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Laparoscopic vs open partial colectomy in elderly patients:Insights from the American College of surgeons- National surgical Quality Improvement Program database

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World Journal of Gastroenterology 2015年第45期21卷 12843-12850页

作者： Umashankkar Kannan Vemuru sunil K Reddy Amar N Mukerji vellore s parithivel Ajay K shah Brian F Gilchrist Daniel T Farkas Department of surgery Bronx-Lebanon Hospital Center

AIM: To compare the outcomes between the laparoscopic and open approaches for partial colectomy in elderly patients aged 65 years and over using the American College of surgeons- National surgical Quality Improvement Program(ACs NsQIP) database. METHODs: The ACs NsQIP database for the years 2005-2011 was queried for all patients 65 years and above who underwent partial colectomy. 1:1 propensity score matching using the nearest- neighbor method was performed to ensure both groups had similar preoperative comorbidities. Outcomes including postoperative complications, length of stay and mortality were compared between the laparoscopic and open groups. χ2 and Fisher's exact test were used for discrete variables and student's t-test for continuous variables. P s considered significant and odds ratios with 95%CI were reported when applicable. REsULTs: The total number of patients in the ACs NsQIP database of the years 2005-2011 was 1777035. We identified 27604 elderly patients who underwent partial colectomy with complete data sets. 12009(43%) of the cases were done laparoscopically and15595(57%) were done with open. After propensity score matching, there were 11008 patients each in the laparoscopic(LC) and open colectomy(OC) cohorts. The laparoscopic approach had lower post-operative complications(LC 15.2%, OC 23.8%, P shorter length of stay(LC 6.61 d, OC 9.62 d, P sION: Even after propensity score matching, elderly patients in the ACs NsQIP database having a laparoscopic partial colectomy had better outcomes than those having open colectomies. In the absence of specific contraindications, elderly patients requiring a partial colectomy should be offered the laparoscopic approach.

关键词： Colectomy Laparoscopic Outcomes Elderly National s

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FEA-based Geometrical Modification of switched Reluctance Motor for Radial Force Reduction

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Chinese Journal of Electrical Engineering 2024年第1期10卷 124-135页

作者： Rani s Jayapragash R school of Electrical Engineering Vellore Institute of TechnologyChennai 600127India

switched reluctance motors(sRMs)are becoming increasingly popular in the automotive sector owing to their robust ***,sRMs are preferred particularly for EV applications owing to their fault tolerance,magnet-free structure,and high power/torque *** main concerns of sRM compared to other machines include torque ripple and *** primary cause of vibration is the radial force created by the sRM.A geometry-based modification of the sRM to reduce the radial force without significantly changing the average torque produced is *** primary goal is to design a 4-phase,8/6 sRM with a lower radial *** possible geometrical alterations are proposed:one with square windows and the other with circular holes on the rotor *** windows are sized and positioned to avoid flux *** criteria are developed for the optimal window size and *** element analysis(FEA)modelling of the sRM is used to validate its *** FEA results are compared with the performance parameters obtained using the analytical *** the multiphysics design tool ANsYs to obtain the natural frequencies and associated deformations through modal *** to the conventional geometry,the radial force is significantly reduced by providing windows.

关键词： 8/6 sRM radial force modal analysis FEA analysis natural frequencies sRM with windows

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Dispersion effect of nano-structure pyrolytic carbon on mechanical,electrical,and microstructural characteristics of cement mortar composite

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International Journal of smart and Nano Materials 2024年第3期15卷 534-578页

作者： N.K.Karthikeyan s.Elavenil school of Civil Engineering Vellore Institute of TechnologyChennaiIndia

In material science,converting waste into nanomaterials by green technologies highlights the interest in producing sus-tainable carbon nanomaterial(sCN).This study first-ever uti-lized tyre-char as a Nano-structure Pyrolytic Carbon(NsPC)to develop electrically conductive *** other car-bon nanomaterials,the efficiency of NsPC particle as an sCN in developing electrically conductive composites was investi-gated in terms of dispersion,mechanical,water absorption,electrical resistivity,and *** results of sedi-mentation and zeta potential(-19 mV)showed the excellent dispersion of NsPC particle in water with the combined effect of superplasticizer and *** compressive(40.37%)and flexural strength(10.76%)than that of plain cement mortar composite(CMC)is achieved with 2 wt.%and 1.5 wt.%of NsPC particle,*** water absorption rate and volume of permeable voids were reduced,exhibiting less *** percolation zone of AC and DC resis-tivity of NsPC composite ranges between 1 and 2 wt.%,and the established percolation threshold is at 2 wt.%.XRD analysis showed C-s-H formation consistently increased with curing age,and HR-sEM with EDAX analysis exhibits the dense micro-structure and well-distributed NsPC particle at the *** experimental outcomes significantly enhanced the relia-bility and provided a framework for tailoring NsPC particle as sCN for developing electrically conductive composite.

关键词： sustainable nanomaterial dispersion mechanical properties electrical resistivity microstructure analysis

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Prediction of Bandwidth of Metamaterial Antenna Using Pearson Kernel-Based Techniques

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Computers, Materials & Continua 2024年第3期78卷 3449-3467页

作者： sherly Alphonse s.Abinaya sourabh Paul school of Computer science and Engineering Vellore Institute of TechnologyChennai600127India school of Electronics Engineering Vellore Institute of TechnologyChennai600127India

The use of metamaterial enhances the performance of a specific class of antennas known as metamaterial *** radiation cost and quality factor of the antenna are influenced by the size of the *** antennas allow for the circumvention of the bandwidth restriction for small *** parameters have recently been predicted using machine learning algorithms in existing *** learning can take the place of the manual process of experimenting to find the ideal simulated antenna *** accuracy of the prediction will be primarily dependent on the model that is *** this paper,a novel method for forecasting the bandwidth of the metamaterial antenna is proposed,based on using the Pearson Kernel as a standard *** with these new approaches,this paper suggests a unique hypersphere-based normalization to normalize the values of the dataset attributes and a dimensionality reduction method based on the Pearson kernel to reduce the dimension.A novel algorithm for optimizing the parameters of Convolutional Neural Network(CNN)based on improved Bat Algorithm-based Optimization with Pearson Mutation(BAO-PM)is also presented in this *** prediction results of the proposed work are better when compared to the existing models in the literature.

关键词： Antenna pearson optimization bandwidth metamaterial

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Enhancing Multicriteria-Based Recommendations by Alleviating scalability and sparsity Issues Using Collaborative Denoising Autoencoder

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Computers, Materials & Continua 2024年第2期78卷 2269-2286页

作者： s.Abinaya K.Uttej Kumar school of Computer science&Engineering Vellore Institute of TechnologyChennaiTamilnadu600127India

A Recommender system(Rs)is a crucial part of several firms,particularly those involved in *** conventional Rs,a user may only offer a single rating for an item-that is insufficient to perceive consumer ***,businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’*** the other hand,the collaborative filtering(CF)algorithm utilizing AutoEncoder(AE)is seen to be effective in identifying user-interested ***,the cost of these computations increases nonlinearly as the number of items and users *** triumph over the issues,a novel expanded stacked autoencoder(EsAE)with Kernel Fuzzy C-Means Clustering(KFCM)technique is proposed with two *** the first phase of offline,the sparse multicriteria rating matrix is smoothened to a complete matrix by predicting the users’intact rating by the EsAE approach and users are clustered using the KFCM *** the next phase of online,the top-N recommendation prediction is made by the EsAE approach involving only the most similar user from multiple *** the EsAE_KFCM model upgrades the prediction accuracy of 98.2%in Top-N recommendation with a minimized recommendation generation *** experimental check on the Yahoo!Movies(YM)movie dataset and TripAdvisor(TA)travel dataset confirmed that the EsAE_KFCM model constantly outperforms conventional Rs algorithms on a variety of assessment measures.

关键词： Recommender systems multicriteria rating collaborative filtering sparsity issue scalability issue stacked-autoencoder Kernel Fuzzy C-Means Clustering

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Acoustical properties of a 3D printed honeycomb structure filled with nanofillers:Experimental analysis and optimization for emerging applications

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Defence Technology（防务技术） 2024年第5期35卷 248-258页

作者： Jeyanthi subramanian Vinoth kumar selvaraj Rohan singh Ilangovan s Naresh Kakur Ruban Whenish school of Mechanical Engineering Vellore Institute of TechnologyChennai600127Tamil NaduIndia Department of Mechanical Engineering SRM Institute of Science and TechnologyRamapuramChennai600089India Department of Chemical Engineering and Materials science University of Southern CaliforniaLos Angeles90089USA Advanced Materials Research Centre Technology Innovation InstituteAbu Dhabi9639UAE Center for Biomaterials Cellular and Molecular TheranosticsVellore Institute of TechnologyVellore632014Tamil NaduIndia

The novelty of this research lies in the successful fabrication of a 3D-printed honeycomb structure filled with nanofillers for acoustic properties,utilizing an impedance tube setup in accordance with AsTM standard E *** Creality Ender-3,a 3D printer,was used for printing the honeycomb structures,and polylactic acid(PLA)material was employed for their *** organic,inorganic,and polymeric compounds within the composites were identified using fourier transformation infrared(FTIR)*** structure and homogeneity of the samples were examined using a field emission scanning electron microscope(FEsEM).To determine the sound absorption coefficient of the 3D printed honeycomb structure,numerous samples were systematically developed using central composite design(CCD)and analysed using response surface methodology(RsM).The RsM mathematical model was established to predict the optimum values of each factor and noise reduction coefficient(NRC).The optimum values for an NRC of 0.377 were found to be 1.116 wt% carbon black,1.025 wt% aluminium powder,and 3.151 mm distance between parallel ***,the results demonstrate that a 3Dprinted honeycomb structure filled with nanofillers is an excellent material that can be utilized in various fields,including defence and aviation,where lightweight and acoustic properties are of great importance.

关键词： 3D printing Honeycomb structure Acoustics Field emission scanning electron microscope Response surface methodology

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secure and efficient prediction of electric vehicle charging demand usingα^(2)-LsTM and AEs-128 cryptography

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Energy and AI 2024年第2期16卷 84-100页

作者： Manish Bharat Ritesh Dash K.Jyotheeswara Reddy A.s.R.Murty Dhanamjayulu C. s.M.Muyeen Department of Electrical and Electronics Engineering Visvesvaraya Technological UniversityBelgaumIndia school of Electrical and Electronics Engineering REVA UniversityBangaloreIndia school of Electrical Engineering Vellore Institute of TechnologyVelloreIndia Department of Electrical Engineering Qatar UniversityDoha2713Qatar

In recent years,there has been a significant surge in demand for electric vehicles(EVs),necessitating accurate prediction of EV charging *** prediction plays a crucial role in evaluating its impact on the power grid,encompassing power management and peak demand *** this paper,a novel deep neural network based onα^(2)-LsTM is proposed to predict the demand for charging from electric vehicles at a 15-minute time ***,we employ AEs-128 for station quantization and secure communication with *** proposed algorithm achieves a 9.2%reduction in both the Root Mean square Error(RMsE)and the mean absolute error compared to LsTM,along with a 13.01%increase in demand *** present a 12-month prediction of EV charging demand at charging stations,accompanied by an effective comparative analysis of Mean Absolute Percentage Error(MAPE)and Mean Percentage Error(MPE)over the last five years using our proposed *** prediction analysis has been conducted using Python programming.

关键词： Charging demand forecasting Deep neural network Electric vehicles LsTM Peak demand management

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Deep Learning-Based Classification of Rotten Fruits and Identification of shelf Life

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Computers, Materials & Continua 2024年第1期78卷 781-794页

作者： s.sofana Reka Ankita Bagelikar Prakash Venugopal V.Ravi Harimurugan Devarajan Centre for smart Grid Technologies School of Electronics EngineeringVellore Institute of TechnologyChennai600127India school of Electronics Engineering Vellore Institute of TechnologyChennai600127India Department of Electrical Engineering Dr.B R Ambedkar National Institute of TechnologyJalandharPunjab144008India

The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional *** primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to *** impact of rotten fruits can foster harmful bacteria,molds and other microorganisms that can cause food poisoning and other illnesses to the *** overall purpose of the study is to classify rotten fruits,which can affect the taste,texture,and appearance of other fresh fruits,thereby reducing their shelf *** agriculture and food industries are increasingly adopting computer vision technology to detect rotten fruits and forecast their shelf ***,this research work mainly focuses on the Convolutional Neural Network’s(CNN)deep learning model,which helps in the classification of rotten *** proposed methodology involves real-time analysis of a dataset of various types of fruits,including apples,bananas,oranges,papayas and ***,machine learningmodels such as GaussianNaïve Bayes(GNB)and random forest are used to predict the fruit’s shelf *** results obtained from the various pre-trained models for rotten fruit detection are analysed based on an accuracy score to determine the best *** comparison to other pre-trained models,the visual geometry group16(VGG16)obtained a higher accuracy score of 95%.Likewise,the random forest model delivers a better accuracy score of 88% when compared with GNB in forecasting the fruit’s shelf *** developing an accurate classification model,only fresh and safe fruits reach consumers,reducing the risks associated with contaminated ***,the proposed approach will have a significant impact on the food industry for efficient fruit distribution and also benefit customers to purchase fresh fruits.

关键词： Rotten fruit detection shelf life deep learning convolutional neural network machine learning gaussian naïve bayes random forest visual geometry group16

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E-voting system using cloud-based hybrid blockchain technology

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Journal of safety science and Resilience 2024年第1期5卷 102-109页

作者： Beulah Jayakumari s Lilly sheeba Maya Eapen Jani Anbarasi Vinayakumar Ravi A.suganya Malathy Jawahar Department of Information Technology Tagore Engineering College Chennai India school of Computer science and Engineering sRM Institute of science and Technology Ramapuram ChennaiIndia Department of Information Technology Jerusalem College of Engineering ChennaiIndia school of Computer science and Engineering vellore Institute of Technology Chennai India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Leather Process Technology Division CsIR-Central Leather Research Institute ChennaiIndia

With the invention of Internet-enabled devices,cloud and blockchain-based technologies,an online voting system can smoothly carry out election *** pandemic situations,citizens tend to develop panic about mass gatherings,which may influence the decrease in the number of *** urges a reliable,flexible,transparent,secure,and cost-effective voting *** proposed online voting system using cloud-based hybrid blockchain technology eradicates the flaws that persist in the existing voting system,and it is carried out in three phases:the registration phase,vote casting phase and vote counting phase.A timestamp-based authentication protocol with digital signature validates voters and candidates during the registration and vote casting *** smart contracts,third-party interventions are eliminated,and the transactions are secured in the blockchain ***,to provide accurate voting results,the practical Byzantine fault tolerance(PBFT)consensus mechanism is adopted to ensure that the vote has not been modified or ***,the overall performance of the proposed system is significantly better than that of the existing *** performance was analyzed based on authentication delay,vote alteration,response time,and latency.

关键词： E-voting Cloud Blockchain smart contract Consensus mechanism

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Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

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Computer Modeling in Engineering & sciences 2024年第4期139卷 801-816页

作者： D.Dorathy Prema Kavitha L.Francis Raj sandeep Kautish Abdulaziz s.Almazyad Karam M.sallam Ali Wagdy Mohamed Department of Mathematics Voorhees CollegeVelloreTamil NaduIndia Chandigarh Group of Colleges JhanjeriMohaliPunjabIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Faculty of science and Technology School of IT and SystemsUniversity of CanberraCanberraAustralia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied science Research Center Applied Science Private UniversityAmman11937Jordan

The intuitive fuzzy set has found important application in decision-making and machine *** enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images *** image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye *** degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal *** proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between *** methodology was used to clarify the input images and make them adequate for the process of glaucoma *** objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert system(FEs)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were *** the peak regions were identified,the recurrence relationships among those peaks were then *** partitioning was done due to varying degrees of similar and dissimilar concentrations in the *** and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and *** distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

关键词： Convolutional Neural Network(CNN) glaucomatous eyes fuzzy difference equation intuitive fuzzy sets image segmentation retinal images

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