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| 1 | An Efficient Unsupervised Learning Approach for Detecting Anomaly in Cloud显示文摘The Cloud system shows its growing functionalities in various industrial applications.The safety towards data transfer seems to be a threat where Network Intrusion Detection System(NIDS)is measured as an essential element to fulfill security.Recently,Machine Learning(ML)approaches have been used for the construction of intellectual IDS.Most IDS are based on ML techniques either as unsupervised or supervised.In supervised learning,NIDS is based on labeled data where it reduces the efficiency of the reduced model to identify attack patterns.Similarly,the unsupervised model fails to provide a satisfactory outcome.Hence,to boost the functionality of unsupervised learning,an effectual auto-encoder is applied for feature selection to select good features.Finally,the Naïve Bayes classifier is used for classification purposes.This approach exposes the finest generalization ability to train the data.The unlabelled data is also used for adoption towards data analysis.Here,redundant and noisy samples over the dataset are eliminated.To validate the robustness and efficiency of NIDS,the anticipated model is tested over the NSL-KDD dataset.The experimental outcomes demonstrate that the anticipated approach attains superior accuracy with 93%,which is higher compared to J48,AB tree,Random Forest(RF),Regression Tree(RT),Multi-Layer Perceptrons(MLP),Support Vector Machine(SVM),and Fuzzy.Similarly,False Alarm Rate(FAR)and True Positive Rate(TPR)of Naive Bayes(NB)is 0.3 and 0.99,respectively.When compared to prevailing techniques,the anticipated approach also delivers promising outcomes. | P.Sherubha S.P.Sasirekha A.Dinesh Kumar Anguraj J.Vakula Rani Raju Anitha S.Phani Praveen R.Hariharan Krishnan | 2023 | Computer Systems Science & Engineering2023，45，4： | 1 |
| 2 | Chemokines in tumor angiogenesis and metastasis显示文摘 | Seema Singh Anguraj Sadanandam Rakesh K. Singh | 2007 | Cancer and Metastasis Reviews (-)2007，，3： | 1 |
| 3 | Existence Results for Non-densely Defined Neutral Impulsive Differential Inclusions with Nonlocal Conditions显示文摘 | Chang Y K Anguraj A Mallika M | 2008 | Journal of Applied Mathematics and Computing2008，28，12： | 1 |
| 4 | Existence of Mild Solutions of Abstract Fractional Differential Equations with Non-Instantaneous Impulsive Conditions显示文摘 | A. Anguraj S. Kanjanadevi | 2016 | Journal of Statistical Science and Application2016，4，1： | 1 |
| 5 | Existence results for an impulsive neutral functional differential equation with state-dependent delay显示文摘 | Anguraj A Arjunan M M Hernández E | 2007 | Appl Anal2007，86，7： | 1 |
| 6 | Existence results for impulsive neutral functional differential equations with infinite delay显示文摘 | Chang Y K Anguraj A Arjunan M M | 2008 | Nonlinear Anal HS2008，2，1： | 1 |
| 7 | Existence results for non-densely defined neutral differential inclusions with nonlocal conditions显示文摘 | Chang Y K Anguraj A Arjunan M M | 2008 | J Appl Math Comput2008，28，12： | 1 |
| 8 | Existence results for an impulsive neutral functional differential equation with state-dependent delay 显示文摘 | Anguraj A Arjunan M M Hernandez E | 2007 | Appl Anal2007，86，7： | 1 |
| 9 | Existence results for impulsive neutral functional differential equations with infinite delay 显示文摘 | Chang Y K Anguraj A Arjunan M M | 2008 | Nonlinear Anal HS2008，2，1： | 1 |
| 10 | Existence results for non-densely defined neutral differential inclusions with nonlocal conditions 显示文摘 | Chang Y K Anguraj A Arjunan M M | 2008 | J Appl Math Comput2008，28，： | 1 |
| 11 | Existence results for impulsive neutral functional differential equations with infinite delay显示文摘 | CHANG Yong-kui ANGURAJ A MALLIKA A M | 2008 | Nonlinear Analysis: Hybrid Systems2008，2，： | 1 |
| 12 | Existence for impulsive neutral integrodifferential inclusions with nonlocal initial conditions via fractional operators显示文摘 | Chang Y K Anguraj A Karthikeyan K | | 0，，： | 1 |
| 13 | Existence results for non-densely defined neutral impulsive differential inclusions with nonlocal conditions显示文摘 | Y.-K. Chang A. Anguraj M. Mallika Arjunan | 2008 | Journal of Applied Mathematics and Computing (-)2008，，1： | 1 |
| 14 | Existence results for an impulsive neutral functional differential equation with state-dependent delay显示文摘 | Anguraj A Mallika Arjunan M E Hernández M | | 0，，： | 1 |
| 15 | Existence,uniqueness and stability results of impulsive stochastic semilinear neutral functional differential equations with infinite delays显示文摘 | Anguraj A Vinodkumar A | | 0，，： | 1 |
| 16 | Reconciliation of classification systems defining molecular subtypes of colorectal cancer显示文摘 | Anguraj Sadanandam Xin Wang Felipe de Sousa E Melo Joe W Gray Louis Vermeulen Douglas Hanahan Jan Paul Medema | 2014 | Cell Cycle2014，，3： | 1 |
| 17 | Chemokines in tumor angiogenesis and metastasis显示文摘 | Seema Singh Anguraj Sadanandam Rakesh K. Singh | 2007 | Cancer and Metastasis Reviews (-)2007，，3： | 1 |
| 18 | Existence Results of Abstract Impulsive Integrodifferential Systems with Measure of Non-compactness显示文摘 | K. Malar A. Anguraj | 2016 | Journal of Statistical Science and Application2016，4，2： | 0 |
| 19 | Modeling a Novel Hyper-Parameter Tuned Deep Learning Enabled Malaria Parasite Detection and Classification显示文摘A theoretical methodology is suggested for finding the malaria parasites’presence with the help of an intelligent hyper-parameter tuned Deep Learning(DL)based malaria parasite detection and classification(HPTDL-MPDC)in the smear images of human peripheral blood.Some existing approaches fail to predict the malaria parasitic features and reduce the prediction accuracy.The trained model initiated in the proposed system for classifying peripheral blood smear images into the non-parasite or parasite classes using the available online dataset.The Adagrad optimizer is stacked with the suggested pre-trained Deep Neural Network(DNN)with the help of the contrastive divergence method to pre-train.The features are extracted from the images in the proposed system to train the DNN for initializing the visible variables.The smear images show the concatenated feature to be utilized as the feature vector in the proposed system.Lastly,hyper-parameters are used to fine-tune DNN to calculate the class labels’probability.The suggested system outperforms more modern methodologies with an accuracy of 91%,precision of 89%,recall of 93%and F1-score of 91%.The HPTDL-MPDC has the primary application in detecting the parasite of malaria in the smear images of human peripheral blood. | Tamal Kumar Kundu Dinesh Kumar Anguraj S.V.Sudha | 2023 | Computers, Materials & Continua2023，77，12： | 0 |
| 20 | Identification of Semaphorin 5A Interacting Protein by Applying Apriori Knowledge and Peptide Complementarity Related to Protein Evolution and Structure显示文摘In the post-genomic era,various computational methods that predict protein-protein interactions at the genome level are available; however,each method has its own advantages and disadvantages,resulting in false predictions. Here we devel-oped a unique integrated approach to identify interacting partner(s) of Semaphorin 5A (SEMA5A),beginning with seven proteins sharing similar ligand interacting residues as putative binding partners. The methods include Dwyer and Root-Bernstein/Dillon theories of protein evolution,hydropathic complementarity of protein structure,pattern of protein functions among molecules,information on domain-domain interactions,co-expression of genes and protein evolution. Among the set of seven proteins selected as putative SEMA5A interacting partners,we found the functions of Plexin B3 and Neuropilin-2 to be associated with SEMA5A. We modeled the semaphorin domain structure of Plexin B3 and found that it shares similarity with SEMA5A. Moreover,a virtual expression database search and RT-PCR analysis showed co-expression of SEMA5A and Plexin B3 and these proteins were found to have co-evolved. In addition,we confirmed the interac-tion of SEMA5A with Plexin B3 in co-immunoprecipitation studies. Overall,these studies demonstrate that an integrated method of prediction can be used at the genome level for discovering many unknown protein binding partners with known ligand binding domains. | Anguraj Sadanandam Michelle L. Varney Rakesh K. Singh | 2008 | Genomics, Proteomics & Bioinformatics2008，6，3： | 0 |
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