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检索条件"任意字段=6th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2009"

共 99 条记录，以下是1-10 订阅

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the Performance Analysis of Several machine learning Methods in Predicting Diabetes 6

The Performance Analysis of Several Machine Learning Methods...

引用

6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Wu, Yuanyuan Xia, Zhuomin Liu, Huizhou Huang, Mengxing Zhang, Yu Hainan University College of Information and Communication Engineering Haikou570228 China Hainan University College of Computer Science and Technology Haikou570228 China

ISBN: (纸本)9798350325485

Diabetes is a chronic disease characterized by hyperglycemia. Early screening of diabetes patients will help reduce the incidence of diabetes. Accurately modeling the early symptoms of diabetes to reduce the incidence of diabetes has become an urgent problem. therefore, it is a valuable research direction to mine and study the information of diabetes patients, predict the occurrence of noninfectious diseases, and assist doctors to make correct diagnoses. the precision of different data mining techniques has been compared in this research, employing machine learning techniques to predict the incidence of diabetes. the University of California, Irvine's open-source dataset has been used as the main database for experiment. Different machine learning classifiers such as random forest, neural network and support vector machine have been employed in this experiment. this research gives a technology review of machine learning algorithms that can be used for diabetes prediction through classification. the experimental results indicate that random forest performs better than other machine learning methods. © 2023 IEEE.

关键词： Decision support systems

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Synthesis of KNN Algorithm in FPGA Technology 1

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6th international conference on Microelectronics, Electromagnetics, and Telecommunications, ICMEET 2021

作者： Payal, Ravi Singh, Amit Prakash CDAC Noida India GGSIPU New Delhi India

ISBN: (数字)9789811685507

ISBN: (纸本)9789811685491

machine learning is coming everywhere, and currently in every field, it is contributing in terms of different applications. machine learning is a type of artificial intelligence (AI), which enables a program to learn, without being explicitly programmed and without human intervention. It is broadly divided into two types, namely supervised and unsupervised learning. Different types of machine learning algorithms are there but slowly and slowly wrt hardware design also it is coming. KNN algorithm is one of the most important algorithms, and it is typically used for pattern recognition, data mining, image recognition, text categorization, and predictive analysis. Here, we tried to implement KNN algorithm by using HDL. It is divided into two parts. First one is to calculating distance between testing point and training datasheet points, and the second part consists of sorting the distances calculated and defining the respective class/group of the testing point. the code is also mapped to FPGA library for the synthesis process. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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the Performance Analysis of Several machine learning Methods in Predicting Diabetes

The Performance Analysis of Several Machine Learning Methods...

引用

pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Yuanyuan Wu Zhuomin Xia Huizhou Liu Mengxing Huang Yu Zhang College of Information and Communication Engineering Hainan University Haikou P.R. China College of Computer Science and Technology Hainan University Haikou P.R. China

Diabetes is a chronic disease characterized by hyperglycemia. Early screening of diabetes patients will help reduce the incidence of diabetes. Accurately modeling the early symptoms of diabetes to reduce the incidence of diabetes has become an urgent problem. therefore, it is a valuable research direction to mine and study the information of diabetes patients, predict the occurrence of noninfectious diseases, and assist doctors to make correct diagnoses. the precision of different data mining techniques has been compared in this research, employing machine learning techniques to predict the incidence of diabetes. the University of California, Irvine’s open-source dataset has been used as the main database for experiment. Different machine learning classifiers such as random forest, neural network and support vector machine have been employed in this experiment. this research gives a technology review of machine learning algorithms that can be used for diabetes prediction through classification. the experimental results indicate that random forest performs better than other machine learning methods.

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Jaśkowski–Fitch Natural Deduction Proofs for Metadata Extraction through machine learning Systems

Jaśkowski–Fitch Natural Deduction Proofs for Metadata Ext...

引用

6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Cuconato, Simone Department of Humanities University of Calabria CS Rende87036 Italy LUISS University - Rome Rome00198 Italy

ISBN: (纸本)9789819780921

In the present paper, I will focus on propositional knowledge, proof theory, and the deductive transmission of information in data mining. the application of proof theory to machine learning-based metadata extraction systems will allow us to intend metadata extraction as mathematical objects. More in detail, I will consider a recent first-order logic (FOL)-based applied ontology for metadata extraction, by the name MAke Decision for Metadata Extraction (MADME), and I will build a specific logical system for declarative sentences applied to the extraction of metadata through machine learning systems. MADME’s strength is that it bases its decision-making procedure on a rigorous logical structure. the objective of this paper is to demonstrate how the logic in MADME can be optimized using Jaśkowski–Fitch natural deduction proofs. Jaśkowski–Fitch systems will determine new operations among the best machine learning-based metadata extraction systems from heterogeneous documents sources and, consequently, will support the research community in knowledge extraction and management. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making

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An Approach Utilizing Linguistic Features for Fake News Detection 17th

An Approach Utilizing Linguistic Features for Fake News Dete...

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17th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations (AIAI) / 6th 5G-PINE Workshop / AI-BIO Workshop / DAAI Workshop / DARE Workshop / EEAI Workshop / 10th MHDW Workshop

作者： Kasseropoulos, Dimitrios Panagiotis Tjortjis, Christos Int Hellen Univ Sch Sci & Technol Data Min & Analyt Res Grp Thessaloniki Greece

ISBN: (纸本)9783030791520;9783030791506;9783030791490

Easy propagation and access to information on the web has the potential to become a serious issue when it comes to disinformation. the term "fake news" describes the intentional propagation of news with the intention to mislead and harm the public and has gained more attention recently. this paper proposes a style-based machine learning (ML) approach, which relies on the textual information from news, such as manually extracted lexical features e.g. part of speech counts, and evaluates the performance of several ML algorithms. We identified a subset of the best performing linguistic features, using information-based metrics, which tend to agree with the literature. We also, combined Named Entity recognition (NER) functionality with the Frequent pattern (FP) Growth association rule algorithm to gain a deeper perspective of the named entities used in the two classes. Both methods reinforce the claim that fake and real news have limited differences in content, setting limitations to style-based methods. Results showed that convolutional neural networks resulted in the best accuracy, outperforming the rest of the algorithms.

关键词： Fake news Social media machine learning (ML) Natural Language Processing (NLP) Association Rule (AR) mining data mining

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Deep learning Envisioned Accident Detection System 6th

Deep Learning Envisioned Accident Detection System

引用

6th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2023

作者： Alam, Intekhab Verma, Ayush Khari, Manju Jawaharlal Nehru University New Delhi110067 India

ISBN: (纸本)9783031530814

the conflict between computational overhead and detection accuracy affects nearly every Automated Accident Detection (AAD) system. Although the accuracy of detection and classification approaches has recently improved dramatically, the systems’ high computational resource requirements make them unsuitable for deployment in situations when immediate feedback is required. this paper suggests a strategy for developing an accurate, cost-efficient automatic collision identification system that can be deployed with a minimal amount of hardware. the AAD system is divided into three key phases—Detection, Tracking, and Classification and offers computationally reduced intensive algorithms for each of these stages. For the detection phase, YOLOv3, it uses a deep learning model that has been trained through knowledge distillation. Its accuracy is on par with that of YOLO (You-Only-Look-Once). On the basis of the information inference and data mining techniques COCO (Common Objects in Context) dataset, YOLOv3 surpasses all other identification techniques considering runtime complexity, averaging an astounding 30 frames/second on a low-end system. It attains an Average Precision (AP) mark of 44.8. For the tracking phase, the system makes use of SORT (Simple Online Real-time Tracking). Since a radial basis kernel with a support vector machine works efficiently, an area under the curve (or AUC) score of 0.92 is achieved. Alongside this, this paper examines a number of machine-learning techniques for the classification stage. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computer vision

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Improving Design Quality of Software Using machine learning Techniques 6

Improving Design Quality of Software Using Machine Learning ...

引用

6th international conference on Advanced Computing and Communication Systems (ICACCS)

作者： Prabha, C. Lakshmi Shivakumar, N. Thiagarajar Coll Engn Comp Sci & Engn Madurai Tamil Nadu India

ISBN: (纸本)9781728151977;9781728151960

Design patterns commonly pre-owned in Object-Software oriented Development. It provides Information helping to overcome recurrent design challenges (problems). recognition of patterns can be described as identification and representation of information based on knowledge obtained from statistical data derived from patterns. Detection of design patterns provides the advantages of an extensive investigation of object-oriented software applications. When using various pattern detection techniques depending on static research issues to identify patterns it detects similar pattern structures. For this, we seek to identify patterns in design of Software by using classification-based methods and information metrics. the JHotDraw, QuickUML, and Junit of object-oriented software are used to detect software problems in the patterns of designing a Software. the datasets are manual creations which are designed utilizing software criteria for classifying learners. It is carried out in two steps, namely the development of a statistics-oriented dataset and the identification of trends in software trends in software design. Tests are carried out by Usage of 3 open source computer programs to test the process, then the tests are examined.

关键词： Design patterns Design pattern mining machine learning techniques Object-oriented metrics

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Community Detection, pattern recognition, and Hypergraph-Based learning: Approaches Using Metric Geometry and Persistent Homology 6

Community Detection, Pattern Recognition, and Hypergraph-Bas...

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6th international conference on Fuzzy Systems and data mining (FSDM)

作者： Nguyen, Dong Quan Ngoc Xing, Lin Lin, Lizhen Univ Notre Dame Dept Appl & Computat Math & Stat Notre Dame IN 46556 USA

ISBN: (纸本)9781643681351;9781643681344

Hypergraph data appear and are hidden in many places in the modern age. they are data structure that can be used to model many real data examples since their structures contain information about higher order relations among data points. One of the main contributions of our paper is to introduce a new topological structure to hypergraph data which bears a resemblance to a usual metric space structure. Using this new topological space structure of hypergraph data, we propose several approaches to study community detection problem, detecting persistent features arising from homological structure of hypergraph data. Also based on the topological space structure of hypergraph data introduced in our paper, we introduce a modified nearest neighbors methods which is a generalization of the classical nearest neighbors methods from machine learning. Our modified nearest neighbors methods have an advantage of being very flexible and applicable even for discrete structures as in hypergraphs. We then apply our modified nearest neighbors methods to study sign prediction problem in hypegraph data constructed using our method.

关键词： Distance matrices Hypergraphs Metric geometry Metric spaces Nearest Neighborhoods Persistent homology

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Improving Design Quality of Software Using machine learning Techniques

Improving Design Quality of Software Using Machine Learning ...

引用

international conference on Advanced Computing and Communication Systems (ICACCS)

作者： C. Lakshmi Prabha N. Shivakumar Computer Science and Engineering Thiagarajar College of Engineering Madurai India

ISBN: (数字)9781728151977

ISBN: (纸本)9781728151984

关键词： Object oriented modeling Unified modeling language machine learning Measurement data mining Software design

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Discriminative Graph learning via Low-Rank Constraint for Semi-Supervised learning 13

Discriminative Graph Learning via Low-Rank Constraint for Se...

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13th EAI international conference on Mobile Multimedia Communications, Mobimedia 2020

作者： Wang, Ying Li, Ao Sun, Gunaglu Liu, Xin School of Computer Science and Technology Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781631902710

Graph learning is more and more widely used in discovering associations and mining relationships between data and data. Our paper proposed a discriminative graph learning for semi-supervised learning method discriminative graph learning with low rank constraints. Traditional semi- supervised learning methods exist the problem of similarity between similar species is not high. How to improve the similarity between similar species has become a problem we need to solve in this field. In order to solve this problem, this paper proposed a new semi-supervised learning means, which mainly includes two parts: 1) Representing the local structure of the research sample by low-rank representation, compressing similar content, and easily finding the focus of image classification;2) In order to improve the similarity between classes, a model that uses similarity constraints between the data to be classified. the proposed means is used for comparative experiments of classification on multiple data sets. through experiments show that the means proposed in this paper performs better than traditional semi-supervised classification methods in terms of accuracy of classification. Copyright © 2020 EAI.

关键词： Image classification

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