Intraday stock trading has become a popular trend in US, Europe, and Indian markets and forecasting these rapid market movements have become an important topic in finance. With the emergence of technology and computin...
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Anomalies in computer networks has increased in the last decades and raised concern to create techniques to identify these unusual traffic patterns. This research aims to use datamining techniques in order to correct...
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The research investigates the efficacy of a comprehensive machinelearning and datamining approach in predictive modeling of stroke risk and post-stroke care management. A holistic methodology is proposed, integratin...
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Human Activity Recognition (HAR) is technically the problem of forecasting an individual's actions based on evidence of their gesture using sensors functioning as accelerometer and gyroscope. It plays a major role...
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ISBN:
(纸本)9781665428644
Human Activity Recognition (HAR) is technically the problem of forecasting an individual's actions based on evidence of their gesture using sensors functioning as accelerometer and gyroscope. It plays a major role in contrasting sectors such as personal biometric signature, daily life monitoring, anti-terrorists along with anti-crime securities, medical-related applications, and so on. These days, smart phones are well-resourced with leading processors and built-in sensors. This comes up with the possibility to unfold a new arena of datamining. This paper signifies the analysis of HAR focused on data composed via accelerometer sensors of smart phones. Further, it illustrates the use of time-domain features which are acquired with the help of a windowing approach termed as overlapping. It is accompanied by a window size of 250ms along with overlapping of 25%. Numerous machinelearning classifiers such as k-nearest neighbors, linear discriminant analysis, bagging classifier, gradient boosting classifier, decision tree, random forest, and support vector machine using three different kernels were practiced. The outcomes exhibit that random forest with 5-fold cross-validation imparts the highest accuracy (92.71%) in recognition of human activities.
Using the capabilities of machinelearning and datamining techniques, we formulated a Decision Tree model to aid hotel management in optimizing resource allocation for profit maximization. Additionally, we constructe...
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Plant diseases may have a major impact on food safety, also a considerable decline in agricultural product output. The great majority of automated systems developed thus far are based on digital pictures, allowing for...
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This research explores the feasibility of AI-powered cryptocurrency mining on traditional workstations. Traditional workstations, while widely available, face challenges in terms of computational efficiency and energy...
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The article considers the task of classifying fractal time series based on the construction of their recurrence plots. Short realizations of EEG signals were used as input data. Two classification machinelearning met...
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The term datamining refers to information elicitation. On the other hand, soft computing deals with information processing. If these two key properties can be combined in a constructive way, then this formation can e...
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ISBN:
(纸本)1853129259
The term datamining refers to information elicitation. On the other hand, soft computing deals with information processing. If these two key properties can be combined in a constructive way, then this formation can effectively be used for knowledge discovery in large databases. Referring to this synergetic combination, the basic merits of datamining and soft computing paradigms are pointed out and novel datamining implementation coupled to a soft computing approach for knowledge discovery is presented. Knowledge modeling by machinelearning together with the computer experiments is described and. the effectiveness of the machinelearning approach employed is demonstrated.
Using big data theory to analyze athletes' performance data, this paper finds out the changing characteristics of athletes' performance. Using the principle of big data, we find the performance characteristics...
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