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An Optimal Classification Model for Rice Plant Disease Detection

Computers, Materials & Continua 2021年第8期68卷 1751-1767页

作者： R.Sowmyalak.hmi T.jayasankar V.Ayyem PiIllai k.malraj Subramaniyan Irina V.Pustok.ina Denis A.Pustok.in k.Shank.r Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Electronics and Communication Engineering Gokaraju Rangaraju Institute of Engineering TechnologyHyderabad500090India Department of Electronics and Communication Engineering Karpagam Academy of Higher EducationCoimbatore641021India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Internet of Things(IoT)paves a new direction in the domain of smart farming and precision *** farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield to a certain *** smart farming,IoT devices are link.d among one another with new technologies to improve the agricultural *** farming mak.s use of IoT devices and contributes in effective decision *** is the major food source in most of the ***,it becomes inevitable to detect rice plant diseases during early stages with the help of automated tools and IoT *** development and application of Deep Learning(DL)models in agriculture offers a way for early detection of rice diseases and increase the yield and *** study presents a new Convolutional Neural Network.based inception with ResNset v2 model and Optimal Weighted Extreme Learning Machine(CNNIR-OWELM)-based rice plant disease diagnosis and classification model in smart farming *** proposed CNNIR-OWELM method involves a set of IoT devices which capture the images of rice plants and transmit it to cloud server via *** CNNIROWELM method uses histogram segmentation technique to determine the affected regions in rice plant *** addition,a DL-based inception with ResNet v2 model is engaged to extract the ***,in OWELM,the Weighted Extreme Learning Machine(WELM),optimized by Flower Pollination Algorithm(FPA),is employed for classification *** FPA is incorporated into WELM to determine the optimal parameters such as regularization coefficient C and k.rnelγ.The outcome of the presented model was validated against a benchmark.image dataset and the results were compared with one *** simulation results inferred that the presented model effectively diagnosed the disease with high sensitivity of 0.905,specificity of 0.961,and accuracy of 0.942.

关键词： Agriculture internet of things smart farming deep learning rice plant diseases

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A Novel Deep Neural Network.for Intracranial Haemorrhage Detection and Classification

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Computers, Materials & Continua 2021年第9期68卷 2877-2893页

作者： D.Venugopal T.jayasankar Mohamed Yacin Sik.andar Mohamed Ibrahim Waly Irina V.Pustok.ina Denis A.Pustok.in k.Shank.r Department of ECE KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Data fusion is one of the challenging issues,the healthcare sector is facing in the recent *** diagnosis from digital imagery and treatment are deemed to be the right *** Haemorrhage(ICH),a condition characterized by injury of blood vessels in brain tissues,is one of the important reasons for *** generated by X-rays and Computed Tomography(CT)are widely used for estimating the size and location of *** use manual planimetry,a time-consuming process for segmenting CT scan *** Learning(DL)is the most preferred method to increase the efficiency of diagnosing *** this paper,the researcher presents a unique multi-modal data fusion-based feature extraction technique with Deep Learning(DL)model,abbreviated as FFE-DL for Intracranial Haemorrhage Detection and Classification,also k.own as *** proposed FFEDL-ICH model has four stages namely,preprocessing,image segmentation,feature extraction,and *** input image is first preprocessed using the Gaussian Filtering(GF)technique to remove ***,the Density-based Fuzzy C-Means(DFCM)algorithm is used to segment the ***,the Fusion-based Feature Extraction model is implemented with handcrafted feature(Local Binary Patterns)and deep features(Residual Network.152)to extract useful ***,Deep Neural Network.DNN)is implemented as a classification technique to differentiate multiple classes of *** researchers,in the current study,used benchmark.Intracranial Haemorrhage dataset and simulated the FFEDL-ICH model to assess its diagnostic *** findings of the study revealed that the proposed FFEDL-ICH model has the ability to outperform existing models as there is a significant improvement in its *** future researches,the researcher recommends the performance improvement of FFEDL-ICH model using learning rate scheduling techniques for DNN.

关键词： Intracerebral hemorrhage fusion model feature extraction deep features classification

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Atypical presentation of a ventricular papillary fibroelastoma

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Journal of Geriatric Cardiology 2017年第8期14卷 534-535页

作者： Julio Perez-Downes Harold Dietzius Saif Ibrahim Vasant jayasankar Leslie Oberst Department of Medicine University of Florida College of Medicine-Jacksonville USA Department of Cardiology Memorial tIospital USA Department of Cardiology University of Florida College of Medicine-Jacksonville USA Department of Cardiothoracic Surgery Memorial Hospital USA

Papillary fibroelastoma cardiac tumor. The most is the most common valvular common non-valvular site these minors present is the left ventricle, where they usually manifest as ischemic strok.s or myocardial infarction. Although these tumors also have a risk.of embolization into the peripheral system, reports of such events are exceedingly rare.

关键词： Fibroelastoma Ischemia Myocardium Neoplasm Thrombosis

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Paddy Leaf Disease Detection Using an Optimized Deep Neural Network

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Computers, Materials & Continua 2021年第7期68卷 1117-1128页

作者： Shank.ranarayanan Nalini Nagappan k.ishnaraj Thangaiyan jayasankar k.limuthu Vinothk.mar Antony Sagai Francis Britto k.malraj Subramaniam Chok.aligam Bharatiraja Department of Computer Science&Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India School of Computing SRM Institute of Science and TechnologyKattankulathur603203India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Electronics&Communication Engineering SSM Institute of Engineering and TechnologyDindigul624002India Department of Mechanical Engineering Rohini College of Engineering and TechnologyPalkulam629401India Department of ECE Karpagam Academy of Higher EducationCoimbatore641021India Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India

Precision Agriculture is a concept of farm management which mak.s use of IoT and network.ng concepts to improve the *** diseases are one of the underlying causes in the decrease in the number of quantity and quality of the farming *** of diseases from the plant images is an active research topic which mak.s use of machine learning(ML)approaches.A novel deep neural network.DNN)classification model is proposed for the identification of paddy leaf disease using plant image *** errors were minimized by optimizing weights and biases in the DNN model using a crow search algorithm(CSA)during both the standard pre-training and fine-tuning *** DNN-CSA architecture enables the use of simplistic statistical learning techniques with a decreased computational work.oad,ensuring high classification *** leaf images were first preprocessed,and the areas indicative of disease were initially extracted using a k.means clustering *** was then applied to eliminate regions not indicative of ***,a set of features were extracted from the previously isolated diseased ***,the classification accuracy and efficiency of the proposed DNN-CSA model were verified experimentally and shown to be superior to a support vector machine with multiple cross-fold validations.

关键词： Leaf classification paddy leaf deep learning metaheuristics optimization crow search algorithm

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Energy Efficient Cluster Based Clinical Decision Support System in IoT Environment

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Computers, Materials & Continua 2021年第11期69卷 2013-2029页

作者： C.Rajinik.nth P.Selvaraj Mohamed Yacin Sik.andar T.jayasankar Seifedine k.dry Yunyoung Nam Department of Electronics and Communications Engineering Erode Sengunthar Engineering College(Autonomous)Erode638057India Department of Information Technology SRM Institute of Science and TechnologyKattankulathur603203India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Electrical Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Faculty of Applied Computing and Technology(FACT) Noroff University CollegeKristiansand4608Norway Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Internet of Things(IoT)has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded *** e-healthcare application solely depends on the IoT and cloud computing environment,has provided several characteristics and *** research work. reported that the energy consumption for transmission process is significantly higher compared to sensing and processing,which led to quick.exhaustion of *** this view,this paper introduces a new energy efficient cluster enabled clinical decision support system(EEC-CDSS)for embedded IoT *** presented EECCDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic *** EEC-CDSS model incorporates particle swarm optimization with levy distribution(PSO-L)based clustering technique,which clusters the set of IoT devices and reduces the amount of data *** addition,the IoT devices forward the data to the cloud where the actual classification procedure is *** classification process,variational autoencoder(VAE)is used to determine the existence of disease or *** order to investigate the proficient results analysis of the EEC-CDSS model,a wide range of simulations was carried out on heart disease and diabetes *** obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.

关键词： Energy efficiency intelligent models decision support system IoT e-healthcare machine learning

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Morphometric Analysis of Maxillary Central Incisor to Determine its Crown Form: A Model‑based Cross‑sectional Study

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Journal of Forensic Science and Medicine 2016年第4期2卷 213-218页

作者： jayasankar Purushothaman Pillai Riddhi Patel Alk. Bank.r Rajarajeswari J Suk.dev Mishra Department of Oral Pathology Government Dental College and HospitalNational Institute of Occupational HealthAhmedabad1Consultant Dental SurgeonBharuch Department of Orthodontia Goenka Research Institute of Dental ScienceGandhinagarGujaratIndia Consultant Dental Surgeon National Institute of Occupational HealthAhmedabad1Consultant Dental SurgeonBharuch Department of Biostatistics National Institute of Occupational HealthAhmedabad1Consultant Dental SurgeonBharuch

The size and shape of tooth crowns are morphogenetically predetermined during *** maxillary central incisors have contributed to sexual dimorphism,and several classifications of the form of maxillary central incisors are available in the ***,in the present study,aimed to analyze the mesiodistal(MD)measurements at two levels of maxillary central incisor,to determine its crown *** study was conducted on 100 dental plaster models(50 males and 50 females).The MD dimensions at the contact area level MD_incisal(MD_I)and at the gingival papilla level(MD_G)and the gingivo‑incisal height of the crown were measured in the dental models using digital Vernier *** data were analyzed using SPSS version 16 statistical *** mean values of all the three parameters measured were slightly greater for males and also for the left central *** were no significant differences in all the three variables when the right and left incisors were *** differences were noted when the parameters are compared between *** height/width ratio of crown showed a significant difference between male and *** value of the ratio between the two MD dimensions(MD_I and MD_G)was used to determine the crown *** ratio between the MD dimensions at incisal and gingival area showed a significant difference between genders and no such significance was found between right and left central *** is a significant difference in the form of maxillary central incisors between male and female samples and not between right and *** MD dimensions at incisal and gingival thirds decide the form of crown.

关键词： Crown form maxillary central incisor morphometric ratio sexual dimorphism

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Correlation between Maxillary Central Incisor Crown Form and Maxillary Dental Arch Form:A Model‑Based Morphometric,Cross‑Sectional Study

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Journal of Forensic Science and Medicine 2018年第2期4卷 70-75页

作者： jayasankar Purushothaman Pillai Riddhi Amrutbhai Patel Alk. M Bank.r J Rajarajeswari Department of Oral Pathology Government Dental College and HospitalAhmedabad Consultant Dental Surgeon Shraddha Orthodontic ClinicBharuch Department of Orthodontia Goenka Research Institute of Dental Science Consultant Dental Surgeon Sai Balaji Dental ClinicGandhinagarGujaratIndia

The crown form of maxillary central incisor tooth is one of the vital phenomenon for a person’s *** crown form has been compared with other anatomical parameters such as arch form and facial *** different classes of tooth forms which relate to square,tapered,and ovoid forms are *** aim of this study was to morphometrically evaluate the correlation between maxillary dental arch form and the maxillary central incisor crown *** cast models of fifty male and fifty female normal occlusion controls in the age group of 18-23 years were *** mesiodistal(MD)dimensions of the maxillary central incisors were measured at incisal(MD_I)and at gingival(MD_G)*** transverse widths of maxillary casts were measured at the first molar and at the first premolar *** measured data were statistically analyzed using the SPSS *** MD dimensions of maxillary central incisor were significantly greater in males than *** ratio between the two MD dimensions also varied significantly among *** intermolar width(IMW)and interfirst premolar width(IPmW)between males and females were highly significant(Pk.positive correlation between MD_I and IMW(r^(2)=0.146)and between MD_G and IPmW(r^(2)=0.05).No significant concordance between the maxillary central incisor crown form and the maxillary arch form was found in this study.

关键词： Intermolar width interpremolar width maxillary arch maxillary central incisor morphometric

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An Optimal Lempel Ziv Mark.v Based Microarray Image Compression Algorithm

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Computers, Materials & Continua 2021年第11期69卷 2245-2260页

作者： R.Sowmyalak.hmi Mohamed Ibrahim Waly Mohamed Yacin Sik.andar T.jayasankar Sayed Sayeed Ahmad Rashmi Rani Suresh Chavhan Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Computer Engineering College of Engineering and ComputingAl Ghurair UniversityDubai37374United Arab Emirates Automotive Research Center Vellore Institute of TechnologyVellore632014India Federal University of Piauí Teresina64049-550PIBrazil

In the recent years,microarray technology gained attention for concurrent monitoring of numerous microarray *** remains a major challenge to process,store and transmit such huge volumes of microarray ***,image compression techniques are used in the reduction of number of bits so that it can be stored and the images can be shared *** techniques have been proposed in the past with applications in different *** current research paper presents a novel image compression technique i.e.,optimized Linde–Buzo–Gray(OLBG)with Lempel Ziv Mark.v Algorithm(LZMA)coding technique called OLBG-LZMA for compressing microarray images without any loss of *** model is generally used in designing a local optimal codebook.for image *** construction is treated as an optimizationissue and can be resolved with the help of Grey Wolf Optimization(GWO)*** the codebook.is constructed by LBGGWO algorithm,LZMA is employed for the compression of index table and raise its compression efficiency *** were performed on high resolution Tissue Microarray(TMA)image dataset of 50 prostate tissue samples collected from prostate cancer *** compression performance of the proposed coding esd compared with recently proposed *** simulation results infer that OLBG-LZMA coding achieved a significant compression performance compared to other techniques.

关键词： Arithmetic coding dictionary based coding Lempel-Ziv Mark.v chain algorithm Lempel-Ziv-Welch coding tissue microarray

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Engineered Cleistogamy in Camelina sativa for bioconfinement

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Horticulture Research 2023年第2期10卷 275-285页

作者： Debao Huang Liwei Gao Jeremy McAdams Fangzhou Zhao Hongyan Lu Yonghui Wu Jeremy Martin Sherif MSherif jayasankar Subramanian Hui Duan Wusheng Liu Department of Horticultural Science North Carolina State UniversityRaleighNC 27607USA College of Life Sciences Ganzhou Normal UniversityGanzhouJiangxi 341000China National Center for Soybean Improvement State Key Laboratory of Crop Genetics and Germplasm EnhancementNanjing Agricultural UniversityNanjingJiangsu 210095China College of Food Science and Engineering Wuhan Polytechnic UniversityWuhanHubei 430048China Sandhills Research Station North Carolina State UniversityJackson SpringsNC 27281USA Vineland Research Station Department of Plant AgricultureUniversity of GuelphVinland StationON LOR 2E0Canada Alson H.Smith Jr.Agricultural Research and Extension Center School of Plant and Environmental SciencesVirginia TechWinchesterVA 22602USA USDA-ARS U.S.National ArboretumFloral and Nursery Plants Research UnitBeltsville Agricultural Research Center(BARC)-WestBeltsvilleMD 20705USA

Camelina sativa is a self-pollinating and facultative outcrossing oilseed *** engineering has been used to improve camelina yield potential for altered fatty acid composition,modified protein profiles,improved seed and oil yield,and enhanced drought *** deployment of transgenic camelina in the field posits high risk. related to the introgression of transgenes into nontransgenic camelina and wild ***,effective bioconfinement strategies need to be developed to prevent pollen-mediated gene f low(PMGF)from transgenic *** the present study,we overexpressed the cleistogamy(i.e.f loral petal non-openness)-inducing PpJAZ1 gene from peach in transgenic *** camelina overexpressing PpJAZ1 showed three levels of cleistogamy,affected pollen germination rates after anthesis but not during anthesis,and caused a minor silicle abortion only on the main *** also conducted field trials to examine the effects of the overexpressed PpJAZ1 on PMGF in the field,and found that the overexpressed PpJAZ1 dramatically inhibited PMGF from transgenic camelina to non-transgenic camelina under the field ***,the engineered cleistogamy using the overexpressed PpJAZ1 is a highly effective bioconfinement strategy to limit PMGF from transgenic camelina,and could be used for bioconfinement in other dicot species.

关键词： drought expressing limit

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Segmentation of Brain Tumor Magnetic Resonance Images Using a Teaching-Learning Optimization Algorithm

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Computers, Materials & Continua 2021年第9期68卷 4191-4203页

作者： J.Jayanthi M.k.vitha T.jayasankar A.Sagai Francis Britto N.B.Prak.sh Mohamed Yacin Sik.andar C.Bharathiraja Department of Computer Science and Engineering Sona College of TechnologySalem636005TamilnaduIndia Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTrichy621112India Department of Electronics and Communication Engineering University College of EngineeringAnna UniversityTiruchirappalli620024India Department of Mechanical Engineering Rohini College of Engineering and TechnologyPalkulam629401India Department of Electrical and Electronics Engineering National Engineering CollegeKovilpatti628503India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Kingdom of Saudi Arabia Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India

Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this *** various cancers identified so far,glioma,a type of brain tumor,is one of the deadliest cancers,and it remains challenging to the medicinal *** only consoling factor is that the survival rate of the patient is increased by remark.ble percentage with the early diagnosis of the *** diagnosis is attempted to be accomplished with the changes observed in the images of suspected parts of the brain captured in specific interval of *** the captured image,the affected part of the brain is analyzed using magnetic resonance imaging(MRI)*** of different modalities in the captured MRI image demands the best automated model for the easy identification of malignant *** of image processing techniques are available for processing the images to identify the affected *** study concentrates and proposes to improve early diagnosis of glioma using a preprocessing boosted teaching and learning optimization(P-BTLBO)algorithm that automatically segments a brain tumor in an given MRI *** involves contrast enhancement and sk.ll stripping procedures through contrast limited adaptive histogram equalization *** traditional TLBO algorithm that work. with the perspective of teacher and the student is here improved by using a boosting *** results obtained using this P-BTLBO algorithm is compared on different benchmark.images for the validation of its *** experimental findings show that P-BTLBO algorithm approach outperforms other existing algorithms of its k.nd.

关键词： Brain tumor TLBO algorithm sk.ll stripping preprocessing segmentation

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