Traditional techniques for detecting materials have been unable to coordinate with the advancement of material science today due to their low accuracy and high cost. Accordingly, machine learning (ML) improves prediction efficiency in material science and high-entropy alloys’ (HEAs’) phase prediction. Unlike traditional alloys, HEAs consist of at least five elements with equal or near-equal atomic sizes. In a previous approach, we presented an HEA interaction network based on its descriptors. In this study, the HEA phase is predicted using a graph-based k-nearest neighbor (KNN) approach. Each HEA compound has its phase, which includes five categories: FCC, BCC, HCP, Multiphase and Amorphous. A composition phase represents a state of matter with a certain energy level. Phase prediction is effective in determining its application. Each compound in the network has some neighbors, and the phase of a new compound can be predicted based on the phase of the most similar neighbors. The proposed approach is performed on the HEA network. The experimental results show that the accuracy of the proposed approach for predicting the phase of new alloys is 88.88%, which is higher than that of other ML methods.

There is a growing trend toward the use of interaction network methods and algorithms, including community-based detection methods, in various fields of science. The approach is already used in many applications, for example, in social sciences and health informatics to analyze behavioral patterns during the COVID-19 pandemic, protein–protein networks in biological sciences, agricultural science, economy, and so forth. This paper attempts to build interaction networks based on high-entropy alloy (HEA) descriptors in order to discover HEA communities with similar functionality. In addition, these communities could be leveraged to discover new alloys not yet included in the data set without any experimental laboratory effort. This research has been carried out using two community detection algorithms, the Louvain algorithm and the enhanced particle swarm optimization (PSO) algorithm. The data set, which is used in this paper, includes 90 HEAs and 6 descriptors. The results reveal 13 alloy communities, and the accuracy of the results is validated by the modularity. The experimental results show that the method with the PSO-based community detection algorithm can achieve alloy communities with an average accuracy improvement of 0.26 compared to the Louvain algorithm. Furthermore, some characteristics of HEAs, for example, their phase composition, could be predicted by the extracted communities. Also, the HEA phase composition has been predicted by the proposed method and achieved about 93% precision.

Although various clinical factors affect the diagnosis of Non-alcoholic Fatty Liver Disease (NAFLD), most studies only use single-source data such as images or laboratory data. Nevertheless, using different categories of features can help to get better results. Hence, one of the most important purposes of this paper is to employ a multi-group of effective factors such as velocimetry, psychological, demographic and anthropometric, and lab test data. Then, some Machine Learning (ML) methods are applied to classify the samples into two healthy and patient with NAFLD groups. The data used here belongs to the PERSIAN Organizational Cohort study at Mashhad University of Medical Sciences. To quantify the scalability of the models, different validity metrics are used. The obtained results illustrate that the proposed method can lead to an increase in the efficiency of the classifiers.

Nowadays, the recommendation is an important task in the decision-making process about the selection of items especially when item space is large, diverse, and constantly updating. As a challenge in the recent systems, the preference and interest of users change over time, and existing recommender systems do not evolve optimal clustering with sufficient accuracy over time. Moreover, the behavior history of the users is determined by their neighbours. The purpose of the time parameter for this system is to extend the time-based priority. This paper has been carried out a time-aware recommender systems based on memetic evolutionary clustering algorithm called RecMem for recommendations. In this system, clusters that evolve over time using the memetic evolutionary algorithm and extract the best clusters at every timestamp, and improve the memetic algorithm using the chaos criterion. The system provides appropriate suggestions to the user based on optimum clustering. The system uses optimal evolutionary clustering using item attributes for the cold-start item problem and demographic information for the cold start user problem. The results show that the proposed method has an accuracy of approximately 0.95, which is more effective than existing systems.