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

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25th international conference on pattern recognition Workshops, ICPR 2020

作者： Antsiperov, Viacheslav Kotelnikov Institute of Radioengineering and Electronics of RAS Mokhovaya 11-7 Moscow Russia

ISBN: (纸本)9783030688202

The paper discusses a new Image mining approach to extracting and exploring relations in the image repositories. The proposed approach, called Maximum Similarity Method, is based on the identification of characteristic fragments in images by a set of predefined patterns. Such an identification is basically carried out as a comparison of the fragment intensity shape with the shapes of already registered patterns - precedents. Mathematically (statistically) such a comparison implies a selection of some measure of similarity and optimization (maximization) of that measure on a set of precedents. In the paper, basing on the principles of machine learning, a special type of similarity measure is proposed, and its reliability is discussed. In fact, this measure represents conditional probability distribution of the registered data - counts of a fragment tested when analogous data for the patterns are given. So, the search for the optimal precedent pattern that maximized the chosen similarity measure constitutes the proposed method. © 2021, Springer Nature Switzerland AG.

关键词： Big data

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The impact of feature quality on SDN traffic learning and classification

The impact of feature quality on SDN traffic learning and cl...

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The 3rd international conference on Distributed Sensing and Intelligent Systems (ICDSIS 2022)

作者： A. Alzu'bi J. Khrais N. Ennab A. Abuarqoub Department of Computer Science Jordan University of Science and Technology Irbid Jordan Cardiff School of Technologies Cardiff Metropolitan University Cardiff UK

Network traffic classification is crucial for facilitating sophisticated network management. Software-defined networks (SDN) provide a featured architecture where the control and data planes are decoupled to enable dynamic configuration. The control plane functionality is implemented in a programmable centralized controller that enables effective use of data-driven based on network traffic classification using efficient machine learning (ML) algorithms. However, there is a lack of standardized benchmarking criteria agreed by the SDN community to evaluate the techniques proposed to collect and classify the network traffic features. This paper presents an empirical analysis to investigate the impact of SDN traffic features on the performance of ML-based classifiers. This study has been conducted using a publicly available SDN traffic dataset. The procedure of feature engineering has also been comprehensively evaluated through three approaches of feature selection, which are low variance filter, high correlation filter, and backward feature elimination. The experimental results show that the performance of ML classifiers is highly driven by the quality traffic features fed to them; the highest quality the traffic features are the best accuracy is achieved.

关键词： computer network security learning (artificial intelligence) pattern classification software defined networking telecommunication control telecommunication network management telecommunication traffic

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data mining: Application of EGARCH dynamic model on the volatility of high-frequency exchange rate data

Data mining: Application of EGARCH dynamic model on the vola...

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2021 3rd international conference on Applied machine learning and data Science, ICAMLDS 2021

作者： Wang, Yang-Chao Tsai, Jui-Jung Chen, Xingyu Newhuadu Business School Minjiang University Fuzhou China Straits Institute Minjiang University Fuzhou China Department of Statistics and Applied Probability UC Santa Barbara Santa BarbaraCA United States

Dynamic model has always been the focus of data science research. The results of dynamic models with different physical backgrounds vary greatly. Considering the time-varying characteristics and heteroskedasticity of long time exchange rate series, the GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model can be used to depict the pattern of variance fluctuation and clustering over time, and the research on variance can be extended to time-varying conditional variance. In this paper, the EGARCH (Exponential GARCH) model is further adopted to analyze the high-frequency exchange rate data from 2005 to 2017, which can reduce the parameter restrictions of the GARCH model, and at the same time, the characteristics of the exponential ensure the non-negative nature of the estimated variance, so as to predict the volatility more accurately. Moreover, the different effects of positive and negative random shocks on volatility can be analyzed to fully describe the data asymmetry. Finally, we use the clustering algorithm of the EGARCH model for data mining, evaluating the influence of endogenous variables and exogenous events on the RMB exchange rate. © Published under licence by IOP Publishing Ltd.

关键词： Dynamic models

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Expression of Concern for: Exploring the Potential Benefits of machine learning for Optimizing Real-Time data Analysis

Expression of Concern for: Exploring the Potential Benefits ...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Ravindra Kumar Nitin N. Sakhare N. Thangarasu K.K. Senthilkumar Pawan S. Wawage A Rengarajan Department of Civil Engineering Vivekananda Global University Jaipur India Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune INDIA Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

This studies paper examines the capability blessings of implementing device getting to know algorithms to optimize real-time information evaluation. The subjects mentioned consist of techniques for predicting traits in actual-time information, the ability of gadget studying algorithms to discover anomalies and outliers in datasets, and how using records mining and herbal language processing (NLP) can improve the accuracy of real-time facts insights. It also evaluates the demanding situations that can arise while applying machine gaining knowledge of algorithms to actual-time statistics, along with scalability problems and overfitting of records. In end, the authors argue that system studying has notable potential to enhance the accuracy and timing of actual-time facts analysis, and that they provide steerage on choosing and enforcing appropriate gadget getting to know algorithms.

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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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Time-Series Deep-learning Classifier for Human Activity recognition Based on Smartphone Built-in Sensors 3

Time-Series Deep-Learning Classifier for Human Activity Reco...

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3rd international Scientific conference of Engineering Sciences and Advances Technologies, IICESAT 2021

作者： Ali, Ghada Qanbar Al-Libawy, Hilal Department of Electrical Engineering University of Babylon Iraq

Human Activity recognition (HAR) is gaining more interest in recent years due to its growing role in many human-related sectors such as the health sector especially with elderly people and motion restricted patients. In recent years, there has been great progress in identifying human activity using various machine learning approaches. However, traditional methods of feature extraction are the most challenging in the feature selection process. Deep learning is a promising approach in the human activity recognition research area and has overcome the feature selection problem. However, several challenges are still open to research issues such as classification performance. This paper describes how to identify specific types of human physical activities using the accelerator and gyroscope data generated by the smartphone user. A deep convolutional neural network architecture has been proposed to perform HAR efficiently and effectively the system has been trained and tested over a dataset generated with the aid of 50 volunteers with four activities (walking, running, walking up-down stairs finally sitting-standing on the chair) events in real-world conditions. We chose four classes, each of which performs well, get to know our range of activities achieving 99% for validation and 99.8% for testing overall accuracy © Published under licence by IOP Publishing Ltd.

关键词： Smartphones

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Activity recognition in the City using Embedded Systems and Anonymous Sensors 11

Activity Recognition in the City using Embedded Systems and ...

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11th international conference on Ambient Systems, Networks and Technologies (ANT) / 3rd international conference on Emerging data and Industry 4.0 (EDI)

作者： Baghezza, R. Bouchard, K. Bouzouane, A. Gouin-Vallerand, C. LIARA Lab UQAC Chicoutimi PQ G7H 2B1 Canada Sherbrooke Univ Sherbrooke PQ J1K 2R1 Canada

This paper presents an embedded system that performs activity recognition in the city. Arduino Due boards with infrared, distance and sound sensors are used to collect data in the city and the activity, profile, and group size recognition performance of different machine learning algorithms (RF, SVM, MLP) are compared. The features were extracted based on fixed-size windows around the observations. We show that it is possible to achieve a high accuracy for binary activity recognition with simple features, and we discuss the optimization of different parameters such as the sensors collection frequency, and the storage buffer size. We highlight the challenges of activity recognition using anonymous sensors in the environment, its possible applications and advantages compared to classical smartphone and wearable based approaches, as well as the improvements that will be made in future versions of this system. This work is a first step towards real-time online activity recognition in smart cities, with the long-term goal of monitoring and offering extended assistance for semi-autonomous people. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Activity recognition Smart City machine learning Anonymous data Healthcare Embedded Systems

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Interpretation of Indian Sign Language to Text and Speech to Communicate with Speech and Hearing Impaired Community

Interpretation of Indian Sign Language to Text and Speech to...

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3rd international conference on machine learning and data Engineering, ICMLDE 2024

作者： Ingoley, Shilpa Bakal, Jagdish TSEC Bandra Mumbai400050 India Pillai HOC Raigadh Rasayani410207 India

One of the necessities of a human being is to express themselves. To express one's thoughts, people typically use speech. However, individuals who are deaf and with speech-impairment convey their feelings by practicing sign language. With this communication medium they perform gestures and interpreting these gestures means knowing sign language(SL). Everyone is not acquainted with SL which is the main obstacle in this communication process. To address this key issue, we recommend the deep learning-based approach which assists in interpreting Indian Sign Language(ISL) to text and speech. Communication between a hearing-impaired individual and an illiterate/blind person is made possible through speech conversion. We created a sample model which interprets numbers used in ISL. Numbers are helpful at many places;financial institutions like banks, cash counters, to mention time/distance. Existing sign language recognition systems do not adequately address the challenges posed by i) left, right and ambidextrous signer ii) signers facing mobile camera in selfie-mode iii) signers wearing gloves on their hands to facilitate communication in cold climates or at hill stations iv) If more than one gesture is present for particular sign then those should be considered. To address the mentioned problems, we have created a diverse dataset named ISL_Num14_AS3N24. We have proposed a system wherein we made use of deep learning technique along with our suggested methodology which utilises the hand keypoints in the pre-processing step. Also, for quicker training, we employ modified two pretrained models Vgg16 and MobileNetV2. Text-to-speech conversion module utilizes gtts library. The system exhibits excellence performance with validation accuracy for Vgg16 as 99.76 and for MobileNetv2 as 99.44. Furthermore, the validation loss of VGG16 and MobileNetv2 is 0.9471 and 1.9921 respectively. © 2025 The Author(s).

关键词： Deep learning

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Sound-Based Respiratory Disease recognition with machine learning 3rd

Sound-Based Respiratory Disease Recognition with Machine Lea...

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3rd international conference on Computing and data Science, CONF-CDS 2021

作者： Xu, Xiaoran Electrical and Computer Engineering Department New York Institute of Technology New York United States

ISBN: (纸本)9789811688843

Due to the effects of respiratory diseases, a large number of people die every year. Last year, the new coronavirus COVID-19 swept the world even more, causing the huge loss of personnel and economic decline around the world. To fight against this huge epidemic, relying on hospitals only to detect the such large number of people is obviously inefficient. Therefore, this paper proposes to relieve the pressure of medical workers through sound detection, in a framework of combing the Mel Frequency Cepstral Coefficient (MFCC) and Convolutional Neural Networks (CNN). To compare different voice types and disease types, we selected three open data sets, namely, ICBHI, Coswara and Verify. Then we compared CNN with the Multi-layer Perceptron, Random Forest and XGBoost models. Finally, it is concluded that CNN has the highest accuracy rate on the three data sets. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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The improvement of hand gesture recognition based on sEMG by moving average filtering for features 3

The improvement of hand gesture recognition based on sEMG by...

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3rd international conference on Artificial Intelligence and Advanced Manufacture, AIAM 2021

作者： Chen, Cai Li, Lijuan Zhang, Xikun Wang, Xingwei Han, Changming Xia, Tian Li, Wenchao Peng, Fulai Shen, Yang An, Jianpeng Shandong Institute of Advanced Technology Chinese Academy of Sciences Jinan China School of Public Health Tongji Medical College Huazhong University of Science and Technology Wuhan China Integrated Traditional Chinese and Western Medicine Hospitial of Tongzhou District Beijing China

ISBN: (纸本)9781450385046

Surface electromyographic signal (sEMG) is a kind of bioelectrical signal, which records the data of muscle activity intensity. Hand gesture recognition based on sEMG plays an important role in the hand rehabilitation robotics. Improving the recognition accuracy of hand gesture is the goal always pursued by researchers. This study purposes to evaluate the performance of moving average filtering for features in improving the accuracy of hand gesture recognition based on sEMG. Firstly, empirical mode decomposition was applied on the effective motion segments extracted by sliding windows with different size and stride. Afterward, 21 features were extracted from original signal and each of the intrinsic mode function. Subsequently, moving average filtering with different orders were used to further suppress the noise in each feature series to improve the recognition performance. Finally, two representative machine learning methods (including decision tree and K nearest neighbor) were used to evaluate the performance. Compared with the original features, the intra-subject recognition accuracy rate after feature processing for KNN and DT increase by 17%, and 6%, respectively. The accuracy of the inter-subject across five subjects after feature processing increase by 28% (KNN), and 12% (DT), respectively. The above results prove that the moving average filtering for features could effectively improve the performance of hand gesture recognition. © 2021 ACM.

关键词： machine learning

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