A Deep Ensemble Learning Method for Effort-Aware Just-In-Time Defect Prediction

Albahli, Saleh

doi:10.3390/fi11120246

Cited by 25 publications

(19 citation statements)

References 25 publications

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“…Figure 7 shows that the average F-measure for the Deep learning for the 5 datasets is higher than Bayes network, SVM, however, slightly less than the F-measure of the Random forest. Deep learning is normally used in two settings; feature extraction and/or feature learning [38]. Deep learning can be used as a simple Neural Network classifier where the features are extracted by non-Deep approaches and Deep learning only learns the class separation.…”

Section: Discussionmentioning

confidence: 99%

Software Defect Prediction Via Deep Learning

Khan*¹,

Albahli²,

Albattah³

et al. 2020

IJITEE

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Existing models on defect prediction are trained on historical limited data which has been studied from a variety of pioneering and researchers. Cross-project defect prediction, which is often reuse data from other projects, works well when the data of training models is completely sufficient to meet the project demands. However, current studies on software defect prediction require some degree of heterogeneity of metric values that does not always lead to accurate predictions. Inspired by the current research studies, this paper takes the benefit with the state-of-the-art of deep learning and random forest to perform various experiments using five different datasets. Our model is ideal for predicting of defects with 90% accuracy using 10-fold cross-validation. The achieved results show that Random Forest and Deep learning are giving more accurate predictions with compared to Bayes network and SVM on all five datasets. We also derived Deep Learning that can be competitive classifiers and provide more robust for detecting defect prediction.

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Section: Discussionmentioning

confidence: 99%

Software Defect Prediction Via Deep Learning

Khan*¹,

Albahli²,

Albattah³

et al. 2020

IJITEE

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“…Deep learning tasks take the advantage of huge data and utilize computationally expensive training techniques to outperform the traditional machine learning tasks [4]. These techniques need to have tremendous amounts of data in order to control latest advances.…”

Section: Chest X-ray Datasetmentioning

confidence: 99%

“…The Covid-19 and other respiratory diseases have seen a great progress due to image processing tools and researchers are using computer techniques and deep learning algorithms in order to achieve these breakthroughs [2][3] [4]. These algorithms were used to make radiographic image classification tools which are very useful in the detection of these respiratory abnormalities.…”

Section: Introductionmentioning

confidence: 99%

Efficient GAN-based Chest Radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia

Albahli¹

2020

Int. J. Med. Sci.

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Background: As 2019 ends coronavirus disease start expanding all over the world. It is highly transmissible disease that can affect respiratory tract and can leads to organ failure. In 2020 it is declared by world health organization as "Public health emergency of international concerns". The current situation of Covid-19 and chest related diseases have already gone through radical change with the advancements of image processing tools. There is no effective method which can accurately identify all chest related diseases and tackle the multiple class problems with reliable results. Method: There are many potentially impactful applications of Deep Learning to fighting the Covid-19 from Chest X-Ray/CT Images, however, most are still in their early stages due to lack of data sharing as it continues to inhibit overall progress in a variety of medical research problems. Based on COVID-19 radiographical changes in CT images, this work aims to detect the possibility of COVID-19 in the patient. This work provides a significant contribution in terms of Gan based synthetic data and four different types of deep learning-based models which provided state of the art comparable results. Results: A Deep Neural Network model provides a significant contribution in terms of detecting COVID-19 and provides effective analysis of chest related diseases with respect to age and gender. Our model achieves 89% accuracy in terms of Gan based synthetic data and four different types of deep learning-based models which provided state of the art comparable results. Conclusion:If the gap in identifying of all viral pneumonias is not filled with effective automation of chest disease detection the healthcare industry may have to bear unfavorable circumstances.

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“…It is an important feature of the proposed model. It includes many applications including data mining, text classification, text mining, real-time analysis, batch queries, machine learning [27]. It would be a beneficial tool for the research community and doctors.…”

Section: Ehr Layer (Cloud Providers Storage Banks)mentioning

confidence: 99%

A Blockchain-Based Architecture for Smart Healthcare System: A Case Study of Saudi Arabia

Albahli¹,

Khan²,

Qamar³

2020

Adv. sci. technol. eng. syst. j.

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eHealth is the use of Information and Communication Technologies (ICT) to enhance health quality access. Health care innovations are an essential element of Vision 2030 and National Transformation Program (NTP) 2020 operational plan that will lead to an improvement in the quality of health care in Saudi Arabia. The objective of this paper is to address the healthcare system limitations in the Kingdom of Saudi Arabia in general and the Qassim region in particular to global standards. In practice, the creation of an infrastructure for storing and seamless sharing of health data between different entities is studied. Furthermore, an in-depth analysis of current practices w.r.t. the health data, as well as finding similarities between NTP 2020 and Health 2020 (European Union) has been performed. A multi-level blockchain eHealth system is proposed to provide seamless electronic health records to the patients. Moreover, the current practices being employed in Qassim province, KSA has been analyzed. It was found that private hospitals give access to medical reports to their patients besides allowing them to manage their appointments. On the other hand, access to the government hospitals' medical records is minimal.

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A Deep Ensemble Learning Method for Effort-Aware Just-In-Time Defect Prediction

Cited by 25 publications

References 25 publications

Software Defect Prediction Via Deep Learning

Software Defect Prediction Via Deep Learning

Efficient GAN-based Chest Radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia

A Blockchain-Based Architecture for Smart Healthcare System: A Case Study of Saudi Arabia

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