Soccer goalkeeper expertise identification based on eye movements

By focusing on high experimental control and realistic presentation, the latest research in expertise assessment of soccer players demonstrates the importance of perceptual skills, especially in decision making. Our work captured omnidirectional in-field scenes displayed through virtual reality glasses to 12 expert players (picked by DFB), 10 regional league intermediate players, and13 novice soccer goalkeepers in order to assess the perceptual skills of athletes in an optimized manner. All scenes were shown from the perspective of the same natural goalkeeper and ended after the return pass to that goalkeeper. Based on the gaze behavior of each player, we classified their expertise with common machine learning techniques. Our results show that eye movements contain highly informative features and thus enable a classification of goalkeepers between three stages of expertise, namely elite youth player, regional league player, and novice, at a high accuracy of 78.2%. This research underscores the importance of eye tracking and machine learning in perceptual expertise research and paves the way for perceptual-cognitive diagnosis as well as future training systems.

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Decision Tree: A Machine Learning for Intrusion Detection

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.f1234.0486s419 ◽

2019 ◽

Vol 8 (6S4) ◽

pp. 1126-1130

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Detection System ◽

Research Work ◽

Machine Learning Techniques ◽

Data Sets ◽

Legitimate User ◽

Learning Techniques ◽

Three Stages

The Intrusion is a major threat to unauthorized data or legal network using the legitimate user identity or any of the back doors and vulnerabilities in the network. IDS mechanisms are developed to detect the intrusions at various levels. The objective of the research work is to improve the Intrusion Detection System performance by applying machine learning techniques based on decision trees for detection and classification of attacks. The methodology adapted will process the datasets in three stages. The experimentation is conducted on KDDCUP99 data sets based on number of features. The Bayesian three modes are analyzed for different sized data sets based upon total number of attacks. The time consumed by the classifier to build the model is analyzed and the accuracy is done.

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A Brief Survey on Text Classification Using Various Machine Learning Techniques

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v8i1.521 ◽

2018 ◽

Vol 8 (1) ◽

pp. 14

Author(s):

Padmavathi .S ◽

M. Chidambaram

Keyword(s):

Machine Learning ◽

Text Classification ◽

Fixed Number ◽

Machine Learning Techniques ◽

Online Information ◽

Rule Based ◽

Learning Techniques ◽

Machine Learning Approach ◽

Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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