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Purpose – The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA). Design/methodology/approach – In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent. Findings – Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models. Research limitations/implications – In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions. Originality/value – In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

A major obstacle when developing convolutional neural networks (CNNs) for medical imaging is the acquisition of training labels: Most current approaches rely on manual class labels from physicians, which may be challenging to obtain. Clinical biomarkers, often measured alongside medical images and used in diagnostic workup, may provide a rich set of data that can be collected retrospectively and utilized to train diagnostic models. In this work, we focused on assessing the potential of blood serum biomarkers, B-type natriuretic peptide (BNP) and NT-pro B-type natriuretic peptide (BNPP), indicative of acute heart failure (HF) and cardiogenic pulmonary edema to be used as continuously valued labels for training a radiographic deep learning algorithm. For this purpose, a CNN was trained using 27748 radiographs to automatically infer BNP and BNPP, and achieved strong performance (AUC =0.903, sensitivity =0.926, specificity =0.857, $r=0.787$ ). Also, the trained models achieved strong performance (AUC =0.801) for pulmonary edema detection when evaluated with radiologist labels. Since relevant radiographic features visible to the CNN may vary greatly based on image resolution, we also assessed the impact of image resolution on model learning and performance, comparing CNNs trained at five image sizes ( $64\times 64$ to $1024\times 1024$ ). Increasing image resolutions had diminishing but positive gains in AUC. Perhaps more importantly, experiments using three activation mapping techniques (saliency, Grad-CAM, XRAI) revealed considerably increased attention in the lungs with larger image sizes. This result emphasizes the need to utilize radiographs near native resolution for optimal CNN performance, which may not be fully captured by summary metrics like AUC.