In the recent pandemic, accurate and rapid testing of patients remained a critical task in the diagnosis and control of COVID-19 disease spread in the healthcare industry. Because of the sudden increase in cases, most countries have faced scarcity and a low rate of testing. Chest X-rays have been shown in the literature to be a potential source of testing for COVID-19 patients, but manually checking X-ray reports is time-consuming and error-prone. Considering these limitations and the advancements in data science, we proposed a Vision Transformer-based deep learning pipeline for COVID-19 detection from chest X-ray-based imaging. Due to the lack of large data sets, we collected data from three open-source data sets of chest X-ray images and aggregated them to form a 30 K image data set, which is the largest publicly available collection of chest X-ray images in this domain to our knowledge. Our proposed transformer model effectively differentiates COVID-19 from normal chest X-rays with an accuracy of 98% along with an AUC score of 99% in the binary classification task. It distinguishes COVID-19, normal, and pneumonia patient’s X-rays with an accuracy of 92% and AUC score of 98% in the Multi-class classification task. For evaluation on our data set, we fine-tuned some of the widely used models in literature, namely, EfficientNetB0, InceptionV3, Resnet50, MobileNetV3, Xception, and DenseNet-121, as baselines. Our proposed transformer model outperformed them in terms of all metrics. In addition, a Grad-CAM based visualization is created which makes our approach interpretable by radiologists and can be used to monitor the progression of the disease in the affected lungs, assisting healthcare.

Automatic screening of diabetic retinopathy (DR) is a well-identified area of research in the domain of computer vision. It is challenging due to structural complexity and a marginal contrast difference between the retinal vessels and the background of the fundus image. As bright lesions are prominent in the green channel, we applied contrast-limited adaptive histogram equalization (CLAHE) on the green channel for image enhancement. This work proposes a novel diabetic retinopathy screening technique using an asymmetric deep learning feature. The asymmetric deep learning features are extracted using U-Net for segmentation of the optic disc and blood vessels. Then a convolutional neural network (CNN) with a support vector machine (SVM) is used for the DR lesions classification. The lesions are classified into four classes, i.e., normal, microaneurysms, hemorrhages, and exudates. The proposed method is tested with two publicly available retinal image datasets, i.e., APTOS and MESSIDOR. The accuracy achieved for non-diabetic retinopathy detection is 98.6% and 91.9% for the APTOS and MESSIDOR datasets, respectively. The accuracies of exudate detection for these two datasets are 96.9% and 98.3%, respectively. The accuracy of the DR screening system is improved due to the precise retinal image segmentation.

Accurate demand forecasting has become extremely important, particularly in the food industry, because many products have a short shelf life, and improper inventory management can result in significant waste and loss for the company. Several machine learning and deep learning techniques recently showed substantial improvements when handling time-dependent data. This paper takes the ’Food Demand Forecasting’ dataset released by Genpact, compares the effect of various factors on demand, extracts the characteristic features with possible influence, and proposes a comparative study of seven regressors to forecast the number of orders. In this study, we used Random Forest Regressor, Gradient Boosting Regressor (GBR), Light Gradient Boosting Machine Regressor (LightGBM), Extreme Gradient Boosting Regressor (XGBoost), Cat Boost Regressor, Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) in particular. The results demonstrate the potential of deep learning models in forecasting and highlight the superiority of LSTM over other algorithms. The RMSLE, RMSE, MAPE, and MAE reach 0.28, 18.83, 6.56%, and 14.18, respectively.

Pregnancy complications significantly impact women and pose potential threats to the developing child’s health. Early identification of these complications is imperative for life-saving interventions. The manual analysis of cardiotocography (CTG) tests, a conventional practice among obstetricians, is both labor-intensive and unreliable. Consequently, the development of efficient fetal health classification models becomes crucial for optimizing medical resources and saving time.This study addresses the imperative for advanced fetal health classification through the application of machine learning (ML) techniques. The objective is to explore, develop, and analyze ML models capable of accurately classifying fetal health based on CTG data. The overarching goal is to enhance diagnostic precision and facilitate timely interventions.Utilizing a freely available cardiotocography data set, despite its relatively small size, the research acknowledges its rich characteristics. Various ML models, including Random Forests, Logistic Regression, Decision Trees, Support Vector Classifiers, Voting Classes, and K-Nearest Neighbors, are deployed on the data set. The analysis involves rigorous training and testing of these models to assess their efficacy in classifying fetal health.The study yields promising outcomes, with the implemented ML models achieving a notable accuracy level of 93%, surpassing previous methods. This underscores the effectiveness of the proposed models in elevating the precision of fetal health classification based on CTG data.The findings advocate for the integration of ML models into routine clinical practices, streamlining fetal health assessments. The study not only underscores the significance of early complication detection but also demonstrates the potential of ML in optimizing medical resource allocation and time efficiency. Further research is warranted to refine and expand ML applications in the context of fetal health assessment, promising advancements in prenatal care.