Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging

The transition to Electric Vehicles (EV) in place of traditional internal combustion engines is increasing societal demand for electricity. The ability to integrate the additional demand from EV charging into forecasting electricity demand is critical for maintaining the reliability of electricity generation and distribution. Load forecasting studies typically exclude households with home EV charging, focusing on offices, schools, and public charging stations. Moreover, they provide point forecasts which do not offer information about prediction uncertainty. Consequently, this paper proposes the Long Short-Term Memory Bayesian Neural Networks (LSTM-BNNs) for household load forecasting in presence of EV charging. The approach takes advantage of the LSTM model to capture the time dependencies and uses the dropout layer with Bayesian inference to generate prediction intervals. Results show that the proposed LSTM-BNNs achieve accuracy similar to point forecasts with the advantage of prediction intervals. Moreover, the impact of lockdowns related to the COVID-19 pandemic on the load forecasting model is examined, and the analysis shows that there is no major change in the model performance as, for the considered households, the randomness of the EV charging outweighs the change due to pandemic.

Bayesian optimization for artificial neural networks: application to Covid-19 image classification

Training a Convolutional Neural Network (CNN) is a difficult task, especially for deep architectures that estimate a large number of parameters. Advanced optimization algorithms should be used. Indeed, it is one of the most important steps to reduce the error between the ground truth and the model prediction. In this sense, many methods have been proposed to solve the optimization problems. In general, regularization, more specifically, non-smooth regularization, can be used in order to build sparse networks, which make the optimization task difficult. The main aim is to develop a novel optimizer based on Bayesian framework. Promising results are obtained when our optimizer is applied on classification of Covid-19 images. By using the proposed approach, an accuracy rate equal to 94% is obtained surpasses all the competing optimizers that do not exceed an accuracy rate of 86%, and 84% for standard Deep Learning optimizers. © 2022 IEEE.

An Optimized Ensemble Model for COVID Detection

The entire world has gone through a pandemic situation due to the spread of novel corona virus. In this paper, the authors have proposed an ensemble learning model for the classification of the subjects to be infected by coronavirus. For this purpose, five types of symptoms are considered. The dataset contains 2889 samples with six attributes and is collected from the Kaggle database. Three different types of classifiers such as Support vector machine (SVM), Gradient boosting, and extreme gradient boosting (XGBoost) are considered for classification purposes. For improving the learning strategy and performance of the proposed models subjected to accuracy, the learning rates are varied for each node of the tree-based ensemble classifiers. Also, the hyperparameters of the XGBoost model are optimized by applying the Bayesian optimization (BO) technique. The best accuracy in SVM classifier is found as 91.69%. 96.58% accuracy is obtained in the modified gradient boosting model. The optimized XGBoost model is providing 100% accuracy which is better than other. © 2022 IEEE.

Analysis of Psychological Distress During COVID-19 Among Professionals

The aftermath of the lockdown caused by the current pandemic generates many challenges and opportunities for the professionals as well as for organizations. Several organizations forced the people to work on-site whereas many of the organizations have been allowing work from home. However, both ways of working are challenging and cause psychological distress. The present work analyses the psychological distress among professionals residing in India during the COVID-19 pandemic. The work considers both the scenarios of working professionals: professionals working from home and professionals working onsite. The work introduces a novel hybrid machine learning approach called GBETRR. GBETRR combines two approaches, namely gradient-boosting classifier and extra-trees regressor repressor. The present work also uses a hybrid parameter optimization algorithm. Multiple performance metrics are used to evaluate the performance evaluation. Results revealed that the professionals with work from home are more stressed as compared to the professionals working onsite. Copyright © 2022 IGI Global.

Hyperparameter Search for CT-Scan Classification Using Hyperparameter Tuning in Pre-Trained Model CNN With MLP

The common approach to find best hyperparameter in CNN training is grid search, by observing one set to another of hyperparameter for obtaining the best result. However, this approach is considered inefficient, time-consuming, and ineffectively computational. In this study, we are observing 2 hyperparameter tuning algorithms (bayesian optimization and random search) in search of the best hyperparameter for CT-Scan classification case. The used dataset is COVID-19 and non-COVID-19 lung CT-Scans. Several CNN architectures are also used such as: InceptionV4, MobileNetV3, and EfficientnetV2 with additional multi-layer perceptron on top layers. Based on the experiments, model EfficientnetV2-L architecture using hyperparameter from bayesian-optimization can outperform other models, with batch size of 32, learning rate of 0.01, dropout 0.5, Adam optimizer and SoftMax activation, resulting in the accuracy rate of 0.94% and a model training time of 50 minutes 40 seconds. © 2022 IEEE.

Forecasting domestic waste generation during successive COVID-19 lockdowns by Bidirectional LSTM super learner neural network

Accurate prediction of domestic waste generation is a challenging task for municipalities to implement sustainable waste management strategies. In the present study, domestic waste generation in the Kingdom of Bahrain, representing a Small Island Developing State (SIDS) case study, has been investigated during successive COVID-19 lockdowns due to the pandemic in 2020. Temporal trends of daily domestic waste generation between 2019 and 2020 and their statistical analyses exhibited remarkable variations highlighting the impact of consecutive COVID-19 lockdowns on domestic waste generation. Machine learning has great potential for predicting solid waste generation rates, but only a few studies utilized deep learning approaches. The state-of-the-art Bidirectional Long Short-Term Memory (BiLSTM) network model as a deep learning method is applied to forecast daily domestic waste data in 2020. Bayesian optimization algorithm (BOA) was hybridized with BiLSTM to generate a super learner approach. The performance of the BOA-BiLSTM super learner model was further compared with the statistical ARIMA model. Performance indicators of the developed models using ARIMA and BiLSTM showed that the latter yielded superior performance for short-term forecasts of domestic waste generation. The MAE, RMSE, MAPE, and R2 were 47.38, 60.73, 256.43, and 0.46, respectively, for the ARIMA model, compared to 3.67, 12.57, 0.24, and 0.96, respectively, for the BiLSTM model. Additionally, the relative errors for the BiLSTM model were lower than those of the ARIMA model. This study highlights that the BiLSTM can be a reliable forecasting tool for solid waste management policymakers during public health emergencies. © 2022 Elsevier B.V.

D2BOF-COVIDNet: A Framework of Deep Bayesian Optimization and Fusion-Assisted Optimal Deep Features for COVID-19 Classification Using Chest X-ray and MRI Scans.

BACKGROUND AND OBJECTIVE: In 2019, a corona virus disease (COVID-19) was detected in China that affected millions of people around the world. On 11 March 2020, the WHO declared this disease a pandemic. Currently, more than 200 countries in the world have been affected by this disease. The manual diagnosis of this disease using chest X-ray (CXR) images and magnetic resonance imaging (MRI) is time consuming and always requires an expert person; therefore, researchers introduced several computerized techniques using computer vision methods. The recent computerized techniques face some challenges, such as low contrast CTX images, the manual initialization of hyperparameters, and redundant features that mislead the classification accuracy. METHODS: In this paper, we proposed a novel framework for COVID-19 classification using deep Bayesian optimization and improved canonical correlation analysis (ICCA). In this proposed framework, we initially performed data augmentation for better training of the selected deep models. After that, two pre-trained deep models were employed (ResNet50 and InceptionV3) and trained using transfer learning. The hyperparameters of both models were initialized through Bayesian optimization. Both trained models were utilized for feature extractions and fused using an ICCA-based approach. The fused features were further optimized using an improved tree growth optimization algorithm that finally was classified using a neural network classifier. RESULTS: The experimental process was conducted on five publically available datasets and achieved an accuracy of 99.6, 98.5, 99.9, 99.5, and 100%. CONCLUSION: The comparison with recent methods and t-test-based analysis showed the significance of this proposed framework.

Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods.

Since the first case of COVID-19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID-19 and COVID-19 findings from computed tomography images using pre-trained models using two different datasets. COVID-19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy-paving pattern, ground-glass opacity, ground-glass opacity and consolidation, ground-glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre-trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine-Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient-weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID-19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID-19 in the lungs was an innovative and successful approach.

Exploring ultrafast flow chemistry by autonomous self-optimizing platform

Automated microreactor platform with Bayesian optimization achieves efficient exploring the unstable intermediate and optimizing the reaction conditions for ultrafast chemistry. [Display omitted] • Fully automated microreactor platform (AMP) was developed and demonstrated. • AI-integrated AMP efficiently optimized reaction conditions of ultrafast chemistry. • Thioquinazolinone derivatives were automatically synthesized within 20 min. The rapid development of novel synthetic routes for pharmaceutical compounds is highly attractive for overcoming pandemic and epidemic-prone diseases like COVID-19. Herein, we report an automated microreactor platform (AMP) with Bayesian optimization (BO) that can autonomously explore the optimal conditions for ultrafast synthesis of biologically active thioquinazolinone. First, AMP operation is successfully demonstrated with full control of quantitative variables, specifically reaction volume, temperature, and flow rate, allowing to sequentially conduct a total of 80 experiments planned by the user. Next, BO enables the AMP to autonomously self-optimize the reaction conditions, demonstrating the high efficiency of the fully automated AMP. The fully automated approach is extended to optimize more complex variables including a categorical variable (i.e. the type of organolithium for synthesis), revealing that phenyllithium (PhLi) gives superior yield for synthesizing thioquinazolinone. In addition, the autonomous AMP is utilized for combinatorial chemistry to sequentially synthesize a library composed of nine types of S-benzylic thioquinazolinone under autonomously optimized conditions within only 20 min. [ FROM AUTHOR]

Toward real-world automated antibody design with combinatorial Bayesian optimization.

Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.

Automatic COVID-19 diagnosis using deep learning features and support vector machines based on Bayesian optimization

Early diagnosis of COVID-19 and detection of infected people are crucial in taking preventative measures and treating the infected people. Artificial intelligence applications based on machine and deep learning techniques are more effective and applicable in such cases. In this work, an approach for automatic COVID-19 diagnosis using chest X-ray images is proposed. In this paper, AlexNet, VGG16, and VGG19 deep learning architectures were used to extract the useful and relevant features. These features were then used as inputs to the support vector machine (SVM) with two discrete outputs: COVID-19 or No-findings. Furthermore, the Bayesian optimization (BO) algorithm was used to tune the parameters of the SVM classifier and choose the optimal parameters. The results of the study indicate that the VGG16-SVM-BO and VGG19-SVM-BO models give the best performance with an accuracy of 99.47%. According to this result, the proposed approach can effectively contribute to the diagnosis of COVID-19. © 2022 IEEE.

COVID-19 classification using chest X-ray images based on fusion-assisted deep Bayesian optimization and Grad-CAM visualization.

The COVID-19 virus's rapid global spread has caused millions of illnesses and deaths. As a result, it has disastrous consequences for people's lives, public health, and the global economy. Clinical studies have revealed a link between the severity of COVID-19 cases and the amount of virus present in infected people's lungs. Imaging techniques such as computed tomography (CT) and chest x-rays can detect COVID-19 (CXR). Manual inspection of these images is a difficult process, so computerized techniques are widely used. Deep convolutional neural networks (DCNNs) are a type of machine learning that is frequently used in computer vision applications, particularly in medical imaging, to detect and classify infected regions. These techniques can assist medical personnel in the detection of patients with COVID-19. In this article, a Bayesian optimized DCNN and explainable AI-based framework is proposed for the classification of COVID-19 from the chest X-ray images. The proposed method starts with a multi-filter contrast enhancement technique that increases the visibility of the infected part. Two pre-trained deep models, namely, EfficientNet-B0 and MobileNet-V2, are fine-tuned according to the target classes and then trained by employing Bayesian optimization (BO). Through BO, hyperparameters have been selected instead of static initialization. Features are extracted from the trained model and fused using a slicing-based serial fusion approach. The fused features are classified using machine learning classifiers for the final classification. Moreover, visualization is performed using a Grad-CAM that highlights the infected part in the image. Three publically available COVID-19 datasets are used for the experimental process to obtain improved accuracies of 98.8, 97.9, and 99.4%, respectively.

Wasserstein-based texture analysis in radiomic studies.

The emerging field of radiomics that transforms standard-of-care images to quantifiable scalar statistics endeavors to reveal the information hidden in these macroscopic images. The concept of texture is widely used and essential in many radiomic-based studies. Practice usually reduces spatial multidimensional texture matrices, e.g., gray-level co-occurrence matrices (GLCMs), to summary scalar features. These statistical features have been demonstrated to be strongly correlated and tend to contribute redundant information; and does not account for the spatial information hidden in the multivariate texture matrices. This study proposes a novel pipeline to deal with spatial texture features in radiomic studies. A new set of textural features that preserve the spatial information inherent in GLCMs is proposed and used for classification purposes. The set of the new features uses the Wasserstein metric from optimal mass transport theory (OMT) to quantify the spatial similarity between samples within a given label class. In particular, based on a selected subset of texture GLCMs from the training cohort, we propose new representative spatial texture features, which we incorporate into a supervised image classification pipeline. The pipeline relies on the support vector machine (SVM) algorithm along with Bayesian optimization and the Wasserstein metric. The selection of the best GLCM references is considered for each classification label and is performed during the training phase of the SVM classifier using a Bayesian optimizer. We assume that sample fitness is defined based on closeness (in the sense of the Wasserstein metric) and high correlation (Spearman's rank sense) with other samples in the same class. Moreover, the newly defined spatial texture features consist of the Wasserstein distance between the optimally selected references and the remaining samples. We assessed the performance of the proposed classification pipeline in diagnosing the coronavirus disease 2019 (COVID-19) from computed tomographic (CT) images. To evaluate the proposed spatial features' added value, we compared the performance of the proposed classification pipeline with other SVM-based classifiers that account for different texture features, namely: statistical features only, optimized spatial features using Euclidean metric, non-optimized spatial features with Wasserstein metric. The proposed technique, which accounts for the optimized spatial texture feature with Wasserstein metric, shows great potential in classifying new COVID CT images that the algorithm has not seen in the training step. The MATLAB code of the proposed classification pipeline is made available. It can be used to find the best reference samples in other data cohorts, which can then be employed to build different prediction models.

Exploring ultrafast flow chemistry by autonomous self-optimizing platform

The rapid development of novel synthetic routes for pharmaceutical compounds is highly attractive for overcoming pandemic and epidemic-prone diseases like COVID-19. Herein, we report an automated microreactor platform (AMP) with Bayesian optimization (BO) that can autonomously explore the optimal conditions for ultrafast synthesis of biologically active thioquinazolinone. First, AMP operation is successfully demonstrated with full control of quantitative variables, specifically reaction volume, temperature, and flow rate, allowing to sequentially conduct a total of 80 experiments planned by the user. Next, BO enables the AMP to autonomously self-optimize the reaction conditions, demonstrating the high efficiency of the fully automated AMP. The fully automated approach is extended to optimize more complex variables including a categorical variable (i.e. the type of organolithium for synthesis), revealing that phenyllithium (PhLi) gives superior yield for synthesizing thioquinazolinone. In addition, the autonomous AMP is utilized for combinatorial chemistry to sequentially synthesize a library composed of nine types of S-benzylic thioquinazolinone under autonomously optimized conditions within only 20 minutes.

MO-MEMES: A method for accelerating virtual screening using multi-objective Bayesian optimization.

The pursuit of potential inhibitors for novel targets has become a very important problem especially over the last 2 years with the world in the midst of the COVID-19 pandemic. This entails performing high throughput screening exercises on drug libraries to identify potential "hits". These hits are identified using analysis of their physical properties like binding affinity to the target receptor, octanol-water partition coefficient (LogP) and more. However, drug libraries can be extremely large and it is infeasible to calculate and analyze the physical properties for each of those molecules within acceptable time and moreover, each molecule must possess a multitude of properties apart from just the binding affinity. To address this problem, in this study, we propose an extension to the Machine learning framework for Enhanced MolEcular Screening (MEMES) framework for multi-objective Bayesian optimization. This approach is capable of identifying over 90% of the most desirable molecules with respect to all required properties while explicitly calculating the values of each of those properties on only 6% of the entire drug library. This framework would provide an immense boost in identifying potential hits that possess all properties required for a drug molecules.

A regularization-driven Mean Teacher model based on semi-supervised learning for medical image segmentation.

Objective.A semi-supervised learning method is an essential tool for applying medical image segmentation. However, the existing semi-supervised learning methods rely heavily on the limited labeled data. The generalization performance of image segmentation is improved to reduce the need for the number of labeled samples and the difficulty of parameter tuning by extending the consistency regularization.Approach.We propose a new regularization-driven Mean Teacher model based on semi-supervised learning for medical image segmentation in this work. We introduce a regularization-driven strategy with virtual adversarial training to improve segmentation performance and the robustness of the Mean Teacher model. We optimize the unsupervised loss function and the regularization term with an entropy minimum to smooth the decision boundary.Main results.We extensively evaluate the proposed method on the International Skin Imaging Cooperation 2017(ISIC2017) and COVID-19 CT segmentation datasets. Our proposed approach gains more accurate results on challenging 2D images for semi-supervised medical image segmentation. Compared with the state-of-the-art methods, the proposed approach has significantly improved and is superior to other semi-supervised segmentation methods.Significance.The proposed approach can be extended to other medical segmentation tasks and can reduce the burden of physicians to some extent.

Effective Diagnosing of Covid-19 from CXR Images Using Deep Learning Approaches and Optimized XG Boost Model

Since the first confirmed incidence of the novel coronavirus Covid-19 in China, it has spread fast around the world, reaching a population of 442,602,593(at the start of 2022), according to World Health Organization figures.Therefore, the diagnosis of the virus is crucial to prevent its separation. However, the tools available for Covid-19 diagnosis are limited compared to the pressure at the increasing number of infected people. Therefore, to prevent the virus thread, it is necessary to find a quick automated system that can handle a bulk amount of data with high accuracy and a lower amount of false positive or false negative. This research presents a hybrid machine learning-based system that uses a pre-trained MobilNet model for feature extraction from chest X-ray images, followed by a dimensionality reduction technique to speed up the classification process and an XGBoost classifier to complete the task. Furthermore, the Bayesian algorithm is used to choose the optimum hyperparameters for the XGBoost classifier. The suggested approach was evaluated on two datasets of X-ray images and produced both high and near results.The results for the first dataset were 97.65% accuracy, 97.63% F1-score, 97.65% recall, and 97.69% precision,and for the second dataset were 96.35% accuracy, 95.82% F1-score, 98.35% recall, and 96.38 precision.

Daily life human activities recognition using a novel AR-DenseNet

With the introduction of mobile-wearable gadgets, the embedding of health-tracking capabilities has advanced dramatically. The Covid-19 years have accelerated research toward wearable sensor-based health monitoring. One of the most important applications in health monitoring is human activity identification. Daily behaviors like walking, sitting and jogging, as well as crucial activities such as falling forward and backward, provide a barrier in HAR (human action recognition) since they are semantically comparable. Previously developed deep learning algorithms have addressed some of these issues. However, these algorithms are hindered by a lack of training data. To cope with nonuniform samples in the human activities data, which can lead to overfitting results, this work introduces the synthetic minority oversampling approach. This article proposes a unique configuration of stacked convolutional neural network (CNN)-AR-DenseNet. With Bayesian optimization, the parameters of AR-DenseNet are optimally optimized. When compared to state-of-the-art stacked CNN network methods, the classification accuracy improved by up to 3.22%, with a substantial improvement of 5.8% over the standard CNN algorithm. © 2022 John Wiley & Sons, Ltd.

Intelligent Forecast of Stock Markets to Handle COVID-19 Economic Crisis by Modified Generative Adversarial Networks

Stock markets have voluminous data and are subjected to uncertainty. The coronavirus disease of 2019 (COVID-19) pandemic has hit the stock markets and the trends of stock markets have accelerated share prices of few companies and has also brought freefall to certain companies. This factor highlights the importance of technical analysis of the stock markets over fundamental analysis. So, the proposed robust model for financial forecasting is built based on the technical indicators and the fake price data generated over a period of time from the stock dataset by a novel architecture of modified generative adversarial network, which uses a dense recurrent neural network as the generator and a dense spectrally normalized convolutional neural network as the discriminator. The hyperparameters used in the network model follow the two-time-scale-update rule and they are tuned by using the Bayesian optimization technique. The feature importance of the technical indicators in predicting the performance by the stock market is enhanced by the XGBoost algorithm. The generative adversarial networks (GAN) used for forecasting in the previous works suffer from problems like mode collapse and non-convergence. So, the proposed work concentrates on building a GAN model, which is stable, robust and converges to Nash equilibrium. The generated GAN model is applied on stock data from the major 100 companies of the S&P 500 stock for a period of 20 years. The modified GAN model predicts prices precise similar to 99 percentage, which maximizes the stock returns. The proposed modified GAN model outperforms the baseline GAN model and other state of the art approaches of forecasting on comparison.

Screening Covid-19 Infection from Chest CT Images using Deep Learning Models based on Transfer Learning

As the global epidemic of Covid19 progresses, accurate diagnosis of Covid19 patients becomes important. The biggest problem in diagnosing test-positive people is the lack or lack of test kits due to the rapid spread of Covid19 in the community. As an alternative rapid diagnostic method, an automated detection system is needed to prevent Covid 19 from spreading to humans. This article proposes to use a convolutional neural network (CNN) to detect patients infected with coronavirus using computer tomography (CT) images. In addition, the transfer learning of the deep CNN model VGG16 is investigated to detect infections on CT scans. The pretrained VGG16 classifier is used as a classifier, feature extractor, and fine tuner in three different sets of tests. Image augmentation is used to boost the model's generalization capacity, while Bayesian optimization is used to pick optimum values for hyperparameters. In order to fine-tune the models and reduce training time, transfer learning is being researched. Surprisingly, all of the proposed models scored greater than 93% accuracy, which is on par with or better than previous deep learning models. The results show that optimization improved generalization in all models and highlight the efficacy of the proposed strategies. © 2022 IEEE.