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| 1 | Comparison of conventional and supported liquid extraction methods for the determination of sitagliptin and simvastatin in rat plasma by LC-ESI-MS/MS显示文摘Three extraction methods were compared for their efficiency to analyze sitagliptin and simvastatin in rat plasma by LC-MS/MS,including(1) liquid-liquid extraction(LLE),(2) solid phase extraction(SPE) and(3) supported liquid extraction(SLE).Comparison of recoveries of analytes with different extraction methods revealed that SLE was the best extraction method.The detection was facilitated with ion trap-mass spectrometer by multiple reactions monitoring(MRM) in a positive ion mode with ESI.The transitions monitored were m/z 441.1→325.2 for simvastatin,408.2→235.1 for sitagliptin and 278.1→260.1 for the IS.The lower limit of quantification(LLOQ) was 0.2 ng/mL for sitagliptin and 0.1 ng/mL for simvastatin.The effective SLE offers enhanced chromatographic selectivity,thus facilitating the potential utility of the method for routine analysis of biological samples along with pharmacokinetic studies. | B.Ramesh N.Manjula S.R.Bijargi V.U.M.Sarma P.Sita Devi | 2015 | Journal of Pharmaceutical Analysis2015，5，3： | 1 |
| 2 | Molecular markers and their applications in wheat breeding显示文摘 | P. K.Gupta R. K.Varshney P. C.Sharma B.Ramesh | 2008 | Plant Breeding2008，，5： | 1 |
| 3 | Multi Head Deep Neural Network Prediction Methodology for High-Risk Cardiovascular Disease on Diabetes Mellitus显示文摘Major chronic diseases such as Cardiovascular Disease(CVD),diabetes,and cancer impose a significant burden on people and healthcare systems around the globe.Recently,Deep Learning(DL)has shown great potential for the development of intelligentmobile Health(mHealth)interventions for chronic diseases that could revolutionize the delivery of health care anytime,anywhere.The aimof this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis,prognosis,management,and treatment of major chronic diseases and advance our understanding of the progress made in this rapidly developing field.Type 2 Diabetes Mellitus(T2DMs)is a regular chronic disorder that is caused by the secretion of insulin,which leads to serious death-related issues and the most complicated ones.Coronary Heart Disease(CHD)is the most frequent issue related to T2DM patients.The major concern is recognizing the high possibility of CHD complications,yet the model is not available to identify it.This work introduces a deep learning technique that can predict heart disease effectively using a hybrid model,which integrates DNNs(Deep Neural Networks)with a Multi-Head Attention Model called MADNN.The scheme canbedesignedtoautomatically learnthe best-quality features fromElectronic Health Records(EHRs),and effectively combine heterogeneous and time-sequencedmedical data for predicting the risk of CVD.The analysis is done using the Kaggle dataset.The outcomes prove that the MADNN has improved accuracy by about 95%and indicates the precise accuracy is higher for the disease compared with SVM,CNN and ANN. | B.Ramesh Kuruva Lakshmanna | 2023 | Computer Modeling in Engineering & Sciences2023，137，12： | 0 |
| 4 | 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 |
| 5 | 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 |
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