Browsing by Author "Vijaya, M.S"

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GRAPH CONVOLUTIONAL NEURAL NETWORK FOR IC50 PREDICTION MODEL USING AMYOTROPHIC LATERAL SCLEROSIS TARGETS
(Springer Link, 2024-02-25) Devipriya, S; Vijaya, M.S
IC50 prediction for neurodegenerative disorders like amyotrophic lateral sclerosis is crucial in biomedical studies. Traditional machine learning models use molecular descriptors for IC50 prediction where most of the descriptors created by different tools are irrelevant and undefined. Hence, graph convolutional neural network, a deep learning algorithm is used in this paper for building more accurate IC50 prediction model based on the structural properties of drug molecules in graph format. The work is implemented with 32 protein targets of amyotrophic lateral sclerosis disorder. IC50 prediction is made by collecting canonical SMILES and their corresponding IC50 values of 2100 drugs from the ChEMBL databases. Featurization and conversion of SMILES to molecular graphs are done by the Deepchem library. The library is used for dataset creation and model building. The results show that the proposed GCNN model with their fine-tuned hyperparameters achieves a prediction rate of 73%.
MALWARE FAMILY CLASSIFICATION MODEL USING USER DEFINED FEATURES AND REPRESENTATION LEARNING
(Springer Link, 2020-11-20) Gayathri, T; Vijaya, M.S
Malware is very dangerous for system and network user. Malware identification is essential tasks in effective detecting and preventing the computer system from being infected, protecting it from potential information loss and system compromise. Commonly, there are 25 malware families exists. Traditional malware detection and anti-virus systems fail to classify the new variants of unknown malware into their corresponding families. With development of malicious code engineering, it is possible to understand the malware variants and their features for new malware samples which carry variability and polymorphism. The detection methods can hardly detect such variants but it is significant in the cyber security field to analyze and detect large-scale malware samples more efficiently. Hence it is proposed to develop an accurate malware family classification model contemporary deep learning technique. In this paper, malware family recognition is formulated as multi classification task and appropriate solution is obtained using representation learning based on binary array of malware executable files. Six families of malware have been considered here for building the models. The feature dataset with 690 instances is applied to deep neural network to build the classifier. The experimental results, based on a dataset of 6 classes of malware families and 690 malware files trained model provides an accuracy of over 86.8% in discriminating from malware families. The techniques provide better results for classifying malware into families.