
Principal Component ◽  
Support Vector ◽  
Linear Discriminant ◽  
Convenient Means ◽  
Electronic Nose System ◽  
Insight Into

As alternative herbal medicine gains soar in popularity around the world, it is necessary to apply a fast and convenient means for classifying and evaluating herbal medicines. In this work, an electronic nose system with seven classification algorithms is used to discriminate between 12 categories of herbal medicines. The results show that these herbal medicines can be successfully classified, with support vector machine (SVM) and linear discriminant analysis (LDA) outperforming other algorithms in terms of accuracy. When principal component analysis (PCA) is used to lower the number of dimensions, the time cost for classification can be reduced while the data is visualized. Afterwards, conformal predictions based on 1NN (1-Nearest Neighbor) and 3NN (3-Nearest Neighbor) (CP-1NN and CP-3NN) are introduced. CP-1NN and CP-3NN provide additional, yet significant and reliable, information by giving the confidence and credibility associated with each prediction without sacrificing of accuracy. This research provides insight into the construction of a herbal medicine flavor library and gives methods and reference for future works.

Cardiovascular Disease Prediction from Electrocardiogram by using Machine Learning Method: A Snapshot from the Subjects of the Malaysian Cohort

2020 ◽  
Author(s):  
Nazrul Anuar Nayan ◽  
Hafifah Ab Hamid ◽  
Mohd Zubir Suboh ◽  
Noraidatulakma Abdullah ◽  
Rosmina Jaafar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Nearest Neighbor ◽  
Total Cholesterol Level ◽  
Population Based ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Cvd Risk ◽  
Linear Discriminant ◽  
Artificial Neural Network Ann

Abstract Background: Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. Results: This study presents a case–control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R–R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, k-nearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. Conclusions: In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population.

scholarly journals A New Approach to Fall Detection Based on Improved Dual Parallel Channels Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2814 ◽  
Author(s):  
Xiaoguang Liu ◽  
Huanliang Li ◽  
Cunguang Lou ◽  
Tie Liang ◽  
Xiuling Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Nearest Neighbor ◽  
Fall Detection ◽  
Classification Performance ◽  
Daily Activities ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Parallel Channels

Falls are the major cause of fatal and non-fatal injury among people aged more than 65 years. Due to the grave consequences of the occurrence of falls, it is necessary to conduct thorough research on falls. This paper presents a method for the study of fall detection using surface electromyography (sEMG) based on an improved dual parallel channels convolutional neural network (IDPC-CNN). The proposed IDPC-CNN model is designed to identify falls from daily activities using the spectral features of sEMG. Firstly, the classification accuracy of time domain features and spectrograms are compared using linear discriminant analysis (LDA), k-nearest neighbor (KNN) and support vector machine (SVM). Results show that spectrograms provide a richer way to extract pattern information and better classification performance. Therefore, the spectrogram features of sEMG are selected as the input of IDPC-CNN to distinguish between daily activities and falls. Finally, The IDPC-CNN is compared with SVM and three different structure CNNs under the same conditions. Experimental results show that the proposed IDPC-CNN achieves 92.55% accuracy, 95.71% sensitivity and 91.7% specificity. Overall, The IDPC-CNN is more effective than the comparison in accuracy, efficiency, training and generalization.

scholarly journals Cardiovascular Disease Prediction from Electrocardiogram by Using Machine Learning

2020 ◽  
Vol 16 (07) ◽  
pp. 34
Author(s):  
Nayan Nazrul Anuar ◽  
Ab Hamid Hafifah ◽  
Suboh Mohd Zubir ◽  
Abdullah Noraidatulakma ◽  
Jaafar Rosmina ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Nearest Neighbor ◽  
Total Cholesterol Level ◽  
Population Based ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Cvd Risk ◽  
Linear Discriminant ◽  
Artificial Neural Network Ann

<p>Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. This study presents a case–control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R–R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, k-nearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population.</p>

scholarly journals An Investigation of Insider Threat Mitigation Based on EEG Signal Classification

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6365
Author(s):  
Jung Hwan Kim ◽  
Chul Min Kim ◽  
Man-Sung Yim
Keyword(s):  
Nearest Neighbor ◽  
Nuclear Industry ◽  
Classification Model ◽  
Support Vector ◽  
Insider Threat ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Nuclear Facilities ◽  
Time Frequency ◽  
Threat Mitigation

This study proposes a scheme to identify insider threats in nuclear facilities through the detection of malicious intentions of potential insiders using subject-wise classification. Based on electroencephalography (EEG) signals, a classification model was developed to identify whether a subject has a malicious intention under scenarios of being forced to become an insider threat. The model also distinguishes insider threat scenarios from everyday conflict scenarios. To support model development, 21-channel EEG signals were measured on 25 healthy subjects, and sets of features were extracted from the time, time–frequency, frequency and nonlinear domains. To select the best use of the available features, automatic selection was performed by random-forest-based algorithms. The k-nearest neighbor, support vector machine with radial kernel, naïve Bayes, and multilayer perceptron algorithms were applied for the classification. By using EEG signals obtained while contemplating becoming an insider threat, the subject-wise model identified malicious intentions with 78.57% accuracy. The model also distinguished insider threat scenarios from everyday conflict scenarios with 93.47% accuracy. These findings could be utilized to support the development of insider threat mitigation systems along with existing trustworthiness assessments in the nuclear industry.

scholarly journals Machine Learning to Classify Intracardiac Electrical Patterns During Atrial Fibrillation

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Mahmood I. Alhusseini ◽  
Firas Abuzaid ◽  
Albert J. Rogers ◽  
Junaid A.B. Zaman ◽  
Tina Baykaner ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Unique Identifier ◽  
Multiple Systems ◽  
Linear Discriminant ◽  
Link Type ◽  
Decision Logic ◽  
The Hilbert Transform

Background: Advances in ablation for atrial fibrillation (AF) continue to be hindered by ambiguities in mapping, even between experts. We hypothesized that convolutional neural networks (CNN) may enable objective analysis of intracardiac activation in AF, which could be applied clinically if CNN classifications could also be explained. Methods: We performed panoramic recording of bi-atrial electrical signals in AF. We used the Hilbert-transform to produce 175 000 image grids in 35 patients, labeled for rotational activation by experts who showed consistency but with variability (kappa [κ]=0.79). In each patient, ablation terminated AF. A CNN was developed and trained on 100 000 AF image grids, validated on 25 000 grids, then tested on a separate 50 000 grids. Results: In the separate test cohort (50 000 grids), CNN reproducibly classified AF image grids into those with/without rotational sites with 95.0% accuracy (CI, 94.8%–95.2%). This accuracy exceeded that of support vector machines, traditional linear discriminant, and k-nearest neighbor statistical analyses. To probe the CNN, we applied gradient-weighted class activation mapping which revealed that the decision logic closely mimicked rules used by experts (C statistic 0.96). Conclusions: CNNs improved the classification of intracardiac AF maps compared with other analyses and agreed with expert evaluation. Novel explainability analyses revealed that the CNN operated using a decision logic similar to rules used by experts, even though these rules were not provided in training. We thus describe a scaleable platform for robust comparisons of complex AF data from multiple systems, which may provide immediate clinical utility to guide ablation. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02997254. Graphic Abstract: A graphic abstract is available for this article.

scholarly journals Analysis of classification methods suitable for band limited spatially filtered EEG signal applicable to non-invasive BCI

2019 ◽  
Vol 8 (9) ◽  
pp. 1689-1694
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Common Spatial Pattern ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Non Invasive ◽  
Band Limited ◽  
Ca Sensitivity

Electroencephalographic (EEG) signals are the preferred input for non-invasive Brain-Computer Interface (BCI). Efficient signal processing strategies, including feature extraction and classification, are required to distinguish the underlying task of BCI. This work proposes the optimized common spatial pattern(CSP) filtering technique as the feature extraction method for collecting the spatially spread variation of the signal. The bandpass filter (BPF) designed for this work assures the availability of event-related synchronized (ERS) and event-related desynchronized (ERD) signal as input to the spatial filter. This work takes consideration of the area-specific electrodes for feature formation. This work further proposes a comparative analysis of classifier algorithms for classification accuracy(CA), sensitivity and specificity and the considered algorithms are Support Vector Machine(SVM), Linear Discriminant Analysis(LDA), and K-Nearest Neighbor(KNN). Performance parameters considered are CA, sensitivity, and selectivity, which can judge the method not only for high CA but also inclining towards the particular class. Thus it will direct in the selection of appropriate classifier as well as tuning the classifier to get the balanced results. In this work, CA, the prior performance parameter is obtained to be 88.2% sensitivity of 94.2% and selectivity 82.2% for the cosine KNN classifier. SVM with linear kernel function also gives the comparable results, thus concluding that the robust classifiers perform well for all parameters in case of CSP for feature extraction.

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