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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Meta-Icvi: Ensemble Validity Metrics For Concise Labeling Of Correct, Under- Or Over-Partitioning In Streaming Clustering, Niklas M. Melton, Sasha A. Petrenko, Donald C. Wunsch
Meta-Icvi: Ensemble Validity Metrics For Concise Labeling Of Correct, Under- Or Over-Partitioning In Streaming Clustering, Niklas M. Melton, Sasha A. Petrenko, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Understanding the performance and validity of clustering algorithms is both challenging and crucial, particularly when clustering must be done online. Until recently, most validation methods have relied on batch calculation and have required considerable human expertise in their interpretation. Improving real-time performance and interpretability of cluster validation, therefore, continues to be an important theme in unsupervised learning. Building upon previous work on incremental cluster validity indices (iCVIs), this paper introduces the Meta- iCVI as a tool for explainable and concise labeling of partition quality in online clustering. Leveraging a time-series classifier and data-fusion techniques, the Meta- iCVI combines the outputs …
Neural Network Supervised And Reinforcement Learning For Neurological, Diagnostic, And Modeling Problems, Donald Wunsch Iii
Neural Network Supervised And Reinforcement Learning For Neurological, Diagnostic, And Modeling Problems, Donald Wunsch Iii
Masters Theses
“As the medical world becomes increasingly intertwined with the tech sphere, machine learning on medical datasets and mathematical models becomes an attractive application. This research looks at the predictive capabilities of neural networks and other machine learning algorithms, and assesses the validity of several feature selection strategies to reduce the negative effects of high dataset dimensionality. Our results indicate that several feature selection methods can maintain high validation and test accuracy on classification tasks, with neural networks performing best, for both single class and multi-class classification applications. This research also evaluates a proof-of-concept application of a deep-Q-learning network (DQN) to …
Deep Neural Network Learning-Based Classifier Design For Big-Data Analytics, Krishnan Raghavan
Deep Neural Network Learning-Based Classifier Design For Big-Data Analytics, Krishnan Raghavan
Doctoral Dissertations
"In this digital age, big-data sets are commonly found in the field of healthcare, manufacturing and others where sustainable analysis is necessary to create useful information. Big-data sets are often characterized by high-dimensionality and massive sample size. High dimensionality refers to the presence of unwanted dimensions in the data where challenges such as noise, spurious correlation and incidental endogeneity are observed. Massive sample size, on the other hand, introduces the problem of heterogeneity because complex and unstructured data types must analyzed. To mitigate the impact of these challenges while considering the application of classification, a two step analysis approach is …
Automated Classification Of Malignant Melanoma Based On Detection Of Atypical Pigment Network In Dermoscopy Images Of Skin Lesions, Nabin K. Mishra
Automated Classification Of Malignant Melanoma Based On Detection Of Atypical Pigment Network In Dermoscopy Images Of Skin Lesions, Nabin K. Mishra
Doctoral Dissertations
“Melanoma causes more deaths than any other form of skin cancer. Early melanoma detection is important to prevent progression to a more deadly stage. Automated computer-based identification of melanoma from dermoscopic images of skin lesions is the most efficient method in early diagnosis. An automated melanoma identification system must include multiple steps, involving lesion segmentation, feature extraction, feature combination and classification. In this research, a classifier-based approach for automatically selecting a lesion border mask for segmentation of dermoscopic skin lesion images is presented. A logistic regression based model selects a single lesion border mask from multiple border masks generated by …
Analysis Of Globule Types In Malignant Melanoma, Jin Xu, Kapil Kumar Gupta, William V. Stoecker, Yamini Krishnamurthy, Harold S. Rabinovitz, Austin Bangert, David A. Calcara, Margaret C. Oliviero, Joseph M. Malters, Rhett J. Drugge, R. Joe Stanley, Randy Hays Moss, Mehmed Emre Celebi
Analysis Of Globule Types In Malignant Melanoma, Jin Xu, Kapil Kumar Gupta, William V. Stoecker, Yamini Krishnamurthy, Harold S. Rabinovitz, Austin Bangert, David A. Calcara, Margaret C. Oliviero, Joseph M. Malters, Rhett J. Drugge, R. Joe Stanley, Randy Hays Moss, Mehmed Emre Celebi
Chemistry Faculty Research & Creative Works
Objective: To identify and analyze subtypes of globules based on size, shape, network connectedness, pigmentation, and distribution to determine which globule types and globule distributions are most frequently associated with a diagnosis of malignant melanoma. Design: Retrospective case series of dermoscopy images with globules. Setting: Private dermatology practices. Participants: Patients in dermatology practices. Intervention: Observation only. Main Outcome Measure: Association of globule types with malignant melanoma. Results: The presence of large globules (odds ratio [OR], 5.25) and globules varying in size (4.72) or shape (5.37) had the highest ORs for malignant melanoma among all globule types and combinations studied. Classical …