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Full-Text Articles in Engineering
Direct-To-Patient Survey For Diagnosis Of Benign Paroxysmal Positional Vertigo, Heidi Richburg, Richard J. Povinelli, David Friedland
Direct-To-Patient Survey For Diagnosis Of Benign Paroxysmal Positional Vertigo, Heidi Richburg, Richard J. Povinelli, David Friedland
Electrical and Computer Engineering Faculty Research and Publications
Given the high incidence of dizziness and its frequent misdiagnosis, we aim to create a clinical support system to classify the presence or absence of benign paroxysmal positional vertigo with high accuracy and specificity. This paper describes a three-phase study currently underway for classification of benign paroxysmal positional vertigo, which includes diagnosis by a specialist in a clinical setting. Patient background information is collected by a survey on an Android tablet and machine learning techniques are applied for classification. Decision trees and wrappers are employed for their ability to provide information about the question set. One goal of the study …
Assessment Of Post-Wildfire Debris Flow Occurrence Using Classifier Tree, Priscilla Addison, Thomas Oommen, Qiuying Sha
Assessment Of Post-Wildfire Debris Flow Occurrence Using Classifier Tree, Priscilla Addison, Thomas Oommen, Qiuying Sha
Michigan Tech Publications
Besides the dangers of an actively burning wildfire, a plethora of other hazardous consequences can occur afterwards. Debris flows are among the most hazardous of these, being known to cause fatalities and extensive damage to infrastructure. Although debris flows are not exclusive to fire affected areas, a wildfire can increase a location’s susceptibility by stripping its protective covers like vegetation and introducing destabilizing factors such as ash filling soil pores to increase runoff potential. Due to the associated dangers, researchers are developing statistical models to isolate susceptible locations. Existing models predominantly employ the logistic regression algorithm; however, previous studies have …
Identifying Criminal Organizations From Their Social Network Structures, Muhammet Serkan Çi̇nar, Burkay Genç, Hayri̇ Sever
Identifying Criminal Organizations From Their Social Network Structures, Muhammet Serkan Çi̇nar, Burkay Genç, Hayri̇ Sever
Turkish Journal of Electrical Engineering and Computer Sciences
Identification of criminal structures within very large social networks is an essential security feat. By identifying such structures, it may be possible to track, neutralize, and terminate the corresponding criminal organizations before they act. We evaluate the effectiveness of three different methods for classifying an unknown network as terrorist, cocaine, or noncriminal. We consider three methods for the identification of network types: evaluating common social network analysis metrics, modeling with a decision tree, and network motif frequency analysis. The empirical results show that these three methods can provide significant improvements in distinguishing all three network types. We show that these …
Optimized Bilevel Classifier For Brain Tumor Type And Grade Discrimination Using Evolutionary Fuzzy Computing, Kavitha Srinivasan, Mohanavalli Subramaniam, Bharathi Bhagavathsingh
Optimized Bilevel Classifier For Brain Tumor Type And Grade Discrimination Using Evolutionary Fuzzy Computing, Kavitha Srinivasan, Mohanavalli Subramaniam, Bharathi Bhagavathsingh
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, an optimized bilevel brain tumor diagnostic system for identifying the tumor type at the first level and grade of the identified tumor at the second level is proposed using genetic algorithm, decision tree, and fuzzy rule-based approach. The dataset is composed of axial MRI of brain tumor types and grades. From the images, various features such as first and second order statistical and textural features are extracted (26 features). In the first level, tumor type classification was done using decision tree constructed with all features. Further evolutionary computing using genetic algorithms (GA) was applied to select the …
A Postpruning Decision Algorithm Based On Loss Minimization, Ahmed M. Ahmed, Ali̇ Hakan Ulusoy, Ahmet Ri̇zaner
A Postpruning Decision Algorithm Based On Loss Minimization, Ahmed M. Ahmed, Ali̇ Hakan Ulusoy, Ahmet Ri̇zaner
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a post-pruning method known as zero-one loss function pruning (ZOLFP) that is based on zero-one loss function is introduced. The proposed ZOLFP method minimizes the expected loss, rather than evaluating the misclassification error rate of a node and its subtree. The subtree is pruned when expected loss of the node is less than or equal to the sum of the loss of its leaves. The experimental results demonstrate that ZOLFP method outperforms. Un-pruned C4.5 Decision Tree (UDT-C4.5) algorithm, reduced error pruning (REP), and minimum error pruning (MEP) in terms of performance accuracy in all used datasets. It …