The year 2020 will remain in the history for the diffusion of the COVID-19 virus, originating a pandemic on a world scale with over a million deaths. From the onset of the pandemic, the scientific community has made numerous efforts to design systems to detect the infected subjects in ever-faster times, allowing both to intervene on them, to avoid dangerous complications, and to contain the pandemic spreading. In this paper, we present an overview of different innovative technologies and devices fielded against the SARS-CoV-2 virus. The various technologies applicable to the rapid and reliable detection of the COVID-19 virus have been explored. Specifically, several magnetic, electrochemical, and plasmonic biosensors have been proposed in the scientific literature, as an alternative to nucleic acid-based real-time reverse transcription Polymerase Chain Reaction (PCR) (RT-qPCR) assays, overcoming the limitations featuring this typology of tests (the need for expensive instruments and reagents, as well as of specialized staff, and their reliability). Furthermore, we investigated the IoT solutions and devices, reported on the market and in the scientific literature, to contain the pandemic spreading, by avoiding the contagion, acquiring the parameters of suspected users, and monitoring them during the quarantine period.

/pdf/an-overview-of-technologies-and-devices-against-covid-19-1x1r13crqd.pdf

An overview of technologies and devices against COVID-19 pandemic diffusion: virus detection and monitoring solutions

Recently ehealth technologies are becoming an overwhelming aspect of public health services that provides seamless access to healthcare information. Machine learning tools associated with IoT technology play an important role in developing such health technologies. This paper proposes a decision support system-based system (DSS) to make diagnosis of cardiovascular diseases. It uses deep learning approaches that classify electrocardiogram (ECG) signals. Thus, a two-stage long-short term memory (LSTM) based neural network architecture, along with an adequate preprocessing of the ECG signals is designed as a diagnosis-aided system for cardiac arrhythmia detection based on an ECG signal analysis. This deep learning based cardio-vascular disease diagnosis system (namely ‘DLCVD’) is built to meet higher performance requirements in terms of accuracy, specificity, and sensitivity. This must also be capable of an online real-time classification. Experimental results using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database show that DLCVD led to outstanding performance

https://ijeecs.iaescore.com/index.php/IJEECS/article/download/26933/16016

A deep learning-based cardio-vascular disease diagnosis system

The classical formulation of the transmission switching problem as a mixed-integer problem is intractable for large systems in real-time control settings. Several heuristics have been proposed in the past to speed up the computation time, which only limits the number of switchable lines. In this paper, a real-time switching heuristic based on neural networks that provides almost instantaneous switching actions, are presented. The findings are shown on case studies of the IEEE 118-bus test system, and the results show that the proposed heuristic is robust to out of distribution data. Additionally, the proposed heuristic has significant computational savings while all other performance metrics like accuracy are similar to state-of-the-art machine learning methods proposed for transmission switching. 

/pdf/real-time-transmission-switching-with-neural-networks-i4wpbmgs.pdf

Real‐time transmission switching with neural networks

Designing integrating systems for support, real-time monitoring, and executing of complex missions is challenging, since they often fail due to high levels of complexity and overwhelming volume of input data. Past attempts have resorted to “ad hoc” solutions, which face issues of being non-updatable, non-upgradable, and not applicable to similar missions, necessitating a complete redesign and reconstruction of the system. In the national defense and security sector, the impact of this reconstruction requirement leads to significant costs and delays. This study presents advanced methodologies for organizing large-scale datasets and handling complex operational procedures systematically, enhancing the capabilities of Decision Support Systems (DSSs). By introducing Complex Mission Support Systems (CMSSs), a novel SS sub-component, improved accuracy and effectiveness are achieved. The CMSS includes mission conceptualization, analysis, real-time monitoring, control dynamics, execution strategies, and simulations. These methods significantly aid engineers in developing DSSs that are highly user-friendly and operational, thanks to human-reasoning-centered design, increasing performance and efficiency. In summary, the systematic development of data cores that support complex processes creates an adaptable and adjustable framework in a wide range of diverse missions. This approach significantly enhances the overall sustainability and robustness of an integrated system. 

Methodologies and Handling Techniques of Large-Scale Information in Decision Support Systems for Complex Missions

Cardiac decision support system has become an effective tool for monitoring, classification and prediction of heart diseases. The common purpose of many researches is to improve its performances. In this paper, Genetic-Fuzzy hybrid approach ensuring accuracy is proposed. It is planned to classify ECG signals into five cardiac types, including, the Normal class (N), Paced class (P), Left Bundle Branch Block class (LBBB), Right Bundle Branch Block class (RBBB) and Premature Ventricular Contraction class (PVC). The main purpose deals with the optimization of a fuzzy system. In fact, the Genetic Algorithm (GA) is applied mainly for tuning the membership and rules parameters. The Root Mean Square Error (RMSE) is considered as the cost function. Accordingly, the investigated approach presents efficient accuracy (RMSE = 0.398) when the Fuzzy Inference System (FIS) is of TakagiSugeno (TS) type and the Gaussian membership is selected. The good linguistic interpretation is the power of GeneticFuzzy hybrid approach regarding others in machine learning.

Genetic-Fuzzy Hybrid Approach for Arrhythmia Classification

The success of an Electrocardiogram (ECG) Decision Support System (DSS) requires the use of an optimum machine learning approach. For this purpose, this paper investigates the use of three feedforward neural networks; the Multilayer Perceptron (MLP), the Radial Basic Function Network (RBF), and the Probabilistic Neural Network (PNN) for recognition of normal and abnormal heartbeats. Feature sets were based on ECG morphology and Discrete Wavelet Transformer (DWT) coefficients. Then, a correlation between features was applied. After that, networks were configured and consequently used for the ECG classification. Next, with respect to the performance criteria fixed by the DSS users, a comparative study between them was deduced. Results show that for classifying the MIT-BIH arrhythmia database signals, the RBF (ACC = 99.9%) was retained as the most accurate network, the PNN (Tr_ttime = 0.070 s) as the rapidest network in the training stage and the MLP (Test_time = 0.096 s) as the rapidest network in testing stage.

/pdf/ecg-decision-support-system-based-on-feedforward-neural-2kkcqnhh2c.pdf

ECG Decision Support System based on feedforward Neural Networks

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ECG Decision Support System based on feedforward Neural Networks