Early detection of breast cancer is an essential procedure to reduce the mortality rate among women. In this paper, a new AI-based computer-aided diagnosis (CAD) framework called ETECADx is proposed by fusing the benefits of both ensemble transfer learning of the convolutional neural networks as well as the self-attention mechanism of vision transformer encoder (ViT). The accurate and precious high-level deep features are generated via the backbone ensemble network, while the transformer encoder is used to diagnose the breast cancer probabilities in two approaches: Approach A (i.e., binary classification) and Approach B (i.e., multi-classification). To build the proposed CAD system, the benchmark public multi-class INbreast dataset is used. Meanwhile, private real breast cancer images are collected and annotated by expert radiologists to validate the prediction performance of the proposed ETECADx framework. The promising evaluation results are achieved using the INbreast mammograms with overall accuracies of 98.58% and 97.87% for the binary and multi-class approaches, respectively. Compared with the individual backbone networks, the proposed ensemble learning model improves the breast cancer prediction performance by 6.6% for binary and 4.6% for multi-class approaches. The proposed hybrid ETECADx shows further prediction improvement when the ViT-based ensemble backbone network is used by 8.1% and 6.2% for binary and multi-class diagnosis, respectively. For validation purposes using the real breast images, the proposed CAD system provides encouraging prediction accuracies of 97.16% for binary and 89.40% for multi-class approaches. The ETECADx has a capability to predict the breast lesions for a single mammogram in an average of 0.048 s. Such promising performance could be useful and helpful to assist the practical CAD framework applications providing a second supporting opinion of distinguishing various breast cancer malignancies.

The COVID-19 outbreak is a disastrous event that has elevated many psychological problems such as lack of employment and depression given abrupt social changes. Simultaneously, psychologists and social scientists have drawn considerable attention towards understanding how people express their sentiments and emotions during the pandemic. With the rise in COVID-19 cases with strict lockdowns, people expressed their opinions publicly on social networking platforms. This provides a deeper knowledge of human psychology at the time of disastrous events. By applying user-produced content on social networking platforms such as Twitter, the sentiments and views of people are analyzed to assist in introducing awareness campaigns and health intervention policies. The modern evolution of artificial intelligence (AI) and natural language processing (NLP) mechanisms has revealed remarkable performance in sentimental analysis (SA). This study develops a new Marine Predator Optimization with Natural Language Processing for Twitter Sentiment Analysis (MPONLP-TSA) for the COVID-19 Pandemic. The presented MPONLP-TSA model is focused on the recognition of sentiments that exist in the Twitter data during the COVID-19 pandemic. The presented MPONLP-TSA technique undergoes data preprocessing to convert the data into a useful format. Furthermore, the BERT model is used to derive word vectors. To detect and classify sentiments, a bidirectional recurrent neural network (BiRNN) model is utilized. Finally, the MPO algorithm is exploited for optimal hyperparameter tuning process, and it assists in enhancing the overall classification performance. The experimental validation of the MPONLP-TSA approach can be tested by utilizing the COVID-19 tweets dataset from the Kaggle repository. A wide comparable study reported a better outcome of the MPONLP-TSA method over current approaches.

Automated oscillometric blood pressure monitors are commonly used to measure blood pressure for many patients at home, office, and medical centers, and they have been actively studied recently. These devices usually provide a single blood pressure point and they are not able to indicate the uncertainty of the measured quantity. We propose a new technique using an ensemble-based recursive methodology to measure uncertainty for oscillometric blood pressure measurements. There are three stages we consider: the first stage is pre-learning to initialize good parameters using the bagging technique. In the second stage, we fine-tune the parameters using the ensemble-based recursive methodology that is used to accurately estimate blood pressure and then measure the uncertainty for the systolic blood pressure and diastolic blood pressure in the third stage.

Respiratory rate monitoring has become necessary for people with respiratory diseases, especially those living alone. Sudden changes in respiratory rate (RR) in these individuals may indicate a severe illness. Hence, estimating the RR is essential for cardiopulmonary health conditions. Capnography is a well-known standard technique for measuring RR. This study presents a novel methodology that uses an automatic feature extraction algorithm integrated with a robust feature selection technique based on exact Gaussian process regression (EGPR) for RR and uncertainty estimations. As EGPR is vital to noisy data and is naturally normalized, it can generate uncertainty estimates. Uncertainty estimation is essential for estimating physiological parameters. In the proposed methodology, data dimension is obtained using a power spectral density feature based on an autocorrelation function; subsequently, the long-resampled wave signal is split to increase the number of input data samples. Selecting an appropriate high-dimensional feature is necessary to predict a response effectively. Herein, we use a robust feature selection method based on neighbor component analysis. The proposed EGPR with automatic feature extraction is more accurate than conventional algorithms for RR and confidence intervals (CIs) estimations. The proposed methodology proves superior to conventional algorithms in terms of saving computer resources. We confirm that the proposed methodology, 2.085 breath per minute (bpm), and EGPR with robust feature selection, 2.049 bpm, show the lowest mean absolute error compared to the conventional algorithms in the Beth Israel Deaconess Medical Center data set.

Estimating the correct respiratory rate (RR) is an essential technique for intensive care units, hospitals, geriatric hospital facilities, and home care services. Capnography is a standard methodology used to monitor carbon dioxide concentrations or partial pressures of respiratory gases to provide the most accurate RR measurements. However, it is inconvenient to use and has been primarily used while administering anesthesia and during intensive care. Many researchers now use electrocardiogram signals to estimate RR. Despite the recent developments, the current hospital environments suffer from inaccurate respiratory monitoring. While various machine learning techniques, including deep learning, have recently been applied to the medical processing sector, only a few studies have been conducted in the field of RR estimation. Therefore, using photoplethysmography, machine-learning techniques such as the ensemble gradient boosting algorithm are being employed in RR estimation. Multi-phases are used based on various feature extraction and selection methodology to improve the performance for RR estimation. In this study, the number of ensembles is increased, and the proposed ensemble methodology is effectively learned to estimate the RR. The proposed ensemble-based gradient boosting algorithm are compared with those of ensemble-based long-short memory network, and ensemble-based supported vector regression techniques, 3.30 breaths per min (bpm), 4.82 bpm and 5.83 bpm based on mean absolute errors. The proposed method shows a more accurate estimate of the respiration rate.

Various machine learning models have been used in the biomedical engineering field, but only a small number of studies have been conducted on respiratory rate estimation. Unlike ensemble models using simple averages of basic learners such as bagging, random forest, and boosting, the gradient boosting algorithm is based on effective iteration strategies. This gradient boosting algorithm is just beginning to be used for respiratory rate estimation. Based on this, we propose a novel methodology combining an autocorrelation function-based power spectral feature extraction process with the gradient boosting algorithm to estimate respiratory rate since we acquire the respiration frequency using the autocorrelation function-based power spectral feature extraction that finds the time domain’s periodicity. The proposed methodology solves overfitting for the training datasets because we obtain the data dimension by applying autocorrelation function-based power spectral feature extraction and then split the long-resampled wave signal to increase the number of input data samples. The proposed model provides accurate respiratory rate estimates and offers a solution for reliably managing the estimation uncertainty. In addition, the proposed method presents a more precise estimate than conventional respiratory rate measurement techniques.