scholarly journals Parameter Analysis of Multiscale Two-Dimensional Fuzzy and Dispersion Entropy Measures Using Machine Learning Classification

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1303
Author(s):  
Ryan Furlong ◽  
Mirvana Hilal ◽  
Vincent O’Brien ◽  
Anne Humeau-Heurtier
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Classification Performance ◽  
Optimal Choice ◽  
Parameter Analysis ◽  
Two Dimensional ◽  
Machine Learning Classification ◽  
Multiple Image ◽  
Average Maximum ◽  
The Impact

Two-dimensional fuzzy entropy, dispersion entropy, and their multiscale extensions (MFuzzyEn2D and MDispEn2D, respectively) have shown promising results for image classifications. However, these results rely on the selection of key parameters that may largely influence the entropy values obtained. Yet, the optimal choice for these parameters has not been studied thoroughly. We propose a study on the impact of these parameters in image classification. For this purpose, the entropy-based algorithms are applied to a variety of images from different datasets, each containing multiple image classes. Several parameter combinations are used to obtain the entropy values. These entropy values are then applied to a range of machine learning classifiers and the algorithm parameters are analyzed based on the classification results. By using specific parameters, we show that both MFuzzyEn2D and MDispEn2D approach state-of-the-art in terms of image classification for multiple image types. They lead to an average maximum accuracy of more than 95% for all the datasets tested. Moreover, MFuzzyEn2D results in a better classification performance than that extracted by MDispEn2D as a majority. Furthermore, the choice of classifier does not have a significant impact on the classification of the extracted features by both entropy algorithms. The results open new perspectives for these entropy-based measures in textural analysis.

Case-based repeatability of machine learning classification performance on breast MRI

2020 ◽  
Author(s):  
Michael Vieceli ◽  
Amy Van Dusen ◽  
Karen Drukker ◽  
Hiroyuki Abe ◽  
Maryellen L. Giger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Breast Mri ◽  
Classification Performance ◽  
Machine Learning Classification ◽  
Case Based

scholarly journals Machine Learning Approach to Classify Rain Type Based on Thies Disdrometers and Cloud Observations

Atmosphere ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 251 ◽  
Author(s):  
Wael Ghada ◽  
Nicole Estrella ◽  
Annette Menzel
Keyword(s):  
Machine Learning ◽  
Geographical Location ◽  
Classification Performance ◽  
Classification Methods ◽  
Reference Dataset ◽  
Machine Learning Classification ◽  
Machine Learning Approach ◽  
Different Types ◽  
Microstructure Parameters

Rain microstructure parameters assessed by disdrometers are commonly used to classify rain into convective and stratiform. However, different types of disdrometer result in different values for these parameters. This in turn potentially deteriorates the quality of rain type classifications. Thies disdrometer measurements at two sites in Bavaria in southern Germany were combined with cloud observations to construct a set of clear convective and stratiform intervals. This reference dataset was used to study the performance of classification methods from the literature based on the rain microstructure. We also explored the possibility of improving the performance of these methods by tuning the decision boundary. We further identified highly discriminant rain microstructure parameters and used these parameters in five machine-learning classification models. Our results confirm the potential of achieving high classification performance by applying the concepts of machine learning compared to already available methods. Machine-learning classification methods provide a concrete and flexible procedure that is applicable regardless of the geographical location or the device. The suggested procedure for classifying rain types is recommended prior to studying rain microstructure variability or any attempts at improving radar estimations of rain intensity.

scholarly journals Utility of MemTrax and Machine Learning Modeling in Classification of Mild Cognitive Impairment

2020 ◽  
Vol 77 (4) ◽  
pp. 1545-1558
Author(s):  
Michael F. Bergeron ◽  
Sara Landset ◽  
Xianbo Zhou ◽  
Tao Ding ◽  
Taghi M. Khoshgoftaar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Performance Metrics ◽  
Early Stage ◽  
Characteristic Curve ◽  
Classification Performance ◽  
Cognitive Screening ◽  
Cross Sectional ◽  
Machine Learning Classification

Background: The widespread incidence and prevalence of Alzheimer’s disease and mild cognitive impairment (MCI) has prompted an urgent call for research to validate early detection cognitive screening and assessment. Objective: Our primary research aim was to determine if selected MemTrax performance metrics and relevant demographics and health profile characteristics can be effectively utilized in predictive models developed with machine learning to classify cognitive health (normal versus MCI), as would be indicated by the Montreal Cognitive Assessment (MoCA). Methods: We conducted a cross-sectional study on 259 neurology, memory clinic, and internal medicine adult patients recruited from two hospitals in China. Each patient was given the Chinese-language MoCA and self-administered the continuous recognition MemTrax online episodic memory test on the same day. Predictive classification models were built using machine learning with 10-fold cross validation, and model performance was measured using Area Under the Receiver Operating Characteristic Curve (AUC). Models were built using two MemTrax performance metrics (percent correct, response time), along with the eight common demographic and personal history features. Results: Comparing the learners across selected combinations of MoCA scores and thresholds, Naïve Bayes was generally the top-performing learner with an overall classification performance of 0.9093. Further, among the top three learners, MemTrax-based classification performance overall was superior using just the top-ranked four features (0.9119) compared to using all 10 common features (0.8999). Conclusion: MemTrax performance can be effectively utilized in a machine learning classification predictive model screening application for detecting early stage cognitive impairment.

scholarly journals Diabetes Prediction Using Machine Learning Techniques

2021 ◽  
pp. 150-152
Author(s):  
Seyma Kiziltas Koc ◽  
Mustafa Yeniad
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
High Performance ◽  
Nearest Neighbor ◽  
Classification Performance ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Machine Learning Classification

Technologies which are used in the healthcare industry are changing rapidly because the technology is evolving to improve people's lifestyles constantly. For instance, different technological devices are used for the diagnosis and treatment of diseases. It has been revealed that diagnosis of disease can be made by computer systems with developing technology.Machine learning algorithms are frequently used tools because of their high performance in the field of health as well as many field. The aim of this study is to investigate different machine learning classification algorithms that can be used in the diagnosis of diabetes and to make comparative analyzes according to the metrics in the literature. In the study, seven classification algorithms were used in the literature. These algorithms are Logistic Regression, K-Nearest Neighbor, Multilayer Perceptron, Random Forest, Decision Trees, Support Vector Machine and Naive Bayes. Firstly, classification performance of algorithms are compared. These comparisons are based on accuracy, sensitivity, precision, and F1-score. The results obtained showed that support vector machine algorithm had the highest accuracy with 78.65%.

Chess Results Analysis Using Elo Measure with Machine Learning

2020 ◽  
Vol 19 (02) ◽  
pp. 2050006
Author(s):  
Fadi Thabtah ◽  
Arun J. Padmavathy ◽  
Andrew Pritchard
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Area Under Curve ◽  
Rule Induction ◽  
Past Performance ◽  
Machine Learning Classification ◽  
Future Performance ◽  
Chess Game ◽  
Prime Objective ◽  
The Impact

An Elo score is a known measure of past performance in chess and other games. This paper investigates the impact of Elo ratings on chess game results and whether this measure can be used to predict future performance in matches. To achieve the aim, various machine learning classification techniques have been evaluated using chess data sourced from an online chess server. We examine how much influence the Elo score has on the prediction power of these techniques based on classifiers they derive. The prime objective of this experiment is to accurately predict the winner of a chess game from attributes that are available before the game starts. We are particularly interested in how large an impact the Elo score has on the prediction when compared with other features. Empirical results reported that classifiers derived by artificial neural network (Multilayer Perceptron), Decision Tree (J48/C4.5), Rule Induction (JRip/RIPPER) and Probabilistic (Naïve Bayes) showed how useful the Elo is at predicting chess results, at least on the dataset considered, improving classifiers’ performance with respect to accuracy, precision, recall and area under curve, among others.

scholarly journals Prospective prediction of PTSD diagnosis in a nationally representative sample using machine learning

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Michelle A. Worthington ◽  
Amar Mandavia ◽  
Randall Richardson-Vejlgaard
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Adverse Event ◽  
Representative Sample ◽  
The United States ◽  
Community Level ◽  
Machine Learning Classification ◽  
Nationally Representative ◽  
The Impact ◽  
New Onset

Abstract Background Recent research has identified a number of pre-traumatic, peri-traumatic and post-traumatic psychological and ecological factors that put an individual at increased risk for developing PTSD following a life-threatening event. While these factors have been found to be associated with PTSD in univariate analyses, the complex interactions of these risk factors and how they contribute to individual trajectories of the illness are not yet well understood. In this study, we examine the impact of prior trauma, psychopathology, sociodemographic characteristics, community and environmental information, on PTSD onset in a nationally representative sample of adults in the United States, using machine learning methods to establish the relative contributions of each variable. Methods Individual risk factors identified in Waves 1 of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) were combined with community-level data for the years concurrent to the NESARC Wave 1 (n = 43,093) and 2 (n = 34,653) surveys. Machine learning feature selection and classification analyses were used at the national level to create models using individual- and community-level variables that would best predict the new onset of PTSD at Wave 2. Results Our classification algorithms yielded 89.7 to 95.6% accuracy for predicting new onset of PTSD at Wave 2. A prior diagnosis of DSM-IV-TR Borderline Personality Disorder, Major Depressive Disorder or Anxiety Disorder conferred the greatest relative influence in new diagnosis of PTSD. Distal risk factors such as prior psychiatric diagnosis accounted for significantly greater relative risk than proximal factors (such as adverse event exposure). Conclusions Our findings show that a machine learning classification approach can successfully integrate large numbers of known risk factors for PTSD into stronger models that account for high-dimensional interactions and collinearity between variables. We discuss the implications of these findings as pertaining to the targeted mobilization emergency mental health resources. These findings also inform the creation of a more comprehensive risk assessment profile to the likelihood of developing PTSD following an extremely adverse event.

Rider and Sunflower optimization-driven neural network for image classification

2021 ◽  
Vol 19 (1-2) ◽  
pp. 41-61
Author(s):  
Hanumantha Rao Nadendla ◽  
A. Srikrishna ◽  
K. Gangadhara Rao
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Classification ◽  
Optimization Algorithm ◽  
Pattern Analysis ◽  
Classification Performance ◽  
Optimization Approach ◽  
Shape Decomposition ◽  
Maximal Sensitivity ◽  
Sensitivity Specificity

Image classification is the classical issue in computer vision, machine learning, and image processing. The image classification is measured by differentiating the image into the prescribed category based on the content of the vision. In this paper, a novel classifier named RideSFO-NN is developed for image classification. The proposed method performs the image classification by undergoing two steps, namely feature extraction and classification. Initially, the images from various sources are provided to the proposed Weighted Shape-Size Pattern Spectra for pattern analysis. From the pattern analysis, the significant features are obtained for the classification. Here, the proposed Weighted Shape-Size Pattern Spectra is designed by modifying the gray-scale decomposition with Weight-Shape decomposition. Then, the classification is done based on Neural Network (NN) classifier, which is trained using an optimization approach. The optimization will be done by the proposed Ride Sunflower optimization (RideSFO) algorithm, which is the integration of Rider optimization algorithm (ROA), and Sunflower optimization algorithm (SFO). Finally, the image classification performance is evaluated using RideSFO-NN based on sensitivity, specificity, and accuracy. The developed RideSFO-NN method achieves the maximal accuracy of 94%, maximal sensitivity of 93.87%, and maximal specificity of 90.52% based on K-Fold.

scholarly journals Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data (Preprint)

2018 ◽  
Author(s):  
Afsaneh Doryab ◽  
Daniella K Villalba ◽  
Prerna Chikersal ◽  
Janine M Dutcher ◽  
Michael Tumminia ◽  
...  
Keyword(s):  
Machine Learning ◽  
College Students ◽  
Data Mining ◽  
Statistical Analysis ◽  
Standard Deviation ◽  
Behavioral Patterns ◽  
Machine Learning Classification ◽  
Passive Sensing ◽  
The Mean ◽  
The Impact

BACKGROUND Feelings of loneliness are associated with poor physical and mental health. Detection of loneliness through passive sensing on personal devices can lead to the development of interventions aimed at decreasing rates of loneliness. OBJECTIVE The aim of this study was to explore the potential of using passive sensing to infer levels of loneliness and to identify the corresponding behavioral patterns. METHODS Data were collected from smartphones and Fitbits (Flex 2) of 160 college students over a semester. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire at the beginning and end of the semester. For a classification purpose, the scores were categorized into high (questionnaire score>40) and low (≤40) levels of loneliness. Daily features were extracted from both devices to capture activity and mobility, communication and phone usage, and sleep behaviors. The features were then averaged to generate semester-level features. We used 3 analytic methods: (1) statistical analysis to provide an overview of loneliness in college students, (2) data mining using the Apriori algorithm to extract behavior patterns associated with loneliness, and (3) machine learning classification to infer the level of loneliness and the change in levels of loneliness using an ensemble of gradient boosting and logistic regression algorithms with feature selection in a leave-one-student-out cross-validation manner. RESULTS The average loneliness score from the presurveys and postsurveys was above 43 (presurvey SD 9.4 and postsurvey SD 10.4), and the majority of participants fell into the high loneliness category (scores above 40) with 63.8% (102/160) in the presurvey and 58.8% (94/160) in the postsurvey. Scores greater than 1 standard deviation above the mean were observed in 12.5% (20/160) of the participants in both pre- and postsurvey scores. The majority of scores, however, fell between 1 standard deviation below and above the mean (pre=66.9% [107/160] and post=73.1% [117/160]). Our machine learning pipeline achieved an accuracy of 80.2% in detecting the binary level of loneliness and an 88.4% accuracy in detecting change in the loneliness level. The mining of associations between classifier-selected behavioral features and loneliness indicated that compared with students with low loneliness, students with high levels of loneliness were spending less time outside of campus during evening hours on weekends and spending less time in places for social events in the evening on weekdays (support=17% and confidence=92%). The analysis also indicated that more activity and less sedentary behavior, especially in the evening, was associated with a decrease in levels of loneliness from the beginning of the semester to the end of it (support=31% and confidence=92%). CONCLUSIONS Passive sensing has the potential for detecting loneliness in college students and identifying the associated behavioral patterns. These findings highlight intervention opportunities through mobile technology to reduce the impact of loneliness on individuals’ health and well-being.

scholarly journals Comparing Multiclass, Binary, and Hierarchical Machine Learning Classification schemes for variable stars

2019 ◽  
Vol 488 (4) ◽  
pp. 4858-4872 ◽  
Author(s):  
Zafiirah Hosenie ◽  
Robert J Lyon ◽  
Benjamin W Stappers ◽  
Arrykrishna Mootoovaloo
Keyword(s):  
Machine Learning ◽  
Binary Stars ◽  
Binary Classification ◽  
Classification Performance ◽  
Variable Stars ◽  
Machine Learning Algorithms ◽  
Accurate Identification ◽  
Data Set ◽  
Machine Learning Classification ◽  
Multiclass Problem

ABSTRACT Upcoming synoptic surveys are set to generate an unprecedented amount of data. This requires an automatic framework that can quickly and efficiently provide classification labels for several new object classification challenges. Using data describing 11 types of variable stars from the Catalina Real-Time Transient Survey (CRTS), we illustrate how to capture the most important information from computed features and describe detailed methods of how to robustly use information theory for feature selection and evaluation. We apply three machine learning algorithms and demonstrate how to optimize these classifiers via cross-validation techniques. For the CRTS data set, we find that the random forest classifier performs best in terms of balanced accuracy and geometric means. We demonstrate substantially improved classification results by converting the multiclass problem into a binary classification task, achieving a balanced-accuracy rate of ∼99 per cent for the classification of δ Scuti and anomalous Cepheids. Additionally, we describe how classification performance can be improved via converting a ‘flat multiclass’ problem into a hierarchical taxonomy. We develop a new hierarchical structure and propose a new set of classification features, enabling the accurate identification of subtypes of Cepheids, RR Lyrae, and eclipsing binary stars in CRTS data.

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