Adaptive Deep Learning Model for Software Bug Detection and Classification

Software is unavoidable in software development and maintenance.In literature,many methods are discussed which fails to achieve efficient software bug detection and classification.In this paper,efficient Adaptive Deep Learning Model(ADLM)is developed for automatic duplicate bug report detection and classification process.The proposed ADLM is a combination of Conditional Random Fields decoding with Long Short-Term Memory(CRF-LSTM)and Dingo Optimizer(DO).In the CRF,the DO can be consumed to choose the efficient weight value in network.The proposed automatic bug report detection is proceeding with three stages like pre-processing,feature extraction in addition bug detection with classification.Initially,the bug report input dataset is gathered from the online source system.In the pre-processing phase,the unwanted information from the input data are removed by using cleaning text,convert data types and null value replacement.The pre-processed data is sent into the feature extraction phase.In the feature extraction phase,the four types of feature extraction method are utilized such as contextual,categorical,temporal and textual.Finally,the features are sent to the proposed ADLM for automatic duplication bug report detection and classification.The proposed methodology is proceeding with two phases such as training and testing phases.Based on the working process,the bugs are detected and classified from the input data.The projected technique is assessed by analyzing performance metrics such as accuracy,precision,Recall,F_Measure and kappa.

Drug–Target Interaction Prediction Model Using Optimal Recurrent Neural Network

Drug-target interactions prediction(DTIP)remains an important requirement in thefield of drug discovery and human medicine.The identification of interaction among the drug compound and target protein plays an essential pro-cess in the drug discovery process.It is a lengthier and complex process for pre-dicting the drug target interaction(DTI)utilizing experimental approaches.To resolve these issues,computational intelligence based DTIP techniques were developed to offer an efficient predictive model with low cost.The recently devel-oped deep learning(DL)models can be employed for the design of effective pre-dictive approaches for DTIP.With this motivation,this paper presents a new drug target interaction prediction using optimal recurrent neural network(DTIP-ORNN)technique.The goal of the DTIP-ORNN technique is to predict the DTIs in a semi-supervised way,i.e.,inclusion of both labelled and unlabelled instances.Initially,the DTIP-ORNN technique performs data preparation process and also includes class labelling process,where the target interactions from the database are used to determine thefinal label of the unlabelled instances.Besides,drug-to-drug(D-D)and target-to-target(T-T)interactions are used for the weight initia-tion of the RNN based bidirectional long short term memory(BiLSTM)model which is then utilized to the prediction of DTIs.Since hyperparameters signifi-cantly affect the prediction performance of the BiLSTM technique,the Adam optimizer is used which mainly helps to improve the DTI prediction outcomes.In order to ensure the enhanced predictive outcomes of the DTIP-ORNN techni-que,a series of simulations are implemented on four benchmark datasets.The comparative result analysis shows the promising performance of the DTIP-ORNN method on the recent approaches.

Skin Melanoma Classification System Using Deep Learning

The deadliest type of skin cancer is malignant melanoma.The diagnosis requires at the earliest to reduce the mortality rate.In this study,an efficient Skin Melanoma Classification(SMC)system is presented using dermoscopic images as a non-invasive procedure.The SMC system consists of four modules;segmentation,feature extraction,feature reduction and finally classification.In the first module,k-means clustering is applied to cluster the colour information of dermoscopic images.The second module extracts meaningful and useful descriptors based on the statistics of local property,parameters of Generalized Autoregressive Conditional Heteroscedasticity(GARCH)model of wavelet and spatial patterns by Dominant Rotated Local Binary Pattern(DRLBP).The third module reduces the features by the t-test,and the last module uses deep learning for the classification.The individual performance shows that GARCH parameters of 3rd DWT level sub-bands provide 92.50%accuracy than local properties(77.5%)and DRLBP(88%)based features for the 1st stage(normal/abnormal).For the 2nd stage(benign/malignant),it is 95.83%(GRACH),90%(DRLBP)and 80.8%(Local Properties).The selected 2%of features from the combination gives 99.5%and 100%for 1st and 2nd stage of the SMC system.The greatest degree of success is achieved on PH2 database images using two stages of deep learning.It can be used as a pre-screening tool as it provides 100%accuracy for melanoma cases.