Expert Systems with Applications

Time series in this dataset are used to create an interaction graph of markets and commodities to be used in machine learning prediction models. We used this dataset in our work and introduced a model named HyS3 and an algorithm named ConKruG.

The attachement is the quesionaire we performed for airport evaluation.

Data sheets with the set of defects used for training and testing the classiffiers. Columns B to BC (if read with Excel) are the value of 54 descriptors. Following two columns are the defect labelling given by an expert during the supervised training (both as a char or as an index). char E -> 6 fish-eye char I -> 0 gel char O -> 1 hole char N -> 2 Black Speck char B -> 3 Bug/Insect char A -> 4 Wrinkle char G -> 5 Rubber char D -> 7 Oil Drop char L -> 8 Light dirt char F -> 9 Fail Transmission First column is the name of the defect. Its first letter should be coherent with last two columns for defects with the following exceptions: - defects named as JXXXX.bmp are defects that can be considered as gel or rubber - defects named as KXXXX.bmp are defects that can be considered as gel or black speck - defects named as MXXXX.bmp are defects that can be considered as rubber or black speck

Instances of distributed reentrant permutation flow shop scheduling problem. The data is in Matlab file format.

Data files: Results

Our proposed data set consist of two part: Normal ( legitimate), and malicious (Phishing) pages. each sample in data set contain 75 features

The zip file contains five datasets used in our study

There are 4 databases, which includes four wind turbines' fault data.

This dataset contains data from 30 China provinces' industrial systems at the year of 2012 for eco-efficiency evaluation.

In this abstract we would like to provide some exciting concrete information including the article’s main impact and significance on expert and intelligent systems. The main impact is that the PTC expert intelligent system fills in the gaps between the human and software decision making processes. This gap analysis is analyzed via empirical triangulation of rail worker data collected from its groups, individuals and the rail industry itself. We utilize an expert intelligent system PTC information technology application to both measure and to improve the alertness of the groups and workers in order to improve the overall safety of the railways through reduced human errors and failures to prevent accidents. Many individual differences in alertness among military, railroad, and other industry workers stem from a lack of sufficient sleep. This continues to be a concern in the railroad industry, even with the implementation of positive train control (PTC) expert system technology. Information technology aids such as PTC cannot prevent all accidents, and errors and failures with PTC may occur. Furthermore, drug interventions are a short-term solution for improving alertness. This study investigated the effect of sleep deprivation on the alertness of railroad signalmen at work, individual differences in alertness, and the information technology available to improve alertness. We investigated various information and communication technology control systems that can be used to maintain operational safety in the railroad industry in the face of incompatible circadian rhythms due to irregular hours, weekend work, and night operations. To fully explain individual differences after the adoption of technology, our approach posits the necessary parameters that one must consider for reason-oriented action, sequential updating, feedback, and technology acceptance in a unified model. This triangulation can help manage workers by efficiently increasing their productivity and improving their health. In our analysis we used R statistical software and Tableau. To test our theory, we issued an Apple watch to a locomotive engineer. The perceived usefulness, perceived ease of use, and actual use he reported led to an analysis of his sleep patterns that eventually ended in his adoption of a sleep apnea device and an improvement in his alertness and effectiveness. His adoption of the technology also resulted in a decrease in his use of chemical interventions to increase his alertness. Our model shows that the alertness of signalmen can be predicted. Therefore, we recommend that the alertness of all railroad workers be predicted given the safety limitations of PTC.