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Defect Prediction Using Akaike and Bayesian Information Criterion

Computer Systems Science & Engineering 2022年第6期41卷 1117-1127页

作者： Saleh Albahli ghulam nabi ahmad hassan yar Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Electrical and Computer Engineering Air UniversityIslamabadPakistan

Data available in software engineering for many applications contains variability and it is not possible to say which variable helps in the process of the *** of the work present in software defect prediction is focused on the selection of best prediction *** this purpose,deep learning and ensemble models have shown promising *** contrast,there are very few researches that deals with cleaning the training data and selection of best parameter values from the *** data available for training the models have high variability and this variability may cause a decrease in model *** deal with this problem we used the Akaike information criterion(AIC)and the Bayesian information criterion(BIC)for selection of the best variables to train the model.A simple ANN model with one input,one output and two hidden layers was used for the training instead of a very deep and complex *** and BIC values are calculated and combination for minimum AIC and BIC values to be selected for the best *** first,variables were narrowed down to a smaller number using correlation *** subsets for all the possible variable combinations were *** the end,an artificial neural network(ANN)model was trained for each subset and the best model was selected on the basis of the smallest AIC and BIC *** was found that combination of only two variables’ns and entropy are best for software defect prediction as it gives minimum AIC and BIC ***,nm and npt is the worst combination and gives maximum AIC and BIC values.

关键词： Software defect prediction machine learning AIC BIC model selection cross-project defect prediction

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Efficient Grad-Cam-Based Model for COVID-19 Classification and Detection

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Computer Systems Science & Engineering 2023年第3期44卷 2743-2757页

Corona Virus(COVID-19)is a novel virus that crossed an animal-human barrier and emerged in Wuhan,*** now it has affected more than 119 million *** of COVID-19 is a critical task and due to a large number of patients,a shortage of doctors has occurred for its *** this paper,a model has been suggested that not only detects the COVID-19 using X-ray and CT-Scan images but also shows the affected *** classes have been defined;COVID-19,normal,and Pneumonia for X-ray *** CT-Scan images,2 classes have been defined COVID-19 and *** classi-fication purposes,pretrained models like ResNet50,VGG-16,and VGG19 have been used with some *** detecting the affected areas Gradient-weighted Class Activation Mapping(GradCam)has been *** the X-rays and ct images are taken at different intensities,so the contrast limited adaptive histogram equalization(CLAHE)has been applied to see the effect on the training of the *** a result of these experiments,we achieved a maximum validation accuracy of 88.10%with a training accuracy of 88.48%for CT-Scan images using the ResNet50 *** for X-ray images we achieved a maximum validation accuracy of 97.31%with a training accuracy of 95.64%using the VGG16 model.

关键词： Convolutional neural networks(CNN) COVID-19 pre-trained models CLAHE Grad-Cam X-ray data augmentation

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