Abstract With recent advancements in the internet of things (IoT), wearables, and sensing technologies, the quality of healthcare services gets improved and it caused a shift from conventional clinical‐based healthcare to real‐time monitoring. The sensors are commonly integrated into several medical gadgets to save the bio‐signals produced by the physiological activities of the human body. At the same time, a biomedical electrocardiogram (ECG) signal is employed as a familiar way to examine and diagnose cardiovascular diseases (CVDs), which is rapid and non‐invasive. Since the increasing number of patients degrades the classification performance due to high differences in the ECG signal patterns among several patients, computer‐assisted automated diagnostic tools are essential for ECG signal classification. With this motivation, this paper introduces a new IoT and deep learning (DL) enabled healthcare disease diagnosis (IoTDL‐HDD) model using biomedical ECG signals. The proposed IoTDL‐HDD model aims to detect the presence of CVDs by the use of DL models in biomedical ECG signals. In addition, the proposed IoTDL‐HDD model utilizes a BiLSTM feature extraction technique to extract useful feature vectors from the ECG signals. For improving the efficiency of the BiLSTM technique, the artificial flora optimization (AFO) algorithm is employed as a hyperparameter optimizer. Besides, a fuzzy deep neural network (FDNN) classifier is employed for assigning proper class labels to the ECG signals. The performance of the IoTDL‐HDD model is examined on biomedical ECG signals and the outcomes are inspected in distinct features. The resultant experimental outcomes pointed out the supremacy of the IoTDL‐HDD model with the maximum accuracy of 93.452%.

Summary: In the last few years, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) has been increasingly used for simultaneous detection of thousands of metabolites and lipids from human tissues and biofluids. To successfully find the most significant differences between two sets of DESI-MSI data (e.g., healthy vs disease) requires the application of accurate computational and statistical methods that can pre-process the data under various normalization settings and help identify these changes among thousands of detected metabolites. Here, we report MassExplorer, a novel computational tool, to help pre-process DESI-MSI data, visualize raw data, build predictive models using the statistical lasso approach to select for a sparse set of significant molecular changes, and interpret selected metabolites. This tool, which is available for both online and offline use, is flexible for both chemists and biologists and statisticians as it helps in visualizing structure of DESI-MSI data and in analyzing the statistically significant metabolites that are differentially expressed across both sample types. Based on the modules in MassExplorer, we expect it to be immediately useful for various biological and chemical applications in mass spectrometry. Availability and implementation: MassExplorer is available as an online R-Shiny application or Mac OS X compatible standalone application. The application, sample performance, source code and corresponding guide can be found at: https://zarelab.com/research/massexplorer-a-tool-to-help-guide-analysis-of-mass-spectrometry-samples/. Supplementary informationMATION: Supplementary data are available at Bioinformatics online.