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| 1 | Results of 102 cases of complete repair of congenital heart defects in patients weighing 700 to 2500 grams显示文摘 | V.Mohan Reddy Doff B. McElhinney Theresa Sagrado Andrew J. Parry David F. Teitel Frank L. Hanley | 1999 | The Journal of Thoracic and Cardiovascular Surgery1999，，2： | 1 |
| 2 | Comparison of ADA 1997 and WHO 1985 criteria for diabetes in south Indians – the Chennai Urban Population Study显示文摘 | R.Deepa S. ShanthiRani G.Premalatha V.Mohan | 2008 | Diabetic Medicine2008，，12： | 1 |
| 3 | Multiple ventricular septal defects: How and when should they be repaired?显示文摘 | Francesco Seddio V.Mohan Reddy Doff B. McElhinney Wayne Tworetzky Norman H. Silverman Frank L. Hanley | 1999 | The Journal of Thoracic and Cardiovascular Surgery1999，，1： | 1 |
| 4 | Differential gene expression of NADPH oxidase (p22phox) and hemoxygenase‐1 in patients with Type?2 diabetes and microangiopathy显示文摘 | A.Adaikalakoteswari M.Balasubramanyam M.Rema V.Mohan | 2006 | Diabetic Medicine2006，，6： | 1 |
| 5 | Is it necessary to routinely fenestrate an extracardiac Fontan?显示文摘 | LeNardo D Thompson Edwin Petrossian Doff B McElhinney Natalia A Abrikosova Phillip Moore V.Mohan Reddy Frank L Hanley | 1999 | Journal of the American College of Cardiology1999，，2： | 1 |
| 6 | IoT-Cloud Empowered Aerial Scene Classification for Unmanned Aerial Vehicles显示文摘Recent trends in communication technologies and unmanned aerial vehicles(UAVs)find its application in several areas such as healthcare,surveillance,transportation,etc.Besides,the integration of Internet of things(IoT)with cloud computing environment offers several benefits for the UAV communication.At the same time,aerial scene classification is one of the major research areas in UAV-enabledMEC systems.In UAV aerial imagery,efficient image representation is crucial for the purpose of scene classification.The existing scene classification techniques generate mid-level image features with limited representation capabilities that often end up in producing average results.Therefore,the current research work introduces a new DL-enabled aerial scene classificationmodel forUAV-enabledMECsystems.The presented model enables theUAVs to capture aerial imageswhich are then transmitted to MEC for further processing.Next,CapsuleNetwork(CapsNet)-based feature extraction technique is applied to derive a set of useful feature vectors from the aerial image.It is important to have an appropriate hyperparameter tuning strategy,since manual parameter tuning of DL model tend to produce several configuration errors.In order to achieve this and to determine the hyperparameters of CapsNetmodel,Shuffled Shepherd Optimization(SSO)algorithm is implemented.Finally,Backpropagation Neural Network(BPNN)classification model is applied to determine the appropriate class labels of aerial images.The performance of SSO-CapsNet model was validated against two openly-accessible datasets namely,UC Merced(UCM)Land Use dataset andWHU-RS dataset.The proposed SSO-CapsNet model outperformed the existing state-of-the-art methods and achieved maximum accuracy of 0.983,precision of 0.985,recall of 0.982,and F-score of 0.983. | K.R.Uthayan G.Lakshmi Vara Prasad V.Mohan C.Bharatiraja Irina V.Pustokhina Denis A.Pustokhin Vicente García Díaz | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 7 | Homogeneous Batch Memory Deduplication Using Clustering of Virtual Machines显示文摘Virtualization is the backbone of cloud computing,which is a developing and widely used paradigm.Byfinding and merging identical memory pages,memory deduplication improves memory efficiency in virtualized systems.Kernel Same Page Merging(KSM)is a Linux service for memory pages sharing in virtualized environments.Memory deduplication is vulnerable to a memory disclosure attack,which uses covert channel establishment to reveal the contents of other colocated virtual machines.To avoid a memory disclosure attack,sharing of identical pages within a single user’s virtual machine is permitted,but sharing of contents between different users is forbidden.In our proposed approach,virtual machines with similar operating systems of active domains in a node are recognised and organised into a homogenous batch,with memory deduplication performed inside that batch,to improve the memory pages sharing efficiency.When compared to memory deduplication applied to the entire host,implementation details demonstrate a significant increase in the number of pages shared when memory deduplication applied batch-wise and CPU(Central processing unit)consumption also increased. | N.Jagadeeswari V.Mohan Raj | 2023 | Computer Systems Science & Engineering2023，44，1： | 0 |
| 8 | Intrusion Detection Using Ensemble Wrapper Filter Based Feature Selection with Stacking Model显示文摘The number of attacks is growing tremendously in tandem with the growth of internet technologies.As a result,protecting the private data from prying eyes has become a critical and tough undertaking.Many intrusion detection solutions have been offered by researchers in order to decrease the effect of these attacks.For attack detection,the prior system has created an SMSRPF(Stacking Model Significant Rule Power Factor)classifier.To provide creative instance detection,the SMSRPF combines the detection of trained classifiers such as DT(Decision Tree)and RF(Random Forest).Nevertheless,it does not generate any accuratefindings that are adequate.The suggested system has built an EWF(Ensemble Wrapper Filter)feature selection with SMSRPF classifier for attack detection so as to overcome this problem.The UNSW-NB15 dataset is used as an input in this proposed research project.Specifically,min–max normalization approach is used to pre-process the incoming data.The feature selection is then carried out using EWF.Based on the selected features,SMSRPF classifiers are utilized to detect the attacks.The SMSRPF is integrated with the trained classi-fiers such as DT and RF to create creative instance detection.After that,the testing data is classified using MCAR(Multi-Class Classification based on Association Rules).The SRPF judges the rules correctly even when the confidence and the lift measures fail.Regarding accuracy,precision,recall,f-measure,computation time,and error,the experimental findings suggest that the new system outperforms the prior systems. | D.Karthikeyan V.Mohan Raj J.Senthilkumar Y.Suresh | 2023 | Intelligent Automation & Soft Computing2023，，1： | 0 |
| 9 | Pattern Recognition of Modulation Signal Classification Using Deep Neural Networks显示文摘In recent times,pattern recognition of communication modulation signals has gained significant attention in several application areas such as military,civilian field,etc.It becomes essential to design a safe and robust feature extraction(FE)approach to efficiently identify the various signal modulation types in a complex platform.Several works have derived new techniques to extract the feature parameters namely instant features,fractal features,and so on.In addition,machine learning(ML)and deep learning(DL)approaches can be commonly employed for modulation signal classification.In this view,this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks(CSM-FFDNN).The goal of the CSM-FFDNN model is to classify the different types of digitally modulated signals.The proposed CSM-FFDNN model involves two major processes namely FE and classification.The proposed model uses Sevcik Fractal Dimension(SFD)technique to extract the fractal features from the digital modulated signals.Besides,the extracted features are fed into the DNN model for modulation signal classification.To improve the classification performance of the DNN model,a barnacles mating optimizer(BMO)is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised.A wide range of simulations takes place to highlight the enhanced performance of the CSM-FFDNN model.The experimental outcomes pointed out the superior recognition rate of the CSM-FFDNN model over the recent state of art methods interms of different evaluation parameters. | D.Venugopal V.Mohan S.Ramesh S.Janupriya Sangsoon Lim Seifedine Kadry | 2022 | Computer Systems Science & Engineering2022，43，11： | 0 |
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