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treat to target in Crohn’s disease:A practical guide for clinicians

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World Journal of Gastroenterology 2024年第1期30卷 50-69页

作者： Ashish R srinivasan Department of Gastroenterology Austin HealthVictoriaMelbourne 3083Australia Department of Gastroenterology Eastern HealthVictoriaMelbourne 3128Australia Department of Medicine University of MelbourneVictoriaMelbourne 3052Australia

A treat-to-target(t2t)approach applies the principles of early intervention and tight disease control to optimise long-term outcomes in Crohn's *** Selecting therapeutic targets in Inflammatory Bowel Disease(StRIDE)-II guidelines specify short,intermediate,and long-term treatment goals,documenting specific treatment targets to be achieved at each of these *** appraisal of Crohn’s disease activity against pre-defined treatment targets at these timepoints remains central to determining whether current therapy should be continued or *** treatment targets in Crohn’s disease comprise combination clinical and patient-reported outcome remission,in conjunction with biomarker normalisation and endoscopic *** the StRIDE-II guidelines endorse the pursuit of endoscopic healing,clinicians must consider that this may not always be appropriate,acceptable,or achievable in all *** underscores the need to engage patients at the outset in an effort to personalise care and individualise treatment *** use of non-invasive biomarkers such as faecal calprotectin in conjunction with cross-sectional imaging techniques,particularly intestinal ultrasound,holds great promise;as do emerging treatment targets such as transmural *** randomised clinical trials,namely,CALM and StARDUSt,have evaluated the efficacy of a t2t approach in achieving endoscopic endpoints in patients with Crohn’s *** from these studies reflect that patient subgroups and Crohn’s disease characteristics likely to benefit most from a t2t approach,remain to be ***,outside of clinical trials,data pertaining to the real-world effectiveness of a t2t approach remains scare,highlighting the need for pragmatic real-world *** the obvious promise of a t2t approach,a lack of guidance to support its integration into real-world clinical practice has the potential to limit its *** highlights the need to describe st

关键词： treat to target Inflammatory bowel disease Crohn’s disease treatment targets Endoscopic remission transmural healing time to response Intestinal ultrasound

Detection and Classification of Diabetic Retinopathy Using DCNN and BSN Models

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Computers, Materials & Continua 2022年第7期72卷 597-609页

作者： S.Sudha A.srinivasan t.Gayathri Devi Department of ECE SRCSASTRA Deemed UniversityKumbakonamIndia

Diabetes is associated with many complications that could lead to *** retinopathy,a complication of diabetes,is difficult to diagnose and may lead to vision *** identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for *** clinical testing involves complex procedures and is timeconsuming,an automated system would help ophthalmologists to detect DR and administer treatment in a timelymanner so that blindness can be *** research works have focused on image processing algorithms,or neural networks,or signal processing techniques alone to detect diabetic ***,we aimed to develop a novel integrated approach to increase the accuracy of *** approach utilized both convolutional neural networks and signal processing *** this proposed method,the biological electro retinogram(ERG)sensor network(BSN)and deep convolution neural network(DCNN)were developed to detect and classify *** the BSN system,electrodes were used to record ERGsignal,which was preprocessed to be *** was performed in the frequency domain by the application of fast Fourier transform(FFt)and mel frequency cepstral coefficients(MFCCs)were *** neural network(ANN)classifier was used to classify the signals of eyes with DR and normal ***,fundus images were captured using a fundus camera,and these were used as the input for DCNN-based *** DCNN consisted of many layers to facilitate the extraction of features and classification of fundus images into normal images,non-proliferative DR(NPDR)or earlystage DR images,and proliferative DR(PDR)or advanced-stage DR ***,it classifiedNPDRaccording tomicroaneurysms,hemorrhages,cotton wool spots,and exudates,and the presence of new blood vessels indicated *** accuracy,sensitivity,and specificity of the ANNclassifier were found to be 94%,95%,and 93%,***

关键词： Deep convolution neural network artificial neural network nonproliferative diabetic retinopathy biological ERG sensor network

Networking Controller Based Real time traffic Prediction in Clustered Vehicular Adhoc Networks

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Intelligent Automation & Soft Computing 2023年第2期35卷 2189-2203页

作者： t.S.Balaji S.srinivasan Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha UniversitySaveetha NagarThandalamChennai602105India Department of Electronics and Communication Engineering College of Engineering and TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026India Department of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversitySaveetha NagarThandalamChennai602105India

the vehicular ad hoc network(VANEt)is an emerging network tech-nology that has gained popularity because to its low cost,ﬂexibility,and seamless *** deﬁned networking(SDN)technology plays a critical role in network administration in the future generation of VANEt withﬁfth generation(5G)*** of the beneﬁts of VANEt,energy economy and trafﬁc control are signiﬁcant architectural *** and real-time trafﬁcﬂow prediction(tFP)becomes critical for managing trafﬁc effectively in the *** controllers are a critical issue in VANEt,which has garnered much interest in recent *** this objective,this study develops the SDNtFP-C technique,a revolutionary SDN controller-based real-time trafﬁcﬂow forecasting technique for clustered *** proposed SDNtFP-C technique combines the SDN controller’s scalability,ﬂexibility,and adaptability with deep learning(DL)***,a novel arithmetic optimization-based clustering technique(AOCA)is developed to cluster automobiles in a *** 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.

关键词： VANEt trafﬁcﬂow prediction clustering metaheuristics SDN controller deep learning

Cross-Validation Convolution Neural Network-Based Algorithm for Automated Detection of Diabetic Retinopathy

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Computer Systems Science & Engineering 2023年第5期45卷 1985-2000页

作者： S.Sudha A.srinivasan t.Gayathri Devi Department of ECE SRCSASTRA Deemed UniversityKumbakonam612001TamilnaduIndia

the substantial vision loss due to Diabetic Retinopathy(DR)mainly damages the blood vessels of the *** feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage,if this problem doesn’t exhibit initially,that leads to permanent ***,this type of disorder can be only screened and identified through the processing of fundus *** different stages in DR are Micro aneurysms(Ma),Hemorrhages(HE),and Exudates,and the stages in lesion show the chance of *** the advancement of early detection of DR in the eye we have developed the CNN-based identification approach on the fundus blood lesion *** CNN-based automated detection of DR proposes the novel Graph cutter-built background and foreground superpixel segmentation technique and the foremost classification of fundus images feature was done through hybrid classifiers as k-Nearest Neighbor(kNN)classifier,Support Vector Machine(SVM)classifier,and Cascaded Rotation Forest(CRF)*** this classifier,the feature cross-validation made the classification more accurate and the comparison is made with the previous works of parameters such as specificity,sensitivity,and accuracy shows that the hybrid classifier attains excellent performance and achieves an overall accuracy of 98%.Among these Cascaded Rotation Forest(CRF)classifier has more accuracy than others.

关键词： CNN networking segmentation hybrid classifier data set crossvalidation fundus image

Fuzzy-Based Secure Clustering with Routing technique for VANEts

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Computer Systems Science & Engineering 2022年第10期43卷 291-304页

作者： t.S.Balaji S.srinivasan S.Prasanna Bharathi B.Ramesh Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Electronics and Communication Engineering College of Engineering&TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026India Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Electronics and Communication Engineering SRM Institute of Science and Technology600026ChennaiIndia Department of Civil Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India

Due to the advanced developments in communication technologies,Internet of vehicles and vehicular adhoc networks(VANEt)offers numerous opportunities for effectively managing transportation *** 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 *** resolve this issue,clustering and routing techniques can be designed using computational intelligence *** this motivation,this paper presents a new type-2 fuzzy sets based clustering with metaheuristic optimization based routing(t2FSCMOR)technique for secure communication in *** t2FSC-MOR technique aims to elect CHs and optimal routes for secure intercluster data transmission in *** proposed model involves t2FSC technique for the selection of CHs and construction of *** 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 ***,trust aware seagull optimization based routing(tASGOR)approach was derived for the optimal selection of routes in *** order to validate the enhanced performance of proposed technique,the set of simulations take place and the outcomes are examined interms of different *** experimental outcomes highlighted the improved performance of the proposed model over the other state of art techniques with a higher throughput of 98%.

关键词： VANEt security clustering routing fuzzy logic systems metaheuristic algorithms

Intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework

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Computers, Materials & Continua 2022年第12期73卷 5543-5557页

作者： thavavel Vaiyapuri S.srinivasan Mohamed Yacin Sikkandar t.S.Balaji Seifedine kadry Maytham N.Meqdad Yunyoung Nam College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha NagarThandalamChennai602105India Department of Medical Equipment technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering College of Engineering&TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026India Department of Applied Data Science Noroff University CollegeKristiansand4608Norway Department of Computer techniques Engineering Al-Mustaqbal University CollegeHillahBabil51001Iraq Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

In past decades,retinal diseases have become more common and affect people of all age grounds over the *** examining retinal eye disease,an artificial intelligence(AI)based multilabel classification model is needed for automated *** analyze the retinal malady,the system proposes a multiclass and multi-label arrangement ***,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 ***,the automated diagnosis of multi-retinal diseases becomes essential,which can be solved by the deep learning(DL)*** this motivation,this paper presents an intelligent deep learningbased multi-retinal disease diagnosis(IDL-MRDD)framework using fundus *** proposed model aims to classify the color fundus images into different classes namely AMD,DR,Glaucoma,Hypertensive Retinopathy,Normal,Others,and Pathological ***,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 *** addition,SqueezeNet based feature extractor is employed to generate a collection of feature ***,the stacked sparse Autoencoder(SSAE)model is applied as a classifier to distinguish the input images into distinct retinal *** efficacy of the IDL-MRDD technique is carried out on a benchmark multi-retinal disease dataset,comprising data instances from different *** experimental values pointed out the superior outcome over the existing techniques with the maximum accuracy of 0.963.

关键词： Multi-retinal disease computer aided diagnosis fundus images deep learning segmentation intelligent models

Optimal Deep Learning Enabled Statistical Analysis Model for traffic Prediction

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Computers, Materials & Continua 2022年第9期72卷 5563-5576页

作者： Ashit kumar Dutta S.srinivasan S.N.kumar t.S.Balaji Won Il Lee Gyanendra Prasad Joshi Sung Won kim Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversitySaveetha NagarThandalamChennai602105India Department of Electrical&Electronics Engineering Amal Jyothi College of EngineeringKanjirappallyKerala686518India Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha UniversitySaveetha NagarThandalamChennai602105India Department of Electronics and Communication Engineering College of Engineering and TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026India Department of Business Administration Hanbat National National UniversityDaejeon34158Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

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,*** traffic prediction model aims to predict the traffic conditions based on the past traffic *** more accurate traffic prediction,this study proposes an optimal deep learning-enabled statistical analysis *** study offers the design of optimal convolutional neural network with attention long short term memory(OCNN-ALStM)model for traffic *** proposed OCNN-ALStM technique primarily preprocesses the traffic data by the use of min-max normalization ***,OCNN-ALStM technique was executed for classifying and predicting the traffic data in real time *** 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 *** design of BSA for optimal hyperparameter tuning of the CNN-ALStM model shows the novelty of the *** experimental validation of the OCNNALStM technique is performed using benchmark datasets and the results are examined under several *** simulation results reported the enhanced outcomes of the OCNN-ALStM model over the recent methods under several dimensions.

关键词： Statistical analysis predictive models deep learning traffic prediction bird swarm algorithm

Rural implementation of the perioperative surgical home:A case-control study

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World Journal of Orthopedics 2023年第3期14卷 123-135页

作者： srinivasan Sridhar Amy Mouat-Hunter Bernadette McCrory Center for Health Outcomes and Policy Evaluation College of Public HealthThe Ohio State UniversityColumbusOH 43210United States Preanesthesia Clinic Bozeman HealthBozemanMT 59715United States Mechanical and Industrial Engineering Montana State UniversityBozemanMT 59715United States

BACkGROUND Perioperative surgical home(PSH)is a novel patient-centric surgical system developed by American Society of Anesthesiologist to improve outcomes and patient *** has proven success in large urban health centers by reducing surgery cancellation,operating room time,length of stay(LOS),and readmission ***,only limited studies have assessed the impact of PSH on surgical outcomes in rural *** to evaluate the newly implemented PSH system at a community hospital by comparing the surgical outcomes using a longitudinal case-control *** the research study was conducted at an 83-bed,licensed level-III trauma rural community hospital.A total of 3096 tJR procedures were collected retrospectively between January 2016 and December 2021 and were categorized as PSH and non-PSH cohorts(n=2305).to evaluate the importance of PSH in the rural surgical system,a case-control study was performed to compare tJR surgical outcomes(LOS,discharge disposition,and 90-d readmission)of the PSH cohort against two control cohorts[Control-1 PSH(C1-PSH)(n=1413)and Control-2 PSH(C2-PSH)(n=892)].Statistical tests including Chi-square test or Fischer’s exact test were performed for categorical variables and Mann-Whitney test or Student’s t-test were performed for continuous *** general linear models(Poisson regression and binomial logistic regression)were performed to fit adjusted *** the LOS was significantly shorter in PSH cohort compared to two control cohorts(median PSH=34 h,C1-PSH=53 h,C2-PSH=35 h)(P valuethe PSH cohort had lower percentages of discharges to other facilities(PSH=3.5%,C1-PSH=15.5%,C2-PSH=6.7%)(P valuethere was no statistical difference observed in 90-d readmission between control and PSH ***,the PSH implementation reduced the 90-d readmission percentage(PSH=4.7%,C1-PSH=6.1%,C2-PSH=3.6%)lower than the national average 30-d readmission percentage which is 5.5%.the PSH system was effecti

关键词： Perioperative surgical home Rural medicine Case-control study total joint replacements Health equity

the impact of process variations on input impedance and mitigation using a circuit technique in FinFEt-based LNA

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Journal of Semiconductors 2015年第4期36卷 104-109页

作者： D.Suresh k.k.Nagarajan R.srinivasan ECE Department Sri Sivasubramaniya Nadar (SSN) College of Engineering It Department Sri Sivasubramaniya Nadar (SSN) College of Engineering

the effect of process variations of a FinFEt-based low noise amplifier （LNA） are mitigated by using the device in an independently driven mode, i.e. an independently driven double gate （IDDG） FinFEt. A 45 nm gate length IDDG FinFEt-based cascoded LNA, operating at 5 GHz, is designed and studied to assess the impact of process variation on the LNA performance metrics such as input impedance, gain and noise figure. Four geometrical parameters, gate length, channel width, gate oxide thickness and fin width, and one non-geometrical parameter, channel doping concentration, are considered in the study. the effect of these variations on the input impedance （the desired value is 50 f2 purely real） of the LNA is compensated by the second gate bias of the IDDG FinFEt.

关键词： FinFEt LNA process variation t-SPICE