The study presents some results of customer paths’ analysis in a shopping mall. Bluetooth-based technology is used to collect data. The event log containing spatiotemporal information is analyzed with process mining. Process mining is a technique that enables one to see the whole process contrary to data-centric methods. The use of process mining can provide a readily-understandable view of the customer paths. We installed iBeacon devices, a Bluetooth-based positioning system, in the shopping mall. During December 2017 and January and February 2018, close to 8000 customer data were captured. We aim to investigate customer behaviors regarding gender by using their paths. We can determine the gender of customers if they go to the men’s bathroom or women’s bathroom. Since the study has a comprehensive scope, we focused on male and female customers’ behaviors. This study shows that male and female customers have different behaviors. Their duration and paths, in general, are not similar. In addition, the study shows that the process mining technique is a viable way to analyze customer behavior using Bluetooth-based technology.

A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.

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Understanding human behavior can assist in the adoption of satisfactory health interventions and improved care. One of the main problems relies on the definition of human behaviors, as human activities depend on multiple variables and are of dynamic nature. Although smart homes have advanced in the latest years and contributed to unobtrusive human behavior tracking, artificial intelligence has not coped yet with the problem of variability and dynamism of these behaviors. Process mining is an emerging discipline capable of adapting to the nature of high-variate data and extract knowledge to define behavior patterns. In this study, we analyze data from 25 in-house residents acquired with indoor location sensors by means of process mining clustering techniques, which allows obtaining workflows of the human behavior inside the house. Data are clustered by adjusting two variables: the similarity index and the Euclidean distance between workflows. Thereafter, two main models are created: (1) a workflow view to analyze the characteristics of the discovered clusters and the information they reveal about human behavior and (2) a calendar view, in which common behaviors are rendered in the way of a calendar allowing to detect relevant patterns depending on the day of the week and the season of the year. Three representative patients who performed three different behaviors: stable, unstable, and complex behaviors according to the proposed approach are investigated. This approach provides human behavior details in the manner of a workflow model, discovering user paths, frequent transitions between rooms, and the time the user was in each room, in addition to showing the results into the calendar view increases readability and visual attraction of human behaviors, allowing to us detect patterns happening on special days.

Online stores assist customers in buying the desired products online. Great competition in the e-commerce sector necessitates technology development. Many e-commerce systems not only present products but also offer similar products to increase online customer interest. Due to high product variety, analyzing products sold together similar to a recommendation system is a must. This study methodologically improves the traditional association rule mining (ARM) method by adding fuzzy set theory. Besides, it extends the ARM by considering not only items sold but also sales amounts. Fuzzy association rule mining (FARM) with the Apriori algorithm can catch the customers’ choice from historical transaction data. It discovers fuzzy association rules from an e-commerce company to display similar products to customers according to their needs in amount. The experimental result shows that the proposed FARM approach produces much information about e-commerce sales for decision-makers. Furthermore, the FARM method eliminates some traditional rules considering their sales amount and can produce some rules different from ARM.

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Consumption rates of polystyrene microplastics (beads of 6, 12, and 26 μm diameter) and their effects on energy metabolism and motor activity of the copepod Calanus helgolandicus living in the Marmara Sea were investigated. All sizes of microplastic particles were actively consumed and excreted via fecal pellets, however, copepods displayed a significant preference for beads sized 6 μm. In a mixture of algae and microplastics beads of 6 μm, microplastics consumption rates linearly (r2 = 0.78, n = 154) increased 800 times from 50.8 ± 17.3 to 8,612 ± 5,972 beads ind–1 day–1 with an increase in bead concentration from 10 to 44,000 beads ml–1. The total and basal metabolic rates as well as time spent swimming for C. helgolandicus, decreased 1.7, 1.8 and about 3-fold, respectively after 7–8 days exposure to microplastic treatments, which was similar to the metabolism and activity of starving animals in filtered water. In copepods consuming microplastics, all vital parameters decreased on the first day of exposure, indicating either accelerated starvation, probably due to increased losses of energy and biological matter in the formation of fecal pellets and/or traumatic/toxic effects of the polystyrene beads on the copepods. Our data from laboratory experiments indicate that the presence of large concentrations of microplastics in water, even when mixed with algae, lowered energy metabolism levels of C. helgolandicus.