scholarly journals The attribute selection process in pattern perception: The effect of constraint redundancy and stimulus exposure time on the classification of spatially represented Markov patterns

1974 ◽  
Vol 2 (1) ◽  
pp. 75-81
Author(s):  
Donald F. Dansereau ◽  
Bill R. Brown
Keyword(s):  
Exposure Time ◽  
Selection Process ◽  
Attribute Selection ◽  
Pattern Perception ◽  
Stimulus Exposure ◽  
Constraint Redundancy

Improved differentiation classification of variable precision artificial intelligence higher education management

2021 ◽  
pp. 1-10
Author(s):  
Chao Dong ◽  
Yan Guo
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Data Mining ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Attribute Selection ◽  
Higher Education Management ◽  
Education Management ◽  
Decision Tree Classification

The wide application of artificial intelligence technology in various fields has accelerated the pace of people exploring the hidden information behind large amounts of data. People hope to use data mining methods to conduct effective research on higher education management, and decision tree classification algorithm as a data analysis method in data mining technology, high-precision classification accuracy, intuitive decision results, and high generalization ability make it become a more ideal method of higher education management. Aiming at the sensitivity of data processing and decision tree classification to noisy data, this paper proposes corresponding improvements, and proposes a variable precision rough set attribute selection standard based on scale function, which considers both the weighted approximation accuracy and attribute value of the attribute. The number improves the anti-interference ability of noise data, reduces the bias in attribute selection, and improves the classification accuracy. At the same time, the suppression factor threshold, support and confidence are introduced in the tree pre-pruning process, which simplifies the tree structure. The comparative experiments on standard data sets show that the improved algorithm proposed in this paper is better than other decision tree algorithms and can effectively realize the differentiated classification of higher education management.

scholarly journals Health State Classification of a Spherical Tank Using a Hybrid Bag of Features and K-Nearest Neighbor

2020 ◽  
Vol 10 (7) ◽  
pp. 2525 ◽  
Author(s):  
Md Junayed Hasan ◽  
Jaeyoung Kim ◽  
Cheol Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Nearest Neighbor ◽  
Selection Process ◽  
Health State ◽  
K Nearest Neighbor ◽  
Bag Of Features ◽  
State Classification ◽  
Spherical Tank ◽  
Art Works ◽  
Health State Classification

Feature analysis puts a great impact in determining the various health conditions of mechanical vessels. To achieve balance between traditional feature extraction and the automated feature selection process, a hybrid bag of features (HBoF) is designed for multiclass health state classification of spherical tanks in this paper. The proposed HBoF is composed of (a) the acoustic emission (AE) features and (b) the time and frequency based statistical features. A wrapper-based feature chooser algorithm, Boruta, is utilized to extract the most intrinsic feature set from HBoF. The selective feature matrix is passed to the multi-class k-nearest neighbor (k-NN) algorithm to differentiate among normal condition (NC) and two faulty conditions (FC1 and FC2). Experimental results demonstrate that the proposed methodology generates an average 99.7% accuracy for all working conditions. Moreover, it outperforms the existing state-of-art works by achieving at least 19.4%.

Stimulus exposure time as a factor in serial learning in an aged sample.

1963 ◽  
Vol 67 (6) ◽  
pp. 594-600 ◽  
Author(s):  
Carl Eisdorfer ◽  
Seymour Axelrod ◽  
Francis L. Wilkie
Keyword(s):  
Exposure Time ◽  
Serial Learning ◽  
Stimulus Exposure ◽  
Aged Sample

Choice Behavior as a Function of Stimulus Exposure

1967 ◽  
Vol 21 (1) ◽  
pp. 213-219 ◽  
Author(s):  
James L. Pate
Keyword(s):  
Exposure Time ◽  
Choice Behavior ◽  
Stimulus Exposure ◽  
Similar Stimulus ◽  
Choice Latency

Two studies which showed significant extra-maze pre-exposure effects are reported. In Exp. 1 hooded rats were exposed to one stimulus and then given a choice between that stimulus and a non-exposed stimulus. Exposure brightness and exposure time were varied and number of changes between the two stimulus areas and time spent in each area were measured. In Exp. 2 rats were exposed to a stimulus and then given a choice between the same area, an area with a similar stimulus and an area with a dissimilar stimulus. Both exposure brightness and exposure time affected choice latency and time in the dissimilar area.

Changes in Lingual Sensitivity as a Function of Age and Stimulus Exposure Time

1982 ◽  
Vol 55 (3_suppl) ◽  
pp. 1083-1090 ◽  
Author(s):  
Linda Petrosino ◽  
Donald Fucci ◽  
Randall R. Robey
Keyword(s):  
Young Adult ◽  
Exposure Time ◽  
Stimulus Duration ◽  
Age Groups ◽  
The Elderly ◽  
Elderly Group ◽  
Vibrotactile Threshold ◽  
Stimulus Exposure ◽  
Adult Group ◽  
Vibrotactile Thresholds

Effects of duration of stimulus exposure on lingual vibrotactile thresholds were examined across three groups of 10 subjects each ( n = 30). Subjects were grouped according to age (child group, mean age = 10.1 yr.; young adult group, mean age = 21.9 yr.; elderly group, mean age = 76.0 yr.). Lingual vibrotactile threshold measurements were obtained for all subjects under 5 conditions of exposure (1, 2, 3, 4, and 5 sec.). Results showed statistically significant differences in threshold among all three age groups. As age increased, thresholds of lingual sensitivity increased (became poorer). Stimulus duration also created significant differences in threshold for all age groups. As stimulus duration increased, thresholds of lingual sensitivity decreased (became better). The children appeared to be the most stable across conditions whereas the elderly group appeared to be the most affected by stimulus duration.

A LITERATURE REVIEW AND CLASSIFICATION OF ENTERPRISE SOFTWARE SELECTION APPROACHES

2009 ◽  
Vol 08 (02) ◽  
pp. 217-238 ◽  
Author(s):  
CEYDA GÜNGÖR ŞEN ◽  
HAYRİ BARAÇLI ◽  
SELÇUK ŞEN
Keyword(s):  
Decision Making ◽  
Literature Review ◽  
Selection Process ◽  
Single Step ◽  
Software Selection ◽  
Enterprise Software ◽  
Software Products ◽  
Functional Perspective ◽  
Comprehensive Classification

The evaluation and selection of enterprise software has become increasingly difficult for decision makers due to a large number of software products available for many applications. Therefore, systematic and repeatable approaches are needed in order to select the appropriate product that best meets the customer requirements. In this paper, we present a literature review and classification of enterprise software selection approaches from the period 1982–2007. In addition to classifying the selected approaches by functional perspective, the decision-making methods used by these approaches in the generic phases of software selection process are also presented. Results are summarized with comprehensive classification tables. This study is intended as a source for academics and practitioners alike interested in software selection problem, especially those who want to see alternative decision-making techniques that can be used to support the every single step of software selection process.

scholarly journals Classification of Online Grooming on Chat Logs Using Two Term Weighting Schemes

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Nur Rafeeqkha Sulaiman ◽  
Maheyzah Md. Siraj
Keyword(s):  
Feature Selection ◽  
Selection Process ◽  
Support Vector ◽  
Term Weighting ◽  
Human Errors ◽  
Weighting Schemes ◽  
Intelligence Gathering ◽  
Internet Users ◽  
Document Frequency

Due to the growth of Internet, it has not only become the medium for getting information, it has also become a platform for communicating. Social Network Service (SNS) is one of the main platform where Internet users can communicate by distributing, sharing of information and knowledge. Chatting has become a popular communication medium for Internet users whereby users can communicate directly and privately with each other. However, due to the privacy of chat rooms or chatting mediums, the content of chat logs is not monitored and not filtered. Thus, easing cyber predators preying on their preys. Cyber groomers are one of cyber predators who prey on children or minors to satisfy their sexual desire. Workforce expertise that involve in intelligence gathering always deals with difficulty as the complexity of crime increases, human errors and time constraints. Hence, it is difficult to prevent undesired content, such as grooming conversation, in chat logs. An investigation on two term weighting schemes on two datasets are used to improve the content-based classification techniques. This study aims to improve the content-based classification accuracy on chat logs by comparing two term weighting schemes in classifying grooming contents. Two term weighting schemes namely Term Frequency – Inverse Document Frequency – Inverse Class Space Density Frequency (TF.IDF.ICSdF) and Fuzzy Rough Feature Selection (FRFS) are used as feature selection process in filtering chat logs. The performance of these techniques were examined via datasets, and the accuracy of their result was measured by Support Vector Machine (SVM). TF.IDF.ICSdF and FRFS are judged based on accuracy, precision, recall and F score measurement.

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