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Development of Gaussian Learning Algorithms for Early Detection of Alzheimer's Disease
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In: FIU Electronic Theses and Dissertations (2020)
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Cognitive discriminative feature selection using variance fractal dimension for the detection of cyber attacks
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Abstract:
The dimensions of data have increased significantly with the abundance of the data that we share. This high-dimensional data results in redundant and irrelevant features that creates challenges to existing machine learning algorithms. Redundant and irrelevant features slow down the training and testing process as a result affecting the performance and run time of a learning algorithm. Conventional feature selection methods showcase great potential to select important features. However, machine enabled feature selection models are unable to solve complex analysis in absence of human cognitive aspect to it. This thesis addresses the challenge concerned with reducing the data dimensions for machine learning algorithms from cognitive aspect. The reduction is carried out by a variance fractal-based complexity analysis to select a reduced set of features of a cyber attack dataset for each attack type that are discriminative in nature. Furthermore, integrated artificial neural network were created with inputs that comprised of the reduced features selected through the complexity analysis. A performance comparison is also provided using our proposed methodology with resulting minimized dataset features of each attack types with non-minimized dataset. The comparative analysis shows that the resultant discriminative features derived from our proposed method not only consume less resource but also speed up the training and testing process while maintaining good detection rates. ; October 2020
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Keyword:
Artificial Neural Network; Cognitive computing; Cognitive Intelligence; Complexity Analysis; Computational intelligence; Cyber attacks; Cyber security; Dimensionality; Discriminative features; Feature Selection; Fractals; Machine Learning; Network Threats; Variance Fractal Dimension
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URL: http://hdl.handle.net/1993/35092
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How to visualize high-dimensional data: a roadmap
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In: Journal of Data Mining and Digital Humanities, Vol Special issue on Visualisations in Historical Linguistics (2020) (2020)
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Investigating Gender-biased Items in a High-stake Language Proficiency Test: Using the Rasch Model Measurement
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In: Applied Linguistics Research Journal, Vol 4, Iss 5, Pp 1-21 (2020) (2020)
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Cognitive artificial intelligence – a complexity based machine learning approach for advanced cyber threats
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Siddiqui, Sana. - : ACM (IWSPA), 2017. : IEEE (IJCNN), 2017. : Springer, 2017. : IEEE (ICCI*CC), 2017
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Using Differential Item Functioning to Investigate the Impact of Reading Proficiency on Science Achievement
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Effect of Tuned Parameters on a LSA Multiple Choice Questions Answering Model
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In: ISSN: 1554-351X ; EISSN: 1554-3528 ; Behavior Research Methods ; https://hal.archives-ouvertes.fr/hal-00336126 ; Behavior Research Methods, Psychonomic Society, Inc, 2009, 41 (4), pp.1201--1209. ⟨10.3758/BRM.41.4.1201⟩ (2009)
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Meaningful term extraction and discriminative term selection in text categorization via unknown-word methodology
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In: http://www.cs.toronto.edu/~gh/Courses/2528/Readings/2002f/Lai+Wu.pdf (2002)
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Medical Data Mining on the Internet: Research on a Cancer Information System
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In: DTIC (1999)
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Dimensionality reduction of electropalatographic data using latent variable models
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Statistical Methods for Neural Network Prediction Models
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In: https://hal.archives-ouvertes.fr/hal-03235964 ; [Research Report] Control Systems Group School of Electronic Engineering. Dublin City University. 1997 (1997)
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Analysis of Defense Language Institute Automated Student Questionnaire Data.
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In: DTIC AND NTIS (1996)
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