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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3244 条记录，以下是301-310 订阅

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Computer-Aided Diagnosis using machine learning Techniques 3

Computer-Aided Diagnosis using Machine Learning Techniques

引用

3rd international conference on Smart data Intelligence, ICSMDI 2023

作者： Prakash, S.Shiva Harshitha, Kompala Charitha, A.O. Janvitha, C. Indu, K. Svec Dept. of Csse Tirupati India

ISBN: (纸本)9781665464871

Computer-aided diagnosis (CAD), a field of medical analysis, is rapidly advancing in a large range and is becoming more complex. Computer-aided recently, there has been a lot of interest in diagnostics for the reason inaccurate medical diagnosis could result in significantly misleading therapies. machine learning (ML) is a key component of computer-aided diagnostic examinations. A simple equation cannot identify an organ in the body with the required accuracy. Therefore, pattern recognition requires learning from examples. Using pattern detection techniques and machine learning (ML) techniques, it is possible to improve the accuracy of diagnosing diseases and making appropriate treatment decisions in the medical field It is important to them that decisions are made objectively. machine learning (ML) provides a reliable approach to the development of improved, automated algorithms for the analysis of high- dimensional, multi-modal biological data. This survey article compares various machine learning approaches and algorithms for a variety of diseases' detection. The range of machine learning (ML) methods and methods utilized in the medical diagnosis and decision- making are analyzed © 2023 IEEE.

关键词： Computer aided diagnosis

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Comparative Study of Classifier for Chronic Kidney Disease prediction using Naive Bayes, KNN and Random Forest 3

Comparative Study of Classifier for Chronic Kidney Disease p...

引用

3rd international conference on Computing Methodologies and Communication (ICCMC)

作者： Devika, R. Avilala, Sai Vaishnavi Subramaniyaswamy, V SASTRA Deemed Univ Sch Comp Thanjavur Tamil Nadu India

ISBN: (纸本)9781538678084

Chronic kidney disease (CKD), is also known as chronic nephritic sickness. It defines constrains which affects your kidneys and reduces your potential to stay healthy. There will be various complication concerns like increased levels in your blood, anemia (low blood count), weak bones, and nerve injury. Detection and treatment should be done prior so it will typically keep chronic uropathy from obtaining a worse condition. data processing is the term used for information discovery from big databases. The task of knowledge mining is to generate regular patterns from historical data and emphasize future conclusions, follows from the convergence of many recent trends: the decreased value of huge knowledge storage devices and therefore the tremendous ease of aggregation knowledge over networks;the development of robust and economical machine learning algorithms to method this data;and therefore the decrease value of machine power, enabling use of computationally intensive strategies for knowledge analysis. machine learning is an important task as it benefits many applications such as analyzing life science outcomes, sleuthing fraud, sleuthing faux users etc. varied knowledge mining classification approaches and machine learning algorithms are applied for prediction of chronic diseases. Therefore, this paper examines the performance of Naive Bayes, K-Nearest Neighbour (KNN) and Random Forest classifier on the basis of its accuracy, preciseness and execution time for CKD prediction. Finally, the outcome after conducted research is that the performance of Random Forest classifier is finest than Naive Bayes and KNN

关键词： data mining machine learning Chronic kidney disease Classification K-Nearest Neighbour Naive Bayes Random Forest

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A Novel Approach of Feature Vector Design for Financial Information Extraction Using Supervised learning 3

A Novel Approach of Feature Vector Design for Financial Info...

引用

3rd international conference on Soft Computing and machine Intelligence (ISCMI)

作者： Dadhich, Mahesh Lewis, James G. Bangalore India Los Angeles USA

ISBN: (纸本)9781509036967

Financial information extraction from big financial reports is a tedious task. This paper speaks about page-wise feature generation and applying learning algorithms for identifying financial information (balance sheets, cash flows, and income statements) in Form 10-K or annual reports of companies. Balance sheets, cash flows, and income statements have some structure in them and are semi-structured information. This approach employs selection of unigrams and bigrams based on frequency of occurrence and expert advice, generation of page wise features, and applying learning models for identifying patterns of specific financial information. Different supervised learning models are applied yielding results with very high accuracy (greater than 99%).

关键词： page wise Information extraction Feature generation SVM Random Forest logistic regression Naive Bayes cross validation 10k reports pattern recognition

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Facial Expression recognition Using Extreme learning machine 3

Facial Expression Recognition Using Extreme Learning Machine

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3rd international conference on Informatics and Computational Sciences (ICICoS)

作者： Shafira, Serenada Salma Ulfa, Nadya Wibawa, Helmie Arif Rismiyati Univ Diponegoro Fac Math & Sci Dept Informat Semarang Indonesia

ISBN: (纸本)9781728146102

Facial expression recognition is one of the technological capabilities in identifying a face image to follow up on research conducted by psychologists. The recognition of facial expressions is very important to know the emotions of someone who is experiencing it. In this study two datasets were used, namely the FER2013 and CK + datasets. The FER2013 dataset and CK+ are datasets designed to identify facial expressions. At the feature extraction stage, it uses the Histogram of Oriented Gradient (HOG) feature dan Local Binary pattern (LBP) feature. Whereas in the classification stage, the Extreme learning machine (ELM) classifier is used. The greatest accuracy by using HOG feature is 63.86% for the FER2013 dataset and 99.79% for the CK + dataset with sigmoid as an activation function. And the greatest accuracy by using LBP feature is 55.11% for the FER2013 dataset and 98.72% for the CK + dataset with RBF as an activation function.

关键词： facial expression recognition histogram of oriented gradient local binary pattern extreme learning machine

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Tell Me Why - A Systematic Literature Review on Outlier Explanation for Tabular data 3

Tell Me Why - A Systematic Literature Review on Outlier Expl...

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3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Nonnenmacher, Jakob Holte, Nils-Christoph Gomez, Jorge Marx University of Oldenburg Oldenburg Germany

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Outlier explanation approaches are used to support analysts in investigating outliers, especially those detected by methods which are not intuitively interpretable such as deep learning or ensemble approaches. There have been several studies on outlier explanation and visualization in the last years. Nonetheless, there has been no overarching survey of existing outlier explanation approaches. We investigate what kind of explanation approaches exist for tabular data and how they can be characterized. For this, we perform a literature review of 418 papers uncovering 26 existing studies which we analyze and categorize according to: approach, model dependence, supported data, output and evaluation. Based on our categorization, we identify trends within outlier explanation. This includes the limited number of underlying approaches and the lack of a common evaluation method. We are also able to highlight avenues for further research such as utilizing different datasets for evaluation or exploring visualization approaches. © 2022 IEEE.

关键词： Anomaly detection

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Comparing Feature learning Methods for Human Activity recognition: Performance study in new user scenario 8

Comparing Feature Learning Methods for Human Activity Recogn...

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Joint 8th international conference on Informatics, Electronics and Vision (ICIEV) / 3rd international conference on Imaging, Vision and pattern recognition (icIVPR) / international conference on Activity and Behavior Computing (ABC)

作者： Lago, Paula Inoue, Sozo Kyushu Inst Technol Fac Engn Dept Basic Sci Kitakyushu Fukuoka Japan Kyushu Inst Technol Fac Engn Grad Sch Biol Engn Kitakyushu Fukuoka Japan

ISBN: (纸本)9781728107882

Designing robust features for human activity recognition (HAR) that performs well across a wide range of users is a hard task. Therefore, more attention is being given to feature learning techniques, to automatically learn features from raw data. In this paper, we present a comparison study among feature learning methods for HAR. Using accelerometer data, we compare four methods for feature learning from raw-sensor data (PCA-based, clustering, matrix factorization, and LSTM networks) to the traditional hand-crafted feature extraction method. We focus on the performance degradation when each model is evaluated using a new user. According to our results, features learned with Principal Component Analysis are the more robust to the new user scenario. Our results evidence the importance of evaluation in unseen user, since the performance difference compared to a random split testing is big.

关键词： feature learning activity recognition accelerometer smartphones

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Two approaches to machine learning classification of time series based on recurrence plots 3

Two approaches to machine learning classification of time se...

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3rd IEEE international conference on data Stream mining and Processing, DSMP 2020

作者： Kirichenko, Lyudmyla Radivilova, Tamara Bulakh, Vitalii Zinchenko, Petro Saif Alghawli, Abed Kharkiv National University of Radio Electronics Department of Applied Mathematics Kharkiv Ukraine Kharkiv National University of Radio Electronics V.V. Popovskyy Dept. of Infocommunication Engineering Kharkiv Ukraine Prince Sattam Bin Abdulaziz University Department of Computer Science Aflaj Saudi Arabia

ISBN: (数字)9781728132143

ISBN: (纸本)9781728132143

The article considers the task of classifying fractal time series based on the construction of their recurrence plots. Short realizations of EEG signals were used as input data. Two classification machine learning methods were considered: in the first case, quantitative fractal and recurrent characteristics of the time series were classification features, in the second case, image recognition of recurrence plots was carried out. The results showed fairly high classification quality for both methods, and relative advantage of the image classification method. © 2020 IEEE.

关键词： Time series

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Optimal Double-Kernel Combination for Classification

引用

6th international conference on machine learning and data mining in pattern recognition

作者： Wang, Feng Zhang, Hongbin Beijing Univ Sci & Technol Coll Comp Sci Beijing 100124 Peoples R China

ISBN: (纸本)9783642030697

Traditional kernelised classification methods Could not perforin well sometimes because of the using of a single and fixed kernel, especially oil sonic complicated data sets. In this paper. a novel optimal double-kernel combination (ODKC) method is proposed for complicated classification tasks. Firstly, data sets are mapped by two basic kernels into different feature spaces respectively, and then three kinds of optimal composite kernels are constructed by integrating information of the two feature spaces. Comparative experiments demonstrate the effectiveness of our methods.

关键词： Classification kernel learning pattern recognition

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Application of Deep learning in Imaging diagnosis of Brain diseases 3

Application of Deep Learning in Imaging diagnosis of Brain d...

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3rd international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Liang, Junchen Wang, Zhaoze Ye, Xiangyang Xi An Jiao Tong Univ Sch Math & Stat Xian Peoples R China Wenzhou Kean Univ Dept Comp Sci Wenzhou Zhejiang Peoples R China Jilin Univ Coll Automot Engn Changchun Peoples R China

ISBN: (纸本)9781665417907

The brain is one of the most important parts of the human body, and the diagnosis of its diseases is of great significance to the treatment of diseases. With the rapid development of deep learning in recent years, its automatically extracted image features have significant advantages compared to traditional artificially extracted features. Therefore, more and more recognition methods based on deep learning are widely used in medical image recognition tasks (such as CT, MRI, PET-CT.). This paper will introduce the application of traditional methods and deep learning methods in various brain diseases. These methods are compared, analyzed, and summarized, then we explored their development status and future development trends.

关键词： brain disease medical image computer-assisted diagnosis deep learning

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Research on Radiation Damage Characteristics of Optical Fiber Materials Based on data mining and machine learning 3rd

Research on Radiation Damage Characteristics of Optical Fibe...

引用

3rd European-Alliance-for-Innovation (EAI) international conference on Advanced Hybrid Information Processing (ADHIP)

作者： Li, Ang Wang, Tian-hui Jilin Univ Zhuhai Coll Zhuhai Peoples R China

ISBN: (纸本)9783030364021;9783030364014

In order to better analyze the damage characteristics of fiber materials under radiation environment, combined with data mining algorithm to calculate the degree of damage of material structure damage. Combine with machine learning method to analyze the calculation results, obtain the damage range of fiber material structure, standardize material damage characteristics and Grade, accurately determine the damage of material structure, and finally improve the radiation damage characteristics of fiber materials. Experiments show that the research on radiation damage characteristics of fiber materials based on data mining and machine learning is accurate and reasonable.

关键词： data mining machine learning Fiber material Radiation damage characteristics

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