检索结果-南通市图书馆

Big Data Analytics with Optimal Deep Learning Model for Medical image classification

Computer Systems Science & Engineering 2023年第2期44卷 1433-1449页

作者： Tariq Mohammed Alqahtani Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia

In recent years,huge volumes of healthcare data are getting generated in various *** advancements made in medical imaging are tremendous owing to which biomedical image acquisition has become easier and *** to such massive generation of big data,the utilization of new methods based on Big Data Analytics(BDA),Machine Learning(ML),and Artiﬁcial Intelligence(AI)have become *** this aspect,the current research work develops a new Big Data Analytics with Cat Swarm Optimization based deep Learning(BDA-CSODL)technique for medical image classiﬁcation on Apache Spark *** aim of the proposed BDA-CSODL technique is to classify the medical images and diagnose the disease ***-CSODL technique involves different stages of operations such as preprocessing,segmentation,fea-ture extraction,and classiﬁ*** addition,BDA-CSODL technique also fol-lows multi-level thresholding-based image segmentation approach for the detection of infected regions in medical ***,a deep convolutional neural network-based Inception v3 method is utilized in this study as feature *** Gradient Descent(SGD)model is used for parameter tuning ***,CSO with Long Short-Term Memory(CSO-LSTM)model is employed as a classiﬁcation model to determine the appropriate class labels to *** SGD and CSO design approaches help in improving the overall image classiﬁcation performance of the proposed BDA-CSODL technique.A wide range of simulations was conducted on benchmark medical image datasets and the com-prehensive comparative results demonstrate the supremacy of the proposed BDA-CSODL technique under different measures.

关键词： Big data analytics healthcare deep learning image classiﬁcation biomedical imaging machine learning

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Automated Red Deer Algorithm with Deep Learning Enabled Hyperspectral image classification

引用

Intelligent Automation & Soft Computing 2023年第2期35卷 2353-2366页

作者： B.Chellapraba D.Manohari K.Periyakaruppan M.S.Kavitha Department of Information Technology Karpagam Institute of TechnologyCoimbatore641032TamilnaduIndia Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119India Department of Computer Science&Engineering SNS College of EngineeringCoimbatore641107India Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035India

Hyperspectral(HS)image classiﬁcation is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,*** recognition of features from the HS images is important for effective classiﬁcation ***,the recent advancements of deep learning(DL)models make it possible in several application *** addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of *** this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classiﬁcation(RDADL-HIC)*** proposed RDADL-HIC technique aims to effectively determine the HSI *** addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad ***,RDA with gated recurrent unit(GRU)approach is used for the identiﬁcation and classiﬁcation of *** design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models *** experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different *** comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches.

关键词： Hyperspectral images image classiﬁcation deep learning adagrad optimizer nasnetlarge model red deer algorithm

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A Novel Handcrafted with Deep Features Based Brain Tumor Diagnosis Model

引用

Intelligent Automation & Soft Computing 2023年第2期35卷 2057-2070页

作者： Abdul Rahaman Wahab Sait Mohamad Khairi Ishak Department of Documents and Archive Center of Documents and Administrative CommunicationKing Faisal UniversityAl Hofuf31982Al-AhsaSaudi Arabia School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong Tebal14300PenangMalaysia

In healthcare sector,image classiﬁcation is one of the crucial problems that impact the quality output from image processing *** purpose of image classiﬁcation is to categorize different healthcare images under various class labels which in turn helps in the detection and management of *** Resonance Imaging(MRI)is one of the effective non-invasive strate-gies that generate a huge and distinct number of tissue contrasts in every imaging *** technique is commonly utilized by healthcare professionals for Brain Tumor(BT)*** recent advancements in Machine Learning(ML)and Deep Learning(DL)models,it is possible to detect the tumor from images automatically,using a computer-aided *** current study focuses on the design of automated Deep Learning-based BT Detection and classiﬁcation model using MRI images(DLBTDC-MRI).The proposed DLBTDC-MRI techni-que aims at detecting and classifying different stages of *** proposed DLBTDC-MRI technique involves medianﬁltering technique to remove the noise and enhance the quality of MRI ***,morphological operations-based image segmentation approach is also applied to determine the BT-affected regions in brain MRI ***,a fusion of handcrafted deep features using VGGNet is utilized to derive a valuable set of feature ***,Artiﬁcial Fish Swarm Optimization(AFSO)with Artiﬁcial Neural Network(ANN)model is utilized as a classiﬁer to decide the presence of *** order to assess the enhanced BT classiﬁcation performance of the proposed model,a comprehensive set of simulations was performed on benchmark dataset and the results were vali-dated under several measures.

关键词： Brain tumor medical imaging image classiﬁcation handcrafted features deep learning parameter optimization

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Hybrid Convolutional Neural Network for Plant Diseases Prediction

引用

Intelligent Automation & Soft Computing 2023年第5期36卷 2393-2409页

作者： S.Poornima N.Sripriya Adel Fahad Alrasheedi S.S.Askar Mohamed Abouhawwash Department of Information Technology Sri Sivasubramaniya Nadar College of EngineeringChennai603110India Department of Statistics and Operations Research College of ScienceKing Saud UniversityRiyadh11451Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Plant diseases prediction is the essential technique to prevent the yield loss and gain high production of agricultural *** monitoring of plant health continuously and detecting the diseases is a signiﬁcant for sustainable *** system to monitor the diseases in plant is time consuming and report a lot of *** is high demand for technology to detect the plant dis-eases *** image processing approach and deep learning approach are highly invited in detection of plant *** diseases like late blight,bacterial spots,spots on Septoria leaf and yellow leaf curved are widely found in *** are the main reasons to affects the plants life and *** identify the diseases earliest,our research presents the hybrid method by com-bining the region based convolutional neural network(RCNN)and region based fully convolutional networks(RFCN)for classifying the *** the leaf images of plants are collected and preprocessed to remove noisy data in *** data normalization,augmentation and removal of background noises are *** images are divided as testing and training,training images are fed as input to deep learning ***,we identify the region of interest(RoI)by using selective *** every region,feature of convolutional neural network(CNN)is extracted independently for further classiﬁ*** plants such as tomato,potato and bell pepper are taken for this *** plant input image is analyzed and classify as healthy plant or unhealthy *** the image is detected as unhealthy,then type of diseases the plant is affected will be *** proposed technique achieves 98.5%of accuracy in predicting the plant diseases.

关键词： Disease detection people detection image classiﬁcation deep learning region based convolutional neural network

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：