Combination Of Classifiers Research Articles

Implications of resampling data to address the class imbalance problem (IRCIP): an evaluation of impact on performance between classification algorithms in medical data.

When correcting for the "class imbalance" problem in medical data, the effects of resampling applied on classifier algorithms remain unclear. We examined the effect on performance over several combinations of classifiers and resampling ratios. Multiple classification algorithms were trained on 7 resampled datasets: no correction, random undersampling, 4 ratios of Synthetic Minority Oversampling Technique (SMOTE), and random oversampling with the Adaptive Synthetic algorithm (ADASYN). Performance was evaluated in Area Under the Curve (AUC), precision, recall, Brier score, and calibration metrics. A case study on prediction modeling for 30-day unplanned readmissions in previously admitted Urology patients was presented. For most algorithms, using resampled data showed a significant increase in AUC and precision, ranging from 0.74 (CI: 0.69-0.79) to 0.93 (CI: 0.92-0.94), and 0.35 (CI: 0.12-0.58) to 0.86 (CI: 0.81-0.92) respectively. All classification algorithms showed significant increases in recall, and significant decreases in Brier score with distorted calibration overestimating positives. Imbalance correction resulted in an overall improved performance, yet poorly calibrated models. There can still be clinical utility due to a strong discriminating performance, specifically when predicting only low and high risk cases is clinically more relevant. Resampling data resulted in increased performances in classification algorithms, yet produced an overestimation of positive predictions. Based on the findings from our case study, a thoughtful predefinition of the clinical prediction task may guide the use of resampling techniques in future studies aiming to improve clinical decision support tools.

Ship detection and classification based on cascaded detection of hull and wake from optical satellite remote sensing imagery

ABSTRACT Satellite remote-sensing provides a cost- and time-effective tool for ship monitoring at sea. Most existing approaches focused on extraction of ship locations using either hull or wake. In this paper, a method of cascaded detection of ship hull and wake was proposed to locate and classify ships using high-resolution satellite imagery. Candidate hulls were fast located through phase spectrum of Fourier transform. A hull refining module was then executed to acquire accurate shapes of candidate hull. False alarms were removed through the shape features and textures of candidate hulls. The probability that a candidate hull is determined as a real one increased with the presence of wakes. After true ships were determined, ship classification was conducted using a fuzzy classifier combining both hull and wake information. The proposed method was implemented to Gaofen-1 panchromatic and multispectral (PMS) imagery and showed good performance for ship detection with recall, precision, overall accuracy, and specificity of 90.1%, 88.1%, 98.8%, and 99.3%, respectively, better than other state-of-the-art coarse-to-fine ship detection methods. Ship classification was successfully achieved for ships with detected wakes. The accuracy of correct classification was 83.8% while the proportion of false classification was 1.0%. Factors influencing the accuracy of the developed method, including texture features and classifiers combination and key parameters of the method, were also discussed.

Comparison of Machine Learning Strategies in Hazardous Asteroids Prediction

The purpose of this study is to use multiple classification algorithm from machine learning to predict hazardous asteroids that orbit Earth. Seven models are Logistic Regression, K-Nearest Neighbor Classifier, Random Forest Classifier, Decision Tree Classifier, Multinomial Naïve Bayes Classifier, Gradient Boosting Classifier, and Voting Classifier. Confusion matrix is used to evaluate those models. Evaluation metrics include accuracy, precision, recall, and f1-score. The result shows that random forest classifier has the greatest overall performance with highest accuracy. Decision Tree classifier, Gradient Boosting classifier, and Voting classifier also perform well. Gradient Boosting classifier is capable of greatly reducing the risk of hazardous asteroid, which is, reduce the number of hazardous asteroids that is predicted as non-hazardous. Because of assumptions of some models like Logistic Regression, data used in the experiment do not follow them, so the overall performance of those models are not well. It would be better to select data for fitting the model. The result shows that combined classifiers perform better. Voting Classifier can be used to assemble those accurate models and get a more accurate result by offsetting disadvantages of each model.

Automatic diagnosis of COVID-19 related respiratory diseases from speech.

In this work, an attempt is made to propose an intelligent and automatic system to recognize COVID-19 related illnesses from mere speech samples by using automatic speech processing techniques. We used a standard crowd-sourced dataset which was collected by the University of Cambridge through a web based application and an android/iPhone app. We worked on cough and breath datasets individually, and also with a combination of both the datasets. We trained the datasets on two sets of features, one consisting of only standard audio features such as spectral and prosodic features and one combining excitation source features with standard audio features extracted, and trained our model on shallow classifiers such as ensemble classifiers and SVM classification methods. Our model has shown better performance on both breath and cough datasets, but the best results in each of the cases was obtained through different combinations of features and classifiers. We got our best result when we used only standard audio features, and combined both cough and breath data. In this case, we achieved an accuracy of 84% and an Area Under Curve (AUC) score of 84%. Intelligent systems have already started to make a mark in medical diagnosis, and this type of study can help better the health system by providing much needed assistance to the health workers.

Trait Energy and Fatigue Influence Inter-Individual Mood and Neurocognitive Responses during Work Done While Sitting, Standing, and Intermittent Walking: A Randomized-Controlled Crossover Design

College students can be sedentary for a majority of the day, which may exacerbate mental health issues or lead to declines in cognitive task performance; however, interventions to address sedentary behaviors may not positively influence everyone. Therefore, the present study sought to identify inter-individual cognitive performance and mood changes of college students during the performance of a cognitive task battery, while seated, standing and with intermittent bouts of walking. Participants (n = 31, age = 25.80 ± 3.61 yrs, 7 male) completed a series of baseline questionnaires including the Trait Mental and Physical Energy and Fatigue survey. Using a randomized controlled cross-over design, participants completed 3 separate testing sessions. At each session, they performed a series of three rounds of cognitive tasks for 27 min and self-reporting mood states for 1 min in the seated position. Each round of cognitive testing was followed by a 2 min break. Each testing day had participants spend the 2 min break in a different condition: sitting, standing, or walking. A series of mixed ANOVAs were used for the primary analysis and a combination of machine learning regressors and classifiers were used for the secondary analysis. Our results suggest that there are unique inter-individual responses to each of the interventions used during the 2 min break. Participants who were low-trait mental and low-trait physical energy benefited the most from the standing desk intervention, while also reporting significant benefits of intermittent walking. However, participants who were low-trait mental fatigue had significant negative consequences of using both standing desks and walking intermittently, while those who were high-trait mental fatigue saw no change in cognitive responses or moods in those conditions. Post hoc machine learning analyses had modest accuracy rates (MAEs < 0.7 for regressors and accuracy rates >60% for classifiers), suggesting that trait mental and physical energy and fatigue may predict inter-individual responses to these interventions. Incorporating standing desks into college classroom settings may result in some students receiving cognitive benefits when inter-individual variability in mood and cognitive responses are accounted for.

Critical Evaluation of a microRNA-Based Risk Classifier Predicting Cancer-Specific Survival in Renal Cell Carcinoma with Tumor Thrombus of the Inferior Vena Cava

(1) Background: Clear cell renal cell carcinoma extending into the inferior vena cava (ccRCCIVC) represents a clinical high-risk setting. However, there is substantial heterogeneity within this patient subgroup regarding survival outcomes. Previously, members of our group developed a microRNA(miR)-based risk classifier-containing miR-21-5p, miR-126-3p and miR-221-3p expression-which significantly predicted the cancer-specific survival (CSS) of ccRCCIVC patients. (2) Methods: Examining a single-center cohort of tumor tissue from n = 56 patients with ccRCCIVC, we measured the expression levels of miR-21, miR-126, and miR-221 using qRT-PCR. The prognostic impact of clinicopathological parameters and miR expression were investigated via single-variable and multivariable Cox regression. Referring to the previously established risk classifier, we performed Kaplan-Meier analyses for single miR expression levels and the combined risk classifier. Cut-off values and weights within the risk classifier were taken from the previous study. (3) Results: miR-21 and miR-126 expression were significantly associated with lymphonodal status at the time of surgery, the development of metastasis during follow-up, and cancer-related death. In Kaplan-Meier analyses, miR-21 and miR-126 significantly impacted CSS in our cohort. Moreover, applying the miR-based risk classifier significantly stratified ccRCCIVC according to CSS. (4) Conclusions: In our retrospective analysis, we successfully validated the miR-based risk classifier within an independent ccRCCIVC cohort.

How Natural Language Processing Can Aid With Pulmonary Oncology Tumor Node Metastasis Staging From Free-Text Radiology Reports: Algorithm Development and Validation

Natural language processing (NLP) is thought to be a promising solution to extract and store concepts from free text in a structured manner for data mining purposes. This is also true for radiology reports, which still consist mostly of free text. Accurate and complete reports are very important for clinical decision support, for instance, in oncological staging. As such, NLP can be a tool to structure the content of the radiology report, thereby increasing the report's value. This study describes the implementation and validation of an N-stage classifier for pulmonary oncology. It is based on free-text radiological chest computed tomography reports according to the tumor, node, and metastasis (TNM) classification, which has been added to the already existing T-stage classifier to create a combined TN-stage classifier. SpaCy, PyContextNLP, and regular expressions were used for proper information extraction, after additional rules were set to accurately extract N-stage. The overall TN-stage classifier accuracy scores were 0.84 and 0.85, respectively, for the training (N=95) and validation (N=97) sets. This is comparable to the outcomes of the T-stage classifier (0.87-0.92). This study shows that NLP has potential in classifying pulmonary oncology from free-text radiological reports according to the TNM classification system as both the T- and N-stages can be extracted with high accuracy.

Machine learning based combination of multi-omics data for subgroup identification in non-small cell lung cancer

Non-small Cell Lung Cancer (NSCLC) is a heterogeneous disease with a poor prognosis. Identifying novel subtypes in cancer can help classify patients with similar molecular and clinical phenotypes. This work proposes an end-to-end pipeline for subgroup identification in NSCLC. Here, we used a machine learning (ML) based approach to compress the multi-omics NSCLC data to a lower dimensional space. This data is subjected to consensus K-means clustering to identify the five novel clusters (C1–C5). Survival analysis of the resulting clusters revealed a significant difference in the overall survival of clusters (p-value: 0.019). Each cluster was then molecularly characterized to identify specific molecular characteristics. We found that cluster C3 showed minimal genetic aberration with a high prognosis. Next, classification models were developed using data from each omic level to predict the subgroup of unseen patients. Decision‑level fused classification models were then built using these classifiers, which were used to classify unseen patients into five novel clusters. We also showed that the multi-omics-based classification model outperformed single-omic-based models, and the combination of classifiers proved to be a more accurate prediction model than the individual classifiers. In summary, we have used ML models to develop a classification method and identified five novel NSCLC clusters with different genetic and clinical characteristics.

Agricultural Robot-Centered Recognition of Early-Developmental Pest Stage Based on Deep Learning: A Case Study on Fall Armyworm (Spodoptera frugiperda)

Accurately detecting early developmental stages of insect pests (larvae) from off-the-shelf stereo camera sensor data using deep learning holds several benefits for farmers, from simple robot configuration to early neutralization of this less agile but more disastrous stage. Machine vision technology has advanced from bulk spraying to precise dosage to directly rubbing on the infected crops. However, these solutions primarily focus on adult pests and post-infestation stages. This study suggested using a front-pointing red-green-blue (RGB) stereo camera mounted on a robot to identify pest larvae using deep learning. The camera feeds data into our deep-learning algorithms experimented on eight ImageNet pre-trained models. The combination of the insect classifier and the detector replicates the peripheral and foveal line-of-sight vision on our custom pest larvae dataset, respectively. This enables a trade-off between the robot's smooth operation and localization precision in the pest captured, as it first appeared in the farsighted section. Consequently, the nearsighted part utilizes our faster region-based convolutional neural network-based pest detector to localize precisely. Simulating the employed robot dynamics using CoppeliaSim and MATLAB/SIMULINK with the deep-learning toolbox demonstrated the excellent feasibility of the proposed system. Our deep-learning classifier and detector exhibited 99% and 0.84 accuracy and a mean average precision, respectively.

Smart Security in Smart City Using Naïve Bayes and RSA

As all major cities adopt the concept of smart cities, concerns arise among the public regarding data security and privacy. The constant threat of attacks on confidentiality, integrity and accessibility of data makes it vulnerable to cyber attacks. The increasing use of IoT devices also increases the potential for cyber attacks that can harm all IoT users. Therefore, it is crucial for city governments to be aware of data security issues related to smart spaces, services, and citizen security, and to provide solutions to existing problems by making maximum policies related to the implementation of smart city concepts. From the above explanation, the author is taking the analysis step with the title "Analysis of Naive Bayes Classifier and Rsa (Rivest Shamir Adleman) Combination in Smart Security in the Implementation of Smart City in Pemko Medan" where the benefits that can be obtained are to gain deeper understanding of Smart Security level, obtain information about the Smart Securty level, and classify the stage of Smart Securty using the combination of Naive Bayes Classifier and Rsa (Rivest Shamir Adleman) in the implementation of Smart City in Pemko Medan.

An efficient and robust Phonocardiography (PCG)-based Valvular Heart Diseases (VHD) detection framework using Vision Transformer (ViT)

Background and objectives:Valvular heart diseases (VHDs) are one of the dominant causes of cardiovascular abnormalities that have been associated with high mortality rates globally. Rapid and accurate diagnosis of the early stage of VHD based on cardiac phonocardiogram (PCG) signal is critical that allows for optimum medication and reduction of mortality rate. Methods:To this end, the current study proposes novel deep learning (DL)-based high-performance VHD detection frameworks that are relatively simpler in terms of network structures, yet effective for accurately detecting multiple VHDs. We present three different frameworks considering both 1D and 2D PCG raw signals. For 1D PCG, Mel frequency cepstral coefficients (MFCC) and linear prediction cepstral coefficients (LPCC) features, whereas, for 2D PCG, various deep convolutional neural networks (D-CNNs) features are extracted. Additionally, nature/bio-inspired algorithms (NIA/BIA) including particle swarm optimization (PSO) and genetic algorithm (GA) have been utilized for automatic and efficient feature selection directly from the raw PCG signal. To further improve the performance of the classifier, vision transformer (ViT) has been implemented levering the self-attention mechanism on the time frequency representation (TFR) of 2D PCG signal. Our extensive study presents a comparative performance analysis and the scope of enhancement for the combination of different descriptors, classifiers, and feature selection algorithms. Main Results:Among all classifiers, ViT provides the best performance by achieving mean average accuracy Acc of 99.90 % and F1-score of 99.95 % outperforming current state-of-the-art VHD classification models. Conclusions:The present research provides a robust and efficient DL-based end-to-end PCG signal classification framework for designing a automated high-performance VHD diagnosis system.

Verification of unemployment benefits’ claims using Classifier Combination method

Verification of unemployment benefits’ claims using Classifier Combination method

Source free unsupervised domain adaptation for electro-mechanical actuator fault diagnosis

A common necessity for prior unsupervised domain adaptation methods that can improve the domain adaptation in unlabeled target domain dataset is access to source domain dataset and target domain dataset simultaneously. However, data privacy makes it not always possible to access source domain dataset and target domain dataset in actual industrial equipment simultaneously, especially for aviation component like Electro-Mechanical Actuator (EMA) whose dataset are often not shareable due to the data copyright and confidentiality. To address this problem, this paper proposes a source free unsupervised domain adaptation framework for EMA fault diagnosis. The proposed framework is a combination of feature network and classifier. Firstly, source domain datasets are only applied to train a source model. Secondly, the well-trained source model is transferred to target domain and classifier is frozen based on source domain hypothesis. Thirdly, nearest centroid filtering is introduced to filter the reliable pseudo labels for unlabeled target domain dataset, and finally, supervised learning and pseudo label clustering are applied to fine-tune the transferred model. In comparison with several traditional unsupervised domain adaptation methods, case studies based on low- and high-frequency monitoring signals on EMA indicate the effectiveness of the proposed method.

Population level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children.

Leveraging a large population-level morphologic, microstructural, and functional neuroimaging dataset, we aimed to elucidate the underlying neurobiology of attention-deficit hyperactivity disorder (ADHD) in children. In addition, we evaluated the applicability of machine learning classifiers to predict ADHD diagnosis based on imaging and clinical information. From the Adolescents Behavior Cognitive Development (ABCD) database, we included 1,798 children with ADHD diagnosis and 6,007 without ADHD. In multivariate logistic regression adjusted for age and sex, we examined the association of ADHD with different neuroimaging metrics. The neuroimaging metrics included fractional anisotropy (FA), neurite density (ND), mean-(MD), radial-(RD), and axial diffusivity (AD) of white matter (WM) tracts, cortical region thickness and surface areas from T1-MPRAGE series, and functional network connectivity correlations from resting-state fMRI. Children with ADHD showed markers of pervasive reduced microstructural integrity in white matter (WM) with diminished neural density and fiber-tracks volumes - most notable in the frontal and parietal lobes. In addition, ADHD diagnosis was associated with reduced cortical volume and surface area, especially in the temporal and frontal regions. In functional MRI studies, ADHD children had reduced connectivity among default-mode network and the central and dorsal attention networks, which are implicated in concentration and attention function. The best performing combination of feature selection and machine learning classifier could achieve a receiver operating characteristics area under curve of 0.613 (95% confidence interval = 0.580-0.645) to predict ADHD diagnosis in independent validation, using a combination of multimodal imaging metrics and clinical variables. Our study highlights the neurobiological implication of frontal lobe cortex and associate WM tracts in pathogenesis of childhood ADHD. We also demonstrated possible potentials and limitations of machine learning models to assist with ADHD diagnosis in a general population cohort based on multimodal neuroimaging metrics.

Radio-Controlled Intelligent UGV as a Spy Robot with Laser Targeting for Military Purposes

The main objective of this unmanned ground vehicle is to deal with the security issues like terrorist activities across the border and in various remote combat missions by reducing the involvement of soldiers. This unmanned ground robot comprises a wireless high-definition camera that can transfer live streams from the robot to headquarters using Wi-Fi. The robot’s movement can be controlled with two modes; one of them is a radio controller working on 2.4 GHz frequency with seven independent channels. Secondly, its movement can also be controlled using a Python-based GUI application. Nowadays, different techniques have been used for face recognition; in our remotely piloted robot, we have used Haar-cascade classifiers in combination with the LBPH algorithm to implement real-time facial recognition. The robot uses a rack and pinion driving mechanism and an ATMEL Cortex-M3 CPU as a controller with 32-bit/s processing speed. In addition, a laser is installed on the turret to shoot the targets down, which can be used in an autonomous mode based on facial recognition results, or it can be used manually either through an RF controller or Python-based GUI. The turret moves in 2-DOF with the help of metallic geared servo motors. Both servo motors can rotate up to 180°. The driving mechanism of the robotic tank is just like DDR, with one difference, the two DC gear motors of the robot are connected diagonally.

Multimodal integration of image, epigenetic and clinical data to predict BRAF mutation status in melanoma

BackgroundIn machine learning, multimodal classifiers can provide more generalised performance than unimodal classifiers. In clinical practice, physicians usually also rely on a range of information from different examinations for diagnosis. In this study, we used BRAF mutation status prediction in melanoma as a model system to analyse the contribution of different data types in a combined classifier because BRAF status can be determined accurately by sequencing as the current gold standard, thus nearly eliminating label noise. MethodsWe trained a deep learning-based classifier by combining individually trained random forests of image, clinical and methylation data to predict BRAF-V600 mutation status in primary and metastatic melanomas of The Cancer Genome Atlas cohort. ResultsWith our multimodal approach, we achieved an area under the receiver operating characteristic curve of 0.80, whereas the individual classifiers yielded areas under the receiver operating characteristic curve of 0.63 (histopathologic image data), 0.66 (clinical data) and 0.66 (methylation data) on an independent data set. ConclusionsOur combined approach can predict BRAF status to some extent by identifying BRAF-V600 specific patterns at the histologic, clinical and epigenetic levels. The multimodal classifiers have improved generalisability in predicting BRAF mutation status.

Classification of Fruits Based on Shape and Color using Combined Nearest Mean Classifiers

Fruit classification is an important task in many agriculture industry. The fruit classification system can be used to identify the types and prices of fruit. Manual classification of fruit is not efficient for large amount of fruits. The advancement of information technology has made possible fruit classification be done by a machine. This research aims to propose a fruit classification methodology based on shape and color. To reduce the effect of lighting variability a color normalization is carried out prior to feature extraction. The color features used in this research are mean and standard deviation. The shape features are area, perimeter, and compactness. The classification of an unknown fruit is carried out using the nearest mean classifier. The method developed in this research is tested using 12 classes of fruits where each class is represented by a number of samples. The experimental results show that the method proposed in this research provides an accuracy of 95.83% for two samples per class and 100% for three samples per class. Experiment on small training samples has been conducted to evaluate the performance of the proposed combined nearest mean classifiers and results obtained showed that the technique was able to provide good accuracy.

Dialogue system based on spoken conversations with access to an unstructured knowledge base

This paper describes an approach for constructing a task-oriented dialog system (a conversational agent) with an unstructured knowledge access based on spoken conversations including: written speech augmentation that simulates the speech recognition results; combination of classifiers; retrieval augmented text generation. The proposed approach provides the training data augmentation in two ways: by converting the original texts into sound waves by a text-to-speech model and then transforming back into texts by an automated speech recognition model; injecting artificially generated errors based on phonetic similarity. A dialogue system with access to the unstructured knowledge base solves the task of detecting a turn, which requires searching for additional information in an unstructured knowledge base. For this purpose, the Support Vector Machine, Convolutional Neural Network, Bidirectional Encoder Representations from Transformers, and Generative Pre-trained Transformer 2 models were trained. The best of the presented models are used in the weighted combination. Next, a suitable text fragment is selected from the knowledge base and a reasonable answer is generated. The tasks are solved by adapting the retrieval augmented text generation model Retrieval Augmented Generation. The proposed method tested on the data from the 10th Dialogue System Technology Challenge. In all metrics, except Precision, the new approach significantly outperformed the results of the basic models proposed by the organizers of the competition. The results of the work can be used to create chat-bot systems that provide automatic processing of user requests in natural language based on an unstructured knowledge access, such as a database of answers to frequently asked questions.

Role of Optimal Features Selection with Machine Learning Algorithms for Chest X-ray Image Analysis.

The objective of the present study is to classify chest X-ray (CXR) images into COVID-positive and normal categories with the optimal number of features extracted from the images. The successful optimal feature selection algorithm that can represent images and the classification algorithm with good classification ability has been determined as the result of experiments. This study presented a framework for the automatic detection of COVID-19 from the CXR images. To enhance small details, textures, and contrast of the images, contrast limited adaptive histogram equalization was used. Features were extracted from the first-order statistics, Gray-Level Co-occurrence Matrix, Gray-Level Run Length Matrix, local binary pattern, Law's Texture Energy Measures, Discrete Wavelet Transform, and Zernikes' Moments using an image feature extraction tool "pyFeats. For the feature selection, three nature-inspired optimization algorithms, Grey Wolf Optimization, Particle Swarm Optimization (PSO), and Genetic Algorithm, were used. For classification, Random Forest classifier, K-Nearest Neighbour classifier, support vector machine (SVM) classifier, and light gradient boosting model classifier were used. For all the feature selection methods, the SVM classifier gives the most accurate and precise result compared to other classification models. Furthermore, in feature selection methods, PSO gives the best result as compared to other methods for feature selection. Using the combination of the SVM classifier with the PSO method, it was observed that the accuracy, precision, recall, and F1-score were 100%. The result of the study indicates that with optimal features with the best choice of the classifier algorithm, the most accurate computer-aided diagnosis of CXR can be achieved. The approach presented in this study with optimal features may be utilized as a complementary tool to assist the radiologist in the early diagnosis of disease and making a more accurate decision.

An Optimized Quadratic Support Vector Machine for EEG Based Brain Computer Interface

The Brain Computer Interface (BCI) has a great impact on mankind. Many researchers have been trying to employ different classifiers to figure out the human brain's thoughts accurately. In order to overcome the poor performance of a single classifier, some researchers used a combined classifier. Others delete redundant information in some channels before applying the classifier as they thought it might reduce the accuracy of the classifier. BCI helps clinicians to learn more about brain problems and disabilities such as stroke to use in recovery. The main objective of this paper is to propose an optimized High-Performance Support Vector Machines (SVM) based classifier (HPSVM-BCI) using the SelectKBest (SKB). In the proposed HPSVM-BCI, the SKB algorithm is used to select the features of the BCI competition III Dataset IVa subjects. Then, to classify the prepared data from the previous phase, SVM with Quadratic kernel (QSVM) were used in the second phase. As well as enhancing the mean accuracy of the dataset, HPSVM-BCI reduces the computational cost and computational time. A major objective of this research is to improve the classification of the BCI dataset. Furthermore, decreased feature count translates to fewer electrodes, a factor that reduces the risk to the human brain. Comparative studies have been conducted with recent models using the same dataset. The results obtained from the study show that HPSVM-BCI has the highest average accuracy, with 99.24% for each subject with 40 channels only.