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检索条件"主题词=Scikit-learn"

共 123 条记录，以下是61-70 订阅

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Performance Evaluation of an Ambient Intelligence Testbed for Improving Quality of Life: Evaluation Using Mean Shift Clustering Algorithm 19

Performance Evaluation of an Ambient Intelligence Testbed fo...

引用

19th International Conference on Network-Based Information Systems (NBiS)

作者： Obukata, Ryoichiro Oda, Tetsuya Elmazi, Donald Ikeda, Makoto Matsuo, Keita Barolli, Leonard Fukuoka Inst Technol Grad Sch Engn Higashi Ku 3-30-1 Wajiro Higashi Fukuoka 8110295 Japan Fukuoka Inst Technol Dept Informat & Commun Engn Higashi Ku 3-30-1 Wajiro Higashi Fukuoka 8110295 Japan

ISBN: (纸本)9781509009794

Ambient intelligence (AmI) deals with a new world of ubiquitous computing devices, where physical environments interact intelligently and unobtrusively with people. AmI environments can be diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, vehicles, tourist attractions, stores, sports facilities, and music devices. In this paper, we present the design and implementation of a testbed for AmI using Raspberry Pi mounted on Raspbian OS. We analyze the performance of mean shift clustering algorithm considering sensing data. For evaluation we considered respiratory rate and heart rate metrics. The simulation results show that the mean shift clustering algorithm has a good performance.

关键词： Ambient Intelligence Quality of Life Machine learning Sensing data Testbed Raspberry Pi Mean Shift scikit-learn

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Virtual Quality control using bidirectional LSTM networks and gradient boosting 17

Virtual Quality control using bidirectional LSTM networks an...

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17th IEEE International Conference on Industrial Informatics (INDIN)

作者： Baghbanpourasl, Amirreza Lughofer, Edwin Meyer-Heye, Pauline Zoerrer, Helmut Eitzinger, Christian Profactor GmbH Steyr Gleink Austria Johannes Kepler Univ Linz Dept Knowledge Based Math Syst Linz Austria

ISBN: (纸本)9781728129273

Quality control is usually performed at the end of production lines. This postponed quality control, in case of deviation of the process values from desired quantities can lead to late detection of batches of defective products. A prediction model allows a virtual online quality control. Observation of any unfavourable trend or degradation in the predicted quality can enable preventive measures. There are many cases where quality control is performed manually and less often. This leads to limited data which is challenging for training any machine learning system used for purpose of prediction. In this paper the goal is prediction of quality measure with respect to the past history of all relevant process values such as sensor readings. We present a machine learning model based on bidirectional Long Short-term Memory (LSTM) recurrent neural networks and gradient boosting. This model is applied to a set of real world data from microfluidic chip production plant and we show that despite limited amount of training data compared to the large input space, it is capable of predicting the relevant quality values, and especially their basic trends over time including drifting phases and changing behaviors.

关键词： neural networks gradient boosting prediction quality control quality assurance zero defect manufacturing Keras scikit-learn tensorflow LSTM

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Estimating Timing of Head Movements Based on the Volume and Pitch of Speech

Estimating Timing of Head Movements Based on the Volume and ...

引用

Human Interface and the Management of Information (HIMI) part of International Conference on Human-Computer Interaction (HCII)

作者： Yanagi, Haruka Oshima, Chika Nakayama, Koichi Saga Univ Saga 8408502 Japan

ISBN: (纸本)9783030226480;9783030226497

Our research aims to create two friendly communication robots to talk with elderly people in nursing facilities. If the robots synchronize their head movements in response to the elderly person, the elderly person may react favorably to the robot. Then, the elderly person can enjoy talking with these two robots. In this paper, we investigated whether the volume and pitch of the speech are useful data for estimating the timing of head movements. Because the robots need to move their heads in real time, when one of the robots or the person is talking, we focus on the volume and pitch of the speech, not the content. Moreover, it was cleared which machine learning method creates suitable classifier models for estimating the timing of head movements. The experimental results showed that Random Forest classifier was the most suitable method.

关键词： Machine learning scikit-learn Communication robot

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Implementation and Analysis of Centroid Displacement-Based k-Nearest Neighbors 18th

Implementation and Analysis of Centroid Displacement-Based k...

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18th International Conference on Advanced Data Mining and Applications (ADMA)

作者： Wang, Alex X. Chukova, Stefanka S. Nguyen, Binh P. Victoria Univ Wellington Sch Math & Stat Wellington New Zealand

ISBN: (纸本)9783031220630;9783031220647

k-NN is a widely used supervised machine learning method in different domains. Despite its simplicity, effectiveness, and robustness, k-NN is limited to the use of the Euclidean distance as the similarity metric, the arbitrarily selected neighborhood size k, the computational challenge from high dimensional data, and the use of the simple majority voting rule. Among different variants of k-NN in classification, we sought to address the last issue and proposed the Centroid Displacementbased k-NN (CDNN), where centroid displacement is used for class determination. In this study, we present an implementation of CDNN for scikit-learn, a well-known machine learning library for the Python programming language, and a comprehensive comparative performance analysis of CDNN with different variants of k-NN in scikit-learn. We open-source our algorithm to benefit the users, and to the best of our knowledge, no similar studies on performance analysis of k-NN and its variants in scikit-learn have been done. We also examine the effectiveness of different distance metrics on the performance of CDNN on different datasets. Extensive experiments on real-world and synthetic datasets verify the effectiveness of CDNN compared to the standard k-NN and other state-of-the-art k-NN-based algorithms. The results from the distance metrics comparison study also show that other distance metrics can further improve the classification performance of CDNN.

关键词： Similarity Nearest neighbors Centroid displacement k-NN scikit-learn

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Classification of user attitudes in Twitter - beginners guide to selected Machine learning libraries 16th

Classification of user attitudes in Twitter - beginners guid...

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16th International-Federation-of-Automatic-Control (IFAC) Conference on Programmable Devices and Embedded Systems PDES

作者： Sokolowska, Marta Mazurek, Maciej Majer, Marcin Podpora, Michal Opole Univ Technol Fac Elect Engn Automat Control & Informat Inst Comp Sci Opole Poland

This paper presents an interesting use case for learning as well as teaching basics of Machine learning systems. Starting from a brief historical outline of the ML, the authors propose and compare a set of popular ML libraries in an interesting exemplary implementation, to present their usability. The paper also describes text classification methods, the aim of which is to distinguish positive and negative labels of particular messages within the Twitter social network. The study is summarized by a brief comparison of the quality of the classification of the libraries and methods used, as an assessment of their suitability. Final thoughts on the importance of teaching ML are included. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Machine learning Natural Language Processing Education Twitter Sentiment Analysis fastText TensorFlow scikit-learn

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Named entity recognition for the contraindication and dosing sections of patient information leaflets with CRFClassifier tools 23

Named entity recognition for the contraindication and dosing...

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23rd International Conference on System Theory, Control and Computing (ICSTCC)

作者： Chirila, Oana Sorina Chirila, Ciprian-Bogdan Stoicu-Tivadar, Lacramioara Univ Politehn Timisoara Dept Automat & Appl Informat Timisoara Romania Univ Politehn Timisoara Dept Comp & Informat Technol Timisoara Romania

ISBN: (纸本)9781728106991

Currently, the interest in medical informatics is to extract medical terms from medical records for later use as searching for the best treatment and use knowledge in cooperation with other physicians. In literature, several algorithms exist that extract certain information of interest using certain models. An area where accurate, real and in-time information is paramount is the medical prescription one, where information is on the rise, and information on drugs, drug interactions or other interactions is vast and complex. In this paper, we use two CRFClassifier algorithms to extract several terms of interest from the contraindication and dosing sections of patient information leaflets in Romanian and later use them in creating decision-support applications. We use the Stanford NER and scikit-learn tools to extract medical information and create a corpus to train the algorithms. Following testing, the best-performing tool proved to be scikit-learn. The obtained data is useful in creating ontologies for further use in decision-support applications reducing medical errors in the prescription process.

关键词： named entity recognition patient information leaflets scikit-learn Stanford NER

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Using the Support Vector Regression Method for Telecommunication Networks Monitoring 3

Using the Support Vector Regression Method for Telecommunica...

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3rd International Scientific-Practical Conference on Problems of Infocommunications Science and Technology (PIC S&T)

作者： Sheremet, Oleksii Sadovoy, Oleksandr Dniprodzerzhinsk State Tech Univ Dept Elect Engn & Machinery Dniprodzerzhinsk Ukraine

ISBN: (纸本)9781509029785

This paper is about possibility of using the support vector regression method for telecommunication networks monitoring. The paper defines a role of regression analysis in the formation of the correlations between telecommunication networks quality parameters. An example of using the regression analysis by support vector machine with scikit-learn implementation is given.

关键词： regression analysis correlation kernel function scikit-learn

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Machine learning Approach for Text Classification in Cybercrime 4

Machine Learning Approach for Text Classification in Cybercr...

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4th International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Kumari, Swati Saquib, Zia Pawar, Sanjay SNDT Univ Usha Mittal Inst Technol Mumbai Maharashtra India Reliance Jio Infocomm Ltd Mumbai Maharashtra India

ISBN: (纸本)9781538652572

Nowadays, use of machine learning is increasing rapidly in every field and plays a key role in sentiment classification. In this project, two-training datasets are used, one is an online training dataset which is available online and another contains pure cybercrime data extracted from Facebook and Twitter using Facepager software tool. The aim is to extract the cybercrime data and according to supervised machine learning, separate the data in two labelled class (i.e. positive and negative) and pre-processing it to get a clean training dataset. The goal is to use cybercrime data to achieve classifier accuracy percent and text classification with confidence value that will be achieved by using NLTK and scikit-learn. The results achieved using both datasets show that using cybercrime datasets gives better classifier accuracy percent.

关键词： Facebook Twitter Data extraction Naive bayes scikit-learn NLTK Sentiment analysis

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Evaluation of Machine learning Algorithms for Multiclass Classification of Voice Calls from Power Systems Operations 7

Evaluation of Machine Learning Algorithms for Multiclass Cla...

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7th International Conference on Engineering and Emerging Technologies (ICEET)

作者： de Lima, Matheus N. S. M. Takigawa, Fabricio Y. K. IFSC Inst Fed Santa Catarina DAE Dept Acad Eletrotecn Florianopolis SC Brazil

ISBN: (纸本)9781665427142

The Brazilian electric system is operated by a non-profit institution called the National Electric System Operator (ONS) which coordinates and controls the operation of energy generation and transmission. For this, the main form of operation occurs through verbal communication between the operators of the system. All telephone calls made between operators are recorded and stored on a server. In this sense, from the transcription and labeling of the audios, it is possible to use Natural Language Processing (NLP) and Machine learning (ML) techniques to classify verbal communication in the operation. The Python programming language will be used together with a scikit-learn library to perform a comparison of multiclass classifiers, Complement Naive Bayes, Linear Support Vector Classification, Stochastic Gradient Descent Classifier, K-Nearest Neighbors Classifier, Multi-Layer Perceptron Classifier and Random Forest Classifier by tunning the hyperparameters and applying evaluation metrics for classification models. As result the model with best performance is the Multi-Layer Perceptron, reaching up to 85% of accuracy and F1 weighted. This work contributes to generation of products that assist the operation of this sector, it can produce performance indicators and even automatization of tasks and actions.

关键词： Dataset text scikit-learn classification machine learning

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Diabetes Disease Prediction Using Machine learning Algorithms

Diabetes Disease Prediction Using Machine Learning Algorithm...

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IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) - Leading Modern Healthcare Technology Enhancing Wellness

作者： Lyngdoh, Arwatki Chen Choudhury, Nurul Amin Moulik, Soumen Natl Inst Technol Meghalaya Dept Comp Sci & Engn Shillong Meghalaya India

ISBN: (纸本)9781728142456

This paper deals with the prediction of Diabetes Disease by performing an analysis of five supervised machine learning algorithms, i.e. K-Nearest Neighbors, Naive Baye, Decision Tree Classifier, Random Forest and Support Vector Machine. Further, by incorporating all the present risk factors of the dataset, we have observed a stable accuracy after classifying and performing cross-validation. We managed to achieve a stable and highest accuracy of 76% with KNN classifier and remaining all other classifiers also give a stable accuracy of above 70%. We analyzed why specific Machine learning classifiers do not yield stable and good accuracy by visualizing the training and testing accuracy and examining model overfitting and model underfitting. The main goal of this paper is to find the most optimal results in terms of accuracy and computational time for Diabetes disease prediction.

关键词： Diabetes disease Machine learning (ML) Disease risk analysis Confusion Matrix scikit-learn Body mass Index (BMI) Precision Recall F1-Score Pandas NumPy and Python

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