An empirical and modelling approach to the evaluation of cruise ships' influence on air quality: The case of La Paz, Mexico.

Maritime activity has diverse environmental consequences impacts in port areas, especially for air quality, and the post-COVID-19 cruise tourism market's potential to recover and grow is causing new environmental concerns in expanding port cities. This research proposes an empirical and modelling approach for the evaluation of cruise ships' influence on air quality concerning NO2 and SO2 in the city of La Paz (Mexico) using indirect measurements. EPA emission factors and the AERMOD modelling system coupled to WRF were used to model dispersions, while street-level mobile monitoring data of air quality from two days of 2018 were used and processed using a radial base function interpolator. The local differential Moran's Index was estimated at the intersection level using both datasets and a co-location clustering analysis was performed to address spatial constancy and to identify the pollution levels. The modelled results showed that cruise ships' impact on air quality had maximum values of 13.66 µg/m3 for NO2 and 15.71 µg/m3 for SO2, while background concentrations of 8.80 for NOx and 0.05 for SOx (µg/m3) were found by analysing the LISA index values for intersections not influenced by port pollution. This paper brings insights to the use of hybrid methodologies as an approach to studying the influence of multiple-source pollutants on air quality in contexts totally devoid of environmental data.

Optimized hierarchical radial basis function neural networks by developing coronavirus herd immunity optimizer for solid oxide fuel cells

A new blackbox technique has been presented in this article for model estimation of solid oxide fuel cells (SOFCs) for providing better results. The proposed method is based on a hierarchical radial basis function (HRBF). The presented method is then developed by a new modified metaheuristic called developed coronavirus herd immunity algorithm (DCHIA). The suggested model has been named DCHIA-HRBF. The proposed model is then trained by some data and prepared for identification and prediction. The model is then analyzed and put in comparison with several latest techniques for validation of the efficiency of the technique. It is also verified by the empirical data to prove its validation with the real data. The results show that the best cost for the performance index which is the network error, is achieved by the proposed developed coronavirus herd immunity algorithm with about 119.442, which is satisfying for the considered function and target against the other state-of-the-art methods. As a result, the simulation results specified that the suggested DCHIA-HRBF delivers high effectiveness as an identifier and prediction tool for the SOFCs. © 2023 John Wiley & Sons, Ltd.

Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup

In the real-world application of COVID-19 misinformation detection, a fundamental challenge is the lack of the labeled COVID data to enable supervised end-to-end training of the models, especially at the early stage of the pandemic. To address this challenge, we propose an unsupervised domain adaptation framework using contrastive learning and adversarial domain mixup to transfer the knowledge from an existing source data domain to the target COVID-19 data domain. In particular, to bridge the gap between the source domain and the target domain, our method reduces a radial basis function (RBF) based discrepancy between these two domains. Moreover, we leverage the power of domain adversarial examples to establish an intermediate domain mixup, where the latent representations of the input text from both domains could be mixed during the training process. Extensive experiments on multiple real-world datasets suggest that our method can effectively adapt misinformation detection systems to the unseen COVID-19 target domain with significant improvements compared to the state-of-the-art baselines. © 2022 IEEE.

Machine learning- based lung disease diagnosis from CT images using Gabor features in Littlewood Paley empirical wavelet transform (LPEWT) and LLE

The term 'lung disease' covers a wide range of conditions that affect the lungs, including asthma, COPD, infections like the flu, pneumonia, tuberculosis, lung cancer, COVID, and numerous other breathing issues. Respiratory failure may result from several respiratory disorders. Recently, various methods have been proposed for lung disease detection, but they are not much more efficient. The proposed model has been tested on the COVID dataset. In this work, Littlewood-Paley Empirical Wavelet Transform (LPEWT) based technique is used to decompose images into their sub-bands. Using locally linear embedding (LLE), linear discriminative analysis (LDA), and principal component analysis (PCA), robust features are identified for lung disease detection after texture-based relevant Gabor features are extracted from images. LLE's outcomes inspire the development of new techniques. The Entropy, ROC, and Student's t-value methods provide ranks for robust features. Finally, LS-SVM is fed with t-value-based ranked features for classification using Morlet wavelet, Mexican-hat wavelet, and radial basis function. This model, which incorporated tenfold cross-validation, exhibited improved classification accuracy of 95.48%, specificity of 95.37%, sensitivity of 95.43%, and an F1 score of.95. The proposed diagnosis method can be a fast disease detection tool for imaging specialists using medical images.Copyright © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Automated diagnosis of COVID stages using texture-based Gabor features in variational mode decomposition from CT images

COVID-19 is a deadly and fast-spreading disease that makes early death by affecting human organs, primarily the lungs. The detection of COVID in the early stages is crucial as it may help restrict the spread of the progress. The traditional and trending tools are manual, time-inefficient, and less accurate. Hence, an automated diagnosis of COVID is needed to detect COVID in the early stages. Recently, several methods for exploiting computed tomography (CT) scan pictures to detect COVID have been developed;however, none are effective in detecting COVID at the preliminary phase. We propose a method based on two-dimensional variational mode decomposition in this work. This proposed approach decomposes pre-processed CT scan pictures into sub-bands. The texture-based Gabor filter bank extracts the relevant features, and the student's t-value is used to recognize robust traits. After that, linear discriminative analysis (LDA) reduces the dimensionality of features and provides ranks for robust features. Only the first 14 LDA features are qualified for classification. Finally, the least square- support vector machine (SVM) (radial basis function) classifier distinguishes between COVID and non-COVID CT lung images. The results of the trial showed that our model outperformed cutting-edge methods for COVID classification. Using tenfold cross-validation, this model achieved an improved classification accuracy of 93.96%, a specificity of 95.59%, and an F1 score of 93%. To validate our proposed methodology, we conducted different relative experiments with deep learning and traditional machine learning-based models like random forest, K-nearest neighbor, SVM, convolutional neural network, and recurrent neural network. The proposed model is ready to help radiologists identify diseases daily. © 2023 Wiley Periodicals LLC.

Predicting Study or Work from Home Satisfaction using Data Mining Technique: A Case Study in Malaysia

The COVID-19 pandemic has urged the government of Malaysia to implement Movement Control Order (MCO) which forces working people to work from home while students to study from home. People's satisfaction on work from home is crucial in determining their work productivity and efficiency whereas student's satisfaction on study from home is important for their learning effectiveness. There is no work has been done yet for exploring data mining techniques to build a model for predicting work or study from home satisfaction using Malaysia as a case study. This paper aimed to identify the best data mining model for predicting the work or study from home satisfaction. The prediction model is learned by analyzing the demographic, the personality traits, and the work from home experience collected from a group of Malaysia people. This study attempts to investigate four data mining techniques that are the decision tree, linear kernel support vector machine, polynomial support vector machine, and radial basis support vector machine. Experiment results show that the radial basis support vector machine outperformed other techniques in predicting the work or study from home satisfaction of Malaysia's community. © 2022 IEEE.

An Efficient Classification Algorithm for Employee Well-Being Prediction Using Deep Learning

The impact of COVID-19 has changed the way work is being done especially in the IT sector. The emergence of work from home as an option has resulted in the evolution of hybrid work culture going forward as the world is moving towards endemic. On these circumstances there has been drastic change in work pattern of employees which clearly impacted the efficiency levels and their wellbeing (both physical and mental). It has also become imperative for the employers to track the efficiency of employees during their working hours in order to ensure maximum productivity in hybrid working model. This paper proposes a system that can detect and track the employee efficiency though facial landmarks by assessing the parameters like drowsiness and stress using deep learning techniques and hybridization of classification algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

SHORT-TERM IMPACTS OF MOBILE TOUCH-SCREEN DEVICE USE ON MUSCULOSKELETAL DISORDERS DURING COVID-19 PANDEMIC: RISK ASSESSMENT MODELLING AND VERIFICATION

The intensive and repetitive use of touch-screens may pose significant problems, such as ergonomic pain or musculoskeletal disorders. This research aims to study the effect of using mobile touch-screen devices on the human musculoskeletal system during the COVID-19 pandemic lockdown and to develop a model for classifying the effects of musculoskeletal stress (pain and discomfort) on the performance of educational activities. The Cornell musculoskeletal discomfort questionnaire was given to 544 participants (71% males and 29% females). An Association Rule Mining approach was applied to illustrate the correlation, and multiple machine-learning models – used to predict the impact of pain and discomfort on different body regions – were applied to determine risk levels that might interfere with the ability to perform daily activities. Most musculoskeletal disorders were reported in the neck region and lower back (64.33% and 55.33% respectively), followed by upper back (44.30%) and the right shoulder (38%). Analysis of association rules showed high positive correlation between the lower back and the neck (support = 43%, confidence = 77%). Additionally, it was found that the radial basis function network has the highest accuracy in prediction (84%). The results of the radial basis function model showed that interference in educational activities can be predicted by using pain indicators in body parts resulting from touch-screen device usage. © 2022, South African Institute of Industrial Engineering. All rights reserved.

Data Mining and RBF Neural Networks to Analyze Data from COVID-19 Patients and Predict New Cases Based on Symptoms

In late 2019, pneumonia epidemics were recorded in Wuhan, China, affecting a large number of individuals. A novel SARS virus was found after a careful investigation of samples from SARS patients. The virus was given the name CORONA because of its coronavirus origin, which was then shortened to COVID-19, which stands for CORONA VIRUS 2019. The World Health Organization declared a pandemic on March 11, 2020. The human brain served as the foundation for civilisation. Neural networks were designed to mimic the human mind's working style in order to profit from their way of thinking. The network of neurons performs similarly to a human neuron. As a result, RBF neural networks have been used to link diagnosis with symptoms because the base-based system is reconciled with the knowledge-based system, which represents symptoms related to the diagnosed disease, and the system becomes suitable for correct disease diagnosis after network training. The current study has two main stages: neuron training, which entails entering the system's parameters and generating random weights ranging from 0 to 1 for each of these parameters, and then applying the RBF neural network function to it and comparing the results with those obtained through COVID-19 health centers. © 2022 IEEE.

Machine Learning-Based Predictors for ICU Admission of COVID-19 Patients

The burden on the health sector has increased when covid-19 was declared as a critical pandemic, making the decision-taking more crucial. This study aimed mainly to build predictors to aid in making decisions for severe patients to predict whether a patient has to be admitted to the intensive care unit (ICU) based only on the vital records. Statistical techniques were used on the electrical health records (EHR) that were accessible for the covid-19 patients. Samples were processed and then extracted based on criteria that support data imputation. Then, several feature selection techniques were utilized based on the field knowledge, Pearson correlation coefficient, and finally by taking the permutation importance of a hypothetical model to retain features that have the highest relationship with the target variable. Then two versions of data were obtained as stateless and grouped data with and without feature selection which were used to build models with various machine learning algorithms;logistic regression, linear support vector machine SVM, SVM with radial basis function RBF, and artificial neural network ANN. In this respect, the models reached an accuracy of more than 95% in most of the used classifiers and the best one scored is RBF-SVM with accuracy up to 98% and achieve 0.95 areas under curve (AUC) performance. These results indicate that trustworthy models were built to fulfill the high demand for accuracy that is more or less commensurate with the cost of accuracy in the health sector relying only on vital information. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Telerehabilitation with Exoskeletons using Adaptive Robust Integral RBF-Neural-Network Impedance Control under Variable Time Delays

With an unprecedented increase in the global aging population and with it, the age-related neuromuscular dysfunction diseases, there is an exorbitant and escalating need for physical rehabilitation. Delivering these services - especially to those that are most vulnerable - under the current COVID-19 pandemic restriction for physical-distancing, is an even greater challenge. Interest in telerehabilitation is spiking, and robotic telerehabilitation could drastically improve patients' access to Some of the major challenges in developing the control methods for these robots are identifying, estimating, and overcoming the effects of dynamic modeling uncertainties, nonlinearities, and disturbances. Having humans in the loop creates the additional need for safety and compliance. Telerehabilitation control methods have the added requirement of delivering telepresence and addressing communication delays which, if not managed, could result in ineffective therapy, destabilize the system, and even cause injury. In this paper, we present a novel adaptive robust integral Radial Basis Function Neural Network Impedance model (RBFNN-I) control method for telerehabilitation with robotic exoskeletons which compensates for dynamic modeling uncertainties in the presence of external human torques and time delays. One of the salient features of the proposed control system is the implementation of a new human torque regulator which improves telepresence. Stability proof using Lyapunov stability theory is shown for the proposed control method. An exoskeleton was designed and used for unilateral and bilateral telerehabilitation simulations. Excellent tracking performance, telepresence and stability was achieved in the presence of large, variable and asymmetric time delays and human torques.

Machine Learning Approach for Autonomous Detection and Classification of COVID-19 Virus.

As people all over the world are vulnerable to be affected by the COVID-19 virus, the automatic detection of such a virus is an important concern. The paper aims to detect and classify corona virus using machine learning. To spot and identify corona virus in CT-Lung screening and Computer-Aided diagnosis (CAD) system is projected to distinguish and classifies the COVID-19. By utilizing the clinical specimens obtained from the corona-infected patients with the help of some machine learning techniques like Decision Tree, Support Vector Machine, K-means clustering, and Radial Basis Function. While some specialists believe that the RT-PCR test is the best option for diagnosing Covid-19 patients, others believe that CT scans of the lungs can be more accurate in diagnosing corona virus infection, as well as being less expensive than the PCR test. The clinical specimens include serum specimens, respiratory secretions, and whole blood specimens. Overall, 15 factors are measured from these specimens as the result of the previous clinical examinations. The proposed CAD system consists of four phases starting with the CT lungs screening collection, followed by a pre-processing stage to enhance the appearance of the ground glass opacities (GGOs) nodules as they originally lock hazy with fainting contrast. A modified K-means algorithm will be used to detect and segment these regions. Finally, the use of detected, infected areas that obtained in the detection phase with a scale of 50×50 and perform segmentation of the solid false positives that seem to be GGOs as inputs and targets for the machine learning classifiers, here a support vector machine (SVM) and Radial basis function (RBF) has been utilized. Moreover, a GUI application is developed which avoids the confusion of the doctors for getting the exact results by giving the 15 input factors obtained from the clinical specimens.

High-performance 3D Unstructured Mesh Deformation Using Rank Structured Matrix Computations

The Radial Basis Function (RBF) technique is an interpolation method that produces high-quality unstructured adaptive meshes. However, the RBF-based boundary problem necessitates solving a large dense linear system with cubic arithmetic complexity that is computationally expensive and prohibitive in terms of memory footprint. In this article, we accelerate the computations of 3D unstructured mesh deformation based on RBF interpolations by exploiting the rank structured property of the matrix operator. The main idea consists in approximating the matrix off-diagonal tiles up to an application-dependent accuracy threshold. We highlight the robustness of our multiscale solver by assessing its numerical accuracy using realistic 3D geometries. In particular, we model the 3D mesh deformation on a population of the novel coronaviruses. We report and compare performance results on various parallel systems against existing state-of-the-art matrix solvers. © 2022 Association for Computing Machinery.

Performance Comparison of Support Vector Machine Kernel Functions in Classifying COVID-19 Sentiment

Support Vector Machine (SVM) algorithm is a machine learning algorithm that is used to classify data by finding the best hyperplane that separates classes. In the SVM algorithm there are several types of kernel methods. Linear, Radial Basis Function (RBF), and polynomial kernel are some of the most commonly used SVM kernels. In previous research, each kernel has been used. However, the comparison of the three kernel function methods on the same dataset using accuracy, sensitivity, and specificity parameters has not been obtained. For this reason, this research is proposed to obtain comparative information of the three kernel functions using accuracy, sensitivity, and specificity parameters. The expected results can later be used as a reference for implementing the best kernel functions. The dataset used is comments on Youtube to analyze public sentiment on the increase in cases at the beginning of the entry of the COVID-19 pandemic in Indonesia. In this study, the accuracy values of the classification model were 0.86 for linear kernel, 0.90 for RBF kernel, and 0.91 for polynomial kernel. The sensitivity values obtained for each model are 0.64 for linear kernel, 0.48 for RBF kernel, and 0.20 for polynomial kernel. While the specificity values obtained for each model are 0.89 for linear kernel, 0.95 for RBF kernel, and 0.99 for polynomial kernel. © 2021 IEEE.

Sentiment Analysis on Social Media (Twitter) about Vaccine-19 Using Support Vector Machine Algorithm

Currently the world is experiencing a Corona Virus Disease (Covid-19) pandemic which attacks the respiratory tract and spreads very quickly to various countries including Indonesia, so the World Health Organization (WHO) has declared Covid-19 as a pandemic. To overcome this pandemic, experts in the medical field also intervened by making vaccinations to strengthen human immunity against the Covid virus. This sentiment analysis was carried out to see opinions on the object, namely the existence of a Covid-19 vaccine. Data collection by crawling data with the keyword 'Covid Vaccine'. The method that will be used is the Support Vector Machine (SVM). The analysis was carried out by comparing the classification accuracy values of the two SVM kernel functions, namely linear and Radial Basic Function (RBF). The results of the study obtained positive sentiment of 43.5%, negative of 19.1%, and neutral of 37.4%. Then the evaluation of the system using the confusion matrix obtained an accuracy value for the linear kernel of 79.15%, a precision value of 77.31%, and a recall value of 78.09%. While the RBF kernel has an accuracy of 84.25%, a precision value of 83.67%, and a recall value of 81.99%. While the cross validation obtained the optimum value at $\mathrm{k}=1$ with an accuracy value of 80.18% for the linear kernel and 85.88% for the RBF kernel. So the RBF kernel has a higher accuracy than the linear kernel. © 2021 IEEE.

MACHINE LEARNING AND RADIOMICS IDENTIFY NOVEL BIOMARKERS OF BONE LOSS

Purpose: Altered bone turnover is a factor in many diseases including osteoarthritis (OA), osteoporosis, inflammation, and viral infection. The absence of obvious symptoms and insufficiently sensitive biomarkers in the early stages of bone loss limits early diagnosis and treatment. Therefore, it is urgent to identify novel, more sensitive, and easy-to-detect biomarkers which can be used in the diagnosis and prognosis of bone health. Our previous data using standard micro-computed tomography (μCT) measurements showed that SARS-CoV-2 infection in mice significantly decreased trabecular bone volume at the lumbar spine, suggesting that decreased bone mass, increased fracture risk, and OA may be underappreciated long-haul comorbidities for COVID patients. In this study, we applied integrated state-of-the-art radiomics and machine learning models to identify more sensitive image-based biomarkers of SARS-CoV-2-induced bone loss from μCT images. These radiomic biomarkers can potentially provide a non-invasive way of quantifying and monitoring systemic bone loss and evaluating treatment efficacy in both research and clinical practices. Methods: All animal use was performed with approval of the Institutional Animal Care and Use Committee. To quantify SARS-CoV-2-induced bone loss, 6-week-old transgenic mice (16 male, 16 female) expressing humanized ACE2 receptors were inoculated with a 2020 strain of SARS-CoV-2 or phosphate-buffered saline (Control) [Fig. A]. Viral infection was confirmed by detection of infectious SARS-CoV-2 in throat swabs and histological identification of SARS-CoV-2 labeled cells. At 6-14 days post-infection, lumbar vertebral bodies (L5) were scanned with μCT (μCT 35, SCANCO Medical AG;6 μm nominal voxel size). The open-source research platform 3D Slicer v2020 with a built-in Python console v3.8 was used for medical image computing and fully automated segmentation of cortical and trabecular bone. Standard μCT assessment of bone microstructure was performed. Radiomic feature extraction and data processing were performed using python based PyRadiomics v3.0.1. A total of 120 radiographic features were extracted from the segmented images [Fig. B]. Principle component analysis (PCA) for feature selection, a support vector machine learning (SVML) predictive model for classification, holdback method for model validation, and all statistical analyses (significance at p<0.05) were performed using JMP Pro v15 (SAS). Results: Using standard μCT methods, SARS-CoV-2 infection significantly reduced the bone volume fraction (BV/TV) by 10 and 10.5% (p= 0.04) and trabecular thickness (Tb.Th) by 8 and 9% (p= 0.02) in male and female mice, respectively, compared to PBS control mice [Fig. C]. Radiomics detected a 20-fold greater magnitude in change over standard methods. SARS-CoV-2 infection significantly changed radiographic parameters with the largest change being a 300% increase in the second-order parameter: cluster shade [Fig. D]. The 45 radiomic features comprising the first 3 principal components were selected for inclusion in the SVML model. The SVML Model (radial basis function kernel;cost = 4.8;gamma = 0.46) produced an area under the receiver operating characteristic curve (AUC) of 1.0 which reflects a perfectly accurate test [Fig. E]. Conclusions: SARS-CoV-2 infection of humanized ACE2 expressing mice caused significant bone changes, suggesting that decreased bone mass, increased fracture risk, OA, and other musculoskeletal complications could be long-term comorbidities for people infected with COVID-19. We developed an open-source, fully automated segmentation and radiomics system to assess systemic bone loss using μCT images. When coupled with machine learning, this system was able to identify novel radiographic biomarkers of bone loss that better discriminate differences in bone microstructure between SARS-CoV-2 infected and control mice than standard bone morphometric indices. The high accuracy of the SVML model in classifying SARS-CoV-2 infected mice opens the possibility of translating these biom rkers to the clinical setting for early detection of skeletal changes associated with long-haul COVID. The methods presented here were demonstrated using SARS-CoV-2 as a model system and can also be adapted to other diseases associated with altered bone turnover. Development of machine-learning methods for radiomic applications is a crucial step toward clinically relevant radiomic biomarkers of bone health and provides a non-invasive way of quantifying and monitoring systemic bone loss and evaluating treatment efficacy. [Formula presented]

Applicability of Grassland Production Estimation Using Remote Sensing for the Mongolian Plateau by Comparing Typical Regions in China and Mongolia

Grasslands on the Mongolian Plateau are critical for supporting local sustainable development. Sufficient measured sample information is the basis of remote sensing modeling and estimation of grassland production. Limited by field inventory costs, it is difficult to collect sufficient and widely distributed samples in the Mongolian Plateau, especially in transboundary areas, which affects the results of grassland production estimation. Here, considering that the measured sample points are sparse, this study took Xilingol League of Inner Mongolia Autonomous Region in China and Dornogovi Province in Mongolia as the study areas, introduced multiple interpolation methods for interpolation experiments, established a statistical regression model based on the above measured and interpolated samples combined with the normalized differential vegetation index, and discussed the applicability of grassland production estimation. The comparison results revealed that the point estimation biased sample hospital-based area disease estimation method and radial basis function showed the best interpolation results for grassland production in Xilingol League and Dornogovi Province, respectively. The power function model was suitable for grassland production estimation in both regions. By inversion, we obtained annual grassland production for 2010–2021 and the uneven spatial distribution of grassland production in both regions. In these two regions, the spatial change in grassland production showed a decreasing trend from northeast to southwest, and the interannual change generally showed a dynamic upward trend. The growth rate of grassland output was faster in Xilingol League than in Dornogovi Province with similar physical geography and climate conditions, indicating that the animal husbandry regulation policies play important roles beyond the influence of climate change. The study recommended grassland estimation methods for an area with sparse samples and the results can be used to support decision making for sustainable animal husbandry and grassland succession management.

Benefit and risk evaluation of inland nuclear generation investment in Kazakhstan combined with an analytical MGT method

Purpose: In the process of building the “Belt and Road” and “Bright Road” community of interests between China and Kazakhstan, this paper proposes the construction of an inland nuclear power plant in Kazakhstan. Considering the uncertainty of investment in nuclear power generation, the authors propose the MGT (Monte-Carlo and Gaussian Radial Basis with Tensor factorization) utility evaluation model to evaluate the risk of investment in nuclear power in Kazakhstan and provide a relevant reference for decision making on inland nuclear investment in Kazakhstan. Design/methodology/approach: Based on real options portfolio combined with a weighted utility function, this study takes into account the uncertainties associated with nuclear power investments through a minimum variance Monte Carlo approach, proposes a noise-enhancing process combined with geometric Brownian motion in solving complex conditions, and incorporates a measure of investment flexibility and strategic value in the investment, and then uses a deep noise reduction encoder to learn the initial values for potential features of cost and investment effectiveness. A Gaussian radial basis function used to construct a weighted utility function for each uncertainty, generate a minimization of the objective function for the tensor decomposition, and then optimize the objective loss function for the tensor decomposition, find the corresponding weights, and perform noise reduction to generalize the nonlinear problem to evaluate the effectiveness of nuclear power investment. Finally, the two dimensions of cost and risk (estimation of investment value and measurement of investment risk) are applied and simulated through actual data in Kazakhstan. Findings: The authors assess the core indicators of Kazakhstan's nuclear power plants throughout their construction and operating cycles, based on data relating to a cluster of nuclear power plants of 10 different technologies. The authors compared it with several popular methods for evaluating the benefits of nuclear power generation and conducted subsequent sensitivity analyses of key indicators. Experimental results on the dataset show that the MGT method outperforms the other four methods and that changes in nuclear investment returns are more sensitive to changes in costs while operating cash flows from nuclear power are certainly an effective way to drive investment reform in inland nuclear power generation in Kazakhstan at current levels of investment costs. Research limitations/implications: Future research could consider exploring other excellent methods to improve the accuracy of the investment prediction further using sparseness and noise interference. Also consider collecting some expert advice and providing more appropriate specific suggestions, which will facilitate the application in practice. Practical implications: The Novel Coronavirus epidemic has plunged the global economy into a deep recession, the tension between China and the US has made the energy cooperation road unusually tortuous, Kazakhstan in Central Asia has natural geographical and resource advantages, so China–Kazakhstan energy cooperation as a new era of opportunity, providing a strong guarantee for China's political and economic stability. The basic idea of building large-scale nuclear power plants in Balkhash and Aktau is put forward, considering the development strategy of building Kazakhstan into a regional international energy base. This work will be a good inspiration for the investment of nuclear generation. Originality/value: This study solves the problem of increasing noise by combining Monte Carlo simulation with geometric Brownian motion under complex conditions, adds the measure of investment flexibility and strategic value, constructs the utility function of noise reduction weight based on Gaussian radial basis function and extends the nonlinear problem to the evaluation of nuclear power investment benefit. © 2022, Emerald Publishing Limited.

Modeling Student's Academic Performance During Covid-19 Based on Classification in Support Vector Machine

This study proposed a statistical investigate the pattern of students' academic performance before and after online learning due to the Movement Control Order (MCO) during pandemic outbreak and a modelling students' academic performance based on classification in Support Vector Machine (SVM). Data sample were taken from undergraduate students of Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris (UPSI). Student's Grade Point Average (GPA) were obtained to developed model of academic performances during Covid-19 outbreak. The prediction model was used to predict the academic performances of university students when online classes was conducted. The algorithm of Support Vector Machine (SVM) was used to develop a model of students' academic performance in university. For the Support Vector Machine (SVM) algorithm, there are two important parameters which are C (misclassification tolerance parameter) and epsilon need to identify before proceed the further analysis. The parameters was applied to four different types of kernel which is linear kernel, radial basis function kernel, polynomial kernel and sigmoid kernel and the result was found that the best accuracy achieved by SVM are 73.68% by using linear kernel and the worst accuracy obtained from a sigmoid kernel which is 67.99% with parameter of misclassification tolerance C is 128 and epsilon is 0.6._

Analysison Students' Learning Habits: Identifyingthe Contributory Factorsof Learning Duringthe Covid-19 PandemicUsing Radial Basis Function (RBF)

The Artificial Neural Network (ANN) is an Artificial Intelligence technique that offer many benefits including the ability to process a vast amount of data, the ability to learn from experiences, and the good generalization capability. It was invented based on the concept of imitation of the human brain and built up of nodes that are like human neurons. The Radial Basis Function (RBF) is one of the established types of ANN. Considering the advantages and great performance of the RBF, this study aims to investigate the contributory factors of students' learning habits during the Coronavirus Disease 2019 (or known as COVID-19) pandemic using RBF. Responses from a total of 420 respondents were collected from Vietnamese students' learning habits during the COVID-19 pandemic dataset that was established from the questionnaires distributed in the period of 7th February 2020 to 28th February 2020. Fifteen independent variables were used as the input for the RBF network which is based on the 15-9-1 structure. Based on the experiment conducted, the implementation of the RBF model was found to be fair and effective with the small number of Sum of Square Error (SSE) and Relative Error (RE) produced. It could also be concluded that the most contributing factor of students' learning habits during the COVID-19 pandemic is the learning hours per day for self-learning before the pandemic.