Nonstationary Gaussian Process Discriminant Analysis With Variable Selection for High-Dimensional Functional Data

High-dimensional classification and feature selection tasks are ubiquitous with the recent advancement in data acquisition technology. In several application areas such as biology, genomics, and proteomics, the data are often functional in their nature and exhibit a degree of roughness and nonstationarity. These structures pose additional challenges to commonly used methods that rely mainly on a two-stage approach performing variable selection and classification separately. We propose in this work a novel Gaussian process discriminant analysis (GPDA) that combines these steps in a unified framework. Our model is a two-layer nonstationary Gaussian process coupled with an Ising prior to identify differentially-distributed locations. Scalable inference is achieved via developing a variational scheme that exploits advances in the use of sparse inverse covariance matrices. We demonstrate the performance of our methodology on simulated datasets and two proteomics datasets: breast cancer and SARS-CoV-2. Our approach distinguishes itself by offering explainability as well as uncertainty quantification in addition to low computational cost, which are crucial to increase trust and social acceptance of data-driven tools. Supplementary materials for this article are available online.

Explainable Mortality Prediction Model for Congestive Heart Failure with Nature-Based Feature Selection Method

A mortality prediction model can be a great tool to assist physicians in decision making in the intensive care unit (ICU) in order to ensure optimal allocation of ICU resources according to the patient's health conditions. The entire world witnessed a severe ICU patient capacity crisis a few years ago during the COVID-19 pandemic. Various widely utilized machine learning (ML) models in this research field can provide poor performance due to a lack of proper feature selection. Despite the fact that nature-based algorithms in other sectors perform well for feature selection, no comparative study on the performance of nature-based algorithms in feature selection has been conducted in the ICU mortality prediction field. Therefore, in this research, a comparison of the performance of ML models with and without feature selection was performed. In addition, explainable artificial intelligence (AI) was used to examine the contribution of features to the decision-making process. Explainable AI focuses on establishing transparency and traceability for statistical black-box machine learning techniques. Explainable AI is essential in the medical industry to foster public confidence and trust in machine learning model predictions. Three nature-based algorithms, namely the flower pollination algorithm (FPA), particle swarm algorithm (PSO), and genetic algorithm (GA), were used in this study. For the classification job, the most widely used and diversified classifiers from the literature were used, including logistic regression (LR), decision tree (DT) classifier, the gradient boosting (GB) algorithm, and the random forest (RF) algorithm. The Medical Information Mart for Intensive Care III (MIMIC-III) dataset was used to collect data on heart failure patients. On the MIMIC-III dataset, it was discovered that feature selection significantly improved the performance of the described ML models. Without applying any feature selection process on the MIMIC-III heart failure patient dataset, the accuracy of the four mentioned ML models, namely LR, DT, RF, and GB was 69.9%, 82.5%, 90.6%, and 91.0%, respectively, whereas with feature selection in combination with the FPA, the accuracy increased to 71.6%, 84.8%, 92.8%, and 91.1%, respectively, for the same dataset. Again, the FPA showed the highest area under the receiver operating characteristic (AUROC) value of 83.0% with the RF algorithm among all other algorithms utilized in this study. Thus, it can be concluded that the use of feature selection with FPA has a profound impact on the outcome of ML models. Shapley additive explanation (SHAP) was used in this study to interpret the ML models. SHAP was used in this study because it offers mathematical assurances for the precision and consistency of explanations. It is trustworthy and suitable for both local and global explanations. It was found that the features that were selected by SHAP as most important were also most common with the features selected by the FPA. Therefore, we hope that this study will help physicians to predict ICU mortality for heart failure patients with a limited number of features and with high accuracy.

Exploring crucial social media marketing factors for improving customer satisfaction and customer loyalty in bed and breakfast sectors in Taiwan

PurposeThe Bed and Breakfast (B&B) enterprises generally lack sufficient human resources and time to conduct research on important social media marketing factors for visitors' satisfaction and visitors' intentions. Therefore, this study aims to provide crucial social media marketing and factors and service quality elements for improving customer satisfaction and customer loyalty in B&B sectors. This study also provides some recommendations for attracting more visitors and increasing customer satisfaction and customer loyalty through social media.Design/methodology/approachFirst, social media marketing factors and service quality elements were identified through the systematic literature review. Then these identified factors and elements were used to design a survey questionnaire for collecting data. The research data included responses of 64 B&B enterprises and 625 customers. The collected data was analyzed by feature selection approaches including Decision Tree algorithm and Information Gain to identify the key factors for improving customer satisfaction and customer loyalty.FindingsThe findings of this study determined that featured choice is an important social media marketing factor, and assurance is the common service quality element for both B&B enterprises and their customers in terms of satisfaction and loyalty.Originality/valueThis study adds a value to the growing literature on customer satisfaction and loyalty in B&B sectors by exploring key social media marketing factors and service quality elements. The study reveals several implications for theories and practices. The findings hopefully help B&B enterprises better social media marketing with less workforce and budget.

A Hybrid Grey Wolf-Whale Optimization Algorithm for Classification of Corona Virus Genome Sequences using Deep Learning

Genome sequence data is widely accepted as complex data and is still growing in an exponential rate. Classification of genome sequences plays a crucial role as it finds its applications in the area of biology, medical and forensics etc. For classification, Genome sequences can be represented in terms of features. More number of less significant features leads to lower accuracy in classification task. Feature selection addresses this issue by selecting the most important features which aids to improve the accuracy and lessens the computational complexity. In this research, Hybrid Grey Wolf-Whale Optimization Algorithm (HGWWOA) is proposed for Genome sequence classification. The proposed algorithm is evaluated using 23 benchmark objective functions along with Convolutional Neural Network classifier and its efficiency is verified using a novel metric namely "Feature Reduction Rate”. The proposed optimization algorithm can be applied for any optimization problems. In this research work, the proposed algorithm is used for classification of Corona Virus genome sequences. Performance comparison of the proposed and existing algorithms was carried out and it is evident that the performance of proposed algorithm exceeds the previous algorithms with an accuracy of 98.2%. © 2023, Zarka Private University. All rights reserved.

Empirical Review of Various Thermography-based Computer-aided Diagnostic Systems for Multiple Diseases

The lifestyle led by today's generation and its negligence towards health is highly susceptible to various diseases. Developing countries are at a higher risk of mortality due to late-stage presentation, inaccessible diagnosis, and high-cost treatment. Thermography-based technology, aided with machine learning, for screening inflammation in the human body is non-invasive and cost-wise appropriate. It requires very little equipment, especially in rural areas with limited facilities. Recently, Thermography-based monitoring has been deployed worldwide at various organizations and public gathering points as a first measure of screening COVID-19 patients. In this article, we systematically compare the state-of-the-art feature extraction approaches for analyzing thermal patterns in the human body, individually and in combination, on a platform using three publicly available Datasets of medical thermal imaging, four Feature Selection methods, and four well-known Classifiers, and analyze the results. We developed and used a two-level sampling method for training and testing the classification model. Among all the combinations considered, the classification model with Unified Feature-Sets gave the best performance for all the datasets. Also, the experimental results show that the classification accuracy improves considerably with the use of feature selection methods. We obtained the best performance with a features subset of 45, 57, and 39 features (from Unified Feature Set) with a combination of mRMR and SVM for DB-DMR-IR and DB-FOOT-IR and a combination of ReF and RF for DB-THY-IR. Also, we found that for all the feature subsets, the features obtained are relevant, non-redundant, and distinguish normal and abnormal thermal patterns with the accuracy of 94.75% on the DB-DMR-IR dataset, 93.14% on the DB-FOOT-IR dataset, and 92.06% on the DB-THY-IR dataset. [ FROM AUTHOR] Copyright of ACM Transactions on Intelligent Systems & Technology is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Ischemic Heart Disease Prediction Using Machine Learning and Deep Learning Techniques

To have control over heart patient health, we need a capable detector which finds out based onhealth records. The idea is to work on coronary artery disease (CAD), which has been the majorhealth issue at present. We took a data set to train our system (machine learning algorithm) towork on the CAD and identify the user's health stage and provide the required information. Asper previous analysis, we got accuracy of 96% now with a minor modification we are trying to impact the accuracy. CAD has been the major health disease that is leading to death in world at present after COVID19, it is causing 33% of death rate by a survey by WHO. So, it is essentialto overcome the disease with proper analysis and prevention, which is all about our project. We are trying to make healthcare handy such that a person that analyze and know about his/her health condition from anywhere and at any time regardless of working hours. © 2023 IEEE.

Determination of households benefits from subsidies by using data mining approaches

Poverty, known as a widespread economic and political challenge (specifically at the times of crisis, like COVID-19), is a very complicated problem, which many countries have been trying for a long time to eradicate. Cash-subsidy allocation procedure using traditional statistical vision is the famous approach, which articles have targeted. Inefficiency of these solutions besides the fact that a pair of households with exact same situation will not be existing leads us to inadequacy and inaccuracy of these methods. This study, by putting data mining and machine learning (as well-known majors in IT and computer Science) visions together, draws a path to overcome this challenge. For this aim, the social, income and expenditure dimensions of a dataset are surveyed from 18885 households considered to measure the population poverty ratio (a fuzzy look at on their eligibility). In respect to the different experimental mode, the effective features are being filtered to use in FCM algorithm in order to determine to what extend the households in the poor or wealthy. Moreover, Genetic Algorithm displays its efficiency in the role of optimizer. Finally, the evaluation results show more accurate outcomes from the feature selection technique (on normalized data) and get the optimized clusters. [ FROM AUTHOR] Copyright of Journal of Information Technology & Politics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Multi-operator Variant of Differential Evolution and its Application in Classification of COVID-19 CT-scan Images

Novel coronavirus disease (COVID-19), caused by the virus (SARS-CoV-2), has drastically impacted human beings' lives since early 2020. The virus is constantly changing, and with mutations, it becomes diverse and spreads more easily. Several automatic COVID-19 diag-nostic tools are proposed that emphasize feature extraction mechanism from radiographical images using modern deep learning technology. The general idea is to leverage smart solutions of pre-trained networks for deep-feature processing. However, all the extracted features may not essentially contribute to the performance of the COVID-19 diag-nostic model, and hence an optimal subset of features must be discov-ered. Motivated by this, we propose a novel feature selection method based on multi-operator differential evolution (MODE), which helps to acquire optimal feature-subset. To show the efficacy of the proposed algorithm, we focus on applying the COVID-19 classification model through medical imaging. Eight advanced pre-trained architectures have been selected for COVID-19 feature extraction from CT-scan medi-cal imaging. After that, the proposed feature selection technique based on MODE is applied. A customized SVM kernel is implemented that supports feature classification. The result analysis shows the perfor-mance of the existing COVID-19 designs with the proposed feature selection technique, MODE, integrated with a customized SVM kernel. It even beats the existing state-of-the-art frameworks carried forward for COVID-19 diagnosis. We have observed that MODE feature selec-tion is suitable for fast COVID-19 detection, having overall accuracy of 85.27%.

Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model.

COVID-19 was first identified in December 2019 at Wuhan, China. At present, the outbreak of COVID-19 pandemic has resulted in severe consequences on both economic and social infrastructures of the developed and developing countries. Several studies have been conducted and ongoing still to design efficient models for diagnosis and treatment of COVID-19 patients. The traditional diagnostic models that use reverse transcription-polymerase chain reaction (rt-qPCR) is a costly and time-consuming process. So, automated COVID-19 diagnosis using Deep Learning (DL) models becomes essential. The primary intention of this study is to design an effective model for diagnosis and classification of COVID-19. This research work introduces an automated COVID-19 diagnosis process using Convolutional Neural Network (CNN) with a fusion-based feature extraction model, called FM-CNN. FM-CNN model has three major phases namely, pre-processing, feature extraction, and classification. Initially, Wiener Filtering (WF)-based preprocessing is employed to discard the noise that exists in input chest X-Ray (CXR) images. Then, the pre-processed images undergo fusion-based feature extraction model which is a combination of Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRM), and Local Binary Patterns (LBP). In order to determine the optimal subset of features, Particle Swarm Optimization (PSO) algorithm is employed. At last, CNN is deployed as a classifier to identify the existence of binary and multiple classes of CXR images. In order to validate the proficiency of the proposed FM-CNN model in terms of its diagnostic performance, extension experimentation was carried out upon CXR dataset. As per the results attained from simulation, FM-CNN model classified multiple classes with the maximum sensitivity of 97.22%, specificity of 98.29%, accuracy of 98.06%, and F-measure of 97.93%.

Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection.

This paper proposes new improved binary versions of the Sine Cosine Algorithm (SCA) for the Feature Selection (FS) problem. FS is an essential machine learning and data mining task of choosing a subset of highly discriminating features from noisy, irrelevant, high-dimensional, and redundant features to best represent a dataset. SCA is a recent metaheuristic algorithm established to emulate a model based on sine and cosine trigonometric functions. It was initially proposed to tackle problems in the continuous domain. The SCA has been modified to Binary SCA (BSCA) to deal with the binary domain of the FS problem. To improve the performance of BSCA, three accumulative improved variations are proposed (i.e., IBSCA1, IBSCA2, and IBSCA3) where the last version has the best performance. IBSCA1 employs Opposition Based Learning (OBL) to help ensure a diverse population of candidate solutions. IBSCA2 improves IBSCA1 by adding Variable Neighborhood Search (VNS) and Laplace distribution to support several mutation methods. IBSCA3 improves IBSCA2 by optimizing the best candidate solution using Refraction Learning (RL), a novel OBL approach based on light refraction. For performance evaluation, 19 real-wold datasets, including a COVID-19 dataset, were selected with different numbers of features, classes, and instances. Three performance measurements have been used to test the IBSCA versions: classification accuracy, number of features, and fitness values. Furthermore, the performance of the last variation of IBSCA3 is compared against 28 existing popular algorithms. Interestingly, IBCSA3 outperformed almost all comparative methods in terms of classification accuracy and fitness values. At the same time, it was ranked 15 out of 19 in terms of number of features. The overall simulation and statistical results indicate that IBSCA3 performs better than the other algorithms.

Future forecasting prediction of Covid-19 using hybrid deep learning algorithm.

Due the quick spread of coronavirus disease 2019 (COVID-19), identification of that disease, prediction of mortality rate and recovery rate are considered as one of the critical challenges in the whole world. The occurrence of COVID-19 dissemination beyond the world is analyzed in this research and an artificial-intelligence (AI) based deep learning algorithm is suggested to detect positive cases of COVID19 patients, mortality rate and recovery rate using real-world datasets. Initially, the unwanted data like prepositions, links, hashtags etc., are removed using some pre-processing techniques. After that, term frequency inverse-term frequency (TF-IDF) andBag of Words (BoW) techniques are utilized to extract the features from pre-processed dataset. Then, Mayfly Optimization (MO) algorithm is performed to pick the relevant features from the set of features. Finally, two deep learning procedures, ResNet model and GoogleNet model, are hybridized to achieve the prediction process. Our system examines two different kinds of publicly available text datasets to identify COVID-19 disease as well as to predict mortality rate and recovery rate using those datasets. There are four different datasets are taken to analyse the performance, in which the proposed method achieves 97.56% accuracy which is 1.40% greater than Linear Regression (LR) and Multinomial Naive Bayesian (MNB), 3.39% higher than Random Forest (RF) and Stochastic gradient boosting (SGB) as well as 5.32% higher than Decision tree (DT) and Bagging techniques if first dataset. When compared to existing machine learning models, the simulation result indicates that a proposed hybrid deep learning method is valuable in corona virus identification and future mortality forecast study.

Cognitively Enhanced Versions of Capuchin Search Algorithm for Feature Selection in Medical Diagnosis: a COVID-19 Case Study.

Feature selection (FS) is a crucial area of cognitive computation that demands further studies. It has recently received a lot of attention from researchers working in machine learning and data mining. It is broadly employed in many different applications. Many enhanced strategies have been created for FS methods in cognitive computation to boost the performance of the methods. The goal of this paper is to present three adaptive versions of the capuchin search algorithm (CSA) that each features a better search ability than the parent CSA. These versions are used to select optimal feature subset based on a binary version of each adapted one and the k-Nearest Neighbor (k-NN) classifier. These versions were matured by applying several strategies, including automated control of inertia weight, acceleration coefficients, and other computational factors, to ameliorate search potency and convergence speed of CSA. In the velocity model of CSA, some growth computational functions, known as exponential, power, and S-shaped functions, were adopted to evolve three versions of CSA, referred to as exponential CSA (ECSA), power CSA (PCSA), and S-shaped CSA (SCSA), respectively. The results of the proposed FS methods on 24 benchmark datasets with different dimensions from various repositories were compared with other k-NN based FS methods from the literature. The results revealed that the proposed methods significantly outperformed the performance of CSA and other well-established FS methods in several relevant criteria. In particular, among the 24 datasets considered, the proposed binary ECSA, which yielded the best overall results among all other proposed versions, is able to excel the others in 18 datasets in terms of classification accuracy, 13 datasets in terms of specificity, 10 datasets in terms of sensitivity, and 14 datasets in terms of fitness values. Simply put, the results on 15, 9, and 5 datasets out of the 24 datasets studied showed that the performance levels of the binary ECSA, PCSA, and SCSA are over 90% in respect of specificity, sensitivity, and accuracy measures, respectively. The thorough results via different comparisons divulge the efficiency of the proposed methods in widening the classification accuracy compared to other methods, ensuring the ability of the proposed methods in exploring the feature space and selecting the most useful features for classification studies.

MonDiaL-CAD: Monkeypox diagnosis via selected hybrid CNNs unified with feature selection and ensemble learning.

Objective: Recently, monkeypox virus is slowly evolving and there are fears it will spread as COVID-19. Computer-aided diagnosis (CAD) based on deep learning approaches especially convolutional neural network (CNN) can assist in the rapid determination of reported incidents. The current CADs were mostly based on an individual CNN. Few CADs employed multiple CNNs but did not investigate which combination of CNNs has a greater impact on the performance. Furthermore, they relied on only spatial information of deep features to train their models. This study aims to construct a CAD tool named "Monkey-CAD" that can address the previous limitations and automatically diagnose monkeypox rapidly and accurately. Methods: Monkey-CAD extracts features from eight CNNs and then examines the best possible combination of deep features that influence classification. It employs discrete wavelet transform (DWT) to merge features which diminishes fused features' size and provides a time-frequency demonstration. These deep features' sizes are then further reduced via an entropy-based feature selection approach. These reduced fused features are finally used to deliver a better representation of the input features and feed three ensemble classifiers. Results: Two freely accessible datasets called Monkeypox skin image (MSID) and Monkeypox skin lesion (MSLD) are employed in this study. Monkey-CAD could discriminate among cases with and without Monkeypox achieving an accuracy of 97.1% for MSID and 98.7% for MSLD datasets respectively. Conclusions: Such promising results demonstrate that the Monkey-CAD can be employed to assist health practitioners. They also verify that fusing deep features from selected CNNs can boost performance.

Co-evolution of neural architectures and features for stock market forecasting: A multi-objective decision perspective

In a multi-objective setting, a portfolio manager's highly consequential decisions can benefit from assessing alternative forecasting models of stock index movement. The present investigation proposes a new approach to identify a set of non-dominated neural network models for further selection by the decision-maker. A new co-evolution approach is proposed to simultaneously select the features and topology of neural networks (collectively referred to as neural architecture), where the features are viewed from a topological perspective as input neurons. Further, the co-evolution is posed as a multi-criteria problem to evolve sparse and efficacious neural architectures. The well-known dominance and decomposition based multi-objective evolutionary algorithms are augmented with a non-geometric crossover operator to diversify and balance the search for neural architectures across conflicting criteria. Moreover, the co-evolution is augmented to accommodate the data-based implications of distinct market behaviors prior to and during the ongoing COVID-19 pandemic. A detailed comparative evaluation is carried out with the conventional sequential approach of feature selection followed by neural topology design, as well as a scalarized co-evolution approach. The results on three market indices (NASDAQ, NYSE, and S&P500) in pre- and peri-COVID time windows convincingly demonstrate that the proposed co-evolution approach can evolve a set of non-dominated neural forecasting models with better generalization capabilities.

Machine Learning Applied to COVID-19: A Review of the Initial Pandemic Period

Diagnostic and decision-making processes in the 2019 Coronavirus treatment have combined new standards using patient chest images, clinical and laboratory data. This work presents a systematic review aimed at studying the Artificial Intelligence (AI) approaches to the patients' diagnosis or evolution with Coronavirus 2019. Five electronic databases were searched, from December 2019 to October 2020, considering the beginning of the pandemic when there was no vaccine influencing the exploration of Artificial Intelligence-based techniques. The first search collected 839 papers. Next, the s were reviewed, and 138 remained after the inclusion/exclusion criteria was performed. After thorough reading and review by a second group of reviewers, 64 met the study objectives. These papers were carefully analyzed to identify the AI techniques used to interpret the images, clinical and laboratory data, considering a distribution regarding two variables: (i) diagnosis or outcome and (ii) the type of data: clinical, laboratory, or imaging (chest computed tomography, chest X-ray, or ultrasound). The data type most used was chest CT scans, followed by chest X-ray. The chest CT scan was the only data type that was used for diagnosis, outcome, or both. A few works combine Clinical and Laboratory data, and the most used laboratory tests were C-reactive protein. AI techniques have been increasingly explored in medical image annotation to overcome the need for specialized manual work. In this context, 25 machine learning (ML) techniques with a highest frequency of usage were identified, ranging from the most classic ones, such as Logistic Regression, to the most current ones, such as those that explore Deep Learning. Most imaging works explored convolutional neural networks (CNN), such as VGG and Resnet. Then transfer learning which stands out among the techniques related to deep learning has the second highest frequency of use. In general, classification tasks adopted two or three datasets. COVID-19 related data is present in all papers, while pneumonia is the most common non-COVID-19 class among them.

FS-Studio: An extensive and efficient feature selection experimentation tool for Weka Explorer

A new tool with a friendly graphical user interface specifically designed to perform feature selection experiments in Weka Explorer allowing parallel computation is proposed in this work. The proposed tool performs Bayesian statistical tests among the selected feature selection techniques to check whether the differences are statistically significant or not. Moreover, the recently published general-purpose metaheuristic named Coronavirus Optimization Algorithm is also adapted for feature selection and integrated in the proposed tool to search for attribute subsets, allowing its use along with any Weka attribute subset evaluation algorithm. After the feature selection process is performed, both classification and regression techniques can be applied to the dataset built with the most relevant features. Finally, the output of the whole process is sent to an exportable table, customizable by means of a bar plot, in order to gather both predicted and actual values as well as the evaluation metrics.

Machine Learning Model to Study the Genetic Susceptibility of Patients with Sever Coronavirus Symptoms

COVID 19 is constantly changing properties because of its contagious as an urgent global challenge, and there are no vaccines or effective drugs. Smart model used to measure and prevent the spread of COVID 19 continues to provide health care services is an urgent need. Previous methods to identify severe symptoms of coronavirus in the early stages, but they have failed to predict the symptoms of coronavirus in an accurate way and also take more time. To overcome these issues the effective severe coronavirus symptoms techniques are proposed. Initially, Gradient Conventional Recursive Neural Classifier based classification and Linear Discriminant Genetic Algorithm used feature selection, mutation, and cross-analysis of features of coronary symptoms. These methods are used to select optimized features and selected features, and then classified by neural network. This Gradient Conventional Recursive Neural Classifier selects features based on the correlation between features that reduce irrelevant features involved in the identification process of coronary symptoms. Gradient Conventional Recursive Neural Classifier based on each function, helping to maximize the correlation between the prediction accuracy of coronavirus symptoms. For this reason, it has always been recommended in an effort to increase the accuracy and reliability of diagnostics to use machine learning to design different classification models. © 2023 IEEE.

Design of COVID19 Detection based on Relative Eccentric Feature Selection using Deep Vectorized Regressive Neural Network for Corona Virus

The world has seen various diseases in different variants, numerous pandemics in the twentieth century like COVID-19. Fly infections are the fundamental driver of contaminations. COVID-19 declared a global pandemic with major impacts on economies and societies around the world. The diagnosis of COVID19 or non-COVID-19 cases early detection at the correct separation early stages of disease are one of the main concerns of the current coronavirus pandemic. At present, accurate detection of coronavirus disease usually takes a long time and is prone to human error. To address this problem, the proposed Deep learning and Design of COVID19 detection based on Relative Eccentric Feature Selection (REFS) Using Deep Vectorized Regressive Neural Network (DVRNN) for corona virus the early detection of the COVID19 virus. Initially collects the COVID19 sample test dataset, then the raw dataset trained into preliminary process is used to remove unwanted noise. After that preliminary processed dataset trained into the feature selection process is done to identify the best features of COVID19 using Ensemble recursive selection. Further, the proposed DVRNN algorithm is done to classify the accurate detection of coronavirus. The proposed model would be useful for the Timely and accurate identification of various stages of coronavirus. Therefore, it can detect the accurate results of COVID19 effectively and accomplish good performance compared with previous methods. © 2023 IEEE.

A Comprehensive Analysis of Clustering Public Utility Bus Passenger's Behavior during the COVID-19 Pandemic: Utilization of Machine Learning with Metaheuristic Algorithm

Public utility bus (PUB) systems and passenger behaviors drastically changed during the COVID-19 pandemic. This study assessed the clustered behavior of 505 PUB passengers using feature selection, K-means clustering, and particle swarm optimization (PSO). The wrapper method was seen to be the best among the six feature selection techniques through recursive feature selection with a 90% training set and a 10% testing set. It was revealed that this technique produced 26 optimal feature subsets. These features were then fed into K-means clustering and PSO to find PUB passengers' clusters. The algorithm was tested using 12 different parameter settings to find the best outcome. As a result, the optimal parameter combination produced 23 clusters. Utilizing the Pareto analysis, the study only considered the vital clusters. Specifically, five vital clusters were found to have comprehensive similarities in demographics and feature responses. The PUB stakeholders could use the cluster findings as a benchmark to improve the current system.

RECENT DEVELOPMENTS IN SOFT COMPUTING BASED TECHNIQUES FOR FEATURE SELECTION AND DISEASE CLASSIFICATION

Computational prediction of diseases is vital in medical research that contributes to computer-aided diagnostics and helps doctors and medical practitioners in critical decision-making for various diseases such as bacterial and viral kinds of disease, including COVID-19 of the current pandemic situation. Feature selection techniques function as a preprocessing phase for classification and prediction algorithms. For disease prediction, these features may be the patient's clinical profiles or genomic features such as gene expression profiles from microarray and read counts from RNA-Seq. The performance of a classifier depends primarily on the selected features. In addition, genomic features are too large in numbers, resulting in the curse of dimensionality problem. In the last few years, several feature selection algorithms have been developed to overcome the existing problems to get rid of eliminating chronic diseases, such as various cancers, Zika virus, Ebola virus, and the COVID-19 pandemic. In this review article, we systematically associate soft computing-based approaches for feature selection and disease prediction by applying three data types: patients' clinical profiles, microarray gene expression profiles, and RNA-Seq sample profiles. According to related work, when the discussion took place, the percentage of medical data types highlighted through pictorial representation and the respective ratio of percentages mentioned were 52%, 27%, 9% and 12% for clinical symptoms, gene expression, MRI-Image and other data types such as signal or text-based utilized, respectively. We also highlight the significant challenges and future directions in this research domain © 2023, Suranaree Journal of Science and Technology.All Rights Reserved.