Different Types Of Classifiers Research Articles

Classification of exercise fatigue levels by multi-class SVM from ECG and HRV.

Among the various physiological signals, electrocardiogram (ECG) is a valid criterion for the classification of various exercise fatigue. In this study, we combine features extracted by deep neural networks with linear features from ECG and heart rate variability (HRV) for exercise fatigue classification. First, the ECG signals are converted into 2-D images by using the short-term Fourier transform (STFT), and image features are extracted by the visual geometry group (VGG) . The extracted image and linear features of ECG and HRV are sent to the different types of classifiers to distinguish distinct exercise fatigue level. To validate performance, the proposed methods are tested on (i) an open-source EPHNOGRAM dataset and (ii) a self-collected dataset (n = 51). The results reveal that the classification based on the concatenated features has the highest accuracy, and the calculation time of the system is also significantly reduced. This demonstrates that the proposed novel hybrid approach can be used to assist in improving the accuracy and timeliness of exercise fatigue classification in a real-time exercise environment. The experimental results show that the proposed method outperforms other recent state-of-the-art methods in terms of accuracy 96.90%, sensitivity 96.90%, F1-score of 0.9687 in EPHNOGRAM and accuracy 92.17%, sensitivity 92.63%, F1-score of 0.9213 in self-collected dataset.

Energy optimization for wireless sensor network using minimum redundancy maximum relevance feature selection and classification techniques

In wireless sensor networks (WSN), conserving energy is usually a basic issue, and several approaches are applied to optimize energy consumption. In this article, we adopt feature selection approaches by using minimum redundancy maximum relevance (MRMR) as a feature selection technique to minimize the number of sensors thereby conserving energy. MRMR ranks the sensors according to their significance. The selected features are then classified by different types of classifiers; SVM with linear kernel classifier, naïve Bayes classifier, and k-nearest neighbors classifier (KNN) to compare accuracy values. The simulation results illustrated an improvement in the lifetime extension factor of sensors and showed that the KNN classifier gives better results than the naïve Bayes and SVM classifier.

Computer-Aided Diagnosis System for Automated Detection of Mri Brain Tumors

Detection and classification of brain tumors in manual or traditional way is an area which could be improved by having such automated detection and clarification system for brain tumors. In this paper, enhanced Computer-Aided Diagnosis CAD software system is introduced for brain tumor detection and classification. Total of 229 brain MRI images was taken as dataset for the purpose of this research; those dataset images include 105 normal brain MRI images, and 124 abnormal brain MRI images. Proposed CAD system is specialized for Meningioma brain tumor detection and classification, and the technique could be generalized and implemented for Glioma, and Pituitary brain tumors as well, and the whole system was implemented using MATLAB software. We started by cropping the region of interest (ROI) of dataset images. Then, feature extraction was implemented using first order statistical features, as well as using of some wavelets filters in combination with the former. T-test is used to exclude features of no statistical significance (p-value < 0.05). After that, different types of classifiers were used to separate the normal set from the abnormal one. Note that, we used an iterative approach to by changing features with many runs until we got best performance, where, best accuracy results were gotten with SVM-Kernel Function (Linear), KNN-1, KNN-3, and KNN-5 classifiers. Note also that, we used convolutional neural networks (CNN) from Deep Learning toolbox of MATLAB as a control method to compare, where the images were fed directly to the CNN. The results were evaluated using performance assessment techniques which are Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, Error Rate, and Area Under the Curve (AUC) of Reciever Operator Characteristic (ROC). With SVM classifier, the best gotten accuracy results were 91 % with CNN classifier, 82% with SVM classifier, and 77 % with KNN classifier. Furthermore, it was very beneficial to find such feature extraction techniques which gave acceptable accuracy results with three different classifiers; this was the case two times as mentioned the study. All proposed CAD system areas was developed and implemented using MATLAB software.

Noun classifiers in Hong Kong Sign Language

Abstract As most other sign languages, Hong Kong Sign Language (HKSL) makes use of verbal classifiers for the purpose of spatial predication. However, a thorough study of the Asian SignBank shows that these same classifier handshapes occur in 76% of all HKSL nouns. In this paper, I argue that these classifier handshapes embedded in nouns are in fact noun classifiers. Under the framework of Distributed Morphology, classifier handshapes are underspecified roots in root compounds which can be assigned the nominal category when merged with a nominal functional head in syntax. The function of these classifiers is to identify, and categorize a discourse referent, and they are full-fledged classifiers according to standards set by the general linguistic literature on classifiers. In addition to verbal and noun classifiers, I also briefly show how HKSL uses mensural numeral classifiers to intervene between mass nouns and numerals within the NP. Taken together, this means that HKSL employs three different types of classifiers: verbal, noun, and numeral classifiers.

DDCNN-F: double decker convolutional neural network 'F' feature fusion as a medical image classification framework

Melanoma is a severe skin cancer that involves abnormal cell development. This study aims to provide a new feature fusion framework for melanoma classification that includes a novel ‘F’ Flag feature for early detection. This novel ‘F’ indicator efficiently distinguishes benign skin lesions from malignant ones known as melanoma. The article proposes an architecture that is built in a Double Decker Convolutional Neural Network called DDCNN future fusion. The network's deck one, known as a Convolutional Neural Network (CNN), finds difficult-to-classify hairy images using a confidence factor termed the intra-class variance score. These hirsute image samples are combined to form a Baseline Separated Channel (BSC). By eliminating hair and using data augmentation techniques, the BSC is ready for analysis. The network's second deck trains the pre-processed BSC and generates bottleneck features. The bottleneck features are merged with features generated from the ABCDE clinical bio indicators to promote classification accuracy. Different types of classifiers are fed to the resulting hybrid fused features with the novel 'F' Flag feature. The proposed system was trained using the ISIC 2019 and ISIC 2020 datasets to assess its performance. The empirical findings expose that the DDCNN feature fusion strategy for exposing malignant melanoma achieved a specificity of 98.4%, accuracy of 93.75%, precision of 98.56%, and Area Under Curve (AUC) value of 0.98. This study proposes a novel approach that can accurately identify and diagnose fatal skin cancer and outperform other state-of-the-art techniques, which is attributed to the DDCNN ‘F’ Feature fusion framework. Also, this research ascertained improvements in several classifiers when utilising the ‘F’ indicator, resulting in the highest specificity of + 7.34%.

A Survey on Automated Emotion Recognition Using Different Classification Models and Approaches

Abstract: Automated emotion detection, is a diverse field with a multitude of applications ranging from software engineering to web customization, education, etc. Several methods and approaches have been devised for automatic emotion recognition, which has taken inspiration from human/natural emotion recognition. I have studied and discussed categorical and dimensional models, which can be subdivided into Circumplex, PANA, vector, and Plutchik's models, for defining a myriad of emotions under varied circumstances. I have stratified the approaches used in emotion detection by trifurcating them into lexicon-based, statistical, and hybrid methods. And, I have presented information on different types of classifiers and classes of neural networks that fall under the category of statistical methods, in a systematized way. I have observed that Support Vector Machines provide the most accurate and clear-cut outcome.

Epilepsy classification using artificial intelligence: A web-based application.

The purpose of the current endeavor was to evaluate the feasibility of using easily accessible and applicable clinical information (based on history taking and physical examination) in order to make a reliable differentiation between idiopathic generalized epilepsy (IGE) versus focal epilepsy using machine learning (ML) methods. The first phase of the study was a retrospective study of a prospectively developed and maintained database. All patients with an electro-clinical diagnosis of IGE or focal epilepsy, at the outpatient epilepsy clinic at Shiraz University of Medical Sciences, Shiraz, Iran, from 2008 until 2022, were included. The first author selected a set of clinical features. Using the stratified random portioning method, the dataset was divided into the train (70%) and test (30%) subsets. Different types of classifiers were assessed and the final classification was made based on their best results using the stacking method. A total number of 1445 patients were studied; 964 with focal epilepsy and 481 with IGE. The stacking classifier led to better results than the base classifiers in general. This algorithm has the following characteristics: precision: 0.81, sensitivity: 0.81, and specificity: 0.77. We developed a pragmatic algorithm aimed at facilitating epilepsy classification for individuals whose epilepsy begins at age 10 years and older. Also, in order to enable and facilitate future external validation studies by other peers and professionals, the developed and trained ML model was implemented and published via an online web-based application that is freely available at http://www.epiclass.ir/f-ige.

Transition-Relevance Places Machine Learning-Based Detection in Dialogue Interactions

A transition-relevance place (TRP) represents a place in a conversation where a change of speaker can occur. The appearance and use of these points in the dialogue ensures a correct and smooth alternation between the speakers. In the presented article, we focused on the study of prosodic speech parameters in the Slovak language, and we tried to experimentally verify the potential of these parameters to detect TRP. To study turn-taking issues in dyadic conversations, the Slovak dialogue corpus was collected and annotated. TRP places were identified by the human annotator in the manual labelling process. The data were then divided into chunks that reflect the length of the interpausal dialogue units and the prosodic features were computed. In the Matlab environment, we compared different types of classifiers based on machine learning in the role of an automatic TRP detector based on pitch and intensity parameters. The achieved results indicate that prosodic parameters can be useful in detecting TRP after splitting the dialogue into interpausal units. The designed approach can serve as a tool for automatic conversational analysis or can be used to label large databases for training predictive models, which can help machines to enhance human-machine spoken dialogue applications.

Preoperative prediction of Ki-67 and p53 status in meningioma using a multiparametric MRI-based clinical-radiomic model.

To investigate the utility of preoperative multiparametric magnetic resonance imaging (mpMRI)-based clinical-radiomic analysis combined with machine learning (ML) algorithms in predicting the expression of the Ki-67 proliferative index and p53 tumor suppressor protein in patients with meningioma. This multicenter retrospective study included 483 and 93 patients from two centers. The Ki-67 index was classified into high (Ki-67≥5%) and low (Ki-67<5%)-expressed groups, and the p53 index was classified into positive (p53≥5%) and negative (p53<5%)-expressed groups. Clinical and radiological features were analyzed using univariate and multivariate statistical analyses. Six ML models were performed with different types of classifiers to predict Ki-67 and p53 status. In the multivariate analysis, larger tumor volumes (p<0.001), irregular tumor margin (p<0.001), and unclear tumor-brain interface (p<0.001) were independently associated with a high Ki-67 status, whereas the presence of both necrosis (p=0.003) and the dural tail sign (p=0.026) were independently associated with a positive p53 status. A relatively better performance was yielded from the model constructed by combined clinical and radiological features. The area under the curve (AUC) and accuracy of high Ki-67 were 0.820 and 0.867 in the internal test, and 0.666 and 0.773 in the external test, respectively. Regarding p53 positivity, the AUC and accuracy were 0.858 and 0.857 in the internal test, and 0.684 and 0.718 in the external test. The present study developed clinical-radiomic ML models to non-invasively predict Ki-67 and p53 expression in meningioma using mpMRI features, and provides a novel non-invasive strategy for assessing cell proliferation.

Spatial-temporal data-augmentation-based functional brain network analysis for brain disorders identification.

Due to the lack of devices and the difficulty of gathering patients, the small sample size is one of the most challenging problems in functional brain network (FBN) analysis. Previous studies have attempted to solve this problem of sample limitation through data augmentation methods, such as sample transformation and noise addition. However, these methods ignore the unique spatial-temporal information of functional magnetic resonance imaging (fMRI) data, which is essential for FBN analysis. To address this issue, we propose a spatial-temporal data-augmentation-based classification (STDAC) scheme that can fuse the spatial-temporal information, increase the samples, while improving the classification performance. Firstly, we propose a spatial augmentation module utilizing the spatial prior knowledge, which was ignored by previous augmentation methods. Secondly, we design a temporal augmentation module by random discontinuous sampling period, which can generate more samples than former approaches. Finally, a tensor fusion method is used to combine the features from the above two modules, which can make efficient use of spatial-temporal information of fMRI simultaneously. Besides, we apply our scheme to different types of classifiers to verify the generalization performance. To evaluate the effectiveness of our proposed scheme, we conduct extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and REST-meta-MDD Project (MDD) dataset. Experimental results show that the proposed scheme achieves superior classification accuracy (ADNI: 82.942%, MDD: 63.406%) and feature interpretation on the benchmark datasets. The proposed STDAC scheme, utilizing both spatial and temporal information, can generate more diverse samples than former augmentation methods for brain disorder classification and analysis.

The Role Classifiers Play in Selecting the Referent of a Word

An important cue to the meaning of a new noun is its accompanying classifier. For example, in English, X in “a sheet of X” should refer to a broad, flat object. A classifier is required in Chinese to quantify nouns. Using children’s overt responses in an object/picture selection task, past research found reliable semantic knowledge of classifiers in Mandarin-reared children at around age three. However, it is unclear how children’s semantic knowledge differs across different types of classifiers and how this difference develops with age. Here we use an arguably more sensitive measure of children’s language knowledge (the intermodal preferential-looking paradigm) to examine Mandarin-reared three-, four-, and five-year-olds’ semantic knowledge of four types of classifiers indicating animacy (human vs. animal distinction), configuration (how objects are arrayed), object shape, and vehicle function. Multiple factors were matched across classifier types: the number of classifiers, perceived familiarity and perceived typicality of the target, and the visual similarity of the two images paired together. Children’s performances differed across classifier types, as they were better with animacy classifiers than with configuration and vehicle function classifiers. Their comprehension was reliable for animacy, object shape, and vehicle function classifiers but not for configuration classifiers. Furthermore, we did not find conclusive evidence for an age-dependent improvement in the child’s performance. The analysis, including the oldest (five-year-olds) and youngest (three-year-olds) children, revealed a marginally significant age effect.

Class-overlap undersampling based on Schur decomposition for Class-imbalance problems

The class-imbalance problem is an important area that plagues machine learning and data mining researchers. It is ubiquitous in all areas of the real world. At present, many methods have been developed to deal with the class-imbalance problem. For the class-imbalance problem, many researchers believe that the class distribution imbalance is not the main factor affecting the performance of the classification model. When the class distribution imbalance coexists with problems such as class overlap, small disjuncts, and noise, the model performance will be severely affected. For the problem of class-distribution imbalance and class-overlap, the existing methods mainly use nearest neighbors to obtain the local similarity of instances in the local domain, and find the overlapping domains in the data set. To the best of our knowledge, no researchers have considered global similarity. In this regard, to find the global similarity of datasets, a novel Schur decomposition class-overlap undersampling method (SDCU) is proposed. SDCU attempts to obtain potentially overlapping instances on global similarity, and is the first to use matrix decomposition to deal with the problem of class-overlap on class-imbalanced data. We conduct comparative experiments on 46 publicly available real datasets. The experimental results show that when using AUC as the performance evaluation metric, the performance of SDCU has obvious advantages compared with other state-of-the-art methods on three different types of classifiers: SVM, CART, and 3NN. In addition, the test results of Friedman ranking and Holm’s post-hoc test also confirmed the conclusions obtained by the experiments.

Fault Detection in Steel Surfaces Using Deep Learning Approaches

There is sustainable growth in the industries in various developing nations. Quality maintenance of the product under development is an essential part of the product development process. Product quality affects the performance of the product. Various kinds of issues in the manufacturing can impact negatively on the product. Therefore, it is needed to make sure that the manufactured products are fault-free by establishing and employing such softwares that will ultimately bring some ease in the fault detection process. This paper aims to diagnose faults on steel surfaces by using convolutional neural networks and classification by making use of 5 different types of classifiers. They are Support Vector machines, Naive Bayes Classifier, Decision Tree, K-nearest Neighbors, and Random Forest. We have used 4 different types of models namely, Alexnet, InceptionV3, Resnet and VGG16. The testing accuracy was found to be maximum for the VGG16 model which was recorded to be 75.02%. Among the classifiers, the best accuracy was found out with Random Forest and Decision Tree classifiers to be 74.9% and 74.3% respectively. The defects are classified among the 4 categories of defects and are highlighted using image segmentation.

Detection of Dyslexic Children Using Machine Learning and Multimodal Hindi Language Eye-Gaze-Assisted Learning System

Children with dyslexia need specific instructions for spelling and word analysis from an early age. It is important to provide appropriate tools using technology for writing aids to such children that can help them to input text, while providing multiple feedback. However, it is unclear how children with dyslexia can efficiently use a gaze-based virtual keyboard (VK). In this study, we propose to use the typing performance of a multimodal Hindi language eye-gaze-assisted learning system based on a VK to help in the reduction of tracking errors for people with writing and reading deficiencies and to detect children with dyslexia. Performance was assessed at three levels: eye tracker, eye tracker with soft switch, and touchscreen as a baseline modality using a predefined copy-typing task. The system was validated through a series of experiments with 32 children (16 dyslexic and 16 control). The results show that the workload and the usability of the system are substantially different for children with dyslexia. Children with dyslexia have a lower typing performance when using the touchscreen modality or the eye tracker only. The detection of children with dyslexia from others was assessed with seven different types of classifiers using the typing speed on different words (AUC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 0.9). These results highlight the need to have fully inclusive VKs. This work demonstrates the superior use of a multimodal system with participants having unique neuropsychological conditions and that the proposed system can be used to detect children with dyslexia.

Detection of Meditation-Induced HRV Dynamics Using Averaging Technique-Based Oversampled Feature Set and Machine Learning Classifiers

In this paper, we propose a textural information-based analysis of scalogram image obtained from continuous wavelet transform of heart rate variability (HRV) signal to study its dynamics during meditation. In addition to features from scalogram image, visibility graph-based complexity measures and multiscale permutation entropies (MPEs) from HRV signal are used to elucidate the modulation in autonomic activity of heart during meditative and non-meditative state. Significant changes in the probability distribution of pixel intensities of scalogram image and undiminished permutation entropy at higher scales are observed during the meditative state. From the extracted features, we have selected the top-ranked features based on ReliefF algorithm and minimum threshold weight of importance set at 0.04. Considering the small sample size (12 subjects) of meditation dataset, we have employed a novel data augmentation technique based on averaging of feature sets to overcome the issue of overfitting. To examine the efficacy of the proposed technique, both the non-augmented and augmented data are applied to four different types of classifiers, namely k-nearest neighbor, support vector machine (SVM), logistic regression and random forest classifiers. Experimental results demonstrate that performance of classifiers in distinguishing meditative and pre-meditative state are much superior with the augmented data as compared to that with regular non-augmented data. Out of these classifiers, radial basis function (RBF)-based SVM results in the best performance with an average accuracy of 96.67%, sensitivity of 95.83% and specificity of 97.5%.

A Reliable Application of MPC for Securing the Tri-Training Algorithm

Due to the widespread use of distributed data mining techniques in a variety of areas, the issue of protecting the privacy of sensitive data has received increasing attention in recent years. Privacy-preserving distributed data mining (PPDDM) focuses on decentralized data analysis without the disclosure of sensitive information from data owner. However, the previous PPDDM mostly works on a limited amount of labeled data. In contrast to the real world, unlabeled data is abundance and labeled data is scarce. The objectives of this paper are to study and to analyze privacy-preserving properties of semi-supervised learning (SSL) algorithm with the combination of labeled and unlabeled data, where data is distributed among multiple data owners. In this paper we propose a Privacy-preserving Distributed Data Mining (PPDDM) method by designing a reliable application of secure MPC to semi-supervised tri-training algorithms. We simulate the original tri-training algorithm and tri-training algorithm with secure MPC using a different types of classifiers and datasets. The simulation results show that tri-training in secure MPC has almost same accuracy compared to original tri-training algorithm. We also compare execution time in addition to performance evaluation of tri-training in secure and the original tri-training algorithms.

Satisfaction prediction of online education in COVID-19 situation using data mining techniques: Bangladesh perspective

&lt;span&gt;This research focuses on the education-based online learning platform. Due to the &lt;a name="_Hlk109304176"&gt;&lt;/a&gt;coronavirus disease (COVID-19) epidemic, online education is gaining global popularity. It has shown how successful it is in investigating the quality of online education at the COVID-19 pandemic situation by 799 students from different academic institutions, schools, colleges, and universities. A Google web form has been utilized as the data gathering mechanism for this survey. This paper perused the prediction of online education through data mining and machine learning approaches in an online program. The data was collected through online questionnaires. To predict online education's satisfaction rate, four different types of classifiers are used e.g., logistic regression classifiers, k-nearest neighbors, support vector machine, naive Bayes classifiers. The key purpose of this research is to find out an answer to a question which is, "are the student's satisfied with starting the new online teaching system, or will it be an ambivalent effect for students in the future?".&lt;/span&gt;

Deep Metric Learning-Based Strawberry Disease Detection With Unknowns.

There has been substantial research that has achieved significant advancements in plant disease detection based on deep object detection models. However, with unknown diseases, it is difficult to find a practical solution for plant disease detection. This study proposes a simple but effective strawberry disease detection scheme with unknown diseases that can provide applicable performance in the real field. In the proposed scheme, the known strawberry diseases are detected with deep metric learning (DML)-based classifiers along with the unknown diseases that have certain symptoms. The pipeline of our proposed scheme consists of two stages: the first is object detection with known disease classes, while the second is a DML-based post-filtering stage. The second stage has two different types of classifiers: one is softmax classifiers that are only for known diseases and the K-nearest neighbor (K-NN) classifier for both known and unknown diseases. In the training of the first stage and the DML-based softmax classifier, we only use the known samples of the strawberry disease. Then, we include the known (a priori) and the known unknown training samples to construct the K-NN classifier. The final decisions regarding known diseases are made from the combined results of the two classifiers, while unknowns are detected from the K-NN classifier. The experimental results show that the DML-based post-filter is effective at improving the performance of known disease detection in terms of mAP. Furthermore, the separate DML-based K-NN classifier provides high recall and precision for known and unknown diseases and achieve 97.8% accuracy, meaning it could be exploited as a Region of Interest (ROI) classifier. For the real field data, the proposed scheme achieves a high mAP of 93.7% to detect known classes of strawberry disease, and it also achieves reasonable results for unknowns. This implies that the proposed scheme can be applied to identify disease-like symptoms caused by real known and unknown diseases or disorders for any kind of plant.

Discrimination of Focal and Non-Focal Epileptic Eeg Signals Using Different Types of Classifiers

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.

A Review on EMG Signal Classification and Applications

Electromyography (EMG) signals are muscles signals that enable the identification of human movements without the need of complex human kinematics calculations. Researchers prefer EMG signals as input signals to control prosthetic arms and exoskeleton robots. However, the proper algorithm to classify human movements from raw EMG signals has been an interesting and challenging topic to researchers. Various studies have been carried out to produce EMG-based human movement classification that gives high accuracy and high reliability. In this paper, the methods used in EMG signal acquisition and processing are reviewed. The different types of feature extraction techniques preferred by researchers are also discussed, including some combination and comparison of feature extraction techniques. This paper also reviews the different types of classifiers favored by researchers to recognize human movements based on EMG signals. The current applications of EMG signals are also reviewed.