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McHUGH, E. S., A. P. SHINN, and J. W. KAY. "Discrimination of the notifiable pathogen Gyrodactylus salaris from G. thymalli (Monogenea) using statistical classifiers applied to morphometric data." Parasitology 121, no. 3 (September 2000): 315–23. http://dx.doi.org/10.1017/s0031182099006381.
Повний текст джерелаАнотація:
The identification and discrimination of 2 closely related and morphologically similar species of Gyrodactylus, G. salaris and G. thymalli, were assessed using the statistical classification methodologies Linear Discriminant Analysis (LDA) and k-Nearest Neighbours (KNN). These statistical methods were applied to morphometric measurements made on the gyrodactylid attachment hooks. The mean estimated classification percentages of correctly identifying each species were 98·1% (LDA) and 97·9% (KNN) for G. salaris and 99·9% (LDA) and 73·2% (KNN) for G. thymalli. The analysis was expanded to include another 2 closely related species and the new classification efficiencies were 94·6% (LDA) and 98·0% (KNN) for G. salaris; 98·2% (LDA) and 72·6% (KNN) for G. thymalli; 86·7% (LDA) and 91·8% (KNN) for G. derjavini; and 76·5% (LDA) and 77·7% (KNN) for G. truttae. The higher correct classification scores of G. salaris and G. thymalli by the LDA classifier in the 2-species analysis over the 4-species analysis suggested the development of a 2-stage classifier. The mean estimated correct classification scores were 99·97% (LDA) and 99·99% (KNN) for the G. salaris–G. thymalli pairing and 99·4% (LDA) and 99·92% (KNN) for the G. derjavini–G. truttae pairing. Assessment of the 2-stage classifier using only marginal hook data was very good with classification efficiencies of 100% (LDA) and 99·6% (KNN) for the G. salaris–G. thymalli pairing and 97·2% (LDA) and 99·2% (KNN) for the G. derjavini–G. truttae pairing. Paired species were then discriminated individually in the second stage of the classifier using data from the full set of hooks. These analyses demonstrate that using the methods of LDA and KNN statistical classification, the discrimination of closely related and pathogenic species of Gyrodactylus may be achieved using data derived from light microscope studies.
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Khan, Asfandyar, Abdullah Khan, Muhammad Muntazir Khan, Kamran Farid, Muhammad Mansoor Alam, and Mazliham Bin Mohd Su’ud. "Cardiovascular and Diabetes Diseases Classification Using Ensemble Stacking Classifiers with SVM as a Meta Classifier." Diagnostics 12, no. 11 (October 26, 2022): 2595. http://dx.doi.org/10.3390/diagnostics12112595.
Повний текст джерелаАнотація:
Cardiovascular disease includes coronary artery diseases (CAD), which include angina and myocardial infarction (commonly known as a heart attack), and coronary heart diseases (CHD), which are marked by the buildup of a waxy material called plaque inside the coronary arteries. Heart attacks are still the main cause of death worldwide, and if not treated right they have the potential to cause major health problems, such as diabetes. If ignored, diabetes can result in a variety of health problems, including heart disease, stroke, blindness, and kidney failure. Machine learning methods can be used to identify and diagnose diabetes and other illnesses. Diabetes and cardiovascular disease both can be diagnosed using several classifier types. Naive Bayes, K-Nearest neighbor (KNN), linear regression, decision trees (DT), and support vector machines (SVM) were among the classifiers employed, although all of these models had poor accuracy. Therefore, due to a lack of significant effort and poor accuracy, new research is required to diagnose diabetes and cardiovascular disease. This study developed an ensemble approach called “Stacking Classifier” in order to improve the performance of integrated flexible individual classifiers and decrease the likelihood of misclassifying a single instance. Naive Bayes, KNN, Linear Discriminant Analysis (LDA), and Decision Tree (DT) are just a few of the classifiers used in this study. As a meta-classifier, Random Forest and SVM are used. The suggested stacking classifier obtains a superior accuracy of 0.9735 percent when compared to current models for diagnosing diabetes, such as Naive Bayes, KNN, DT, and LDA, which are 0.7646 percent, 0.7460 percent, 0.7857 percent, and 0.7735 percent, respectively. Furthermore, for cardiovascular disease, when compared to current models such as KNN, NB, DT, LDA, and SVM, which are 0.8377 percent, 0.8256 percent, 0.8426 percent, 0.8523 percent, and 0.8472 percent, respectively, the suggested stacking classifier performed better and obtained a higher accuracy of 0.8871 percent.
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R. Essa, Raghad, Hanadi Abbas Jaber, and Abbas A. Jasim. "Features selection for estimating hand gestures based on electromyography signals." Bulletin of Electrical Engineering and Informatics 12, no. 4 (August 1, 2023): 2087–94. http://dx.doi.org/10.11591/beei.v12i4.5048.
Повний текст джерелаАнотація:
Hand prosthesis controlled by surface electromyography (sEMG) is promising due to the control capabilities and the noninvasive technique that machine learning (ML) offers to help physically disabled people during daily life. Nevertheless, dexterous prostheses are still infrequently popular due to control problems and limited robustness. This paper proposes a new set of time domain (TD) features to improve the EMG pattern recognition performance. The effect of five feature sets is evaluated based on the three classifiers k-nearest neighbor (KNN), linear discriminate analysis (LDA), and support vector machine (SVM). The EMG signals are obtained from database-5 (DB5) of the ninapro project datasets. In this study, the long-term signals of DB5 are segmented into short-term signals to perform short-term recognition. The results showed that the LDA classifier based on the proposed features achieved high classification accuracy for classifing 17 gestures. The LDA classifier achieved about 96.47% compared to 94.12%, and 93.82% for KNN and SVM classifiers, respectively. The results confirm that the suitable features extracted from short term signals with the appropriate classifier, has an important impact on improving the performance of gesture classification.
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R. Essa, Raghad, Hanadi Abbas Jaber, and Abbas A. Jasim. "Features selection for estimating hand gestures based on electromyography signals." Bulletin of Electrical Engineering and Informatics 12, no. 4 (August 1, 2023): 2087–94. http://dx.doi.org/10.11591/eei.v12i4.5048.
Повний текст джерелаАнотація:
Hand prosthesis controlled by surface electromyography (sEMG) is promising due to the control capabilities and the noninvasive technique that machine learning (ML) offers to help physically disabled people during daily life. Nevertheless, dexterous prostheses are still infrequently popular due to control problems and limited robustness. This paper proposes a new set of time domain (TD) features to improve the EMG pattern recognition performance. The effect of five feature sets is evaluated based on the three classifiers k-nearest neighbor (KNN), linear discriminate analysis (LDA), and support vector machine (SVM). The EMG signals are obtained from database-5 (DB5) of the ninapro project datasets. In this study, the long-term signals of DB5 are segmented into short-term signals to perform short-term recognition. The results showed that the LDA classifier based on the proposed features achieved high classification accuracy for classifing 17 gestures. The LDA classifier achieved about 96.47% compared to 94.12%, and 93.82% for KNN and SVM classifiers, respectively. The results confirm that the suitable features extracted from short term signals with the appropriate classifier, has an important impact on improving the performance of gesture classification.
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Zou, Xiuguo, Chenyang Wang, Manman Luo, Qiaomu Ren, Yingying Liu, Shikai Zhang, Yungang Bai, Jiawei Meng, Wentian Zhang, and Steven W. Su. "Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine." Sensors 22, no. 8 (April 14, 2022): 2997. http://dx.doi.org/10.3390/s22082997.
Повний текст джерелаАнотація:
Apples are one of the most widely planted fruits in the world, with an extremely high annual production. Several issues should be addressed to avoid the damaging of samples during the quality grading process of apples (e.g., the long detection period and the inability to detect the internal quality of apples). In this study, an electronic nose (e-nose) detection system for apple quality grading based on the K-nearest neighbor support vector machine (KNN-SVM) was designed, and the nasal cavity structure of the e-nose was optimized by computational fluid dynamics (CFD) simulation. A KNN-SVM classifier was also proposed to overcome the shortcomings of the traditional SVMs. The performance of the developed device was experimentally verified in the following steps. The apples were divided into three groups according to their external and internal quality. The e-nose data were pre-processed before features extraction, and then Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to reduce the dimension of the datasets. The recognition accuracy of the PCA–KNN-SVM classifier was 96.45%, and the LDA–KNN-SVM classifier achieved 97.78%. Compared with other commonly used classifiers, (traditional KNN, SVM, Decision Tree, and Random Forest), KNN-SVM is more efficient in terms of training time and accuracy of classification. Generally, the apple grading system can be used to evaluate the quality of apples during storage.
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Bilucaglia, Marco, Gian Marco Duma, Giovanni Mento, Luca Semenzato, and Patrizio E. Tressoldi. "Applying machine learning EEG signal classification to emotion‑related brain anticipatory activity." F1000Research 9 (October 13, 2021): 173. http://dx.doi.org/10.12688/f1000research.22202.3.
Повний текст джерелаАнотація:
Machine learning approaches have been fruitfully applied to several neurophysiological signal classification problems. Considering the relevance of emotion in human cognition and behaviour, an important application of machine learning has been found in the field of emotion identification based on neurophysiological activity. Nonetheless, there is high variability in results in the literature depending on the neuronal activity measurement, the signal features and the classifier type. The present work aims to provide new methodological insight into machine learning applied to emotion identification based on electrophysiological brain activity. For this reason, we analysed previously recorded EEG activity measured while emotional stimuli, high and low arousal (auditory and visual) were provided to a group of healthy participants. Our target signal to classify was the pre-stimulus onset brain activity. Classification performance of three different classifiers (LDA, SVM and kNN) was compared using both spectral and temporal features. Furthermore, we also contrasted the performance of static and dynamic (time evolving) approaches. The best static feature-classifier combination was the SVM with spectral features (51.8%), followed by LDA with spectral features (51.4%) and kNN with temporal features (51%). The best dynamic feature‑classifier combination was the SVM with temporal features (63.8%), followed by kNN with temporal features (63.70%) and LDA with temporal features (63.68%). The results show a clear increase in classification accuracy with temporal dynamic features.
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Bilucaglia, Marco, Gian Marco Duma, Giovanni Mento, Luca Semenzato, and Patrizio E. Tressoldi. "Applying machine learning EEG signal classification to emotion‑related brain anticipatory activity." F1000Research 9 (October 8, 2021): 173. http://dx.doi.org/10.12688/f1000research.22202.2.
Повний текст джерелаАнотація:
Machine learning approaches have been fruitfully applied to several neurophysiological signal classification problems. Considering the relevance of emotion in human cognition and behaviour, an important application of machine learning has been found in the field of emotion identification based on neurophysiological activity. Nonetheless, there is high variability in results in the literature depending on the neuronal activity measurement, the signal features and the classifier type. The present work aims to provide new methodological insight into machine learning applied to emotion identification based on electrophysiological brain activity. For this reason, we analysed previously recorded EEG activity measured while emotional stimuli, high and low arousal (auditory and visual) were provided to a group of healthy participants. Our target signal to classify was the pre-stimulus onset brain activity. Classification performance of three different classifiers (LDA, SVM and kNN) was compared using both spectral and temporal features. Furthermore, we also contrasted the performance of static and dynamic (time evolving) approaches. The best static feature-classifier combination was the SVM with spectral features (51.8%), followed by LDA with spectral features (51.4%) and kNN with temporal features (51%). The best dynamic feature‑classifier combination was the SVM with temporal features (63.8%), followed by kNN with temporal features (63.70%) and LDA with temporal features (63.68%). The results show a clear increase in classification accuracy with temporal dynamic features.
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P, Sunil Kumar, and Harikumar Rajaguru. "ASSESSMENT OF EPILEPSY CLASSIFICATION USING TECHNIQUES SUCH AS SINGULAR VALUE DECOMPOSITION, APPROXIMATE ENTROPY, AND WEIGHTED K-NEAREST NEIGHBORS MEASURES." Asian Journal of Pharmaceutical and Clinical Research 9, no. 5 (September 1, 2016): 91. http://dx.doi.org/10.22159/ajpcr.2016.v9i5.12196.
Повний текст джерелаАнотація:
ABSTRACTObjective: The main aim of this research is to reduce the dimension of the epileptic Electroencephalography (EEG) signals and then classify it usingvarious post classifiers. For the evaluation and easy treatment of neurological diseases, EEG signals are used. The reflection of the electrical activitiesof the human brain is obtained by the measurement of potentials in EEG. To study and explore the brain functions in an exhaustive manner, EEG is usedby both physicians and scientists. The study of the electrical activity of the brain which is done through EEG recording is a vital tool for the diagnosis ofmany neurological diseases which include epilepsy, sleep disorders, injuries in head, dementia etc. One of the most commonly occurring and prevalentneurological disorders is epilepsy and it is easily characterized by recurrent seizures.Methods: This paper employs the concept of dimensionality reduction concepts like Fuzzy Mutual Information (FMI), Independent ComponentAnalysis (ICA), Linear Graph Embedding (LGE), Linear Discriminant Analysis (LDA) and finally Variational Bayesian Matrix Factorization (VBMF).The epilepsy risk levels are also classified using post classifiers like Singular Value Decomposition (SVD), Approximate Entropy (ApEn) and WeightedKNN (W-KNN) classifiers.Results: The highest accuracy is obtained when LDA is combined with Weighted KNN (W-KNN) Classifiers and it is of 97.18%. Conclusion: Thus the EEG signals not only represent the brain function but also the status of the whole body. The best result obtained was whenLDA is engaged as a dimensionality reduction technique followed by the usage of the W-KNN as post classifier for the classification of epilepsy risklevels from EEG signals. Future work may incorporate the possible usage of different dimensionality reduction techniques with various other types ofclassifiers for the perfect classification of epilepsy risk levels from EEG signals.Keywords: FMI, ICA, LGE, LDA, W-KNN, EEG
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Bai, Junjie, Kan Luo, Jun Peng, Jinliang Shi, Ying Wu, Lixiao Feng, Jianqing Li, and Yingxu Wang. "Music Emotions Recognition by Machine Learning With Cognitive Classification Methodologies." International Journal of Cognitive Informatics and Natural Intelligence 11, no. 4 (October 2017): 80–92. http://dx.doi.org/10.4018/ijcini.2017100105.
Повний текст джерелаАнотація:
Music emotions recognition (MER) is a challenging field of studies addressed in multiple disciplines such as musicology, cognitive science, physiology, psychology, arts and affective computing. In this article, music emotions are classified into four types known as those of pleasing, angry, sad and relaxing. MER is formulated as a classification problem in cognitive computing where 548 dimensions of music features are extracted and modeled. A set of classifications and machine learning algorithms are explored and comparatively studied for MER, which includes Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Neuro-Fuzzy Networks Classification (NFNC), Fuzzy KNN (FKNN), Bayes classifier and Linear Discriminant Analysis (LDA). Experimental results show that the SVM, FKNN and LDA algorithms are the most effective methodologies that obtain more than 80% accuracy for MER.
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Singh, Law Kumar, Pooja, Hitendra Garg, and Munish Khanna. "Histogram of Oriented Gradients (HOG)-Based Artificial Neural Network (ANN) Classifier for Glaucoma Detection." International Journal of Swarm Intelligence Research 13, no. 1 (January 1, 2022): 1–32. http://dx.doi.org/10.4018/ijsir.309940.
Повний текст джерелаАнотація:
Glaucoma is a severe condition of the optic nerve resulting in the loss of eyesight. The proposed methodology has introduced the extraction of HOG (histogram of oriented gradients) features from the retinal fundus image. After the removal of HOG features, the authors compare the performance of five different machine learning techniques like k-nearest neighbour (KNN), support vector machine (SVM), linear discriminant analysis (LDA), naïve bayes, and artificial neural network. The process of image classification is based on analyzing the numerical properties of the obtained image features and classifying the data into different categories. In the paper, the authors intend to classify whether the image belongs to the glaucomatous category or the healthy category. After the application of the different classification algorithms to the test data and further analysis of the results, they could conclude that the SVM classifier provided an accuracy of 90%, KNN 86%, Naïve Bayes 96%, LDA 86%, and ANN 96.90% on the dataset in hand.
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Asghar, Ali, Saad Jawaid Khan, Fahad Azim, Choudhary Sobhan Shakeel, Amatullah Hussain, and Imran Khan Niazi. "Inter-classifier comparison for upper extremity EMG signal at different hand postures and arm positions using pattern recognition." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 236, no. 2 (October 22, 2021): 228–38. http://dx.doi.org/10.1177/09544119211053669.
Повний текст джерелаАнотація:
The utilization of surface EMG and intramuscular EMG signals has been observed to create significant improvement in pattern recognition approaches and myoelectric control. However, there is less data of different arm positions and hand postures available. Hand postures and arm positions tend to affect the combination of surface and intramuscular EMG signal acquisition in terms of classifier accuracy. Hence, this study aimed to find a robust classifier for two scenarios: (1) at fixed arm position (FAP) where classifiers classify different hand postures and (2) at fixed hand posture (FHP) where classifiers classify different arm positions. A total of 20 healthy male participants (30.62 ± 3.87 years old) were recruited for this study. They were asked to perform five motion classes including hand grasp, hand open, rest, hand extension, and hand flexion at four different arm positions at 0°, 45°, 90°, and 135°. SVM, KNN, and LDA classifier were deployed. Statistical analysis in the form of pairwise comparisons was carried out using SPSS. It is concluded that there is no significant difference among the three classifiers. SVM gave highest accuracy of 75.35% and 58.32% at FAP and FHP respectively for each motion classification. KNN yielded the highest accuracies of 69.11% and 79.04% when data was pooled and was classified at different arm positions and at different hand postures respectively. The results exhibited that there is no significant effect of changing arm position and hand posture on the classifier accuracy.
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Mokdad, Aicha, Sidi Mohammed El Amine Debbal, and Fadia Meziani. "Diagnosis of amyotrophic lateral sclerosis (ALS) disorders based on electromyogram (EMG) signal analysis and feature selection." Polish Journal of Medical Physics and Engineering 26, no. 3 (September 1, 2020): 155–60. http://dx.doi.org/10.2478/pjmpe-2020-0018.
Повний текст джерелаАнотація:
AbstractElectromyogram signal (EMG) provides an important source of information for the diagnosis of neuromuscular disorders. In this study, we proposed two methods of analysis which concern the bispectrum and continuous wavelet transform (CWT) of the EMG signal then a comparison is made to select which one is the most suitable to identify an abnormality in biceps brachii muscle in the main purpose is to assess the pathological severity in bifrequency and time-frequency analysis applying respectively bispectrum and CWT. Then four time and frequency features are extracted and three popular machine learning algorithms are implemented to differentiate neuropathy and healthy conditions of the selected muscle. The performance of these time and frequency features are compared using support vector machine (SVM), linear discriminate analysis (LDA) and K-Nearest Neighbor (KNN) classifier performance. The results obtained showed that the SVM classifier yielded the best performance with an accuracy of 95.8%, precision of 92.59% and specificity of 92%. followed by respectively KNN and LDA classifier that achieved respectively an accuracy of 92% and 91.5%, precision of 92% and 85.4%, and specificity of 92% and 83%.
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Dar, Basra Farooq, Malik Sajjad Ahmed Nadeem, Samina Khalid, Farzana Riaz, Yasir Mahmood, and Ghias Hameed. "Improving the Classification Ability of Delegating Classifiers Using Different Supervised Machine Learning Algorithms." Computer and Information Science 16, no. 3 (August 23, 2023): 22. http://dx.doi.org/10.5539/cis.v16n3p22.
Повний текст джерелаАнотація:
Cancer Classification & Prediction Is Vitally Important for Cancer Diagnosis. DNA Microarray Technology Has Provided Genetic Data That Has Facilitated Scientists Perform Cancer Research. Traditional Methods of Classification Have Certain Limitations E.G. Traditionally A Proposed DSS Uses A Single Classifier at A Time for Classification or Prediction Purposes. To Increase Classification Accuracy, Reject Option Classifiers Has Been Proposed in Machine Learning Literature. In A Reject Option Classifier, A Rejection Region Is Defined and The Samples Fall in That Region Are Not Classified by The Classifier. The Unclassifiable Samples Are Rejected by Classifier in Order to Improve Classifier’s Accuracy. However, These Rejections Affect the Prediction Rate of The Classifier as Well. To Overcome the Problem of Low Prediction Rates by Increased Rejection of Samples by A Single Classifier, the “Delegating Classifiers” Provide Better Accuracy at Less Rejection Rate. Different Classifiers Such as Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), K Nearest Neighbor (KNN) Etc. Have Been Proposed. Moreover, Traditionally, Same Learner Is Used As “Delegated” And “Delegating” Classifier. This Research Has Investigated the Effects of Using Different Machine Learning Classifiers in Both of The Delegated and Delegating Classifiers, And the Results Obtained Showed That Proposed Method Gives High Accuracy and Increases the Prediction Rate.
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Darapureddy, Nagadevi, Nagaprakash Karatapu, and Tirumala Krishna Battula. "Comparative Analysis of Texture Patterns on Mammograms for Classification." Traitement du Signal 38, no. 2 (April 30, 2021): 379–86. http://dx.doi.org/10.18280/ts.380215.
Повний текст джерелаАнотація:
Breast cancer is a cancerous tumor that arrives within the tissues of the breast. Women are mostly attacked than men. To detect early cancer medical specialists, suggest mammography for screening. Algorithms in Machine learning were executed on mammogram images to classify whether the tissues are deleterious or not. An analysis is done based on the texture feature extraction using different techniques like Frequency decoded local binary pattern (FDLBP), Local Bit-plane Decoded Pattern (LBDP), Local Diagonal Extrema Pattern (LDEP), Local Directional Order Pattern (LDOP), Local Wavelet Pattern (LWP). The features extracted are tested on 322 images from MIA’s database of three different classes. The algorithms in Machine learning like K-Nearest Neighbor classifier (KNN), Support vector classifier (SVC), Decision Tree classifier (DTC), Random Forest classifier (RFC), AdaBoost classifier (AC), Gradient Boosting classifier (GBC), Gaussian Naive Bayes classifier (GNB), Linear Discriminant Analysis classifier (LDA), Quadratic Discriminant Analysis classifier (QDA) were used to evaluate the accuracy of classification. This paper examines the comparison of accuracy using different texture features. KNN algorithm with LDEP for texture feature extraction gives classification accuracy of 64.61%, SVC with all the texture patterns mentioned gives classification accuracy of 63.07%, DTC with FDLBP, LBDP gives classification accuracy of 47.69, RFC with LBDP and AC with LDOP and GBC with FDLBP gives 61.53% classification accuracy, GNB and LDA with FDLBP gives 60% and 63.07% classification accuracy respectively, QDA with LBDP gives 64.61 classification accuracy. Of all the Algorithms support vector classifier gives good accuracy results with all the texture patterns mentioned.
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Alfarhan, Khudhur A., Mohd Yusoff Mashor, Ammar Zakaria, and Mohammad Iqbal Omar. "Automated Electrocardiogram Signals Based Risk Marker for Early Sudden Cardiac Death Prediction." Journal of Medical Imaging and Health Informatics 8, no. 9 (December 1, 2018): 1769–75. http://dx.doi.org/10.1166/jmihi.2018.2531.
Повний текст джерелаАнотація:
Sudden cardiac death (SCD) is one of the cardiovascular diseases that lead to millions of deaths worldwide every year. The aim of the present work is to propose a method for reducing the mortality rate of the SCD patients by an early prediction for SCD from the ECG signal. Normal and SCD MIT databases were used in this research work. One minute segments of ECG signals were segmented from MIT databases where these segments are ten minutes before sudden cardiac arrest (SCA) onset. The collected raw ECG signals were subjected to filter to remove the noise and then normalized. A frequency-domain feature and time-domain features were extracted from the Q-T segment, Q-T interval, R-R interval and QRS interval. The features were normalized to improve the performance of the classifier. Artificial intelligence classifiers; namely, K-nearest neighbor (KNN) and linear discriminant analysis (LDA) were used separately on SCD and normal ECG signals. The highest classification accuracy obtained for KNN and LDA are 97% and 95.5% respectively.
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Said, Sherif, Abdullah S. Karar, Taha Beyrouthy, Samer Alkork, and Amine Nait-ali. "Biometrics Verification Modality Using Multi-Channel sEMG Wearable Bracelet." Applied Sciences 10, no. 19 (October 5, 2020): 6960. http://dx.doi.org/10.3390/app10196960.
Повний текст джерелаАнотація:
Electrical biosignals have the potential for use as biometric authenticators, owing to their ability to facilitate liveness detection and concealed nature. In this work, the viability of using surface electromyogram (sEMG) as a biometric modality for users verification is investigated. A database of multi-channel sEMG signals is created using a wearable armband from able-bodied users. Each user used his/her muscles to form a password that consists of a unique combination of specific hand gestures. A total of 18 features are extracted from the signals in order to distinguish between the users. Several features are extracted in the frequency domain after estimating the power spectral density while using the Welch’s method. Specifically, average frequency, signal power, median frequency, Kurtosis, Deciles, coefficient of dissymmetry, and the peak frequency of the sEMG signal are considered. To further increase the accuracy of the classifier, time domain features are also extracted through segmentation of the signal into 10 segments, and then calculating both the root mean square and length of the signal. Several classifiers that are based on K-nearest Neighbors (KNN), Linear Discernment Analysis (LDA), and Ensemble of Classifiers are constructed, trained, and statistically compared, resulting in an average accuracy in 97.4%, 98.3%, and 98.5%, respectively. False acceptance rate (FAR) and False Rejection Rate (FRR) are estimated for each classifier in order to determine the effectiveness of the biometrics verification system. Although the ensemble classifier accuracy was found to be the highest, the results show that the KNN classifier exhibits a FAR of 0.2% and FRR of 2.9%. Thus, the KNN classifier was found to he the optimum classifier after the extraction of all 18 features. This work demonstrates the usefulness of sEMG as a biometric authenticator in user verification.
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Uddin, M. A., and M. S. Ahmed. "Modified naive Bayes classifier for classification of protein-protein interaction sites." Journal of Bioscience and Agriculture Research 26, no. 02 (December 10, 2020): 2177–84. http://dx.doi.org/10.18801/jbar.260220.266.
Повний текст джерелаАнотація:
The prediction of protein-protein interaction sites (PPIs) is a vital importance in biology for understanding the physical and functional interactions between molecules in living systems. There are several classification approaches for the prediction of PPI sites; the naïve Bayes classifier is one of the most popular candidates. But the ordinary naïve Bayes classifier is sensitive to unusual protein sequence profiling feature dataset and sometimes it gives ambiguous prediction results. To overcome this problem we have been modified the naïve Bayes classifier by radial basis function (RBF) kernel for the prediction of PPI sites. We investigate the performance of our proposed method compared with the popular classifiers like linear discriminant analysis (LDA), naïve Bayes classifier (NBC), support vector machine (SVM), AdaBoost and k-nearest neighbor (KNN) by the protein sequence profiling data analysis. The mNBC method showed sensitivity (86%), specificity (81%), accuracy (83%) and MCC (65%) for prediction of PPI sites.
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Riri, Hicham, Mohammed Ed-Dhahraouy, Abdelmajid Elmoutaouakkil, Abderrahim Beni-Hssane, and Farid Bourzgui. "Extracted features based multi-class classification of orthodontic images." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (August 1, 2020): 3558. http://dx.doi.org/10.11591/ijece.v10i4.pp3558-3567.
Повний текст джерелаАнотація:
The purpose of this study is to investigate computer vision and machine learning methods for classification of orthodontic images in order to provide orthodontists with a solution for multi-class classification of patients’ images to evaluate the evolution of their treatment. Of which, we proposed three algorithms based on extracted features, such as facial features and skin colour using YCbCrcolour space, assigned to nodes of a decision tree to classify orthodontic images: an algorithm for intra-oral images, an algorithm for mould images and an algorithm for extra-oral images. Then, we compared our method by implementing the Local Binary Pattern (LBP) algorithm to extract textural features from images. After that, we applied the principal component analysis (PCA) algorithm to optimize the redundant parameters in order to classify LBP features with six classifiers; Quadratic Support Vector Machine (SVM), Cubic SVM, Radial Basis Function SVM, Cosine K-Nearest Neighbours (KNN), Euclidian KNN, and Linear Discriminant Analysis (LDA). The presented algorithms have been evaluated on a dataset of images of 98 different patients, and experimental results demonstrate the good performances of our proposed method with a high accuracy compared with machine learning algorithms. Where LDA classifier achieves an accuracy of 84.5%.
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ZHANG, YUE, GANGSHENG CAO, TONGTONG ZHAO, HANYANG ZHANG, JUNTIAN ZHANG, and CHUNMING XIA. "A PILOT STUDY OF MECHANOMYOGRAPHY-BASED HAND MOVEMENTS RECOGNITION EMPHASIZING ON THE INFLUENCE OF FABRICS BETWEEN SENSOR AND SKIN." Journal of Mechanics in Medicine and Biology 20, no. 08 (October 2020): 2050054. http://dx.doi.org/10.1142/s0219519420500542.
Повний текст джерелаАнотація:
Multi-channel mechanomyography (MMG) signals were acquired from the forearm when the subjects were performing eight classes of hand movements related to rehabilitation training. Ten time domain (TD) features and wavelet packet node energy (WPNE) features were extracted from each channel of MMG, and the hand movements were classified by support vector machine (SVM), extreme learning machine (ELM), linear discriminant analysis (LDA) and [Formula: see text]-nearest neighborhood (KNN) and the classifying results of three methods of collecting MMG (sensors directly on skin, sensors on cotton fabric and sensors on acrylic fiber) were compared. When all TD features were selected and SVM was adopted as the classifier, the total recognition rates of hand movements were 94.0%, 93.9% and 93.6%, respectively, of three collection methods. Using ELM can obtain similar results as SVM, with the recognition rates of 94.3%, 94.3% and 94.1%, respectively, better than using LDA (88.5%, 88.6% and 88.0%) or KNN (88.9%, 89.4% and 89.0%). For each algorithm, using TD features can acquire the highest recognition rates. Once the feature set and the classifier were selected, the total recognition rates were almost equally among three collection methods (especially for some feature sets, the differences are smaller than 1%). The results confirmed that satisfactory effects could be acquired even when the MMG was collected from sensors on fabrics with specific material, thus indicating that MMG has a unique potential value for developing wearable devices.
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Wuryani, Nanik, and Sarifah Agustiani. "Random Forest Classifier untuk Deteksi Penderita COVID-19 berbasis Citra CT Scan." Jurnal Teknik Komputer 7, no. 2 (July 16, 2021): 187–93. http://dx.doi.org/10.31294/jtk.v7i2.10468.
Повний текст джерелаАнотація:
Covid-19 merupakan virus yang menyebar dan meluas sehingga berubah menjadi suatu pandemi. Virus Covid-19 menyerang melalui organ vital manusia yaitu paru-patu, oleh karena itu peneliti lebih berfokus untuk mengidentifikasi Covid-19 pada paru-paru. Penelitian ini dilakukan dengan menggunakan citra CT Scan paru-paru dan bertujuan untuk mendeteksi ada tidaknya virus dengan cara mengklasifikasikan citra Covid-19 ke dalam tiga kelas menggunakan algoritma Random Forest serta mengkombinasikannya dengan menyertakan beberapa ekstraksi fitur yaitu Haralick, Color Histogram, dan Hu-Moments. Penelitian dimulai dengan hanya memasukkan satu fitur ke dalam percobaan, lalu mengkombinasikan dengan fitur yang lain, kemudian membandingkannya menggunakan klasifikasi oleh algoritma lain seperti K-Nearest Neighbor (KNN), Decision Tree, Linear Discriminant Analysis (LDA), Logistic Regression, Support Vector Machine (SVM), dan Naive Bayes. Hasil penelitian menunjukkan bahwa akurasi tertinggi dihasilkan oleh algoritma Random Forest dengan memasukkan fitur Haralick dan Color Histogram ke dalam proses yaitu sebesar 96,9%, diikuti oleh KNN sebesar 96,5%, Decision Tree sebesar 95,5%, dan yang paling rendah yaitu Naive Bayes sebesar 42,4%
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Siti Khotimatul Wildah, Sarifah Agustiani, Ali Mustopa, Nanik Wuryani, Hendri Mahmud Nawawi, and Rizky Ade Safitri. "Pengenalan Wajah Menggunakan Pembelajaran Mesin Berdasarkan Ekstraksi Fitur Pada Gambar Wajah Berkualitas Rendah." INFOTECH : Jurnal Informatika & Teknologi 2, no. 2 (December 31, 2021): 95–103. http://dx.doi.org/10.37373/infotech.v2i2.189.
Повний текст джерелаАнотація:
Wajah merupakan bagian dari sistem biometric dimana wajah manusia memiliki bentuk dan karakteristik yang berbeda antara satu dengan lainnya sehingga wajah dapat dijadikan sebagai alternatif pengamanan suatu sistem. Proses pengenalan wajah didasarkan pada proses pencocokan dan perbandingan citra yang dimasukan dengan citra yang telah tersimpan di database. Akan tetapi pengenalan wajah menjadi permasalahan yang cukup menantang dikarenakan illuminasi, pose dan ekspresi wajah serta kualitas citra. Oleh sebab itu pada penelitian ini bertujuan untuk melakukan pengenalan wajah dengan menggunakan metode machine learning seperti Logistic Regression (LR), Linear Discriminant Analysis (LDA), Decision Tree Classifier, Random Forest Classifier (RF), Gaussian NB, K Neighbors Classifier (KNN) dan Support Vector Machine (SVM) dan beberapa metode ekstraksi fitur Hu-Moment, HOG dan Haralick pada dataset Yale Face. Berdasarkan pengujian yang dilakukan metode ekstraksi fitur gabungan Hu-Moment, HOG dan Haralick dengan algoritma Linear Discriminant Analysis (LDA) menghasilkan nilai akurasi tertinggi sebesar 79,71% dibandingkan dengan metode ekstraksi fitur dan algoritma klasifikasi lainnya.
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Murariu, Mădălina-Giorgiana, and Daniela Tărniceriu. "Discrimination of Focal and Non-Focal Epileptic Eeg Signals Using Different Types of Classifiers." Bulletin of the Polytechnic Institute of Iași. Electrical Engineering, Power Engineering, Electronics Section 68, no. 2 (June 1, 2022): 61–79. http://dx.doi.org/10.2478/bipie-2022-0011.
Повний текст джерелаАнотація:
Abstract Epilepsy is a neurological disorder characterized by recurrent seizures and has a high incidence rate. The aim of this research is to classify EEG signals as either focal and non-focal in order to identify the epileptogenic area of the brain, which can be surgically treated to manage epilepsy. In this paper, was proposed a classification method based on higher order spectra (HOS) parameters and four different classifiers: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-Nearest neighbors (KNN), and Mahalanobis distance (MD). The method was evaluated using a public dataset that consists in EEG recordings from epileptic patients. The classifiers performances were evaluated and it was shown that KNN classifier achieves a maximum classification rate of 99.55%, sensitivity of 100%, and specificity of 99.09%. The data classification was performed with maximum values of 0.96 for F1-score, and 0.91 for both Kappa and Matthews Coefficient. The results demonstrate the efficiency of the proposed method to identify the type of EEG signals.
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Yaman, Aris, Bagus Sartono, Ariani Indrawati, Yulia Aris Kartika, and Agus M. Soleh. "Automated Multi Label Classification on Fertilizer Themed Patent Documents in Indonesia." DESIDOC Journal of Library & Information Technology 42, no. 4 (July 19, 2022): 218–26. http://dx.doi.org/10.14429/djlit.42.4.17733.
Повний текст джерелаАнотація:
Patent literature research has a high scientific value for the industrial, commercial, legal, and policymaking communities. Therefore, patent analysis has become crucial. Patent topic classification is an important process in patent topic modeling analysis. However, the classification process is time-consuming and expensive, as it is usually carried out manually by an expert. Moreover, a patent document may be categorised in more than one category or label, further complicating the task. As the number of patent documents submitted increases, creating an automated patent classification system that yields accurate results becomes increasingly critical. Therefore, in this paper, we analyse the performance of two algorithms with regard to multi-label classification in patent documents: multi-label k-nearest neighbor (ML-KNN) and classifier chain k-nearest neighbor (CC-KNN), combined with latent Dirichlet allocation (LDA). These two methods have a considerable advantage in handling the continuously updated dataset; they also exhibit superior performance compared to other multi-label learning algorithms. This study also compares these two algorithms with the term frequency (TF)-weighting measure. The optimal value obtained is based on the following evaluation parameters: micro F1, accuracy, Hamming loss, and one error. The result shows that the ML-KNN method is better than the CC-KNN method and that the multi-label classification based on topics (patent LDA) is better than the TF-weighting technique.
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Posada-Quintero, Hugo F., Natasa Reljin, Aurelie Moutran, Dimitrios Georgopalis, Elaine Choung-Hee Lee, Gabrielle E. W. Giersch, Douglas J. Casa, and Ki H. Chon. "Mild Dehydration Identification Using Machine Learning to Assess Autonomic Responses to Cognitive Stress." Nutrients 12, no. 1 (December 23, 2019): 42. http://dx.doi.org/10.3390/nu12010042.
Повний текст джерелаАнотація:
The feasibility of detecting mild dehydration by using autonomic responses to cognitive stress was studied. To induce cognitive stress, subjects (n = 17) performed the Stroop task, which comprised four minutes of rest and four minutes of test. Nine indices of autonomic control based on electrodermal activity (EDA) and pulse rate variability (PRV) were obtained during both the rest and test stages of the Stroop task. Measurements were taken on three consecutive days in which subjects were “wet” (not dehydrated) and “dry” (experiencing mild dehydration caused by fluid restriction). Nine approaches were tested for classification of “wet” and “dry” conditions: (1) linear (LDA) and (2) quadratic discriminant analysis (QDA), (3) logistic regression, (4) support vector machines (SVM) with cubic, (5) fine Gaussian kernel, (6) medium Gaussian kernel, (7) a k-nearest neighbor (KNN) classifier, (8) decision trees, and (9) subspace ensemble of KNN classifiers (SE-KNN). The classification models were tested for all possible combinations of the nine indices of autonomic nervous system control, and their performance was assessed by using leave-one-subject-out cross-validation. An overall accuracy of mild dehydration detection was 91.2% when using the cubic SE-KNN and indices obtained only at rest, and the accuracy was 91.2% when using the cubic SVM classifiers and indices obtained only at test. Accuracy was 86.8% when rest-to-test increments in the autonomic indices were used along with the KNN and QDA classifiers. In summary, measures of autonomic function based on EDA and PRV are suitable for detecting mild dehydration and could potentially be used for the noninvasive testing of dehydration.
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Geethanjali, P., and K. K. Ray. "STATISTICAL PATTERN RECOGNITION TECHNIQUE FOR IMPROVED REAL-TIME MYOELECTRIC SIGNAL CLASSIFICATION." Biomedical Engineering: Applications, Basis and Communications 25, no. 02 (April 2013): 1350026. http://dx.doi.org/10.4015/s1016237213500269.
Повний текст джерелаАнотація:
The authors in this paper propose a statistical technique for pattern recognition of electromyogram (EMG) signals along with effective feature ensemble to achieve an improved classification performance with less processing time and memory space. In this study, EMG signals from 10 healthy subjects and two transradial amputees for six motions of hand and wrist is considered for identification of the intended motion. From four channels myoelectric signals, the extracted time domain features are grouped into three ensembles to identify the effectiveness of feature ensemble in classification. The three feature ensembles obtained from multichannel continuous EMG signals are applied to the new classifiers namely simple logistic regression (SLR), J48 algorithm for decision tree (DT), logistic model tree (LMT) and feature subspace ensemble using k-nearest neighbor (kNN). Novel classifiers SLR, DT and LMT, select only the dominant features during training to develop the model for pattern recognition. This selection of features reduces the processing time as well as memory space of the controller for real-time application. The performance of SLR, DT, LMT and feature subspace ensemble using kNN classifiers are compared with other conventional classifiers, such as neural network (NN), simple kNN and linear discriminant analysis (LDA). The average classification accuracy with SLR is found to be better with feature ensemble-1 compared to the other classifiers. Also, the statistical Kruscal–Wallis test shows, the classification performance of SLR is not only better but also takes less time and memory space compared to other classifiers for classification. Also the performance of the classifier is tested in real-time with transradial amputees for actuation of drive for two intended motions with TMS320F28335eZdsp controller. The experimental results show that the SLR classifier improves the controller response in real-time.
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Alhudhaif, Adi. "An effective classification framework for brain-computer interface system design based on combining of fNIRS and EEG signals." PeerJ Computer Science 7 (May 6, 2021): e537. http://dx.doi.org/10.7717/peerj-cs.537.
Повний текст джерелаАнотація:
Background The brain-computer interface (BCI) is a relatively new but highly promising special field that is actively used in basic neuroscience. BCI includes interfaces for human-computer communication based directly on neural activity concerning mental processes. Fundamental BCI components consist of different units. In the first stage, the EEG and NIRS signals obtained from the individuals are preprocessed, and the signals are brought to a certain standard. Methods In order to realize proposed framework, a dataset containing Motor Imaginary and Mental Activity tasks are prepared with Electroencephalography (EEG) and Near-Infrared Spectroscopy (NIRS) signal. First of all, HbO and HbR curves are obtained from NIRS signals. Hbo, HbR, HbO+HbR, EEG, EEG+HbO and EEG+HbR features tables are created with the features obtained by using HbO, HbR, and EEG signals, and feature weighted is carried out with the k-Means clustering centers based attribute weighting method (KMCC-based) and the k-Means clustering centers difference based attribute weighting method (KMCCD-based). Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and k-Nearest Neighbors algorithm (kNN) classifiers are used to see the classifier differences in the study. Results As a result of this study, an accuracy rate of 99.7% (with kNN classifier and KMCCD-based weighting) is obtained in the data set of Motor Imaginary. Similarly, an accuracy rate of 99.9% (with SVM and kNN classifier and KMCCD-based weighting) is obtained in the Mental Activity dataset. The weighting method is used to increase the classification accuracy, and it has been shown that it will contribute to the classification of EEG and NIRS BCI systems. The results show that the proposed method increases classifiers’ performance, offering less processing power and ease of application. In the future, studies could be carried out by combining the k-Means clustering center-based weighted hybrid BCI method with deep learning architectures. Further improved classifier performances can be achieved by combining both systems.
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Ahmadi, Mohsen, Fatemeh Dashti Ahangar, Nikoo Astaraki, Mohammad Abbasi, and Behzad Babaei. "FWNNet: Presentation of a New Classifier of Brain Tumor Diagnosis Based on Fuzzy Logic and the Wavelet-Based Neural Network Using Machine-Learning Methods." Computational Intelligence and Neuroscience 2021 (November 22, 2021): 1–13. http://dx.doi.org/10.1155/2021/8542637.
Повний текст джерелаАнотація:
In this paper, we present a novel classifier based on fuzzy logic and wavelet transformation in the form of a neural network. This classifier includes a layer to predict the numerical feature corresponded to labels or classes. The presented classifier is implemented in brain tumor diagnosis. For feature extraction, a fractal model with four Gaussian functions is used. The classification is performed on 2000 MRI images. Regarding the results, the accuracy of the DT, KNN, LDA, NB, MLP, and SVM is 93.5%, 87.6%, 61.5%, 57.5%, 68.5%, and 43.6%, respectively. Based on the results, the presented FWNNet illustrates the highest accuracy of 100% with the fractal feature extraction method and brain tumor diagnosis based on MRI images. Based on the results, the best classifier for diagnosis of the brain tumor is FWNNet architecture. However, the second and third high-performance classifiers are the DT and KNN, respectively. Moreover, the presented FWNNet method is implemented for the segmentation of brain tumors. In this paper, we present a novel supervised segmentation method based on the FWNNet layer. In the training process, input images with a sweeping filter should be reshaped to vectors that correspond to reshaped ground truth images. In the training process, we performed a PSO algorithm to optimize the gradient descent algorithm. For this purpose, 80 MRI images are used to segment the brain tumor. Based on the results of the ROC curve, it can be estimated that the presented layer can segment the brain tumor with a high true-positive rate.
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Qi, Zuxuan, Xiaohong Wu, Yangjian Yang, Bin Wu, and Haijun Fu. "Discrimination of the Red Jujube Varieties Using a Portable NIR Spectrometer and Fuzzy Improved Linear Discriminant Analysis." Foods 11, no. 5 (March 7, 2022): 763. http://dx.doi.org/10.3390/foods11050763.
Повний текст джерелаАнотація:
In order to quickly, nondestructively, and effectively distinguish red jujube varieties, based on the combination of fuzzy theory and improved LDA (iLDA), fuzzy improved linear discriminant analysis (FiLDA) algorithm was proposed to classify near-infrared reflectance (NIR) spectra of red jujube samples. FiLDA shows performs better than iLDA in dealing with NIR spectra containing noise. Firstly, the portable NIR spectrometer was employed to gather the NIR spectra of five kinds of red jujube, and the initial NIR spectra were pretreated by standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky-Golay smoothing (S-G smoothing), mean centering (MC) and Savitzky-Golay filter (S-G filter). Secondly, the high-dimensional spectra were processed for dimension reduction by principal component analysis (PCA). Then, linear discriminant analysis (LDA), iLDA and FiLDA were applied to extract features from the NIR spectra, respectively. Finally, K nearest neighbor (KNN) served as a classifier for the classification of red jujube samples. The highest classification accuracy of this identification system for red jujube, by using FiLDA and KNN, was 94.4%. These results indicated that FiLDA combined with NIR spectroscopy was an available method for identifying the red jujube varieties and this method has wide application prospects.
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Liang, Jing, Xiaoli Li, Panpan Zhu, Ning Xu, and Yong He. "Hyperspectral Reflectance Imaging Combined with Multivariate Analysis for Diagnosis of Sclerotinia Stem Rot on Arabidopsis Thaliana Leaves." Applied Sciences 9, no. 10 (May 21, 2019): 2092. http://dx.doi.org/10.3390/app9102092.
Повний текст джерелаАнотація:
Sclerotinia stem rot (SSR) is one of the most destructive diseases in the world caused by Sclerotinia sclerotiorum (S. sclerotiorum), resulting in significant yield loss. Early and high-throughput detection would be critical to prevent SSR from spreading. This study aimed to propose a feasible method for SSR detection based on the hyperspectral imaging coupled with multivariate analysis. The performance of different detecting algorithms were compared by combining the extreme learning machine (ELM), K-nearest neighbor algorithm (KNN), linear discriminant analysis (LDA), naïve Bayes classifier (NB) and the support vector machine (SVM) with the random frog (RF), successive projection algorithm (SPA) and sequential forward selection (SFS). The similarity of selected optimal wavelengths by three different feature selection methods indicated a high correlation between selected wavelengths and SSR. Compared with KNN, LDA, NB, and SVM, three wavelengths (455, 671 and 747 nm) selected by SFS-CA combined with ELM could achieve relatively better results with the overall accuracy of 93.7% and the lowest false negative rate of 2.4%. These results demonstrated the potential of the presented method using hyperspectral reflectance imaging combined with multivariate analysis for SSR diagnosis.
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Ropelewska, Ewa, Ahmed M. Rady, and Nicholas J. Watson. "Apricot Stone Classification Using Image Analysis and Machine Learning." Sustainability 15, no. 12 (June 8, 2023): 9259. http://dx.doi.org/10.3390/su15129259.
Повний текст джерелаАнотація:
Apricot stones have high commercial value and can be used for manufacturing functional foods, cosmetic products, active carbon, and biodiesel. The optimal processing of the stones is dependent on the cultivar and there is a need for methods to sort among different cultivars (which are often mixed in processing facilities). This study investigates the effectiveness of two low-cost colour imaging systems coupled with supervised learning to develop classification models to determine the cultivar of different stones. Apricot stones of the cultivars ‘Bella’, ‘Early Orange’, ‘Harcot’, ‘Skierniewicka Słodka’, and ‘Taja’ were used. The RGB images were acquired using a flatbed scanner or a digital camera; and 2172 image texture features were extracted within the R, G, B; L, a, b; X, Y, Z; U, and V colour coordinates. The most influential features were determined and resulted in 103 and 89 selected features for the digital camera and the flatbed scanner, respectively. Linear and nonlinear classifiers were applied including Linear Discriminant Analysis (LDA), Decision Trees (DT), k-Nearest Neighbour (kNN), Support Vector Machines (SVM), and Naive Bayes (NB). The models resulting from the flatbed scanner and using selected features achieved an accuracy of 100% via either quadratic diagonal LDA or kNN classifiers. The models developed using images from the digital camera and all or selected features had an accuracy of up to 96.77% using the SVM classifier. This study presents novel and simple-to-implement at-line (flatbed scanner) and online (digital camera) methodologies for apricot stone sorting. The developed procedure combining colour imaging and machine learning may be used for the authentication of apricot stone cultivars and quality evaluation of apricot from sustainable production.
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Ozcift, Akin, and Arif Gulten. "Assessing Effects of Pre-Processing Mass Spectrometry Data on Classification Performance." European Journal of Mass Spectrometry 14, no. 5 (April 1, 2008): 267–73. http://dx.doi.org/10.1255/ejms.938.
Повний текст джерелаАнотація:
Disease prediction through mass spectrometry (MS) data is gaining importance in medical diagnosis. Particularly in cancerous diseases, early prediction is one of the most life saving stages. High dimension and the noisy nature of MS data requires a two-phase study for successful disease prediction; first, MS data must be pre-processed with stages such as baseline correction, normalizing, de-noising and peak detection. Second, a dimension reduction based classifier design is the main objective. Having the data pre-processed, the prediction accuracy of the classifier algorithm becomes the most significant factor in the medical diagnosis phase. As health is the main concern, the accuracy of the classifier is clearly very important. In this study, the effects of the pre-processing stages of MS data on classifier performances are addressed. Three pre-processing stages—baseline correction, normalization and de-noising—are applied to three MS data samples, namely, high-resolution ovarian cancer, low-resolution prostate cancer and a low-resolution ovarian cancer. To measure the effects of the pre-processing stages quantitatively, four diverse classifiers, genetic algorithm wrapped K-nearest neighbor (GA-KNN), principal component analysis-based least discriminant analysis (PCA-LDA), a neural network (NN) and a support vector machine (SVM) are applied to the data sets. Calculated classifier performances have demonstrated the effects of pre-processing stages quantitatively and the importance of pre-processing stages on the prediction accuracy of classifiers. Results of computations have been shown clearly.
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Md Isa, N. E., A. Amir, M. Z. Ilyas, and M. S. Razalli. "Motor imagery classification in Brain computer interface (BCI) based on EEG signal by using machine learning technique." Bulletin of Electrical Engineering and Informatics 8, no. 1 (March 1, 2019): 269–75. http://dx.doi.org/10.11591/eei.v8i1.1402.
Повний текст джерелаАнотація:
This paper focuses on classification of motor imagery in Brain Computer Interface (BCI) by using classifiers from machine learning technique. The BCI system consists of two main steps which are feature extraction and classification. The Fast Fourier Transform (FFT) features is extracted from the electroencephalography (EEG) signals to transform the signals into frequency domain. Due to the high dimensionality of data resulting from the feature extraction stage, the Linear Discriminant Analysis (LDA) is used to minimize the number of dimension by finding the feature subspace that optimizes class separability. Five classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes, Decision Tree and Logistic Regression are used in the study. The performance was tested by using Dataset 1 from BCI Competition IV which consists of imaginary hand and foot movement EEG data. As a result, SVM, Logistic Regression and Naïve Bayes classifier achieved the highest accuracy with 89.09% in AUC measurement.
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Yang, Huan, Cheng Wang, Han Zhang, Ya’nan Zhou, and Bin Luo. "Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms." PeerJ Computer Science 9 (May 10, 2023): e1354. http://dx.doi.org/10.7717/peerj-cs.1354.
Повний текст джерелаАнотація:
Purity is an important factor of maize seed quality that affects yield, and traditional seed purity identification methods are costly or time-consuming. To achieve rapid and accurate detection of the purity of maize seeds, a method for identifying maize seed varieties, using random subspace integrated learning and hyperspectral imaging technology, was proposed. A hyperspectral image of the maize seed endosperm was collected to obtain a spectral image cube with a wavelength range of 400∼1,000 nm. Methods, including Standard Normal Variate (SNV), multiplicative Scatter Correction (MSC), and Savitzky–Golay First Derivative (SG1) were used to preprocess raw spectral data. Iteratively retains informative variables (IRIV) and competitive adaptive reweighted sampling (CARS) were used to reduce the dimensions of the spectral data. A recognition model of maize seed varieties was established using k-nearest neighbor (KNN), support vector machine (SVM), line discrimination analysis (LDA) and decision tree (DT). Among the preprocessing methods, MSC has the best effect. Among the dimensionality reduction methods, IRIV has the best performance. Among the base classifiers, LDA had the highest precision. To improve the precision in identifying maize seed varieties, LDA was used as the base classifier to establish a random subspace ensemble learning (RSEL) model. Using MSC-IRIV-RSEL, precision increased from 0.9333 to 0.9556, and the Kappa coefficient increased from 0.9174 to 0.9457. This study shows that the method based on hyperspectral imaging technology combined with subspace ensemble learning algorithm is a new method for maize seed purity recognition.
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Bishnoi, Sudha, Nadhir Al-Ansari, Mujahid Khan, Salim Heddam, and Anurag Malik. "Classification of Cotton Genotypes with Mixed Continuous and Categorical Variables: Application of Machine Learning Models." Sustainability 14, no. 20 (October 21, 2022): 13685. http://dx.doi.org/10.3390/su142013685.
Повний текст джерелаАнотація:
Mixed data is a combination of continuous and categorical variables and occurs frequently in fields such as agriculture, remote sensing, biology, medical science, marketing, etc., but only limited work has been done with this type of data. In this study, data on continuous and categorical characters of 452 genotypes of cotton (Gossypium hirsutum) were obtained from an experiment conducted by the Central Institute of Cotton Research (CICR), Sirsa, Haryana (India) during the Kharif season of the year 2018–2019. The machine learning (ML) classifiers/models, namely k-nearest neighbor (KNN), Classification and Regression Tree (CART), C4.5, Naïve Bayes, random forest (RF), bagging, and boosting were considered for cotton genotypes classification. The performance of these ML classifiers was compared to each other along with the linear discriminant analysis (LDA) and logistic regression. The holdout method was used for cross-validation with an 80:20 ratio of training and testing data. The results of the appraisal based on hold-out cross-validation showed that the RF and AdaBoost performed very well, having only two misclassifications with the same accuracy of 97.26% and the error rate of 2.74%. The LDA classifier performed the worst in terms of accuracy, with nine misclassifications. The other performance measures, namely sensitivity, specificity, precision, F1 score, and G-mean, were all together used to find out the best ML classifier among all those considered. Moreover, the RF and AdaBoost algorithms had the highest value of all the performance measures, with 96.97% sensitivity and 97.50% specificity. Thus, these models were found to be the best in classifying the low- and high-yielding cotton genotypes.
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Amini, Morteza, MirMohsen Pedram, AliReza Moradi, and Mahshad Ouchani. "Diagnosis of Alzheimer’s Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN)." Computational and Mathematical Methods in Medicine 2021 (April 27, 2021): 1–15. http://dx.doi.org/10.1155/2021/5514839.
Повний текст джерелаАнотація:
The automatic diagnosis of Alzheimer’s disease plays an important role in human health, especially in its early stage. Because it is a neurodegenerative condition, Alzheimer’s disease seems to have a long incubation period. Therefore, it is essential to analyze Alzheimer’s symptoms at different stages. In this paper, the classification is done with several methods of machine learning consisting of K -nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), linear discrimination analysis (LDA), and random forest (RF). Moreover, novel convolutional neural network (CNN) architecture is presented to diagnose Alzheimer’s severity. The relationship between Alzheimer’s patients’ functional magnetic resonance imaging (fMRI) images and their scores on the MMSE is investigated to achieve the aim. The feature extraction is performed based on the robust multitask feature learning algorithm. The severity is also calculated based on the Mini-Mental State Examination score, including low, mild, moderate, and severe categories. Results show that the accuracy of the KNN, SVM, DT, LDA, RF, and presented CNN method is 77.5%, 85.8%, 91.7%, 79.5%, 85.1%, and 96.7%, respectively. Moreover, for the presented CNN architecture, the sensitivity of low, mild, moderate, and severe status of Alzheimer patients is 98.1%, 95.2%,89.0%, and 87.5%, respectively. Based on the findings, the presented CNN architecture classifier outperforms other methods and can diagnose the severity and stages of Alzheimer’s disease with maximum accuracy.
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Imanian, Kamal, Razieh Pourdarbani, Sajad Sabzi, Ginés García-Mateos, Juan Ignacio Arribas, and José Miguel Molina-Martínez. "Identification of Internal Defects in Potato Using Spectroscopy and Computational Intelligence Based on Majority Voting Techniques." Foods 10, no. 5 (April 30, 2021): 982. http://dx.doi.org/10.3390/foods10050982.
Повний текст джерелаАнотація:
Potatoes are one of the most demanded products due to their richness in nutrients. However, the lack of attention to external and, especially, internal defects greatly reduces its marketability and makes it prone to a variety of diseases. The present study aims to identify healthy-looking potatoes but with internal defects. A visible (Vis), near-infrared (NIR), and short-wavelength infrared (SWIR) spectrometer was used to capture spectral data from the samples. Using a hybrid of artificial neural networks (ANN) and the cultural algorithm (CA), the wavelengths of 861, 883, and 998 nm in Vis/NIR region, and 1539, 1858, and 1896 nm in the SWIR region were selected as optimal. Then, the samples were classified into either healthy or defective class using an ensemble method consisting of four classifiers, namely hybrid ANN and imperialist competitive algorithm (ANN-ICA), hybrid ANN and harmony search algorithm (ANN-HS), linear discriminant analysis (LDA), and k-nearest neighbors (KNN), combined with the majority voting (MV) rule. The performance of the classifier was assessed using only the selected wavelengths and using all the spectral data. The total correct classification rates using all the spectral data were 96.3% and 86.1% in SWIR and Vis/NIR ranges, respectively, and using the optimal wavelengths 94.1% and 83.4% in SWIR and Vis/NIR, respectively. The statistical tests revealed that there are no significant differences between these datasets. Interestingly, the best results were obtained using only LDA, achieving 97.7% accuracy for the selected wavelengths in the SWIR spectral range.
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Balkhi, Parinaz, and Mehrdad Moallem. "A Multipurpose Wearable Sensor-Based System for Weight Training." Automation 3, no. 1 (February 16, 2022): 132–52. http://dx.doi.org/10.3390/automation3010007.
Повний текст джерелаАнотація:
In recent years, there has been growing interest in automated tracking and detection of sports activities. Researchers have shown that providing activity information to individuals during their exercise routines can greatly help them in achieving their exercise goals. In particular, such information would help them to maximize workout efficiency and prevent overreaching and overtraining. This paper presents the development of a novel multipurpose wearable device for automatic weight detection, activity type recognition, and count repetition in sports activities such as weight training. The device monitors weights and activities by using an inertial measurement unit (IMU), an accelerometer, and three force sensors mounted in a glove, and classifies them by utilizing developed machine learning models. For weight detection purposes, different classifiers including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Multi-layer Perceptron Neural Networks (MLP) were investigated. For activity recognition, the K nearest neighbor (KNN), Decision Tree (DT), Random Forest (RF), and SVM models were trained and examined. Experimental results indicate that the SVM classifier can achieve the highest accuracy for weight detection whereas RF can outperform other classifiers for activity recognition. The results indicate feasibility of developing a wearable device that can provide in-situ accurate information regarding the lifted weight and activity type with minimum physical intervention.
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Norali, A. N., M. N. Anas, Z. Zakaria, M. Asymawi, A. H. Abu Bakar, and Y. F. Chong. "Electromyography Signal Pattern Recognition for Movement of Shoulder." Journal of Physics: Conference Series 2071, no. 1 (October 1, 2021): 012049. http://dx.doi.org/10.1088/1742-6596/2071/1/012049.
Повний текст джерелаАнотація:
Abstract Pectoralis major and deltoid are two muscles that are associated with the movement of the shoulder. Electromyography (EMG) signal acquired from these two muscles can be used to classify the movement of the shoulder based on pattern recognition. In this paper, an experiment for EMG data collection involves eight healthy male subjects who perform four shoulder movements which are flexion, extension, internal rotation and external rotation. Feature extraction of EMG data is done using root mean square (RMS), variance (VAR) and zero crossing (ZC). For pattern recognition, the classifiers that are used are Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA). Classification results shows highest accuracy on ZC feature using an SVM classifier with cubic kernel. The study on shoulder movement using EMG of pectoralis and deltoid muscles could be extended on arm amputees based on hypothesis that the EMG signal could be utilized for control of robotic prosthetic arm.
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Abidine, M’hamed Bilal, and Belkacem Fergani. "Activity recognition from smartphone data using weighted learning methods." Intelligenza Artificiale 15, no. 1 (July 28, 2021): 1–15. http://dx.doi.org/10.3233/ia-200059.
Повний текст джерелаАнотація:
Mobile phone based activity recognition uses data obtained from embedded sensors to infer user’s physical activities. The traditional approach for activity recognition employs machine learning algorithms to learn from collected labeled data and induce a model. To enhance the accuracy and hence to improve the overall efficiency of the system, the good classifiers can be combined together. Fusion can be done at the feature level and also at the decision level. In this work, we propose a new hybrid classification model Weighted SVM-KNN to perform automatic recognition of activities that combines a Weighted Support Vector Machines (WSVM) to learn a model with a Weighted K-Nearest Neighbors (WKNN), to classify and identify the ongoing activity. The sensory inputs to the classifier are reduced with the Linear Discriminant Analysis (LDA). We demonstrate how to train the hybrid approach in this setting, introduce an adaptive regularization parameter for WSVM approach, and illustrate how our method outperforms the state-of-the-art on a large benchmark datasets.
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Avots, Egils, Klāvs Jermakovs, Maie Bachmann, Laura Päeske, Cagri Ozcinar, and Gholamreza Anbarjafari. "Ensemble Approach for Detection of Depression Using EEG Features." Entropy 24, no. 2 (January 28, 2022): 211. http://dx.doi.org/10.3390/e24020211.
Повний текст джерелаАнотація:
Depression is a public health issue that severely affects one’s well being and can cause negative social and economic effects to society. To raise awareness of these problems, this research aims at determining whether the long-lasting effects of depression can be determined from electroencephalographic (EEG) signals. The article contains an accuracy comparison for SVM, LDA, NB, kNN, and D3 binary classifiers, which were trained using linear (relative band power, alpha power variability, spectral asymmetry index) and nonlinear (Higuchi fractal dimension, Lempel–Ziv complexity, detrended fluctuation analysis) EEG features. The age- and gender-matched dataset consisted of 10 healthy subjects and 10 subjects diagnosed with depression at some point in their lifetime. Most of the proposed feature selection and classifier combinations achieved accuracy in the range of 80% to 95%, and all the models were evaluated using a 10-fold cross-validation. The results showed that the motioned EEG features used in classifying ongoing depression also work for classifying the long-lasting effects of depression.
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Stančić, Ivo, Josip Musić, Tamara Grujić, Mirela Kundid Vasić, and Mirjana Bonković. "Comparison and Evaluation of Machine Learning-Based Classification of Hand Gestures Captured by Inertial Sensors." Computation 10, no. 9 (September 14, 2022): 159. http://dx.doi.org/10.3390/computation10090159.
Повний текст джерелаАнотація:
Gesture recognition is a topic in computer science and language technology that aims to interpret human gestures with computer programs and many different algorithms. It can be seen as the way computers can understand human body language. Today, the main interaction tools between computers and humans are still the keyboard and mouse. Gesture recognition can be used as a tool for communication with the machine and interaction without any mechanical device such as a keyboard or mouse. In this paper, we present the results of a comparison of eight different machine learning (ML) classifiers in the task of human hand gesture recognition and classification to explore how to efficiently implement one or more tested ML algorithms on an 8-bit AVR microcontroller for on-line human gesture recognition with the intention to gesturally control the mobile robot. The 8-bit AVR microcontrollers are still widely used in the industry, but due to their lack of computational power and limited memory, it is a challenging task to efficiently implement ML algorithms on them for on-line classification. Gestures were recorded by using inertial sensors, gyroscopes, and accelerometers placed at the wrist and index finger. One thousand and eight hundred (1800) hand gestures were recorded and labelled. Six important features were defined for the identification of nine different hand gestures using eight different machine learning classifiers: Decision Tree (DT), Random Forests (RF), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) with linear kernel, Naïve Bayes classifier (NB), K-Nearest Neighbours (KNN), and Stochastic Gradient Descent (SGD). All tested algorithms were ranged according to Precision, Recall, and F1-score (abb.: P-R-F1). The best algorithms were SVM (P-R-F1: 0.9865, 0.9861, and 0.0863), and RF (P-R-F1: 0.9863, 0.9861, and 0.0862), but their main disadvantage is their unusability for on-line implementations in 8-bit AVR microcontrollers, as proven in the paper. The next best algorithms have had only slightly poorer performance than SVM and RF: KNN (P-R-F1: 0.9835, 0.9833, and 0.9834) and LR (P-R-F1: 0.9810, 0.9810, and 0.9810). Regarding the implementation on 8-bit microcontrollers, KNN has proven to be inadequate, like SVM and RF. However, the analysis for LR has proved that this classifier could be efficiently implemented on targeted microcontrollers. Having in mind its high F1-score (comparable to SVM, RF, and KNN), this leads to the conclusion that the LR is the most suitable classifier among tested for on-line applications in resource-constrained environments, such as embedded devices based on 8-bit AVR microcontrollers, due to its lower computational complexity in comparison with other tested algorithms.
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Anam, Khairul, and Adel Al-Jumaily. "Optimized Kernel Extreme Learning Machine for Myoelectric Pattern Recognition." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 1 (February 1, 2018): 483. http://dx.doi.org/10.11591/ijece.v8i1.pp483-496.
Повний текст джерелаАнотація:
Myoelectric pattern recognition (MPR) is used to detect user’s intention to achieve a smooth interaction between human and machine. The performance of MPR is influenced by the features extracted and the classifier employed. A kernel extreme learning machine especially radial basis function extreme learning machine (RBF-ELM) has emerged as one of the potential classifiers for MPR. However, RBF-ELM should be optimized to work efficiently. This paper proposed an optimization of RBF-ELM parameters using hybridization of particle swarm optimization (PSO) and a wavelet function. These proposed systems are employed to classify finger movements on the amputees and able-bodied subjects using electromyography signals. The experimental results show that the accuracy of the optimized RBF-ELM is 95.71% and 94.27% in the healthy subjects and the amputees, respectively. Meanwhile, the optimization using PSO only attained the average accuracy of 95.53 %, and 92.55 %, on the healthy subjects and the amputees, respectively. The experimental results also show that SW-RBF-ELM achieved the accuracy that is better than other well-known classifiers such as support vector machine (SVM), linear discriminant analysis (LDA) and k-nearest neighbor (kNN).
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Alkhammash, Eman H., Myriam Hadjouni, and Ahmed M. Elshewey. "A Hybrid Ensemble Stacking Model for Gender Voice Recognition Approach." Electronics 11, no. 11 (May 31, 2022): 1750. http://dx.doi.org/10.3390/electronics11111750.
Повний текст джерелаАнотація:
Gender recognition by voice is a vital research subject in speech processing and acoustics, as human voices have many remarkable characteristics. Voice recognition is beneficial in a variety of applications, including mobile health care systems, interactive systems, crime analysis, and recognition systems. Several algorithms for voice recognition have been developed, but there is still potential for development in terms of the system’s accuracy and efficiency. Recent research has focused on combining ensemble learning with a variety of machine learning models in order to create more accurate classifiers. In this paper, a stacked ensemble for gender voice recognition model is presented, using four classifiers, namely, k-nearest neighbor (KNN), support vector machine (SVM), stochastic gradient descent (SGD), and logistic regression (LR) as base classifiers and linear discriminant analysis (LDA) as meta classifier. The dataset used includes 3168 instances and 21 features, where 20 features are the predictors, and one feature is the target. Several prediction evaluation metrics, including precision, accuracy, recall, F1 score, and area under the receiver operating characteristic curve (AUC), were computed to verify the execution of the proposed model. The results obtained illustrated that the stacked model achieved better results compared to other conventional machine learning models. The stacked model achieved high accuracy with 99.64%.
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Sáenz Oviedo, Mayra A., William R. Kuhn, Martin A. Rondon Sepulveda, John Abbott, Jessica L. Ware, and Melissa Sanchez-Herrera. "Are wing contours good classifiers for automatic identification in Odonata? A view from the Targeted Odonata Wing Digitization (TOWD) project." International Journal of Odonatology 25 (December 8, 2022): 96–106. http://dx.doi.org/10.48156/1388.2022.1917184.
Повний текст джерелаАнотація:
In recent decades, a lack of available knowledge about the magnitude, identity and distribution of biodiversity has given way to a taxonomic impediment where species are not being described as fast as the rate of extinction. Using Machine Learning methods based on seven different algorithms (LR, CART, KNN, GNB, LDA, SVM and RFC) we have created an automatic identification approach for odonate genera, through images of wing contours. The training population is composed of the collected specimens that have been digitized in the framework of the NSF funded Odomatic and TOWD projects. Each contour was pre-processed, and 80 coefficients were extracted for each specimen. These form a database with 4656 rows and 80 columns, which was divided into 70% for training and 30% for testing the classifiers. The classifier with the best performance was a Linear Discriminant Analysis (LDA), which discriminated the highest number of classes (100) with an accuracy value of 0.7337, precision of 0.75, recall of 0.73 and a F1 score of 0.73. Additionally, two main confusion groups are reported, among genera within the suborders of Anisoptera and Zygoptera. These confusion groups suggest a need to include other morphological characters that complement the wing information used for the classification of these groups thereby improving accuracy of classification. Likewise, the findings of this work open the door to the application of machine learning methods for the identification of species in Odonata and in insects more broadly which would potentially reduce the impact of the taxonomic impediment.
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Sun, Dawei, Yueming Zhu, Haixia Xu, Yong He, and Haiyan Cen. "Time-Series Chlorophyll Fluorescence Imaging Reveals Dynamic Photosynthetic Fingerprints of sos Mutants to Drought Stress." Sensors 19, no. 12 (June 12, 2019): 2649. http://dx.doi.org/10.3390/s19122649.
Повний текст джерелаАнотація:
Resistance to drought stress is one of the most favorable traits in breeding programs yet drought stress is one of the most poorly addressed biological processes for both phenomics and genetics. In this study, we investigated the potential of using a time-series chlorophyll fluorescence (ChlF) analysis to dissect the ChlF fingerprints of salt overly sensitive (SOS) mutants under drought stress. Principle component analysis (PCA) was used to identify a shifting pattern of different genotypes including sos mutants and wild type (WT) Col-0. A time-series deep-learning algorithm, sparse auto encoders (SAEs) neural network, was applied to extract time-series ChlF features which were used in four classification models including linear discriminant analysis (LDA), k-nearest neighbor classifier (KNN), Gaussian naive Bayes (NB) and support vector machine (SVM). The results showed that the discrimination accuracy of sos mutants SOS1-1, SOS2-3, and wild type Col-0 reached 95% with LDA classification model. Sequential forward selection (SFS) algorithm was used to obtain ChlF fingerprints of the shifting pattern, which could address the response of sos mutants and Col-0 to drought stress over time. Parameters including QY, NPQ and Fm, etc. were significantly different between sos mutants and WT. This research proved the potential of ChlF imaging for gene function analysis and the study of drought stress using ChlF in a time-series manner.
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Syahrial, Syahrial, Rosmin Ilham, Zulaika F. Asikin, and St Surya Indah Nurdin. "Stunting Classification in Children's Measurement Data Using Machine Learning Models." Journal La Multiapp 3, no. 2 (March 31, 2022): 52–60. http://dx.doi.org/10.37899/journallamultiapp.v3i2.614.
Повний текст джерелаАнотація:
The study conducted a stunting classification of measurement data for children under 5 years old. The dataset has attributes such as: gender, age, weight (BB), height (TB), weight / height (BBTB), weight / age (BBU), and height / age (TBU). The research uses the CRISP-DM methodology in processing the data. The data were tested on several classification models, namely: logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (KNN), classification and regression trees (CART), nave bayes (NB), support vector machine - linear kernel (SVM-Linear), support vector machine - rbf kernel (SVM-RBF), random forest classifier (RPC), adaboost (ADA), and neural network (MLPC). These models were tested on the dataset to find out the best model in accuracy. The test results show that SVM-RBF produces an accuracy of 78%. SVM-RBF has consistently been at the highest accuracy in several tests. Testing through k-fold cross validation with k=10.
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Zhang, Jintao, Shuang Lai, Huahua Yu, Erjie Wang, Xizhe Wang, and Zixuan Zhu. "Fruit Classification Utilizing a Robotic Gripper with Integrated Sensors and Adaptive Grasping." Mathematical Problems in Engineering 2021 (September 3, 2021): 1–15. http://dx.doi.org/10.1155/2021/7157763.
Повний текст джерелаАнотація:
As the core component of agricultural robots, robotic grippers are widely used for plucking, picking, and harvesting fruits and vegetables. Secure grasping is a severe challenge in agricultural applications because of the variation in the shape and hardness of agricultural products during maturation, as well as their variety and delicacy. In this study, a fruit identification method utilizing an adaptive gripper with tactile sensing and machine learning algorithms is reported. An adaptive robotic gripper is designed and manufactured to perform adaptive grasping. A tactile sensing information acquisition circuit is built, and force and bending sensors are integrated into the robotic gripper to measure the contact force distribution on the contact surface and the deformation of the soft fingers. A robotic manipulator platform is developed to collect the tactile sensing data in the grasping process. The performance of the random forest (RF), k-nearest neighbor (KNN), support vector classification (SVC), naive Bayes (NB), linear discriminant analysis (LDA), and ridge regression (RR) classifiers in identifying and classifying five types of fruits using the adaptive gripper is evaluated and compared. The RF classifier achieves the highest accuracy of 98%, while the accuracies of the other classifiers vary from 74% to 97%. The experiment illustrates that efficient and accurate fruit identification can be realized with the adaptive gripper and machine learning classifiers, and that the proposed method can provide a reference for controlling the grasping force and planning the robotic motion in the plucking, picking, and harvesting of fruits and vegetables.
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Li, Wenlong, and Haibin Qu. "Wavelet-based classification and influence matrix analysis method for the fast discrimination of Chinese herbal medicines according to the geographical origins with near infrared spectroscopy." Journal of Innovative Optical Health Sciences 07, no. 04 (July 2014): 1350061. http://dx.doi.org/10.1142/s1793545813500612.
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A discriminant analysis technique using wavelet transformation (WT) and influence matrix analysis (CAIMAN) method is proposed for the near infrared (NIR) spectroscopy classification. In the proposed methodology, NIR spectra are decomposed by WT for data compression and a forward feature selection is further employed to extract the relevant information from the wavelet coefficients, reducing both classification errors and model complexity. A discriminant-CAIMAN (D-CAIMAN) method is utilized to build the classification model in wavelet domain on the basis of reduced wavelet coefficients of spectral variables. NIR spectra data set of 265 salviae miltiorrhizae radix samples from 9 different geographical origins is used as an example to test the classification performance of the algorithm. For a comparison, k-nearest neighbor (KNN), linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods are also employed. D-CAIMAN with wavelet-based feature selection (WD-CAIMAN) method shows the best performance, achieving the total classification rate of 100% in both cross-validation set and prediction set. It is worth noting that the WD-CAIMAN classifier also shows improved sensitivity, selectivity and model interpretability in the classifications.
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S, Archana. "A Comparison of Various Machine Learning Algorithms and Deep Learning Algorithms for Prediction of Loan Eligibility." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 4558–64. http://dx.doi.org/10.22214/ijraset.2023.54495.
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Abstract: Banking serves as a link between people who have money in excess and those who lack it for any reason, making it a crucial component of the entire financial system. People depend on banks to borrow money to meet their needs Banks provide money as loans with an interest rate that has to be repaid. The loans are approved by banks based on the borrower's numerous characteristics. The development of artificial intelligence gave financial organizations a method to broaden their lending practices without taking on excessive financial risk. The process can be sped up, made more effective, and less error-prone by using machine learning and deep learning models for loan approval. In this study, we compare ten Machine Learning models, including Decision Tree, Logistic Regression, K Nearest Neighbour (KNN), Random Forest Classifier, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Gaussian Naive Bayes, XGBoost, Gradient Boosting, Adaboost, and Deep Learning models, including Deep Neural Network (DNN) and Long Short Term Memory network (LSTM), to predict loan applicants who deserve the money.
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Güney, Selen, Sema Arslan, Adil Deniz Duru, and Dilek Göksel Duru. "Identification of Food/Nonfood Visual Stimuli from Event-Related Brain Potentials." Applied Bionics and Biomechanics 2021 (September 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/6472586.
Повний текст джерелаАнотація:
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.