A computer-assisted instructional method based on machine learning in software testing class

2018 ◽  
Vol 26 (5) ◽  
pp. 1150-1158 ◽  
Author(s):  
Wei Zheng ◽  
Yutong Bai ◽  
Haoxuan Che
Keyword(s):  
Machine Learning ◽  
Software Testing ◽  
Instructional Method ◽  
Computer Assisted ◽  
Testing Class

scholarly journals Software Testing: Issues and Challenges of Artificial Intelligence & Machine Learning

2021 ◽  
Vol 12 (1) ◽  
pp. 101-112
Author(s):  
Kishore Sugali ◽  
Chris Sprunger ◽  
Venkata N Inukollu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Software Testing ◽  
Future Research ◽  
Critical Component ◽  
Development Methodology ◽  
Testing Strategies ◽  
History Of ◽  
Shed Light ◽  
The Way

The history of Artificial Intelligence and Machine Learning dates back to 1950’s. In recent years, there has been an increase in popularity for applications that implement AI and ML technology. As with traditional development, software testing is a critical component of an efficient AI/ML application. However, the approach to development methodology used in AI/ML varies significantly from traditional development. Owing to these variations, numerous software testing challenges occur. This paper aims to recognize and to explain some of the biggest challenges that software testers face in dealing with AI/ML applications. For future research, this study has key implications. Each of the challenges outlined in this paper is ideal for further investigation and has great potential to shed light on the way to more productive software testing strategies and methodologies that can be applied to AI/ML applications.

scholarly journals Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257901
Author(s):  
Yanjing Bi ◽  
Chao Li ◽  
Yannick Benezeth ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Computer Assisted ◽  
Long Distance ◽  
Relationship Analysis ◽  
Vector Machines

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density.

Effects of Student Choice of Instructional Method on the Learning Outcomes of Students With Comorbid Learning and Emotional/Behavioral Disabilities

2018 ◽  
Vol 41 (4) ◽  
pp. 213-226
Author(s):  
Glenna M. Billingsley ◽  
Cathy N. Thomas ◽  
Jo A. Webber
Keyword(s):  
Learning Outcomes ◽  
Secondary Students ◽  
Stated Preference ◽  
Mathematics Learning ◽  
Instructional Method ◽  
Learning Preferences ◽  
Computer Assisted ◽  
Single Subject ◽  
Before And After ◽  
Students With Ld

The purpose of this exploratory study was to determine whether learning preferences of secondary students identified with concomitant learning disabilities (LD) and emotional disturbance (ED) align with the instructional method with which they best learned mathematics. Responses to a preference survey given before and after instruction were compared with learning outcomes in a single-subject, alternating treatments research design investigating the effects of direct teach, computer-assisted instruction (CAI), and a combination of both methods. Results showed that most students were able to predict, by choice or stated preference, conditions under which they will learn best and, in hindsight, indicate the condition that provided the best learning outcome. This small study contributes to the literature on secondary mathematics learning for students with LD, and makes unique contributions regarding (a) the learning of students with comorbid LD and ED and (b) implementation of instruction that combines direct teach with CAI.

scholarly journals Computer-assisted framework for machine-learning–based delineation of GTV regions on datasets of planning CT and PET/CT images

2017 ◽  
Vol 58 (1) ◽  
pp. 123-134 ◽  
Author(s):  
Koujiro Ikushima ◽  
Hidetaka Arimura ◽  
Ze Jin ◽  
Hidetake Yabu-uchi ◽  
Jumpei Kuwazuru ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ground Glass Opacity ◽  
Global Optimum ◽  
Image Features ◽  
Support Vector ◽  
Computer Assisted ◽  
Dice Similarity Coefficient ◽  
Radiation Oncologists ◽  
Tumor Region ◽  
Leave One Out

Abstract We have proposed a computer-assisted framework for machine-learning–based delineation of gross tumor volumes (GTVs) following an optimum contour selection (OCS) method. The key idea of the proposed framework was to feed image features around GTV contours (determined based on the knowledge of radiation oncologists) into a machine-learning classifier during the training step, after which the classifier produces the ‘degree of GTV’ for each voxel in the testing step. Initial GTV regions were extracted using a support vector machine (SVM) that learned the image features inside and outside each tumor region (determined by radiation oncologists). The leave-one-out-by-patient test was employed for training and testing the steps of the proposed framework. The final GTV regions were determined using the OCS method that can be used to select a global optimum object contour based on multiple active delineations with a LSM around the GTV. The efficacy of the proposed framework was evaluated in 14 lung cancer cases [solid: 6, ground-glass opacity (GGO): 4, mixed GGO: 4] using the 3D Dice similarity coefficient (DSC), which denotes the degree of region similarity between the GTVs contoured by radiation oncologists and those determined using the proposed framework. The proposed framework achieved an average DSC of 0.777 for 14 cases, whereas the OCS-based framework produced an average DSC of 0.507. The average DSCs for GGO and mixed GGO were 0.763 and 0.701, respectively, obtained by the proposed framework. The proposed framework can be employed as a tool to assist radiation oncologists in delineating various GTV regions.

Machine Learning Applied to Software Testing: A Systematic Mapping Study

2019 ◽  
Vol 68 (3) ◽  
pp. 1189-1212 ◽  
Author(s):  
Vinicius H. S. Durelli ◽  
Rafael S. Durelli ◽  
Simone S. Borges ◽  
Andre T. Endo ◽  
Marcelo M. Eler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Software Testing ◽  
Systematic Mapping Study ◽  
Mapping Study ◽  
Systematic Mapping

P31 Optimising trajectories in computer assisted planning for cranial laser interstitial thermal therapy: a machine learning approach

2019 ◽  
Vol 90 (3) ◽  
pp. e33.1-e33
Author(s):  
K Li ◽  
VN Vakharia ◽  
R Sparks ◽  
LGS França ◽  
A McEvoy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Hippocampal Sclerosis ◽  
Thermal Therapy ◽  
Machine Learning Techniques ◽  
Target Point ◽  
Computer Assisted ◽  
Laser Interstitial Thermal Therapy ◽  
Entry Points

ObjectivesOptimal trajectory planning for cranial laser interstitial thermal therapy (cLITT) in drug resistant focal mesial temporal lobe epilepsy (MTLE).DesignA composite ablation score of ablated AHC minus ablated PHG volumes were calculated and normalised. Random forest and linear regression were implemented to predict composite ablation scores and determine the optimal entry and target point combinations to maximize this.SubjectsTen patients with hippocampal sclerosis were included.MethodsComputer Assisted Planning (CAP) cLITT trajectories were generated using entry regions that include the inferior occipital gyri (IOG), middle occipital gyri (MOG), inferior temporal gyri (ITG) and middle temporal gyri (MTG). Target points were varied by sequential erosions and transformations of the centroid of the amygdala. In total 760 trajectory combinations were generated per patient and ablation volumes were calculated based on a conservative 15 mm maximum ablation diameter.ResultsLinear regression was superior to random forest predictions. Linear regression indicated that maximal composite ablation scores were associated with entry points that clustered around the junction of the IOG, MOG and MTG. The optimal target point was a translation of the centroid of the amygdala anteriorly and medially.ConclusionsMachine learning techniques accurately predict composite ablation scores with linear regression outperforming the random forest approach. Optimal CAP entry points for cLITT maximize ablation of the AHC and spare the PHG.

Software defect prediction: A multi-criteria decision-making approach

2020 ◽  
Vol 15 (1) ◽  
pp. 35-42
Author(s):  
A.O. Balogun ◽  
A.O. Bajeh ◽  
H.A. Mojeed ◽  
A.G. Akintola
Keyword(s):  
Machine Learning ◽  
Software Testing ◽  
Evaluation Metrics ◽  
Defect Prediction ◽  
Software Systems ◽  
Software Defect Prediction ◽  
Learning Models ◽  
Decision Method ◽  
Software Defect ◽  
Machine Learning Models

Failure of software systems as a result of software testing is very much rampant as modern software systems are large and complex. Software testing which is an integral part of the software development life cycle (SDLC), consumes both human and capital resources. As such, software defect prediction (SDP) mechanisms are deployed to strengthen the software testing phase in SDLC by predicting defect prone modules or components in software systems. Machine learning models are used for developing the SDP models with great successes achieved. Moreover, some studies have highlighted that a combination of machine learning models as a form of an ensemble is better than single SDP models in terms of prediction accuracy. However, the efficiency of machine learning models can change with diverse predictive evaluation metrics. Thus, more studies are needed to establish the effectiveness of ensemble SDP models over single SDP models. This study proposes the deployment of Multi-Criteria Decision Method (MCDM) techniques to rank machine learning models. Analytic Network Process (ANP) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) which are types of MCDM techniques are deployed on 9 machine learning models with 11 performance evaluation metrics and 11 software defects datasets. The experimental results showed that ensemble SDP models are best appropriate SDP models as Boosted SMO and Boosted PART ranked highest for each of the MCDM techniques. Besides, the experimental results also validated the stand of not considering accuracy as the only performance evaluation metrics for SDP models. Conclusively, more performance metrics other than predictive accuracy should be considered when ranking and evaluating machine learning models. Keywords: Ensemble; Multi-Criteria Decision Method; Software Defect Prediction

scholarly journals Application of Machine Learning in Auditing Teaching: A Case Study of Predicting the Audit Report Type of China ST Listed Companies

2020 ◽  
Vol 2 (1) ◽  
pp. 023-040
Author(s):  
Shi-Ming Huang Shi-Ming Huang ◽  
Chang-ping Chen Shi-Ming Huang ◽  
Tzu-ching Wong Chang-ping Chen
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Listed Company ◽  
Accounting Students ◽  
Computer Assisted ◽  
Research Database ◽  
Audit Report ◽  
Professional Fields ◽  
Future Work

<p>Artificial intelligence is an important emerging technology in the accounting industry. Fear and hype associated with artificial intelligence and its impact on accounting and auditing jobs have pervaded the professional fields of accounting and auditing. It is important to develop AI competency in accountants and auditors. This paper presents a teaching case for a professor or lecturer to use for teaching machine learning to accounting students. The case is based on openly available data from the China Stock Market & Accounting Research database and aims to teach students how to predict the future audit report type of a China ST listed company. Through case teaching, students can learn skills related to computer-assisted auditing tools and machine learning (such as ACL) develop the confidence to apply artificial intelligence in their education and future work.</p> <p>&nbsp;</p>

scholarly journals Haar Wavelet Pyramid-Based Melanoma Skin Cancer Identification With Ensemble of Machine Learning Algorithms

2021 ◽  
Vol 16 (4) ◽  
pp. 1-15
Author(s):  
Sudeep D. Thepade ◽  
Gaurav Ramnani
Keyword(s):  
Machine Learning ◽  
Skin Cancer ◽  
Health Informatics ◽  
Performance Metrics ◽  
Learning Algorithms ◽  
Haar Wavelet ◽  
Machine Learning Algorithms ◽  
Computer Assisted ◽  
Marginal Improvement ◽  
Wavelet Pyramid

Melanoma is a mortal type of skin cancer. Early detection of melanoma significantly improves the patient’s chances of survival. Detection of melanoma at an early juncture demands expert doctors. The scarcity of such expert doctors is a major issue with healthcare systems globally. Computer-assisted diagnostics may prove helpful in this case. This paper proposes a health informatics system for melanoma identification using machine learning with dermoscopy skin images. In the proposed method, the features of dermoscopy skin images are extracted using the Haar wavelet pyramid various levels. These features are employed to train machine learning algorithms and ensembles for melanoma identification. The consideration of higher levels of Haar Wavelet Pyramid helps speed up the identification process. It is observed that the performance gradually improves from the Haar wavelet pyramid level 4x4 to 16x16, and shows marginal improvement further. The ensembles of machine learning algorithms have shown a boost in performance metrics compared to the use of individual machine learning algorithms.

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