Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices

We aimed to develop a prediction model for intensive care unit (ICU) hospitalization of Coronavirus disease-19 (COVID-19) patients using artificial neural networks (ANN). We assessed 25 laboratory parameters at first from 248 consecutive adult COVID-19 patients for database creation, training, and development of ANN models. We developed a new alpha-index to assess association of each parameter with outcome. We used 166 records for training of computational simulations (training), 41 for documentation of computational simulations (validation), and 41 for reliability check of computational simulations (testing). The first five laboratory indices ranked by importance were Neutrophil-to-lymphocyte ratio, Lactate Dehydrogenase, Fibrinogen, Albumin, and D-Dimers. The best ANN based on these indices achieved accuracy 95.97%, precision 90.63%, sensitivity 93.55%. and F1-score 92.06%, verified in the validation cohort. Our preliminary findings reveal for the first time an ANN to predict ICU hospitalization accurately and early, using only 5 easily accessible laboratory indices

Improved hybridization of evolutionary algorithms with a sensitivity-based decision-making technique for the optimal planning of shunt capacitors in radial distribution systems

In this paper, an improved hybridization of an evolutionary algorithm, named permutated oppositional differential evolution sine cosine algorithm (PODESCA) and also a sensitivity-based decision-making technique (SBDMT) are proposed to tackle the optimal planning of shunt capacitors (OPSC) problem in different-scale radial distribution systems (RDSs). The evolved PODESCA uniquely utilizes the mechanisms of differential evolution (DE) and an enhanced sine-cosine algorithm (SCA) to constitute the algorithm's main structure. In addition, quasi-oppositional technique (QOT) is applied at the initialization stage to generate the initial population, and also inside the main loop. PODESCA is implemented to solve the OPSC problem, where the objective is to minimize the system's total cost with the presence of capacitors subject to different operational constraints. Moreover, SBDMT is developed by using a multi-criteria decision-making (MCDM) approach; namely the technique for the order of preference by similarity to ideal solution (TOPSIS). By applying this approach, four sensitivity-based indices (SBIs) are set as inputs of TOPSIS, whereas the output is the highest potential buses for SC placement. Consequently, the OPSC problem's search space is extensively and effectively reduced. Hence, based on the reduced search space, PODESCA is reimplemented on the OPSC problem, and the obtained results with and without reducing the search space by the proposed SBDMT are then compared. For further validation of the proposed methods, three RDSs are used, and then the results are compared with different methods from the literature. The performed comparisons demonstrate that the proposed methods overcome several previous methods and they are recommended as effective and robust techniques for solving the OPSC problem

A novel combined evolutionary algorithm for optimal planning of distributed generators in radial distribution systems

In this paper, a novel, combined evolutionary algorithm for solving the optimal planning of distributed generators (OPDG) problem in radial distribution systems (RDSs) is proposed. This algorithm is developed by uniquely combining the original differential evolution algorithm (DE) with the search mechanism of Lévy flights (LF). Furthermore, the quasi-opposition based learning concept (QOBL) is applied to generate the initial population of the combined DELF. As a result, the new algorithm called the quasi-oppositional differential evolution Lévy flights algorithm (QODELFA) is presented. The proposed technique is utilized to solve the OPDG problem in RDSs by taking three objective functions (OFs) under consideration. Those OFs are the active power loss minimization, the voltage profile improvement, and the voltage stability enhancement. Different combinations of those three OFs are considered while satisfying several operational constraints. The robustness of the proposed QODELFA is tested and verified on the IEEE 33-bus, 69-bus, and 118-bus systems and the results are compared to other existing methods in the literature. The conducted comparisons show that the proposed algorithm outperforms many previous available methods and it is highly recommended as a robust and efficient technique for solving the OPDG problem

Prediction of concrete and FRC properties at high temperature using machine and deep learning: A review of recent advances and future perspectives

Concrete structures when exposed to elevated temperature significantly decline their original properties. High temperatures substantially affect the concrete physical and chemical properties causing significant structural decay and generalized damage impairing the safety and serviceability of the structure. Due to the great importance of concrete behavior at elevated temperatures and under fire, many studies have been conducted on cementitious composites, and the most relevant properties have been studied and evaluated. In particular, fiber-reinforced concrete (FRC) has been a subject of great interest in the last decade due to its superior properties compared to ordinary concrete. Several experimental studies and analytical models have been presented to predict concrete and FRC properties. Among the predictive models, machine learning (ML) tools have shown great merits over other analytical models due to their relative accuracy, generalization abilities, flexible mathematical framework, and cost-effective features. Among the ML, the deep learning (DL) models show remarkable performance when predicting the concrete and the FRC properties at high temperatures because of their ability to deal with more complex nonlinear correlations or difficult regression problems. This review paper presents a pioneering survey of the various ML and DL model implementations predicting concrete and FRC properties at high temperatures. The manuscript aims to establish a solid platform on the state of the art for machine and deep learning prediction of cementitious composites’ properties at elevated temperatures. It aims to provide interested researchers with research indications, directions, challenges, recommendations, and future perspectives