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| 1 | E‐satisfaction and e‐loyalty: A contingency framework显示文摘 | Rolph E.Anderson Srini S.Srinivasan | 2003 | Mark2003，，2： | 2 |
| 2 | Diabetes and the enteric nervous system显示文摘 | B.Chandrasekharan S.Srinivasan | 2007 | Neurogastroenterology & Motility2007，，12： | 1 |
| 3 | Analysis of nitrite reductase (nirK and nirS) genes and cultivation reveal depauperate community of denitrifying bacteria in the Black Sea suboxic zone显示文摘 | Brian B.Oakley Christopher A.Francis Kathryn J.Roberts Clara A.Fuchsman S.Srinivasan James T.Staley | 2006 | Environmental Microbiology2006，，1： | 1 |
| 4 | E‐satisfaction and e‐loyalty: A contingency framework显示文摘 | Rolph E.Anderson Srini S.Srinivasan | 2003 | Psychol Mark2003，，2： | 1 |
| 5 | Diabetes and the enteric nervous system显示文摘 | B.Chandrasekharan S.Srinivasan | 2007 | Neurogastroenterology & Motility2007，，12： | 1 |
| 6 | E‐satisfaction and e‐loyalty: A contingency framework显示文摘 | Rolph E.Anderson Srini S.Srinivasan | 2003 | Psychol Mark2003，，2： | 1 |
| 7 | E‐satisfaction and e‐loyalty: A contingency framework显示文摘 | Rolph E.Anderson Srini S.Srinivasan | 2003 | Psychol Mark2003，，2： | 1 |
| 8 | Optimal Deep Learning Enabled Statistical Analysis Model for Traffic Prediction显示文摘Due to the advances of intelligent transportation system(ITSs),traffic forecasting has gained significant interest as robust traffic prediction acts as an important part in different ITSs namely traffic signal control,navigation,route mapping,etc.The traffic prediction model aims to predict the traffic conditions based on the past traffic data.For more accurate traffic prediction,this study proposes an optimal deep learning-enabled statistical analysis model.This study offers the design of optimal convolutional neural network with attention long short term memory(OCNN-ALSTM)model for traffic prediction.The proposed OCNN-ALSTM technique primarily preprocesses the traffic data by the use of min-max normalization technique.Besides,OCNN-ALSTM technique was executed for classifying and predicting the traffic data in real time cases.For enhancing the predictive outcomes of the OCNN-ALSTM technique,the bird swarm algorithm(BSA)is employed to it and thereby overall efficacy of the network gets improved.The design of BSA for optimal hyperparameter tuning of the CNN-ALSTM model shows the novelty of the work.The experimental validation of the OCNNALSTM technique is performed using benchmark datasets and the results are examined under several aspects.The simulation results reported the enhanced outcomes of the OCNN-ALSTM model over the recent methods under several dimensions. | Ashit Kumar Dutta S.Srinivasan S.N.Kumar T.S.Balaji Won Il Lee Gyanendra Prasad Joshi Sung Won Kim | 2022 | Computers, Materials & Continua2022，，9： | 1 |
| 9 | Feature Extraction and Classification of Plant Leaf Diseases Using Deep Learning Techniques显示文摘In India’s economy, agriculture has been the most significantcontributor. Despite the fact that agriculture’s contribution is decreasing asthe world’s population grows, it continues to be the most important sourceof employment with a little margin of difference. As a result, there is apressing need to pick up the pace in order to achieve competitive, productive,diverse, and long-term agriculture. Plant disease misinterpretations can resultin the incorrect application of pesticides, causing crop harm. As a result,early detection of infections is critical as well as cost-effective for farmers.To diagnose the disease at an earlier stage, appropriate segmentation of thediseased component from the leaf in an accurate manner is critical. However,due to the existence of noise in the digitally captured image, as well asvariations in backdrop, shape, and brightness in sick photographs, effectiverecognition has become a difficult task. Leaf smut, Bacterial blight andBrown spot diseases are segmented and classified using diseased Apple (20),Cercospora (60), Rice (100), Grape (140), and wheat (180) leaf photos in thesuggested work. In addition, a superior segmentation technique for the ROIfrom sick leaves with living backdrop is presented here. Textural features of thesegmented ROI, such as 1st and 2nd order WPCA Features, are discoveredafter segmentation. This comprises 1st order textural features like kurtosis,skewness, mean and variance as well as 2nd procedure textural features likesmoothness, energy, correlation, homogeneity, contrast, and entropy. Finally,the segmented region of interest’s textural features is fed into four differentclassifiers, with the Enhanced Deep Convolutional Neural Network provingto be the most precise, with a 96.1% accuracy. | K.Anitha S.Srinivasan | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 10 | Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs显示文摘Recently,COVID-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its treatment.The researchers are making persistent efforts to derive probable solutions formanaging the pandemic in their areas.One of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography(CT)scans.At the same time,the recent advances in machine learning(ML)and deep learning(DL)models show promising results in medical imaging.Particularly,the convolutional neural network(CNN)model can be applied to identifying abnormalities on chest radiographs.While the epidemic of COVID-19,much research is led on processing the data compared with DL techniques,particularly CNN.This study develops an improved fruit fly optimization with a deep learning-enabled fusion(IFFO-DLEF)model for COVID-19 detection and classification.The major intention of the IFFO-DLEF model is to investigate the presence or absence of COVID-19.To do so,the presented IFFODLEF model applies image pre-processing at the initial stage.In addition,the ensemble of three DL models such as DenseNet169,EfficientNet,and ResNet50,are used for feature extraction.Moreover,the IFFO algorithm with a multilayer perceptron(MLP)classification model is utilized to identify and classify COVID-19.The parameter optimization of the MLP approach utilizing the IFFO technique helps in accomplishing enhanced classification performance.The experimental result analysis of the IFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented IFFO-DLEF model over recent approaches. | Mohamed Yacin Sikkandar K.Hemalatha M.Subashree S.Srinivasan Seifedine Kadry Jungeun Kim Keejun Han | 2023 | Computer Systems Science & Engineering2023，47，10： | 0 |
| 11 | Design of QoS Aware Routing Protocol for IoT Assisted Clustered WSN显示文摘In current days,the domain of Internet of Things(IoT)and Wireless Sensor Networks(WSN)are combined for enhancing the sensor related data transmission in the forthcoming networking applications.Clustering and routing techniques are treated as the effective methods highly used to attain reduced energy consumption and lengthen the lifetime of the WSN assisted IoT networks.In this view,this paper presents an Ensemble of Metaheuristic Optimization based QoS aware Clustering with Multihop Routing(EMOQoSCMR)Protocol for IoT assisted WSN.The proposed EMO-QoSCMR protocol aims to achieve QoS parameters such as energy,throughput,delay,and lifetime.The proposed model involves two stage processes namely clustering and routing.Firstly,the EMO-QoSCMR protocol involves crossentropy rain optimization algorithm based clustering(CEROAC)technique to select an optimal set of cluster heads(CHs)and construct clusters.Besides,oppositional chaos game optimization based routing(OCGOR)technique is employed for the optimal set of routes in the IoT assisted WSN.The proposed model derives a fitness function based on the parameters involved in the IoT nodes such as residual energy,distance to sink node,etc.The proposed EMOQoSCMR technique has resulted to an enhanced NAN of 64 nodes whereas the LEACH,PSO-ECHS,E-OEERP,and iCSHS methods have resulted in a lesser NAN of 2,10,42,and 51 rounds.The performance of the presented protocol has been evaluated interms of energy efficiency and network lifetime. | Ashit Kumar Dutta S.Srinivasan Bobbili Prasada Rao B.Hemalatha Irina V.Pustokhina Denis A.Pustokhin Gyanendra Prasad Joshi | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 12 | Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment显示文摘At the present time,the Industrial Internet of Things(IIoT)has swiftly evolved and emerged,and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data.The use of image sensors as an automa-tion tool for the IIoT is increasingly becoming more common.Due to the fact that this organisation transfers an enormous number of photographs at any one time,one of the most significant issues that it has is reducing the total quantity of data that is sent and,as a result,the available bandwidth,without compromising the image quality.Image compression in the sensor,on the other hand,expedites the transfer of data while simultaneously reducing bandwidth use.The traditional method of protecting sensitive data is rendered less effective in an environment dominated by IoT owing to the involvement of third parties.The image encryp-tion model provides a safe and adaptable method to protect the confidentiality of picture transformation and storage inside an IIoT system.This helps to ensure that image datasets are kept safe.The Linde–Buzo–Gray(LBG)methodology is an example of a vector quantization algorithm that is extensively used and a rela-tively new form of picture reduction known as vector quantization(VQ).As a result,the purpose of this research is to create an artificial humming bird optimi-zation approach that combines LBG-enabled codebook creation and encryption(AHBO-LBGCCE)for use in an IIoT setting.In the beginning,the AHBO-LBGCCE method used the LBG model in conjunction with the AHBO algorithm in order to construct the VQ.The Burrows-Wheeler Transform(BWT)model is used in order to accomplish codebook compression.In addition,the Blowfish algorithm is used in order to carry out the encryption procedure so that security may be attained.A comprehensive experimental investigation is carried out in order to verify the effectiveness of the proposed algorithm in comparison to other algorithms.The experimental values ensure that the suggested approach and the outcomes are examined in a variety of different perspectives in order to further enhance them. | Adepu Shravan Kumar S.Srinivasan | 2023 | Intelligent Automation & Soft Computing2023，，6： | 0 |
| 13 | Networking Controller Based Real Time Traffic Prediction in Clustered Vehicular Adhoc Networks显示文摘The vehicular ad hoc network(VANET)is an emerging network tech-nology that has gained popularity because to its low cost,flexibility,and seamless services.Software defined networking(SDN)technology plays a critical role in network administration in the future generation of VANET withfifth generation(5G)networks.Regardless of the benefits of VANET,energy economy and traffic control are significant architectural challenges.Accurate and real-time trafficflow prediction(TFP)becomes critical for managing traffic effectively in the VANET.SDN controllers are a critical issue in VANET,which has garnered much interest in recent years.With this objective,this study develops the SDNTFP-C technique,a revolutionary SDN controller-based real-time trafficflow forecasting technique for clustered VANETs.The proposed SDNTFP-C technique combines the SDN controller’s scalability,flexibility,and adaptability with deep learning(DL)mod-els.Additionally,a novel arithmetic optimization-based clustering technique(AOCA)is developed to cluster automobiles in a VANET.The TFP procedure is then performed using a hybrid convolutional neural network model with atten-tion-based bidirectional long short-term memory(HCNN-ABLSTM).To optimise the performance of the HCNN-ABLSTM model,the dingo optimization techni-que was used to tune the hyperparameters(DOA).The experimental results ana-lysis reveals that the suggested method outperforms other current techniques on a variety of evaluation metrics. | T.S.Balaji S.Srinivasan | 2023 | Intelligent Automation & Soft Computing2023，，2： | 0 |
| 14 | 一款抗敏产品对缓解牙本质敏感的作用显示文摘在双盲、平行设计的三个组的随机对照临床试验(RCT)中,对一款抗敏产品在一次刷牙后的即刻抗敏效果,和在连续使用一周、四周和八周后的抗敏效果进行评价。招募有至少两颗敏感牙的受试者进行研究(筛选时要求Schiff指数达到2或者3),敏感牙源自:牙颈部酸蚀、磨损或是牙龈退缩所导致的牙根暴露。 | 孙佳凝 陶丹英 杨雯洁 S.Srinivasan A.Gupta 张琼 S.Bisignano M.Mantzourani | 2019 | 口腔护理用品工业2019，29，5： | 0 |
| 15 | Fuzzy-Based Secure Clustering with Routing Technique for VANETs显示文摘Due to the advanced developments in communication technologies,Internet of vehicles and vehicular adhoc networks(VANET)offers numerous opportunities for effectively managing transportation problems.On the other,the cloud environment needs to disseminate the emergency message to the vehicles which are consistently distributed on the roadway so that every vehicle gets the messages from closer vehicles in a straightforward way.To resolve this issue,clustering and routing techniques can be designed using computational intelligence approaches.With this motivation,this paper presents a new type-2 fuzzy sets based clustering with metaheuristic optimization based routing(T2FSCMOR)technique for secure communication in VANET.The T2FSC-MOR technique aims to elect CHs and optimal routes for secure intercluster data transmission in VANET.The proposed model involves T2FSC technique for the selection of CHs and construction of clusters.The T2FSC technique uses different parameters namely traveling speed(TS),link quality(LQ),trust factor(TF),inter-vehicle distance(IVD),and neighboring node count(NCC).The inclusion of trust factor helps to select the proper cluster heads(CHs)for secure data dissemination process.Moreover,trust aware seagull optimization based routing(TASGOR)approach was derived for the optimal selection of routes in VANET.In order to validate the enhanced performance of proposed technique,the set of simulations take place and the outcomes are examined interms of different measures.The experimental outcomes highlighted the improved performance of the proposed model over the other state of art techniques with a higher throughput of 98%. | T.S.Balaji S.Srinivasan S.Prasanna Bharathi B.Ramesh | 2022 | Computer Systems Science & Engineering2022，43，10： | 0 |
| 16 | Hybrid Deep Learning-Improved BAT Optimization Algorithm for Soil Classification Using Hyperspectral Features显示文摘Now a days,Remote Sensing(RS)techniques are used for earth observation and for detection of soil types with high accuracy and better reliability.This technique provides perspective view of spatial resolution and aids in instantaneous measurement of soil’s minerals and its characteristics.There are a few challenges that is present in soil classification using image enhancement such as,locating and plotting soil boundaries,slopes,hazardous areas,drainage condition,land use,vegetation etc.There are some traditional approaches which involves few drawbacks such as,manual involvement which results in inaccuracy due to human interference,time consuming,inconsistent prediction etc.To overcome these draw backs and to improve the predictive analysis of soil characteristics,we propose a Hybrid Deep Learning improved BAT optimization algorithm(HDIB)for soil classification using remote sensing hyperspectral features.In HDIB,we propose a spontaneous BAT optimization algorithm for feature extraction of both spectral-spatial features by choosing pure pixels from the Hyper Spectral(HS)image.Spectral-spatial vector as training illustrations is attained by merging spatial and spectral vector by means of priority stacking methodology.Then,a recurring Deep Learning(DL)Neural Network(NN)is used for classifying the HS images,considering the datasets of Pavia University,Salinas and Tamil Nadu Hill Scene,which in turn improves the reliability of classification.Finally,the performance of the proposed HDIB based soil classifier is compared and analyzed with existing methodologies like Single Layer Perceptron(SLP),Convolutional Neural Networks(CNN)and Deep Metric Learning(DML)and it shows an improved classification accuracy of 99.87%,98.34%and 99.9%for Tamil Nadu Hills dataset,Pavia University and Salinas scene datasets respectively. | S.Prasanna Bharathi S.Srinivasan G.Chamundeeswari B.Ramesh | 2023 | Computer Systems Science & Engineering2023，45，4： | 0 |
| 17 | Dimensionality Reduction Using Optimized Self-Organized Map Technique for Hyperspectral Image Classification显示文摘The high dimensionalhyperspectral image classification is a challenging task due to the spectral feature vectors.The high correlation between these features and the noises greatly affects the classification performances.To overcome this,dimensionality reduction techniques are widely used.Traditional image processing applications recently propose numerous deep learning models.However,in hyperspectral image classification,the features of deep learning models are less explored.Thus,for efficient hyperspectral image classification,a depth-wise convolutional neural network is presented in this research work.To handle the dimensionality issue in the classification process,an optimized self-organized map model is employed using a water strider optimization algorithm.The network parameters of the self-organized map are optimized by the water strider optimization which reduces the dimensionality issues and enhances the classification performances.Standard datasets such as Indian Pines and the University of Pavia(UP)are considered for experimental analysis.Existing dimensionality reduction methods like Enhanced Hybrid-Graph Discriminant Learning(EHGDL),local geometric structure Fisher analysis(LGSFA),Discriminant Hyper-Laplacian projection(DHLP),Group-based tensor model(GBTM),and Lower rank tensor approximation(LRTA)methods are compared with proposed optimized SOM model.Results confirm the superior performance of the proposed model of 98.22%accuracy for the Indian pines dataset and 98.21%accuracy for the University of Pavia dataset over the existing maximum likelihood classifier,and Support vector machine(SVM). | S.Srinivasan K.Rajakumar | 2023 | Computer Systems Science & Engineering2023，47，11： | 0 |
| 18 | Intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework显示文摘In past decades,retinal diseases have become more common and affect people of all age grounds over the globe.For examining retinal eye disease,an artificial intelligence(AI)based multilabel classification model is needed for automated diagnosis.To analyze the retinal malady,the system proposes a multiclass and multi-label arrangement method.Therefore,the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge,which tends to be time-consuming,vulnerable generalization ability,and unfeasible in massive datasets.Therefore,the automated diagnosis of multi-retinal diseases becomes essential,which can be solved by the deep learning(DL)models.With this motivation,this paper presents an intelligent deep learningbased multi-retinal disease diagnosis(IDL-MRDD)framework using fundus images.The proposed model aims to classify the color fundus images into different classes namely AMD,DR,Glaucoma,Hypertensive Retinopathy,Normal,Others,and Pathological Myopia.Besides,the artificial flora algorithm with Shannon’s function(AFA-SF)basedmulti-level thresholding technique is employed for image segmentation and thereby the infected regions can be properly detected.In addition,SqueezeNet based feature extractor is employed to generate a collection of feature vectors.Finally,the stacked sparse Autoencoder(SSAE)model is applied as a classifier to distinguish the input images into distinct retinal diseases.The efficacy of the IDL-MRDD technique is carried out on a benchmark multi-retinal disease dataset,comprising data instances from different classes.The experimental values pointed out the superior outcome over the existing techniques with the maximum accuracy of 0.963. | Thavavel Vaiyapuri S.Srinivasan Mohamed Yacin Sikkandar T.S.Balaji Seifedine Kadry Maytham N.Meqdad Yunyoung Nam | 2022 | Computers, Materials & Continua2022，，12： | 0 |
| 19 | Diagnosis of Leukemia Disease Based on Enhanced Virtual Neural Network显示文摘White Blood Cell(WBC)cancer or leukemia is one of the serious cancers that threaten the existence of human beings.In spite of its prevalence and serious consequences,it is mostly diagnosed through manual practices.The risks of inappropriate,sub-standard and wrong or biased diagnosis are high in manual methods.So,there is a need exists for automatic diagnosis and classification method that can replace the manual process.Leukemia is mainly classified into acute and chronic types.The current research work proposed a computer-based application to classify the disease.In the feature extraction stage,we use excellent physical properties to improve the diagnostic system’s accuracy,based on Enhanced Color Co-Occurrence Matrix.The study is aimed at identification and classification of chronic lymphocytic leukemia using microscopic images of WBCs based on Enhanced Virtual Neural Network(EVNN)classification.The proposed method achieved optimum accuracy in detection and classification of leukemia from WBC images.Thus,the study results establish the superiority of the proposed method in automated diagnosis of leukemia.The values achieved by the proposed method in terms of sensitivity,specificity,accuracy,and error rate were 97.8%,89.9%,76.6%,and 2.2%,respectively.Furthermore,the system could predict the disease in prior through images,and the probabilities of disease detection are also highly optimistic. | K.Muthumayil S.Manikandan S.Srinivasan JoséEscorcia-Gutierrez Margarita Gamarra Romany F.Mansour | 2021 | Computers, Materials & Continua2021，，11： | 0 |
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