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检索条件"主题词=Classification Algorithms"

共 26309 条记录，以下是201-210 订阅

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Tweet Sentiment Analysis for 2019 Indonesia Presidential Election Results using Various classification algorithms

Tweet Sentiment Analysis for 2019 Indonesia Presidential Ele...

引用

Information Technology, Advanced Mechanical and Electrical Engineering (ICITAMEE), International Conference on

作者： Febby Apri Wenando Regiolina Hayami Bakaruddin Ali Yunda Novermahakim Informatic Engineering Universitas Muhammadiyah Riau Indonesia Management Universitas Muhammadiyah Riau Indonesia Informatic Engineering Universitas Islam Riau Indonesia

ISBN: (数字)9781728183091

ISBN: (纸本)9781728183107

The Presidential general election on 2019 became one of the most popular topics on twitter nowdays. Sentiment analysis over Twitter offers people a effective way to measure the public's feelings towards their party and politicians. The society give their opinion about the pair of candidates that they are support through the social media. This paper addresses the sentiment analysis on Twitter data, The dataset was used based on the tweet on the @jokowi twitter account. The retrieval of data by using the Tweepy library with the Python 2.7 programming language. We utilized few of machine learning algorithm to build our classifier and classified the test data as positive and negative sentiment for the training dataset. This research proposes the weighting word method Unigram, Bigram, Trigram, N-Gram (1-2) and N -Gram (1-3) combined with several machine learning algorithms to compare the best algorithm.

关键词： Training Sentiment analysis Machine learning algorithms Social networking (online) Voting Blogs classification algorithms

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A comparative study of classification algorithms for risk prediction in pregnancy

A comparative study of classification algorithms for risk pr...

引用

IEEE Region 10 International Conference TENCON

作者： Lakshmi B.N. Indumathi T.S. Nandini Ravi PG Research Centre Visvesvaraya Technological University Bangalore Karanataka India Obstetrics and Gynaecology Bangalore Karnataka India

ISBN: (纸本)9781479986422

Pregnancy is one of the most delicate stages in every woman's life requiring increased medical care and attention. Pregnancy induces a variety of abnormalities which may lead to severe complications if unnoticed or neglected. Pregnancy complications are health problems that are caused due to changes in physiological parameters during the period of gestation. Pregnancy complications can lead to severe maternal illness by women during pregnancy, at delivery and after delivery. The aim of this paper is to predict the present complications in the health of a pregnant woman using two classification algorithms namely C4.5 decision tree classification algorithm and Naive Bayes classification Algorithm. The selected algorithms are powerful and popular tools used for the tasks of classification and prediction in Data mining. These two algorithms use pregnancy data collected from pregnant women in different stages of pregnancy to predict their present health state and the associated health complications. This study focuses on identifying the best algorithm among the two classification algorithms to predict the health status of each pregnant woman and its associated complication. Applying these classification techniques on pregnancy related data the status of risk in the health of any pregnant women can be identified and maternal and fetal morality rate can be reduced.

关键词： Pregnancy classification algorithms Decision trees Prediction algorithms Algorithm design and analysis Pediatrics Blood pressure

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Comparison of classification algorithms Towards Subject-Specific and Subject-Independent BCI

Comparison of Classification Algorithms Towards Subject-Spec...

引用

International Winter Workshop on Brain-Computer Interface (BCI)

作者： Parisa Ghane Narges Zarnaghinaghsh Ulisses Braga-Neto Texas A&M University College Station United States

ISBN: (数字)9781728184852

ISBN: (纸本)9781728184869

Motor imagery brain computer interface designs are considered difficult due to limitations in subject-specific data collection and calibration, as well as demanding system adaptation requirements. Recently, subject-independent (SI) designs received attention because of their possible applicability to multiple users without prior calibration and rigorous system adaptation. SI designs are challenging and have shown low accuracy in the literature. Two major factors in system performance are the classification algorithm and the quality of available data. This paper presents a comparative study of classification performance for both SS and SI paradigms. The present classification algorithms include two parametric (LDA and SVM) and two non-parametric (k-NN and CART) methods. Our results show that classification algorithms for SS models display large variance in performance. Therefore, distinct classification algorithms per subject may be required. SI models display lower variance in performance but should only be used if a relatively large sample size is available. For SI models, LDA and CART had the highest accuracy for small and moderate sample size, respectively. The source code of this paper is available at ***/parisaghane/eegbciMI.

关键词： Support vector machines Training System performance Brain modeling Brain-computer interfaces classification algorithms Calibration

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Packet classification algorithms

Packet classification algorithms

引用

IEEE International Symposium on Industrial Electronics (ISIE)

作者： Mrudul Dixit B. V. Barbadekar Ashwinee B. Barbadekar E & TC Department Cummins College of Engineering for women Pune Maharashtra India LT. Department Vishwakarma Institute of Technology Pune Maharashtra India

The process of categorizing packets into flows in an internet router is called packet classification. All packets belonging to the same flow obey pre-defined rules and are processed in a similar manner by the router. Packet classification is needed for non best-effort services, such as firewalls and quality of service, services that require the capability to distinguish and isolate traffic in different flows for suitable processing. The different services are packet filtering, policy routing, accounting and billing, traffic rate limiting, traffic shaping etc. Packet classification can be done on a single field or multiple fields. There are a variety of algorithms proposed for packet classification. They can be broadly categorized as basic data structures / search algorithms, geometric algorithms, heuristic algorithms and hardware specific algorithms. In this paper a review is taken of various packet classification algorithms of the above mentioned categories.

关键词： classification algorithms Routing Heuristic algorithms Data structures Web and internet services Industrial electronics IEEE members Educational institutions Quality of service Filtering

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Evaluation of classification algorithms for Software Defect Prediction

Evaluation of Classification Algorithms for Software Defect ...

引用

International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE)

作者： Ma. José Hernández-Molinos Ángel J. Sánchez-García R. Erandi Barrientos-Martínez Juan Carlos Pérez-Arriaga Facultad de Estadística e Informátic Universidad Veracruzana Universidad Veracruzana Institute de Investigaciones en Inteligencia Artificial Xalapa México

ISBN: (数字)9781665495400

ISBN: (纸本)9781665495417

For Software Engineering industry is essential to build software with high quality, but this has become difficult these days with the fact that size and complexity of developed software is high. For this reason, it is important to predict the quality of software in early phases, by this way, it helps to reduce testing resources. Some classification algorithms have been used for software defect prediction. In this paper five algorithms were evaluated and compared according to accuracy metric using three PROMISE datasets. For the evaluation, two classifiers were used which are focus on decision trees and three Bayesian classifiers. Results shows that even when percentages of accuracy are high on each classifier, decision trees got the highest percentage compared to Bayesian classifiers.

关键词： Measurement Mechatronics Software algorithms Prediction algorithms Software classification algorithms Bayes methods

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A Comparative Analysis of Machine Learning classification algorithms for Cardiovascular Disease Prediction

A Comparative Analysis of Machine Learning Classification Al...

引用

International Conference on Adaptive Science & Technology (ICAST)

作者： Elliot Kojo Attipoe Alimatu Saadia Yussiff Maame Gyamfua Asante-Mensah Dept. of Computer Science and Information Technology University of Cape Coast Cape Coast Ghana

ISBN: (数字)9798350385403

ISBN: (纸本)9798350385410

Cardiovascular disease is a life-threatening condition accounting for over 17 million deaths globally every year. Several studies have revealed the advancement of machine learning algorithms in predictive modeling and risk assessment, leading to improved chronic disease management and preventive measures. The algorithms analyze complex medical datasets to uncover hidden patterns, trends, and correlations that might be difficult for the medical practitioner, thereby improving the efficiency and accuracy of the medical decision-making process. This study utilizes a cardiovascular disease dataset comprising 70,000 instances to compare the performance of ten classification algorithms in predicting cardiovascular diseases. A new machine learning model is proposed, which performs better than all existing models using the same dataset. The results show the XGBoost classifier as the best model for predicting cardiovascular diseases, with an accuracy of 73.3% and an AUC score of 80%.

关键词： Training Machine learning algorithms Accuracy Stacking Predictive models Prediction algorithms classification algorithms Cardiovascular diseases Tuning Testing

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Physical activity recognition using inertial wearable sensors — A review of supervised classification algorithms

Physical activity recognition using inertial wearable sensor...

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International Conference on Advances in Biomedical Engineering (ICABME)

作者： Khaled Safi Ferhat Attal Samer Mohammed Mohamad Khalil Yacine Amirat Laboratoire LISSI Paris-Est University France AZM center EDST Lebanese university Lebanon

The goal of this paper is to compare the performances of various supervised algorithms in classifying physical daily living activities. Six healthy subjects were asked to perform twelve static and dynamic activities such as walking, running, sitting down, standing-up, lying, climbing stairs, etc. Three triaxial accelerometers are used to measure the human movements resulting from each activity. Seven supervised classification techniques are used: K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Random Forest (RF), Multi-Layer Perceptron (MLP), classification And Regression Tree (CART), Naive Bayes (NB), and Gaussian Mixture Model (GMM). These methods are compared in terms of correct classification rate (Accuracy), Recall, Precision, F-measure and execution time. The 10-fold cross validation is used as a validation procedure. The obtained results show that K-NN gives the best results with 96.26 % of correct classification rate.

关键词： Radio frequency Support vector machines Legged locomotion Yttrium Europe classification algorithms Conferences

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Initial study for the investigation of breast tumour response with classification algorithms using a microwave radar prototype

Initial study for the investigation of breast tumour respons...

引用

European Conference on Antennas and Propagation, EuCAP

作者： Raquel C. Conceição Dallan Byrne J. A. Noble Ian Craddock Faculdade de Ciências Universidade de Lisboa Portugal Institute of Biomedical Engineering University of Oxford Oxford UK Department of Electrical & Electronic Engineering University of Bristol UK

In this paper classification algorithms will be used to investigate the presence of tumours in the breast, from signals collected with a radar microwave imaging prototype from the University of Bristol. A number of features will be extracted from the scattering of breast tumours and will then be used in classification algorithms such as Linear Discriminant Analysis or Quadratic Discriminant Analysis. The results from the classifier will allow creating an image of the considered synthetic breast phantom in which normal breast tissue is classified as a “miss” and tumour tissue is classified as a “hit”.

关键词： Breast Phantoms Microwave imaging Radar imaging classification algorithms Tumors

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The method of elf-files identification based on the metric classification algorithms

The method of elf-files identification based on the metric c...

引用

Open Innovations Association and Seminar on Information Security and Protection of Information Technology (FRUCT-ISPIT), Conference of

作者： Igor Zikratov Igor Pantiukhin Irina Krivtsova Nikita Druzhinin ITMO University Saint Petersburg Russia

ISBN: (纸本)9781509025008

When performing the internal audit of computer equipment an important problem is to identify the elf (executable and linkable format) files stored on the investigated hard drive. To solve this problem, we propose a method of identification of unknown elf-ffles based on the metric classification algorithms. The method consists of three stages. On the first stage the preparation of the training sample by the disassembly of each file and submitting it in the form of an ordered set of the 118 elements is implemented. Each of these elements is the frequency of occurrences of the 118 most commonly used commands in the assembler code. Each program in the sample is represented by several sets, corresponding to different versions or operating systems in which this software is installed. Then, the Minkowski metric of each sample file and identifiable file is calculated. For the method of potential functions the selection of the reference elements of each set is obtained. On the third stage using the metric classification algorithms we evaluate affiliation of the identifiable file for a particular program from the sample. To approbate proposed method the experiment with the use of this method was conducted; results showing the accuracy of identification of elf-files was equal to 89,60% were obtained. The results indicate that this method is applicable in problems of identification of elf-files while conducting the internal audit of computer equipment. The advantages of the method are the accuracy of program identification regardless of the elf-files versions in the Linux operating systems. The ease of implementation of our method and the identification execution speed can be used not only in tasks of internal audit, but also in other tasks of computer forensics.

关键词： Training classification algorithms Standards Measurement Computers Operating systems Mathematical model

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Machine Learning for Enhanced Autism Screening: A Comparative Evaluation of classification algorithms

Machine Learning for Enhanced Autism Screening: A Comparativ...

引用

Cyber Resilience (ICCR), International Conference on

作者： May Y. Al-Nashashibi Mohammad Alauthman Wael Hadi Department of Data Science and Artificial Intelligence University of Petra Amman Jordan Department of Information Security University of Petra Amman Jordan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Detecting autism spectrum disorder (ASD) is challenging due to its varying symptoms across individuals. This research assessed four common machine learning classifiers - Logistic Regression, Naïve Bayes, Support Vector Machines, and K-Nearest Neighbors - using four ASD datasets. Two experiments evaluated the algorithms on the original datasets and versions modified by combining three datasets, adding an age feature, and reducing features. Support vector machines showed the most consistently strong performance on all metrics in both experiments. The adult dataset performed best among original datasets, while the modified combined dataset was most successful, with substantial gains across measures. Overall, the results demonstrate machine learning's promise for developing more accurate and accessible ASD screening, complementing standard diagnostic practices. Further research should evaluate these techniques on expanded and diverse ASD data.

关键词： Support vector machines Autism Logistic regression Machine learning algorithms Machine learning Vectors classification algorithms

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