Online ISSN: 2515-8260

Keywords : classification models

A Survey of different machine learning models for software defect testing

Arpitha Kotte; Dr.Ahmad Abdul Moiz Qyser

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 3256-3268

As the size of the defects increases, it becomes difficult to predict the different types of software defects with high true positive rate. The main objective of the machine learning models for software defect-based testing application is to improve the defect prediction rate with less error rate. Evaluating the software metrics and defect prediction are the two key quality features that determine the success of a software product. Most of the conventional meta-heuristic based software defect testing models are independent of dynamic parameters estimation. Also, these conventional models are used to predict the defect in the homogeneous software testing systems with limited number of feature space. In this paper, different types of software defect prediction systems and its models are discussed along with the limitations on various metrics.

Brain Tumor And Intracranial Haemorrhage Feature Extraction And Classification Using Conventional And Deep Learning Methods

R. Aruna Kirithika; S. Sathiya; M. Balasubramanian; P. Sivaraj

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 7, Pages 237-258

Presently, brain tumor (BT) and Intracranial hemorrhage (ICH) detection and classification processes become essential to save human lives. Automated diagnosis model using deep learning (DL) models finds useful to attain improved diagnostic outcome. This paper presents an ensemble of handcrafted and deep features for BT and ICH diagnosis. The proposed model comprises of three important processes, such as preprocessing, feature extraction and classification. The preprocessing of the input image takes place in three ways namely skull stripping, bilateral filtering (BF) and contrast limited adaptive histogram equalization (CLAHE) based contrast enhancement. In addition, scale invariant feature transform (SIFT) and AlexNet models are used for feature extraction process. In order to classify the existence of BT and ICH, two classification models is carried out such as gaussian naïve bayes (GNB) and random forest (RF).For validating the effective diagnostic performance of the proposed model, a set of simulations were carried out to determine the different class labels. The simulation outcome indicated the effective performance with the maximum sensitivity of 92.41%, specificity of 100%, and accuracy of 94.26%.