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It is standard cardiology practice for patients suffering from ventricular arrhythmias (the main cause of sudden cardiac death) belonging to high risk populations to be treated via the implantation of Subcutaneous Implantable cardioverter-defibrillators (S-ICDs). S-ICDs carry a risk of so-called T wave over sensing (TWOS), which can lead to inappropriate shocks that carry an inherent health risk. For this reason, according to current practice patients' Electrocardiograms (ECGs) are manually screened by a cardiologist over 10 s to assess the T:R ratio—the ratio between the amplitudes of the T and R waves which is used as a marker for the likelihood of TWOS—with a plastic template. Unfortunately, the temporal variability of a patient' T:R ratio can render such a screening procedure, which relies on an inevitably short ECG segment due to its manual nature, unreliable. In this paper, we propose and investigate a tool based on deep learning for the automatic prediction of the T:R ratios from multiple 10-second segments of ECG recordings capable of carrying out a 24-hour automated screening. Thanks to the significantly increased screening window, such a screening would provide far more reliable T:R ratio predictions than the currently utilized 10-second, template-based, manual screening is capable of. Our tool is the first, to the best of our knowledge, to fully automate such an otherwise manual and potentially inaccurate procedure. From a methodological perspective, we evaluate different deep learning model architectures for our tool, assess a range of stochastic-gradient-descent-based optimization methods for training their underlying deep-learning model, perform hyperparameter tuning, and create ensembles of the best performing models in order to identify which combination leads to the best performance. We find that the resulting model, which has been integrated into a prototypical tool for use by clinicians, is able to predict T:R ratios with very high accuracy. Thanks to this, our automated T:R ratio detection tool will enable clinicians to provide a completely automated assessment of whether a patient is eligible for S-ICD implantation which is more reliable than current practice thanks to adopting a significantly longer ECG screening window which better and more accurately captures the behavior of the patient's T:R ratio than the current manual practice. [ABSTRACT FROM AUTHOR]

This survey paper attempts to cover a broad range of topics related to computational biomedicine. The field has been attracting great attention due to a number of benefits it can provide the society with. New technological and theoretical advances have made it possible to progress considerably. Traditionally, problems emerging in this field are challenging from many perspectives. In this paper, we considered the influence of big data on the field, problems associated with massive datasets in biomedicine and ways to address these problems. We analyzed the most commonly used machine learning and feature mining tools and several new trends and tendencies such as deep learning and biological networks for computational biomedicine. [ABSTRACT FROM AUTHOR]

In the context of wireless sensor networks (WSNs), the utilization of artificial intelligence (AI)-based solutions and systems is on the ascent. These technologies offer significant potential for optimizing services in today's interconnected world. AI and nature-inspired algorithms have emerged as promising approaches to tackle various challenges in WSNs, including enhancing network lifespan, data aggregation, connectivity, and achieving optimal coverage of the targeted area. Coverage optimization poses a significant problem in WSNs, and numerous algorithms have been proposed to address this issue. However, as the number of sensor nodes within the sensor range increases, these algorithms often encounter difficulties in escaping local optima. Hence, exploring alternative global metaheuristic and bio-inspired algorithms that can be adapted and combined to overcome local optima and achieve global optimization in resolving wireless sensor network coverage problems is crucial. This paper provides a comprehensive review of the current state-of-the-art literature on wireless sensor networks, coverage optimization, and the application of machine learning and nature-inspired algorithms to address coverage problems in WSNs. Additionally, we present unresolved research questions and propose new avenues for future investigations. By conducting bibliometric analysis, we have identified that binary and probabilistic sensing model are widely employed, target and k-barrier coverage are the most extensively studied coverage scenarios in WSNs, and genetic algorithm and particle swarm optimization are the most commonly used nature-inspired algorithms for coverage problem analysis. This review aims to assist researchers in exploring coverage problems by harnessing the potential of nature-inspired and machine-learning algorithms. It provides valuable insights into the existing literature, identifies research gaps, and offers guidance for future studies in this field. [ABSTRACT FROM AUTHOR]

This paper addresses a generalization of the coupled-operations scheduling problem in the context of a flow shop environment. We consider the two-machine scheduling problem with the objective of minimizing the makespan. Each job consists of a coupled-operation to be processed first on the first machine and a single operation to be then processed on the second machine. A coupled-operation contains two operations separated by an exact time delay. The single operation can start on the second machine only when the coupled-operation on the first machine is completed. We prove the NP-completeness of two restricted versions of the general problem, whereas we also exhibit several other well solvable cases. [ABSTRACT FROM AUTHOR]

Machine learning and deep learning are powerful tools for quantitative investment. To examine the effectiveness of the models in different markets, this paper applies random forest and DNN models to forecast stock prices and construct statistical arbitrage strategies in five stock markets, including mainland China, the United States, the United Kingdom, Canada and Japan. Each model is applied to the price of major stock indices constituting stocks in these markets from 2005 to 2020 to construct a long-short portfolio with 20 selected stocks by the model. The results show that the a particular model obtains significantly different profits in different markets, among which DNN has the best performance, especially in the Chinese stock market. We find that DNN models generally perform better than other machine learning models in all markets. [ABSTRACT FROM AUTHOR]