scholarly journals A Study on the Evaluation Method of Personality Education Using Machine Learning

2020 ◽  
Vol 14 (2) ◽  
pp. 221-231
Author(s):  
Kyung-Mi Lee
Keyword(s):  
Machine Learning ◽  
Evaluation Method

scholarly journals Machine Learning Empowered Trust Evaluation Method for IoT Devices

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 65066-65077
Author(s):  
Wei Ma ◽  
Xing Wang ◽  
Mingsheng Hu ◽  
Qinglei Zhou
Keyword(s):  
Machine Learning ◽  
Evaluation Method ◽  
Trust Evaluation ◽  
Iot Devices

scholarly journals Automated Assessment of Psychiatric Disorders Using Speech: A Systematic Review

2019 ◽  
Author(s):  
Daniel Mark Low ◽  
Kate H. Bentley ◽  
Satrajit S Ghosh
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Systematic Review ◽  
Evaluation Method ◽  
Psychiatric Symptoms ◽  
Meta Analysis ◽  
Learning Technology ◽  
Acoustic Features ◽  
Post Traumatic Stress ◽  
Health Assessments

Objective: There are many barriers to accessing mental health assessments including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited partly due to the episodic nature of psychiatric symptoms. Therefore, machine learning technology using speech samples obtained in the clinic or remotely could one day be a biomarker to improve diagnosis and treatment. To date, reviews have only focused on using acoustic features from speech to detect depression and schizophrenia. Here we present the first systematic review of studies using speech for automated assessments across a broader range of psychiatric disorders.Methods: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We included studies from the last 10 years using speech to identify the presence or severity of disorders within the Diagnostic and Statistical Manual of Mental Disorders (DSM–5). For each disorder we describe sample size, clinical evaluation method, speech-eliciting tasks, machine learning methodology, performance, and other relevant findings. Results: 1395 studies were screened of which 127 studies met the inclusion criteria. The majority of studies were on depression, schizophrenia, and bipolar disorder, and the remaining on post-traumatic stress disorder, anxiety disorders, and eating disorders. 63% of studies built machine learning predictive models, and the remaining 37% performed null-hypothesis testing only. We provide an online database with our search results and synthesize how acoustic features appear in each disorder.Conclusion: Speech processing technology could aid mental health assessments, but there are many obstacles to overcome, especially the need for comprehensive transdiagnostic and longitudinal studies. Given the diverse types of datasets, feature extraction, computational methodologies, and evaluation criteria, we provide guidelines for both acquiring data and building machine learning models with a focus on testing hypotheses, open science, reproducibility, and generalizability.

scholarly journals A Machine Learning Approach to Predict Hypotensive Events in ICU Settings

2019 ◽  
Author(s):  
Mina Chookhachizadeh Moghadam ◽  
Ehsan Masoumi ◽  
Nader Bagherzadeh ◽  
Davinder Ramsingh ◽  
Guann-Pyng Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Evaluation Method ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Physiological Status ◽  
Time Prediction ◽  
Evaluation Approach ◽  
High Positive Predictive Value ◽  
Data Points

AbstractPurposePredicting hypotension well in advance provides physicians with enough time to respond with proper therapeutic measures. However, the real-time prediction of hypotension with high positive predictive value (PPV) is a challenge due to the dynamic changes in patients’ physiological status under the drug administration which is limiting the amount of useful data available for the algorithm.MethodsTo mimic real-time monitoring, we developed a machine learning algorithm that uses most of the available data points from patients’ record to train and test the algorithm. The algorithm predicts hypotension up to 30 minutes in advance based on only 5 minutes of patient’s physiological history. A novel evaluation method is proposed to assess the algorithm performance as a function of time at every timestamp within 30 minutes prior to hypotension. This evaluation approach provides statistical tools to find the best possible prediction window.ResultsDuring 181,000 minutes of monitoring of about 400 patients, the algorithm demonstrated 94% accuracy, 85% sensitivity and 96% specificity in predicting hypotension within 30 minutes of the events. A high PPV of 81% obtained and the algorithm predicted 80% of the events 25 minutes prior to their onsets. It was shown that choosing a classification threshold that maximizes the F1 score during the training phase contributes to a high PPV and sensitivity.ConclusionThis study reveals the promising potential of the machine learning algorithms in real-time prediction of hypotensive events in ICU setting based on short-term physiological history.

Automatic Cognitive Load Classification Using High-Frequency Interaction Events

2013 ◽  
Vol 9 (3) ◽  
pp. 73-88 ◽  
Author(s):  
Tao Lin ◽  
Xiao Li ◽  
Zhiming Wu ◽  
Ningjiu Tang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Cognitive Load ◽  
High Frequency ◽  
Evaluation Method ◽  
Back Propagation ◽  
Machine Learning Techniques ◽  
Selection Methods ◽  
Learning Techniques ◽  
Frequency Interaction

There is still a challenge of creating an evaluation method which can not only unobtrusively collect data without supplement equipment but also objectively, quantitatively and in real-time evaluate cognitive load of user based the data. The study explores the possibility of using the features extracted from high-frequency interaction events to evaluate cognitive load to respond to the challenge. Specifically, back-propagation neural networks, along with two feature selection methods (nBset and SFS), were used as the classifier and it was able to use a set of features to differentiate three cognitive load levels with an accuracy of 74.27%. The main contributions of the research are: (1) demonstrating the use of combining machine learning techniques and the HFI features in automatically evaluating cognitive load; (2) showing the potential of using the HFI features in discriminating different cognitive load when suitable classifier and features are adopted.

Applying Fuzzy Set in Elevator Safety Management Evaluation Method

2013 ◽  
Vol 710 ◽  
pp. 712-715
Author(s):  
Bai Lin Liu ◽  
Hui Yun Zou ◽  
Xi Chen
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Fuzzy Set ◽  
Evaluation Method ◽  
Safety Management ◽  
System Management ◽  
Assessment And Evaluation ◽  
Management Evaluation ◽  
Corrective Measures ◽  
Human Effects

In order to solve low accuracy, human effects and complexity in elevator safety management evaluation, a method based on machine learning was proposed. The method adopts safety checklist to collect data of elevator safety related conditions, comprehensively considering the importance and influence of every factor, which influences the safety on the basis of the safety checklist analysis and fuzzy set. To complete the process of the risk assessment and evaluation, we use machine learning combined with maintenance knowledge of evaluation, which provide users with comprehensive and effective corrective measures and suggestions. Applications show that the method can find potential leak of elevator system management.

scholarly journals Predicting the Local Response of Metastatic Brain Tumor to Gamma Knife Radiosurgery by Radiomics With a Machine Learning Method

2021 ◽  
Vol 10 ◽  
Author(s):  
Daisuke Kawahara ◽  
Xueyan Tang ◽  
Chung K. Lee ◽  
Yasushi Nagata ◽  
Yoichi Watanabe
Keyword(s):  
Machine Learning ◽  
Treatment Outcome ◽  
Prediction Model ◽  
Gamma Knife ◽  
Evaluation Method ◽  
Gamma Knife Radiosurgery ◽  
Training Data ◽  
Local Response ◽  
Visual Evaluation ◽  
Group I

PurposeThe current study proposed a model to predict the response of brain metastases (BMs) treated by Gamma knife radiosurgery (GKRS) using a machine learning (ML) method with radiomics features. The model can be used as a decision tool by clinicians for the most desirable treatment outcome.Methods and MaterialUsing MR image data taken by a FLASH (3D fast, low-angle shot) scanning protocol with gadolinium (Gd) contrast-enhanced T1-weighting, the local response (LR) of 157 metastatic brain tumors was categorized into two groups (Group I: responder and Group II: non-responder). We performed a radiomics analysis of those tumors, resulting in more than 700 features. To build a machine learning model, first, we used the least absolute shrinkage and selection operator (LASSO) regression to reduce the number of radiomics features to the minimum number of features useful for the prediction. Then, a prediction model was constructed by using a neural network (NN) classifier with 10 hidden layers and rectified linear unit activation. The training model was evaluated with five-fold cross-validation. For the final evaluation, the NN model was applied to a set of data not used for model creation. The accuracy and sensitivity and the area under the receiver operating characteristic curve (AUC) of the prediction model of LR were analyzed. The performance of the ML model was compared with a visual evaluation method, for which the LR of tumors was predicted by examining the image enhancement pattern of the tumor on MR images.ResultsBy the LASSO analysis of the training data, we found seven radiomics features useful for the classification. The accuracy and sensitivity of the visual evaluation method were 44 and 54%. On the other hand, the accuracy and sensitivity of the proposed NN model were 78 and 87%, and the AUC was 0.87.ConclusionsThe proposed NN model using the radiomics features can help physicians to gain a more realistic expectation of the treatment outcome than the traditional method.

Predicting Soil erosion based on the machine learning in Loess Plateau of China 

2021 ◽  
Author(s):  
Chenlu Huang ◽  
Qinke Yang
Keyword(s):  
Machine Learning ◽  
Soil Erosion ◽  
Loess Plateau ◽  
Evaluation Method ◽  
Interpolation Method ◽  
Dynamic Change ◽  
Control Factor ◽  
Change Analysis ◽  
Slope Length ◽  
Entire Study

<p>Soil erosion is one of the global ecological and environmental problems, which is an important factor leading to land degradation. To scientifically and effectively control soil erosion, it’s necessary to improve soil erosion evaluation methods that can obtain the actual rates of soil erosion, rather than potential erosion. For this, about 300 sampling units deployed in the Loess Plateau used as the basic data in our study, combining the seven soil erosion factors (rainfall-runoff erosivity factor, soil erodibility factor, slope length and steepness factor, biological-control factor, engineering-control factor, tillage practices factor) involved in the CSLE model and 50 soil erosion covariates related to climate, soil, topography, vegetation, human activities, etc. Using machine learning methods to establish an optimal model, and spatially predict the soil erosion rate and make a soil erosion mapof the entire study area. The prediction results show that the explanation degree of the random forest spatial prediction model is 73%. Among the selected optimal characteristic parameters, terrain and vegetation-related variables are the most important factors affecting soil erosion, from high to low, the order is LS > B > NDVI (May to September). Compared to previous studies with USLE/RUSLE/CSLE and GIS integrated mapping methods, or sampling survey based interpolation method, improvements in this paper can be concluded to : (1) the use of machine learning instead of simple multiply by soil erosion factors (linear regression), (2) higher resolution interpretation results supported by the project of “Pan-Third Pole Project”, which provide soil erosion that closed to the actual rates of soil erosion. (3) considerate additional related covariates such as population density, precipitation, soil conservation measures and so on. Further development of soil erosion prediction could provide a more accurate soil erosion evaluation method. This method can not only monitor and evaluate soil erosion in real time, and provide the possibility for the dynamic change analysis? of soil erosion in the future, but also help decision makers take effective measures in the process of mitigating soil erosion risk.</p>

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