Automatic detection of novel corona virus (SARS-CoV-2) infection in computed tomography scan based on local adaptive thresholding and kernel-support vectors

The recent studies have indicated the requisite of computed tomography scan analysis by radiologists extensively to find out the suspected patients of SARS-CoV-2 (COVID-19). The existing deep learning methods distribute one or more of the subsequent bottlenecks. Therefore, a straight forward method for detecting COVID-19 infection using real-world computed tomography scans is presented. The detection process consists of image processing techniques such as segmentation of lung parenchyma and extraction of effective texture features. The kernel-based support vector machine is employed over feature vectors for classification. The performance parameters of the proposed method are calculated and compared with the existing methodology on the same dataset. The classification results are found outperforming and the method is less probabilistic which can be further exploited for developing more realistic detection system.

On the value of apricot kernel in modern medicine and its future development

Apricot kernels are one of the most regularly used traditional Chinese medicinal ingredients in Asia. The medical significance of apricot kernels is highlighted since Traditional Chinese Medicine (TCM) demonstrated its favourable impact when apricot kernels were used in the prevention and treatment of Corona Virus Disease 2019 (COVID-19). Furthermore, apricot kernels are high in fat, protein, dietary fibre, and specific amygdalin, making them a new form of dried fruit in comparison to almond kernels, with a bigger market opportunity. This paper systematically reviewed the active components of apricot kernels and their application in medicine, especially for molecular mechanisms of anti-tumors of amygdalin, providing scientific theoretical foundations for modern medicine treatment with COVID-19-induced lung disease, and for the development of high value-added apricot kernels.

An inaugural survey of cereal, oilseed and vegetable crop diseases in the Yukon Territory in 2020

Various kinds of field crops growing on two commercial farms in the Whitehorse area of the southern Yukon Territory were surveyed for diseases in summer 2020 by staff of the Agriculture Branch of the Government of Yukon. They included barley, wheat, canola, beets, broccoli, cabbage, carrots, potatoes and turnips. Fields were visited one or more times during July and August. The incidence and severity of diseases were visually assessed on a crop-by-crop basis and samples were collected for laboratory analysis of the pathogens present, if any. Both infectious and non-infectious diseases were present on most crops. The infectious diseases were caused by various species of plant pathogenic bacteria and fungi that were common on these crops growing in other areas of Canada. INTRODUCTION AND METHODS: The 2020 field crop disease survey is believed to be the first organized study of its kind on agricultural crops in the Territory. In his book, "An Annotated Index of Plant Diseases in Canada . . . ", I.L. Conners lists over 300 records of plant diseases on trees, shrubs, herbs and grasses in the Yukon that were published by individuals who were surveying forests and native vegetation mainly for federal government departments, universities and other agencies (Conners 1967). The objectives of the 2020 survey were: (1) to determine the kinds and levels of diseases on selected Yukon crops, (2) to identify the major pathogen species attacking Yukon crops, and (3) to use the results to plan future surveillance activities aimed at helping producers to improve their current disease management programs. All of the fields included in the 2020 survey were situated on two commercial farms, which were designated as Farm #1 and #2, in the Whitehorse area in the southern Yukon (Fig. 1). The crops surveyed included cereals (barley and wheat), oilseeds (canola) and vegetables (beets, broccoli, cabbage, carrots, potatoes and turnips). Fields were visited one or more times in the mid- to late growing season (July/August) at a time when damage from diseases was most noticeable. Symptoms were visually assessed on a crop-by-crop basis by determining their incidence and severity. Incidence was represented by the percentage of plants, leaves, heads, kernels, etc., damaged in the target crop, while severity was estimated to be the proportion of the leaf, fruit, head, root/canopy area, etc., affected by a specific disease as follows: Proportion of the canopy affected based on a 0-4 rating scale, where: 0 = no disease symptoms, 1 = 1-10% of the crop canopy showing symptoms;2 = 11-25% showing symptoms, 3 = 26- 50% showing symptoms, and 4 = > 50% showing symptoms. Photographs of affected plants were taken and sent to plant pathologists across Western Canada for their opinions on causation. Where possible, representative samples of plants with disease symptoms were packaged and sent to the Alberta Plant Health Lab (APHL) in Edmonton, AB for diagnostic analyses. Background information, such as the general cultural practices and cropping history, was obtained from the producers wherever possible. GPS coordinates were obtained for each field to enable future mapping Cereals: Individual fields of barley (11 ha) and wheat (30 ha) located at Farm #1 were surveyed. The barley was a two-row forage cultivar 'CDC Maverick', while the wheat was an unspecified cultivar of Canada Prairie Spring (CPS) Wheat. Plant samples were taken along a W-shaped transect for a total of five sampling points for the barley field (< 20 ha) and ten sampling points for the wheat field (> 20 ha). The first visit, which occurred on July 30, involved visual inspection and destructive sampling wherein plants were collected and removed from the field for a detailed disease assessment at a lab space in Whitehorse. There, the roots were rinsed off and the plants were examined for disease symptoms. The second visit to these fields, which occurred on August 27, only involved visual examination of the standing crop. Oilseeds: A single 40 ha field of Polish canola (cv. 'Synergy') was examined o

Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images.

Radiomic analysis allows for the detection of imaging biomarkers supporting decision-making processes in clinical environments, from diagnosis to prognosis. Frequently, the original set of radiomic features is augmented by considering high-level features, such as wavelet transforms. However, several wavelets families (so called kernels) are able to generate different multi-resolution representations of the original image, and which of them produces more salient images is not yet clear. In this study, an in-depth analysis is performed by comparing different wavelet kernels and by evaluating their impact on predictive capabilities of radiomic models. A dataset composed of 1589 chest X-ray images was used for COVID-19 prognosis prediction as a case study. Random forest, support vector machine, and XGBoost were trained (on a subset of 1103 images) after a rigorous feature selection strategy to build-up the predictive models. Next, to evaluate the models generalization capability on unseen data, a test phase was performed (on a subset of 486 images). The experimental findings showed that Bior1.5, Coif1, Haar, and Sym2 kernels guarantee better and similar performance for all three machine learning models considered. Support vector machine and random forest showed comparable performance, and they were better than XGBoost. Additionally, random forest proved to be the most stable model, ensuring an appropriate balance between sensitivity and specificity.

Forecasting the Asian stock market volatility: Evidence from WTI and INE oil futures

This study investigates whether China's crude oil futures (INE) and West Texas Intermediate (WTI) markets hold valuable information for estimating the realized volatility of seven Asian stock markets. This study has several notable findings. First, China's oil futures can trigger forecast accuracy for three equity indices (Nikkei 225, NSEI, and FT Straits Times), whereas WTI helps forecast the volatility of the two indices (KSE 100 and KOSPI). Second, comparing China's crude oil futures with WTI's crude oil futures, we find that the former could be an effective indicator for all seven Asian stock markets during a high-volatility period, while WTI information is helpful in forecasting the volatility of the KSE 100, NSEI, and FT Strait Times during the low-volatility period. Further, information of both oil futures is ineffective for the Hang Seng and SSEC equity indices. Our results are robust in several robustness checks, including alternative evaluation methods, recursive window approach, and alternative realized measures, even during the COVID-19 pandemic.

The utilization of palm kernel shells and waste plastics in asphaltic mix for sustainable pavement construction. (Special Issue: Technology and innovation for a post COVID recovery and growth: the contribution of industry and academia.)

Natural aggregates are being depleted due to the high demand for road and building construction and need to be replaced with alternative materials. This study investigated the potential of using Palm kernel shells (PaKS) as a partial replacement for natural aggregates (NA) and waste plastics (WP) as a binder. The physical and volumetric properties of the different asphaltic mixes (AM) were assessed using the Marshall Method. The bitumen content of the mix design samples was varied from 4.0% to 7.5% of the total weight of aggregates utilized. According to the Marshall parameters, at 5.5% bitumen content, the maximum Marshall Stability value of the different mix designs increased from 9.8 kN to 12.1 kN and the flow value increased from 3.0 mm to 3.7 mm. The experimental results based on the optimum bitumen content determined by the Marshall method demonstrate that PaKS and WP can be utilized to modify AM. However, additional tests will be needed to evaluate the use of this composition in road construction.

Analyzing the Role of Emotional Intelligence on the Performance of Small and Medium Enterprises (SMEs) Using AI-Based Convolutional Neural Networks (CNNs)

Human emotion detection is necessary for social interaction and plays an important role in our daily lives. Artificial intelligence research is rising, focusing on automated emotion detection. The capability to identify the emotion, which is considered one of the traits of emotional intelligence, is a component of human intelligence. Although the study is limited dependent on facial expressions or voice is flourishing, it is identifying emotions via body movements, a less researched issue. To attain emotional intelligence, this study suggests a deep learning approach. Here initially the video can be converted into image frames after the converted image frames can be preprocessed using the Glitter bandpass butter worth filter and contrast stretch histogram equalization. Then from the enhanced image, the features can be clustered using the hybrid Gaussian BIRCH algorithm. Then the specialized features are retrieved from the body of human gestures using the AdaDelta bacteria foraging optimization algorithm, and the selected features are fed to a supervised Kernel Boosting LENET deep-learning algorithm. The experiment is conducted using Geneva multimodal emotion portrayals (GEMEPs) corpus data set. This data set includes, human body gestures portraying the archetypes of five emotions, such as anger, fear, joy, pride, and sad. In these emotion detection techniques, the suggested Kernel Boosting LENET classifier achieves 98.5% accuracy, 94% precision, 95% sensitivity, and F-Score 93% outperformed better than the other existing classifiers. As a result, emotional acknowledgment may help small and medium enterprises (SMEs) to improve their performance and entrepreneurial orientation. The correlation coefficient of 188 and the significance coefficient of 0.00 show that emotional intelligence and SMEs performance have a significant and positive association.

Fractal-fractional operator for COVID-19 (Omicron) variant outbreak with analysis and modeling.

The fractal-fraction derivative is an advanced category of fractional derivative. It has several approaches to real-world issues. This work focus on the investigation of 2nd wave of Corona virus in India. We develop a time-fractional order COVID-19 model with effects of disease which consist system of fractional differential equations. Fractional order COVID-19 model is investigated with fractal-fractional technique. Also, the deterministic mathematical model for the Omicron effect is investigated with different fractional parameters. Fractional order system is analyzed qualitatively as well as verify sensitivity analysis. The existence and uniqueness of the fractional-order model are derived using fixed point theory. Also proved the bounded solution for new wave omicron. Solutions are derived to investigate the influence of fractional operator which shows the impact of the disease on society. Simulation has been made to understand the actual behavior of the OMICRON virus. Such kind of analysis will help to understand the behavior of the virus and for control strategies to overcome the disseise in community.

The Geographically Weighted Multivariate Poisson Inverse Gaussian Regression Model and Its Applications

This study aims to develop a method for multivariate spatial overdispersion count data with mixed Poisson distribution, namely the Geographically Weighted Multivariate Poisson Inverse Gaussian Regression (GWMPIGR) model. The parameters of the GWMPIGR model are estimated locally using the maximum likelihood estimation (MLE) method by considering spatial effects. Therefore, the significance of the regression parameter differs for each location. In this study, four GWMPIGR models are evaluated based on the exposure variable and the spatial weighting function. We compare the performance of those four models in real-world application using data on the number of infant, under-5 and maternal deaths in East Java in 2019 using five predictor variables. In this study, the GWMPIGR model uses one exposure variable and three exposure variables. Compared to the fixed kernel Gaussian weighting function, the GWMPIGR model with the fixed kernel bisquare weighting function and one exposure variable has a better fit based on the AICc value. Furthermore, according to the best GWMPIGR model, there are several regional groups formed based on predictors that significantly affected each event in East Java in 2019.

Multi-kernel CNN block-based detection for COVID-19 with imbalance dataset

COVID-19, which originated from Wuhan, rapidly spread throughout the world and became a public health crisis. Recognizing the positive cases at the earliest stage was crucial in order to restrain the spread of this virus and to perform medical treatment quickly for patients affected. However, the limited supply of RT-PCR as a diagnosis tool caused greatly delay in obtaining examination results of the suspected patients. Previous research stated that using radiologic images could be utilized to detect COVID-19 before the symptoms appeared. With the rapid development of Artificial intelligence in medical imaging in recent years, deep learning as the core of this technology could achieve human-level-performance in diagnostic accuracy. In this paper, deep learning was implemented to detect COVID-19 using a chest X-ray dataset. The proposed model employed a multi-kernel convolution neural network (CNN) block combined with pre-trained ResNet-34 to overcome an imbalanced dataset. The model block adopted different kernel sizes as follows 1x1, 3x3, 5x5, and 7x7. The findings show that the proposed model is capable of performing binary and three class classification tasks with an accuracy of 100% and 93.51% in the validation phase and 95% and 83% in the test phase, respectively.

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.

Classification of Covid-19 Patients Requiring Intensive Care Unit

As the world faced the covid-19 pandemic, there was a surge in the number of patients that overwhelmed many hospitals. Due to the limited number of Intensive Care Units (ICUs), some hospitals also find it difficult to meet ICU needs for covid-19 patients. So there is a need to set priorities for patients who really need to get treatment in ICU. In this paper, a classification modelling of Covid-19 patients requiring ICU was carried out using Support Vector Machine (SVM) algorithm. The data used to build the model was data from Mexican government obtained from the Kaggle website. Tests were carried out on 3 types of SVM kernels, namely Linear Kernel, Polynomial Kernel, and Gaussian RBF Kernel toward dataset before and after balancing process. From the results of validation testing using 3-fold and 5-fold cross validation, the best accuracy of 87.1055% was obtained using the three kernels toward dataset without balancing. © 2021 IEEE.

Regional Disparities and Influencing Factors of Eco-Efficiency of Arable Land Utilization in China

Eco-efficiency of arable land utilization (EALU) emphasizes efficient coordination between land use systems and ecosystems. It is therefore of great significance for agricultural sustainability based on the systematic assessment of EALU. This study took carbon emissions and non-point source pollution resulting from arable land utilization into the measurement system of EALU, and a super-SBM model, kernel density estimation and Tobit regression model were used to analyze regional differences and influencing factors of EALU for 31 provinces in China from 2000 to 2019. The results showed that there was an upward trend in EALU in China from 0.4393 in 2000 to 0.8929 in 2019, with an average annual growth rate of 4.01%. At the regional level, the EALU of three categories of grain functional areas generally maintains an increasing trend, with the highest average value of EALU in main grain marketing areas (MGMAs), followed by grain producing and marketing balance areas (GPMBAs) and main grain producing areas (MGPAs). There are obvious differences in EALU among provinces, and the number of provinces with high eco-efficiency has increased significantly, showing a spatial distribution pattern of “block” clustering. In terms of dynamic evolution, kernel density curves reflect the evolution of EALU in China and grain functional areas with different degrees of polarization characteristics. The results of Tobit regression show that natural conditions, financial support for agriculture, science and technology inputs, level of industrialization, agricultural mechanization, and the living standards of farmers are significant factors resulting in regional disparities of EALU. Therefore, this study proposes the implementation of differentiated arable land use/agricultural management strategies to improve the sustainable utilization of arable land.

Bidirectional Convolutional-LSTM based Network for lung segmentation of chest X-ray images

Deep Neural Networks (DNN)-based methods, particularly UNet, are considered as state-of-the-art for many medical imaging tasks. However, despite remarkable progress on segmenting the normal lung, performance of the UNet is unsatisfactory on challenging chest X-ray (CXR) images. This could be due to mainly two limiting factors: (1) skip connections that merge feature maps of similar size from encoding and decoding paths, and (2) loss of spatial information due to repetitive down-sampling operations. To overcome these problems, in this study, we propose a DNN-based new architecture that replaces the skip connections with a bidirectional convolutional-LSTM (BC-LSTM) module that allows exchange of more information between encoder and decoder paths and also capture spatiotemporal information. For further improvement, we add a multiple kernel pooling (MKP) block at the lowest level of UNet to encode more spatial information by different sized pooling operations. To evaluate the performance of our method, we use CXR images with different pulmonary diseases such as tuberculosis, pneumoconiosis, and Covid-19 from four public datasets as well as a private dataset and compare its performance with a standard UNet model. Results suggest that the proposed framework outperforms the UNet for all five datasets on lung segmentation, in terms of two evaluation metrics, namely Dice Coefficient (DC) and Jaccard Index (JI). © 2021 IEEE.

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._

Inferring human microbe–drug associations via multiple kernel fusion on graph neural network

Complex and diverse microbial communities have certain impacts on human health, and specific drugs are needed to treat diseases caused by microbes. However, most of the discovery of associations between microbes and drugs is through biological experiments, which are time-consuming and expensive. Therefore, it is crucial to develop an effective and computational model to detect novel microbe–drug associations. In this study, we propose a model based on Multiple Kernel fusion on Graph Convolutional Network, called MKGCN, for inferring novel microbe–drug associations. Our model is built on the heterogeneous network of microbes and drugs to extract multi-layer features, through Graph Convolutional Network (GCN). Then, we respectively calculate the kernel matrix by embedding features on each layer, and fuse multiple kernel matrices based on the average weighting method. Finally, Dual Laplacian Regularized Least Squares is used to infer new microbe–drug associations by the combined kernel in microbe and drug spaces. Compared with the existing tools for detecting biological bipartite networks, our model has excellent prediction effect on three datasets via three types of cross-validation. Furthermore, we also conduct a case study of the SARS-Cov-2 virus and make a deduction about drugs that may be able to associate with COVID-19. We have proved the accuracy of the prediction results through the existing literature. © 2021

Identifying the Structure of CSCL Conversations Using String Kernels

Computer-Supported Collaborative Learning tools are exhibiting an increased popularity in education, as they allow multiple participants to easily communicate, share knowledge, solve problems collaboratively, or seek advice. Nevertheless, multi-participant conversation logs are often hard to follow by teachers due to the mixture of multiple and many times concurrent discussion threads, with different interaction patterns between participants. Automated guidance can be provided with the help of Natural Language Processing techniques that target the identification of topic mixtures and of semantic links between utterances in order to adequately observe the debate and continuation of ideas. This paper introduces a method for discovering such semantic links embedded within chat conversations using string kernels, word embeddings, and neural networks. Our approach was validated on two datasets and obtained state-of-the-art results on both. Trained on a relatively small set of conversations, our models relying on string kernels are very effective for detecting such semantic links with a matching accuracy larger than 50% and represent a better alternative to complex deep neural networks, frequently employed in various Natural Language Processing tasks where large datasets are available.

Teaching Simple Strategies to Foster Emotional Well-Being.

The COVID-19 pandemic has presented considerable disruptions to routines that have challenged emotional well-being for children and their caregivers. One direction for supporting emotional well-being includes strategies that help children feel their best in the moment, which can bolster their capacity to respond appropriately to thoughts and behaviors. Strengthening emotional well-being equitably, however, must include opportunities in settings that are easily accessible to all, such as schools. In this paper, we focus on simple, evidence-informed strategies that can be used in schools to promote positive feelings in the moment and build coping behaviors that facilitate tolerance of uncertainty. We focus on those strategies that educators can easily and routinely use across ages, stages, and activities. Selected strategies are primarily tied to cognitive behavioral theory, with our review broadly organized across categories of self-awareness, self-soothing, and social relationships. We review evidence for each, providing examples that illustrate ease of use in school settings.

Machine learning and network medicine approaches for drug repositioning for COVID-19.

We present two machine learning approaches for drug repurposing. While we have developed them for COVID-19, they are disease-agnostic. The two methodologies are complementary, targeting SARS-CoV-2 and host factors, respectively. Our first approach consists of a matrix factorization algorithm to rank broad-spectrum antivirals. Our second approach, based on network medicine, uses graph kernels to rank drugs according to the perturbation they induce on a subnetwork of the human interactome that is crucial for SARS-CoV-2 infection/replication. Our experiments show that our top predicted broad-spectrum antivirals include drugs indicated for compassionate use in COVID-19 patients; and that the ranking obtained by our kernel-based approach aligns with experimental data. Finally, we present the COVID-19 repositioning explorer (CoREx), an interactive online tool to explore the interplay between drugs and SARS-CoV-2 host proteins in the context of biological networks, protein function, drug clinical use, and Connectivity Map. CoREx is freely available at: https://paccanarolab.org/corex/.

Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations.

We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.