检索结果-南通市图书馆

An Efficient Smooth Quantile Boost Algorithm for binary classification

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Advances in Pure Mathematics 2016年第9期6卷 615-624页

作者： Zhefeng Wang Wanzhou Ye Department of Mathematics College of Science Shanghai University Shanghai China

In this paper, we propose a Smooth Quantile Boost classification (SQBC) algorithm for binary classification problem. The SQBC algorithm directly uses a smooth function to approximate the “check function” of the quantile regression. Compared to other boost-based classification algorithms, the proposed algorithm is more accurate, flexible and robust to noisy predictors. Furthermore, the SQBC algorithm also can work well in high dimensional space. Extensive numerical experiments show that our proposed method has better performance on randomly simulations and real data.

关键词： Boosting Quantile Regression Smooth Check Function binary classification

Cost-sensitive multi-layer perceptron for binary classification with imbalanced data

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Cost-sensitive multi-layer perceptron for binary classificat...

Detection of Angioectasias and Haemorrhages Incorporated into a Multi-Class classification Tool for the GI Tract Anomalies by Using binary CNNs

第37届中国控制会议

作者： Zheng Liu Sen Zhang Wendong Xiao Yan Di School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology Beijing

Currently, class imbalance has been a challenge for classification due to its highly imbalanced instances of distinct classes. With the advantage in quantity, the majority classes can get high accuracy in classification while many instances belonging to minority classes are inclined to be classified as majority classes. In this paper, we propose a novel cost-sensitive method based on multi-layer perceptron（CMMLP） for binary classification with imbalanced data. The proposed cost matrix is used to modify the construction of loss function so as to encourage classifier to pay more attention to the accuracy of minority class by minimizing training error. In order to verify the effectiveness of CMMLP, CMMLP is applied to some benchmark datasets and Click-Through Rate（CTR） prediction datasets. Experimental results illustrate that the new cost-sensitive approach can achieve better performance for binary classification with imbalanced data than the original MLP（OMLP）.

关键词： Imbalance learning binary classification CMMLP CTR prediction

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Journal of Biomedical Science and Engineering 2021年第12期14卷 402-414页

作者： Christos Barbagiannis Alexios Polydorou Michail Zervakis Andreas Polydorou Eleftheria Sergaki School of Electrical and Computer Engineering (ECE) School Technical University of Crete Chania Greece Department of Surgery School of Medicine National and Kapodistrian University of Athens Athens Greece

The proposed deep learning algorithm will be integrated as a binary classifier under the umbrella of a multi-class classification tool to facilitate the automated detection of non-healthy deformities, anatomical landmarks, pathological findings, other anomalies and normal cases, by examining medical endoscopic images of GI tract. Each binary classifier is trained to detect one specific non-healthy condition. The algorithm analyzed in the present work expands the ability of detection of this tool by classifying GI tract image snapshots into two classes, depicting haemorrhage and non-haemorrhage state. The proposed algorithm is the result of the collaboration between interdisciplinary specialists on AI and Data Analysis, Computer Vision, Gastroenterologists of four University Gastroenterology Departments of Greek Medical Schools. The data used are 195 videos (177 from non-healthy cases and 18 from healthy cases) videos captured from the PillCam(R) Medronics device, originated from 195 patients, all diagnosed with different forms of angioectasia, haemorrhages and other diseases from different sites of the gastrointestinal (GI), mainly including difficult cases of diagnosis. Our AI algorithm is based on convolutional neural network (CNN) trained on annotated images at image level, using a semantic tag indicating whether the image contains angioectasia and haemorrhage traces or not. At least 22 CNN architectures were created and evaluated some of which pre-trained applying transfer learning on ImageNet data. All the CNN variations were introduced, trained to a prevalence dataset of 50%, and evaluated of unseen data. On test data, the best results were obtained from our CNN architectures which do not utilize backbone of transfer learning. Across a balanced dataset from no-healthy images and healthy images from 39 videos from different patients, identified correct diagnosis with sensitivity 90%, specificity 92%, precision 91.8%, FPR 8%, FNR 10%. Besides, we comp

关键词： Capsule Endoscopy (CE) Small Bowel Bleeding (SBB) Angioectasia Haemorrhage Gatrointestinal (GI) Small Bowel Capsule Endoscopy (SBCE) Convolutional Neural Network (CNN) Computer Aided Diagnosis (CAD) Image Level Annotation Pixel Level Annotation binary classification

Valuable Data Extraction for Resistivity Imaging Logging Interpretation

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Tsinghua Science and Technology 2020年第2期25卷 281-293页

作者： Yili Ren Renbin Gong Zhou Feng Meichao Li Institute of Computer Application Technology PetroChina Research Institute of Petroleum Exploration and Development(RIPED)Beijing 100083China Department of Well Logging&Remote Sensing Technology of RIPED Beijing 100083China

Imaging logging has become a popular means of well logging because it can visually represent the lithologic and structural characteristics of *** manual interpretation of imaging logging is affected by the limitations of the naked eye and experiential *** a result,manual interpretation accuracy is ***,it is highly useful to develop effective automatic imaging logging interpretation by machine *** imaging logging is the most widely used technology for imaging *** this paper,we propose an automatic extraction procedure for the geological features in resistivity imaging logging *** procedure is based on machine learning and achieves good results in practical *** that the existence of valueless data significantly affects the recognition effect,we propose three strategies for the identification of valueless data based on binary *** compare the effect of the three strategies both on an experimental dataset and in a production environment,and find that the merging method is the best performing of the three *** effectively identifies the valueless data in the well logging images,thus significantly improving the automatic recognition effect of geological features in resistivity logging images.

关键词： machine learning binary classification multiclass classification outlier detection imaging logging

A comparative study on machine learning-based classification to find photothrombotic lesion in histological rabbit brain images

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Journal of Innovative Optical Health Sciences 2021年第6期4卷 81-89页

作者： Sang Hee Jo Yoonhee Kim Yoon Bum Lee Sung Suk Oh Jong-ryul Choi School of Biomedical Engineering Daegu Catholic University(DCU)Gyeongsan 38430Republic of Korea Medical Device Development Center Daegu-Gyeongbuk Medical Innovation Foundation(DGMIF) Daegu 41061Republic of Korea Laboratory Animal Center Daegu-Gyeongbuk Medical Innovation Foundation(DGMIF) Daegu 41061Republic of Korea

Recently,research has been conducted to assist in the processing and analysis of histopathological images using machine learning *** this study,we established machine learning-based algorithms to detect photothrombotic lesions in histological images of photothrombosis-induced rabbit *** machine learning-based algorithms for binary classification were applied,and the accu-racies were compared to classify normal tissues and photothrombotic *** lesion classification model consisting of a 3-layered neural network with a rectified linear unit(ReLU)activation function,Xavier initialization,and Adam optimization using datasets with a unit size of 128×128 pixels yielded the highest accuracy(0.975).In the validation using the tested histological images,it was confirmed that the model could identify regions where brain damage occurred due to photochemical ischemic *** the development of machine learning-based photothrombotic lesion classi-fication models and performance comparisons,we confirmed that machine learning algorithms have the potential to be utilized in histopathology and various medical diagnostic techniques.

关键词： Machine learning histopathological images photothrombotic lesion rabbit brain binary classification logistic regression multi-layer neural networks

Hybrid Models for Breast Cancer Detection via Transfer Learning Technique

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第2期74卷 3063-3083页

作者： Sukhendra Singh Sur Singh Rawat Manoj Gupta B.K.Tripathi Faisal Alanzi Arnab Majumdar Pattaraporn Khuwuthyakorn Orawit Thinnukool Department of Information Technology JSS Academy of Technical EducationNoidaIndia JSS Academy of Technical Education NoidaIndia Department of Electronics and Communication Engineering JECRC University JaipurRajasthanIndia Harcourt Butler Technological University Kanpur India Department of Electrical Engineering Prince Sattam Bin Abdulaziz UniversityCollege of EngineeringAl Kharj16278Saudi Arabia Faculty of Engineering Imperial College LondonLondonSW72AZUK College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Currently,breast cancer has been amajor cause of deaths in women worldwide and the World Health Organization(WHO)has confirmed *** severity of this disease can be minimized to the large extend,if it is diagnosed properly at an early stage of the ***,the proper treatment of a patient having cancer can be processed in better way,if it can be diagnosed properly as early as possible using the better ***,it has been currently observed that the deep neural networks have delivered remarkable performance for detecting cancer in histopathological images of breast *** address the above said issues,this paper presents a hybrid model using the transfer learning to study the histopathological images,which help in detection and rectification of the disease at a low *** dataset experiments were carried out to validate the suggested hybrid model in this *** experimental results show that the proposed model outperformed the baseline methods,with F-scores of 0.81 for DenseNet+Logistic Regression hybrid model,(F-score:0.73)for Visual Geometry Group(VGG)+Logistic Regression hybrid model,(F-score:0.74)for VGG+Random Forest,(F-score:0.79)for DenseNet+Random Forest,and(F-score:0.79)for VGG+Densenet+Logistic Regression hybrid model on the dataset of histopathological images.

关键词： Histopathological deep neural network machine learning breast cancer binary classification transfer learning

Diabetic Retinopathy Detection: A Hybrid Intelligent Approach

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第9期80卷 4561-4576页

作者： Atta Rahman Mustafa Youldash Ghaida Alshammari Abrar Sebiany Joury Alzayat Manar Alsayed Mona Alqahtani Noor Aljishi Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia

Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy(DR).Early detection and treatment are crucial to prevent complete blindness or partial vision *** detection methods,which involve ophthalmologists examining retinal fundus images,are subjective,expensive,and ***,this study employs artificial intelligence(AI)technology to perform faster and more accurate binary classifications and determine the presence of *** this regard,we employed three promising machine learning models namely,support vector machine(SVM),k-nearest neighbors(KNN),and Histogram Gradient Boosting(HGB),after carefully selecting features using transfer learning on the fundus images of the Asia Pacific Tele-Ophthalmology Society(APTOS)(a standard dataset),which includes 3662 images and originally categorized DR into five levels,now simplified to a binary format:No DR and DR(Classes 1-4).The results demonstrate that the SVM model outperformed the other approaches in the literature with the same dataset,achieving an excellent accuracy of 96.9%,compared to 95.6%for both the KNN and HGB *** approach is evaluated by medical health professionals and offers a valuable pathway for the early detection of DR and can be successfully employed as a clinical decision support system.

关键词： Diabetic retinopathy transfer learning machine learning fundus images binary classification APTOS

Prediction of COVID-19 Cases Using Machine Learning for Effective Public Health Management

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2021年第3期66卷 2265-2282页

作者： Fahad Ahmad Saleh N.Almuayqil Mamoona Humayun Shahid Naseem Wasim Ahmad Khan Kashaf Junaid Department of Computer Sciences Kinnaird College for WomenLahore54000Pakistan Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaAljouf72341Saudi Arabia Division of Computer Science&Information Technology University of EducationLahore54000Pakistan School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Clinical Laboratory Sciences College of Applied Medical SciencesJouf UniversitySakakaAljouf72341Saudi Arabia

COVID-19 is a pandemic that has affected nearly every country in the *** present,sustainable development in the area of public health is considered vital to securing a promising and prosperous future for ***,widespread diseases,such as COVID-19,create numerous challenges to this goal,and some of those challenges are not yet *** this study,a Shallow Single-Layer Perceptron Neural Network(SSLPNN)and Gaussian Process Regression(GPR)model were used for the classification and prediction of confirmed COVID-19 cases in five geographically distributed regions of Asia with diverse settings and environmental conditions:namely,China,South Korea,Japan,Saudi Arabia,and *** environmental and non-environmental features were taken as the input dataset,and confirmed COVID-19 cases were taken as the output dataset.A correlation analysis was done to identify patterns in the cases related to fluctuations in the associated *** results of this study established that the population and air quality index of a region had a statistically significant influence on the ***,age and the human development index had a negative influence on the *** proposed SSLPNN-based classification model performed well when predicting the classes of confirmed *** training,the binary classification model was highly accurate,with a Root Mean Square Error(RMSE)of ***,the results of the regression analysis using the GPR technique with Matern 5/2 were highly accurate(RMSE=0.95239)when predicting the number of confirmed COVID-19 cases in an ***,dynamic management has occupied a core place in studies on the sustainable development of public health but dynamic management depends on proactive strategies based on statistically verified approaches,like Artificial Intelligence(AI).In this study,an SSLPNN model has been trained to fit public health associated data into an appropriate class,allowing GPR to predict the number of confir

关键词： Public health sustainable development artificial intelligence SARSCoV-2 shallow single-layer perceptron neural network binary classification gaussian process regression

classification with Gaussians and convex loss Ⅱ:improving error bounds by noise conditions

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China Mathematics 2011年第1期54卷 165-171页

作者： XIANG DaoHong Department of Mathematics Zhejiang Normal University Jinhua 321004 China

We continue our study on classification learning algorithms generated by Tikhonov regularization schemes associated with Gaussian kernels and general convex loss functions. Our main purpose of this paper is to improve error bounds by presenting a new comparison theorem associated with general convex loss functions and Tsybakov noise conditions. Some concrete examples are provided to illustrate the improved learning rates which demonstrate the effect of various loss functions for learning algorithms. In our analysis, the convexity of the loss functions plays a central role.

关键词： reproducing kernel Hilbert space binary classification general convex loss Tsybakov noise condition Sobolev space