Classification of Post-COVID-19 Emotions with Residual-Based Separable Convolution Networks and EEG Signals

The COVID-19 epidemic has created highly unprocessed emotions that trigger stress, anxiety, or panic attacks. These attacks exhibit physical symptoms that may easily lead to misdiagnosis. Deep-learning (DL)-based classification approaches for emotion detection based on electroencephalography (EEG) signals are computationally costly. Nowadays, limiting memory potency, considerable training, and hyperparameter optimization are always needed for DL models. As a result, they are inappropriate for real-time applications, which require large computational resources to detect anxiety and stress through EEG signals. However, a two-dimensional residual separable convolution network (RCN) architecture can considerably enhance the efficiency of parameter use and calculation time. The primary aim of this study was to detect emotions in undergraduate students who had recently experienced COVID-19 by analyzing EEG signals. A novel separable convolution model that combines residual connection (RCN-L) and light gradient boosting machine (LightGBM) techniques was developed. To evaluate the performance, this paper used different statistical metrics. The RCN-L achieved an accuracy (ACC) of 0.9263, a sensitivity (SE) of 0.9246, a specificity (SP) of 0.9282, an F1-score of 0.9264, and an area under the curve (AUC) of 0.9263 when compared to other approaches. In the proposed RCN-L system, the network avoids the tedious detection and classification process for post-COVID-19 emotions while still achieving impressive network training performance and a significant reduction in learnable parameters. This paper also concludes that the emotions of students are highly impacted by COVID-19 scenarios.

Tourist Trajectory Prediction Based on Improved LightGBM

Considering the influences of the COVID-19 disease, systemic risks with respect to the tourism industry and the erratic preferences of the tourists have fiercely affected the performance of machine learning models for tourist trajectory prediction. This paper introduces a noise-reduced and Bayesian optimized light gradient boosting machine(LightGBM) to forecast the likelihood of visitors entering the consequent scenic attraction, accommodating to the variability of tourism attributes. The empirical evidence of tourism data in Luoyang City Hall from March 2020 to November 2021 illustrates that our practice surpasses the baseline LightGBM mechanism as well as a random search-based technique regarding prediction loss by 5.39% and 4.42% correspondingly. The proposed research demonstrates a promising stride in the improvement of intelligent tourism in the experimental area by enhancing tourist experiences and allocating tourism resources efficiently, which can also be smoothly applied to other scenic spots. © 2023 SPIE.

Designing Hybrid CNN-SVM Model for COVID-19 Classification Based on X-ray Images Using LGBM Feature Selection

COVID-19 still exists at an alarming level;hence, early diagnosis is important for treating and controlling this disease due to its rapid spread. The use of X-rays in medical image analysis can play an essential role in fast and affordable diagnosis. This study used a two-level feature selection in hybrid deep convolutional features obtained from the extraction of X-ray images. The transfer learning-based approach was implemented using five convolutional neural networks (CNNs) named VGG16, VGG19, ResNet50, InceptionV3, and Xception. The combination of two or three CNNs' performance as a feature extractor was then carefully analyzed. We selected the features obtained from multiple CNNs in a particular layer with a specified percentage of features in the first level for getting relevant features from various models. Then, we combined those features and did the second level of feature selection to select the most informative features. Both levels of feature selection were carried out using the light gradient boosting machine (LightGBM) algorithm. The final feature set has been used to classify COVID-19 and non-COVID-19 chest X-ray images using the support vector machines (SVM) classifier. The proposed model's performance was evaluated and analyzed on the open-access dataset. The highest accuracy was 99.80% using only 5% of the features extracted from ResNet50 and Xception. The other way of combining the ensemble of deep features and a few recent works for the classification of COVID-19 were also compared with the proposed model. As a result, our proposed model has achieved the best success rate for this dataset and may be deployed to support decision systems for radiologists © IJASEIT is licensed under a Creative Commons Attribution-Share Alike 4.0 International License

Prediction of Used Car Prices Using Machine Learning

As the Indian auto-industry entered BS-VI era from April 2020, the value proposition of used cars grew stronger, as the new cars became expensive due to additional technology costs. Moreover, the unavailability of public transport and fear of infection force people toward self-mobility during the outbreak of Covid-19 pandemic. But, the surge in demand for used cars made some car sellers to take advantage from customers by listing higher prices than normal. In order to help consumers aware of market trends and prices for used cars, there comes the need to create a model that can predict the cost of used cars by taking into consideration about different features and prices of other cars present in the country. In this paper, we have used different machine learning algorithms such as k-nearest neighbor (KNN), random forest regression, decision tree, and light gradient boosting machine (LightGBM) which is able to predict the price of used cars based on different features specific to Indian buyers, and we have implemented the best model by comparing with other models to serve our cause. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

GLCM Based LGBM classification for COVID-19 Analysis using Chest X-ray Images

The novel corona virus (COVID-19) has turned out to be the biggest challenge of 21 century. Since, it is spreading at a very high pace all over the world, fast and accurate detection of this virus becomes a necessity. However, the human annotation of images is time-consuming;it is not a good strategy for dealing with big amounts of medical imaging data. This work looks at the experimental examination of features that are well-suited for examining X-ray pictures in COVID19. This investigation encompasses the series of steps, including data augmentation, pre-processing, feature extraction using GLCM followed by feature selection using PCA, and finally classification is performed by Light Gradient Boosting Machine. The proposed method was validated by comparing it to COVID-19 X-ray dataset, with an accuracy achieved is 92.40%. © 2022 IEEE.

Integrated model for COVID-19 diagnosis based on computed tomography artificial intelligence, and clinical features: a multicenter cohort study.

Background: We developed and validated a machine learning diagnostic model for the novel coronavirus (COVID-19) disease, integrating artificial-intelligence-based computed tomography (CT) imaging and clinical features. Methods: We conducted a retrospective cohort study in 11 Japanese tertiary care facilities that treated COVID-19 patients. Participants were tested using both real-time reverse transcription polymerase chain reaction (RT-PCR) and chest CTs between January 1 and May 30, 2020. We chronologically split the dataset in each hospital into training and test sets, containing patients in a 7:3 ratio. A Light Gradient Boosting Machine model was used for the analysis. Results: A total of 703 patients were included, and two models-the full model and the A-blood model-were developed for their diagnosis. The A-blood model included eight variables (the Ali-M3 confidence, along with seven clinical features of blood counts and biochemistry markers). The areas under the receiver-operator curve of both models [0.91, 95% confidence interval (CI): 0.86 to 0.95 for the full model and 0.90, 95% CI: 0.86 to 0.94 for the A-blood model] were better than that of the Ali-M3 confidence (0.78, 95% CI: 0.71 to 0.83) in the test set. Conclusions: The A-blood model, a COVID-19 diagnostic model developed in this study, combines machine-learning and CT evaluation with blood test data and performs better than the Ali-M3 framework existing for this purpose. This would significantly aid physicians in making a quicker diagnosis of COVID-19.

Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review.

The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.

Personalized Prediction of Hospital Mortality in COVID-19-Positive Patients.

OBJECTIVE: To develop predictive models for in-hospital mortality and length of stay (LOS) for coronavirus disease 2019 (COVID-19)-positive patients. PATIENTS AND METHODS: We performed a multicenter retrospective cohort study of hospitalized COVID-19-positive patients. A total of 764 patients admitted to 14 different hospitals within the Cleveland Clinic from March 9, 2020, to May 20, 2020, who had reverse transcriptase-polymerase chain reaction-proven coronavirus infection were included. We used LightGBM, a machine learning algorithm, to predict in-hospital mortality at different time points (after 7, 14, and 30 days of hospitalization) and in-hospital LOS. Our final cohort was composed of 764 patients admitted to 14 different hospitals within our system. RESULTS: The median LOS was 5 (range, 1-44) days for patients admitted to the regular nursing floor and 10 (range, 1-38) days for patients admitted to the intensive care unit. Patients who died during hospitalization were older, initially admitted to the intensive care unit, and more likely to be white and have worse organ dysfunction compared with patients who survived their hospitalization. Using the 10 most important variables only, the final model's area under the receiver operating characteristics curve was 0.86 for 7-day, 0.88 for 14-day, and 0.85 for 30-day mortality in the validation cohort. CONCLUSION: We developed a decision tool that can provide explainable and patient-specific prediction of in-hospital mortality and LOS for COVID-19-positive patients. The model can aid health care systems in bed allocation and distribution of vital resources.