A Machine Learning-Based Reliability Assessment Model for Critical Software Systems

2007 ◽  
Author(s):  
Venkata U.B. Challagulla ◽  
Farokh B. Bastani ◽  
Raymond A. Paul ◽  
Wei-Tek Tsai ◽  
Yinong Chen
Keyword(s):  
Machine Learning ◽  
Reliability Assessment ◽  
Assessment Model ◽  
Software Systems

A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 300 ◽  
Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Quality Standard ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Health Care Insurance ◽  
Software Defect ◽  
Learning Techniques ◽  
Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

scholarly journals A Novel Hybrid Method for Landslide Susceptibility Mapping-Based GeoDetector and Machine Learning Cluster: A Case of Xiaojin County, China

2021 ◽  
Vol 10 (2) ◽  
pp. 93
Author(s):  
Wei Xie ◽  
Xiaoshuang Li ◽  
Wenbin Jian ◽  
Yang Yang ◽  
Hongwei Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Method ◽  
Roc Curve ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Assessment Model ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Susceptibility Map ◽  
Area Index

Landslide susceptibility mapping (LSM) could be an effective way to prevent landslide hazards and mitigate losses. The choice of conditional factors is crucial to the results of LSM, and the selection of models also plays an important role. In this study, a hybrid method including GeoDetector and machine learning cluster was developed to provide a new perspective on how to address these two issues. We defined redundant factors by quantitatively analyzing the single impact and interactive impact of the factors, which was analyzed by GeoDetector, the effect of this step was examined using mean absolute error (MAE). The machine learning cluster contains four models (artificial neural network (ANN), Bayesian network (BN), logistic regression (LR), and support vector machines (SVM)) and automatically selects the best one for generating LSM. The receiver operating characteristic (ROC) curve, prediction accuracy, and the seed cell area index (SCAI) methods were used to evaluate these methods. The results show that the SVM model had the best performance in the machine learning cluster with the area under the ROC curve of 0.928 and with an accuracy of 83.86%. Therefore, SVM was chosen as the assessment model to map the landslide susceptibility of the study area. The landslide susceptibility map demonstrated fit with landslide inventory, indicated the hybrid method is effective in screening landslide influences and assessing landslide susceptibility.

Agreement and Reliability Analysis of Machine Learning Scaling in the Assessment of Acute Proximal Weakness by Experts and Non-Experts: Clinical Feasibility Study (Preprint)

2021 ◽  
Author(s):  
Eunjeong Park ◽  
Kijeong Lee ◽  
Taehwa Han ◽  
Hyo Suk Nam
Keyword(s):  
Machine Learning ◽  
Reliability Analysis ◽  
Gold Standard ◽  
Assessment Tool ◽  
Expert Knowledge ◽  
Hybrid Approach ◽  
Assessment Model ◽  
Lower Limbs ◽  
Neurological Deficits ◽  
Percent Agreement

BACKGROUND Assessing the symptoms of proximal weakness caused by neurological deficits requires expert knowledge and experienced neurologists. Recent advances in artificial intelligence and the Internet of Things have resulted in the development of automated systems that emulate physicians’ assessments. OBJECTIVE This study provides an agreement and reliability analysis of using an automated scoring system to evaluate proximal weakness by experts and non-experts. METHODS We collected 144 observations from acute stroke patients in a neurological intensive care unit to measure the symptom of proximal weakness of upper and lower limbs. A neurologist performed a gold standard assessment and two medical students performed identical tests as non-expert assessments for manual and machine learning-based scaling of Medical Research Council (MRC) proximal scores. The system collects signals from sensors attached on patients’ limbs and trains a machine learning assessment model using the hybrid approach of data-level and algorithm-level methods for the ordinal and imbalanced classification in multiple classes. For the agreement analysis, we investigated the percent agreement of MRC proximal scores and Bland-Altman plots of kinematic features between the expert- and non-expert scaling. In the reliability analysis, we analysed the intra-class correlation coefficients (ICCs) of kinematic features and Krippendorff’s alpha of the three observers’ scaling. RESULTS The mean percent agreement between the gold standard and the non-expert scaling was 0.542 for manual scaling and 0.708 for IoT-assisted machine learning scaling, with 30.63% enhancement. The ICCs of kinematic features measured using sensors ranged from 0.742 to 0.850, whereas the Krippendorff’s alpha of manual scaling for the three observers was 0.275. The Krippendorff’s alpha of machine learning scaling increased to 0.445, with 61.82% improvement. CONCLUSIONS Automated scaling using sensors and machine learning provided higher inter-rater agreement and reliability in assessing acute proximal weakness. The enhanced assessment supported by the proposed system can be utilized as a reliable assessment tool for non-experts in various emergent environments.

scholarly journals Random Forest Model in the Diagnosis of Dementia Patients with Normal Mini-Mental State Examination Scores

2022 ◽  
Vol 12 (1) ◽  
pp. 37
Author(s):  
Jie Wang ◽  
Zhuo Wang ◽  
Ning Liu ◽  
Caiyan Liu ◽  
Chenhui Mao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Random Forest ◽  
Mental State ◽  
Mini Mental State Examination ◽  
Machine Learning Algorithms ◽  
Assessment Model ◽  
Test Time ◽  
Cognitive Screening ◽  
State Examination

Background: Mini-Mental State Examination (MMSE) is the most widely used tool in cognitive screening. Some individuals with normal MMSE scores have extensive cognitive impairment. Systematic neuropsychological assessment should be performed in these patients. This study aimed to optimize the systematic neuropsychological test battery (NTB) by machine learning and develop new classification models for distinguishing mild cognitive impairment (MCI) and dementia among individuals with MMSE ≥ 26. Methods: 375 participants with MMSE ≥ 26 were assigned a diagnosis of cognitively unimpaired (CU) (n = 67), MCI (n = 174), or dementia (n = 134). We compared the performance of five machine learning algorithms, including logistic regression, decision tree, SVM, XGBoost, and random forest (RF), in identifying MCI and dementia. Results: RF performed best in identifying MCI and dementia. Six neuropsychological subtests with high-importance features were selected to form a simplified NTB, and the test time was cut in half. The AUC of the RF model was 0.89 for distinguishing MCI from CU, and 0.84 for distinguishing dementia from nondementia. Conclusions: This simplified cognitive assessment model can be useful for the diagnosis of MCI and dementia in patients with normal MMSE. It not only optimizes the content of cognitive evaluation, but also improves diagnosis and reduces missed diagnosis.

Optimization Design of Rolling Bearing Based on Reliability Assessment

2012 ◽  
Vol 215-216 ◽  
pp. 115-118
Author(s):  
Jia Hong Zheng ◽  
Yu Chun Liu ◽  
Min Li
Keyword(s):  
Optimization Design ◽  
Reliability Assessment ◽  
Rolling Bearing ◽  
Transmission System ◽  
Assessment Model ◽  
Rolling Bearings ◽  
Transmission Systems ◽  
Modern Times ◽  
Wind Generator ◽  
Time Optimization

Today, transmission systems are playing important roles in engineering. For all of the components in them, status of the rolling bearing has been rising. In modern times, people are making great efforts on the optimization of transmission system. At the same time, optimization design of all the internal components was also important. For all the techniques, research to the rolling bearings has become increasingly mature. In this paper, the rolling bearing in growth of box of the 2 MW wind generator was took as the research object. The load coefficient of rolling bearing was been calculated, then the reliability assessment model of the rolling bearing was been made. On the basis of which, the optimization design on the reliability was done.

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