In this paper, based on the discussion of some important issues related to cooperative design, and the analysis for niche technology, a group classifier algorithm and a sharing learning algorithm in a multi-agent coop...
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ISBN:
(纸本)9781424435340
In this paper, based on the discussion of some important issues related to cooperative design, and the analysis for niche technology, a group classifier algorithm and a sharing learning algorithm in a multi-agent cooperative system are put forward. The aim is to use socio-cultural perspectives and niche technology for supporting design reuse and share in a cooperative design system.
Supervised machine learning is a method that attempts to emulate experienced-based learning similar to how humans learn. This study leverages supervised machine learning algorithms to determine the most important fact...
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ISBN:
(纸本)9781665403962
Supervised machine learning is a method that attempts to emulate experienced-based learning similar to how humans learn. This study leverages supervised machine learning algorithms to determine the most important factors of the on-time performance of a San Antonio's VIA bus system using public data sets collected from the 2019 San Antonio Datathon event. A variety of algorithms are used to create models that are capable of predicting on-time performance to see if a bus route met standards on a given day. The algorithms present in the python libraries Sci Kit Learn and Stats Models. Once an algorithm has proven to be highly accurate at predicting the on-time performance, a further study was conducted into the decision-making process of each algorithm to find out which feature it determined to be most important. By knowing the most important feature of the on-time performance, the study gives insight as to what factors affect the performance of bus routes.
Data Mining classification task is categorized as a part of knowledge acquisition process, which can be implemented through the analysis procedure in related databases. In this study, we aimed to employ this technique...
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ISBN:
(纸本)9781612842127
Data Mining classification task is categorized as a part of knowledge acquisition process, which can be implemented through the analysis procedure in related databases. In this study, we aimed to employ this technique to perform talent knowledge acquisition process in Human Resource (HR) by using talent databases. In HR, among the challenges of HR professionals is to manage organization's talents, especially to ensure the right person assign to the right job at the right time. In this case, knowledge discovered from talent knowledge acquisition process can be used by professionals in HR to handle various tasks in talent management. In this article, we present an experimental study to identify the potential data mining classification technique for talent knowledge acquisition. Talent knowledge discovered from related databases can be used to classify the appropriate talent among employees. In experimental phase, we used selected classification algorithms in order to propose the suitable classifier from talent datasets. As a result, the C4.5 classifier algorithm from decision tree family is recommended as a suitable classifier for the datasets. Classification model performed by this classifier can be used in talent management especially for talent classification or prediction.
Slope geological disaster is a type of natural disaster that is prevalent in the plateau mountainous area. Its occurrence frequently brings substantial harm to the local human population and living environment. The ai...
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Slope geological disaster is a type of natural disaster that is prevalent in the plateau mountainous area. Its occurrence frequently brings substantial harm to the local human population and living environment. The aim of this research is to investigate the suitability of various mapping units for different classifiers in the process of creating susceptibility zoning maps for slope geological disasters and to evaluate the effectiveness of each model. Concurrently, the susceptibility map produced by the high-accuracy model will assist relevant authorities in conducting disaster management tasks and also serve as a reference for modeling the susceptibility assessment of environmental geological disasters in other mountainous regions. Fuyuan County of China was considered as the research area, based on two different mapping units and four classifiers. Nine factors leading to disasters, such as elevation and topographic relief, were chosen as the evaluation indexes. With the help of the ArcGIS platform, the zoning map of geo-logical disaster susceptibility is drawn. Ultimately, the accuracy of the evaluation results was verified by the receiver operating characteristic curve and the confusion matrix. The findings indicate that all approaches are capable of producing susceptibility maps for geological disasters. However, the outcomes from the four classifiers that utilize slope units are more precise and logical than those that employ grid units. Lithology and the water system emerge as the most significant factors causing disasters in the study area, while the influence of fault zones is found to be minimal. The integration of the slope unit with the random forest classifier achieves the highest accuracy in predictions, maximizing the capabilities of both, and presents a promising application in the susceptibility mapping of slope geological disasters.
A smart city is an idea that is realized by the computing of a large amount of data collected through sensors, cameras, and other electronic methods to provide services, manage resources and solve daily life problems....
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A smart city is an idea that is realized by the computing of a large amount of data collected through sensors, cameras, and other electronic methods to provide services, manage resources and solve daily life problems. The transformation of the conventional grid to a smart grid is one step in the direction towards smart city realization. An electric grid is composed of control stations, generation centres, transformers, communication lines, and distributors, which helps in transferring power from the power station to domestic and commercial consumers. Present electric grids are not smart enough that they can estimate the varying power requirement of the consumer. Also, these conventional grids are not enough robust and scalable. This has become the motivation for shifting from a conventional grid to a smart grid. The smart grid is a kind of power grid, which is robust and adapts itself to the varying needs of the consumer and self-healing in nature. In this way, the transformation from a conventional grid to a smart grid will help the government to make a smart city. The emergence of machine learning has helped in the prediction of the stability of the grid under the dynamically changing requirement of the consumer. Also, the usage of a variety of sensors will help in the collection of real-time consumption data. Through machine learning algorithms, we can gain an insight view of the collected data. This has helped the smart grid to convert into a robust smart grid, as this will help in avoiding the situation of failure. In this work, the authors have applied logistic regression, decision tree, support vector machine, linear discriminant analysis, quadratic discriminant analysis, naive Bayes, random forest, and k-nearest neighbour algorithms to predict the stability of the grid. The authors have used the smart grid stability dataset freely available on Kaggle to train and test the models. It has been found that a model designed using the support vector machine algori
Objective Prompt, accurate, objective assessment of concussion is crucial as delays can lead to increased short and long-term consequences. The purpose of this study was to derive an objective multimodal concussion i...
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Objective Prompt, accurate, objective assessment of concussion is crucial as delays can lead to increased short and long-term consequences. The purpose of this study was to derive an objective multimodal concussion index (CI) using EEG at its core, to identify concussion, and to assess change over time throughout recovery. Methods Male and female concussed (N = 232) and control (N = 206) subjects 13–25 years were enrolled at 12 US colleges and high schools. Evaluations occurred within 72 h of injury, 5 days post-injury, at return-to-play (RTP), 45 days after RTP (RTP + 45); and included EEG, neurocognitive performance, and standard concussion assessments. Concussed subjects had a witnessed head impact, were removed from play for ≥ 5 days using site guidelines, and were divided into those with RTP < 14 or ≥14 days. Part 1 describes the derivation and efficacy of the machine learning derived classifier as a marker of concussion. Part 2 describes significance of differences in CI between groups at each time point and within each group across time points. Results Sensitivity = 84.9%, specificity = 76.0%, and AUC = 0.89 were obtained on a test Hold-Out group representing 20% of the total dataset. EEG features reflecting connectivity between brain regions contributed most to the CI. CI was stable over time in controls. Significant differences in CI between controls and concussed subjects were found at time of injury, with no significant differences at RTP and RTP + 45. Within the concussed, differences in rate of recovery were seen. Conclusions The CI was shown to have high accuracy as a marker of likelihood of concussion. Stability of CI in controls supports reliable interpretation of CI change in concussed subjects. Objective identification of the presence of concussion and assessment of readiness to return to normal activity can be aided by use of the CI, a rapidly obtained, point of care assessment tool.
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