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检索条件"任意字段=5th International Conference on Signal Processing and Machine Learning, SPML 2022"

共 777 条记录，以下是61-70 订阅

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Handling Concept Drift in Financial Time Series Data: Recurrent Xavier On-line Sequential Extreme learning machine 22

Handling Concept Drift in Financial Time Series Data: Recurr...

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5th international conference on signal processing and machine learning, spml 2022

作者： Liu, Zongying Zhang, Wenru Sun, Hui Li, Shaoxi Li, Hangqi Faculty of Navigation Dalian Maritime University China

ISBN: (纸本)9781450396912

Financial time series prediction attracts more attention from industry and research field. Recent decade, On-line Sequential Extreme learning machine (OS-ELM) and its variant algorithms are widely used for time series prediction with efficient and accuracy performance. However, due to random weights selection in OS-ELM and characteristic of non-stationary data, the unstable prediction performance and the concept drift problem appear in the training process. In this study, two proposed methods are used in the conventional Online Sequential Extreme learning machine (OS-ELM). Firstly, new Xavier weights selection method is employed to replace the conventional weights selection method in OS-ELM, which reduces the unstable performance and improves the forecasting ability. Secondly, to solve concept drift problem, an Adaptive Forgetting Factor method is proposed. Finally, based on these two methods, a novel time series prediction model called Recurrent Xavier Online Sequential Extreme learning machine with Adaptive Forgetting Factor (RXOS-ELM-AFF) is proposed for the long-term time series prediction. the ability of our model is assessed on one artificial data set and five financial data. According to the experimental results, our proposed RXOS-ELM-AFF has superior forecasting ability than the conventional OS-ELM. © 2022 Association for Computing machinery.

关键词： Forecasting

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Comparison of SVM and Naive Bayes for Sentiment Classification using BERT data 5

Comparison of SVM and Naive Bayes for Sentiment Classificati...

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5th international conference on Multimedia, signal processing and Communication Technologies (IMPACT)

作者： Rana, Shivani Kanji, Rakesh Jain, Shruti Jaypee Univ Informat & Technol Dept Comp Sci & Engn Solan HP India Jaypee Univ Informat & Technol Dept Elect & Commun Engn Solan HP India

ISBN: (纸本)9781665476478

Expressing views related to any common interest of area is becoming very general due to the excessive use of internet on the handheld devices like mobiles. Everyone is free to share his opinions within his social network. Having some useful knowledge from those opinions is the challenge for text classification techniques. In this work, the movies related opinions in the form of long and more than one sentence are analyzed to categorize them as positive or negative sentiments towards the movie. In this paper, the machine learning approach using the supervised learning methods is used for the implementation of the model. the data is prepared using the BERT model where plain text is directly converted into a numerical data form. the experiments are conducted using the Support Vector machine and Naive Bayes algorithms. Accuracy, F1 score, and other metrics have been evaluated for the comparison of the results of both algorithms. the results show that the predictions by SVM have high accuracy than Naive Bayes.

关键词： machine learning Sentiment Analysis BERT Naive Bayes SVM

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Investigation into Bearing Fault Classification using Various Feature Set Combinations in KNN 5

Investigation into Bearing Fault Classification using Variou...

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5th international conference on Multimedia, signal processing and Communication Technologies (IMPACT)

作者： Saleem, Zaufisha Mudassir, Mantaza Khanam, Sidra Aligarh Muslim Univ Zakir Hussain Coll Engn & Technol Dept Mech Engn Aligarh Uttar Pradesh India

ISBN: (纸本)9781665476478

In many rotating types of machinery, rolling element bearings are considered "crucial", as rotating components cause the majority of failures, with proper performance monitoring;imminent failures can be identified and corrected. At the initial phase, there may exist many distinct features embedded in the data that may be able to identify a bearing fault which are non-traceable by using the physical models. Due to which, machine learning (ML) algorithms are currently the growing area of research for diagnostic and prognostic purposes. this work presents a study on the fault classification accuracy by K-Nearest Neighbor (KNN) machine learning classifier wherein the effect of different Time domain, Frequency domain and a combination of Time and Frequency domain features individually and in combinations have been investigated. the classification accuracy of Fine, Medium, and Coarse KNN have been examined and from the results obtained, Fine KNN has been used to further study the classification accuracy for various feature set combinations. Furthermore, features have been ranked using One-way ANOVA and their classification accuracies obtained using those top ranked features and their combinations have been investigated.

关键词： bearing fault diagnosis machine learning feature selection KNN classifier

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Enhancing the Precision of Smartphone GNSS Positioning Using machine learning 23

Enhancing the Precision of Smartphone GNSS Positioning Using...

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5th international conference on Image, Video and signal processing, IVSP 2023

作者： Yang, Haocheng Computer Science and Design Singapore University of Technology and Design 8 Somapah Rd Singapore487372 Singapore

ISBN: (纸本)9781450398381

the rapid development of mobile Internet has made the smartphone a nearly ubiquitous device. As a result, the positioning of a smartphone becomes much more important for people's daily life. In particular, this measurement of positioning is usually conducted by the Global Navigation Satellite System (GNSS). therefore, it is a significant and crucial task to improve the precision of smartphone GNSS positioning. In this work, we utilize the machine learning techniques to enhance the precision of smartphone GNSS positioning, where the weighted least squares (WLS) estimation and the precise single-point positioning (PPP) algorithm are applied. Here, the results of WLS are denoised by the carrier smoothing and Kalman filtering;PPP algorithm is implemented using the RTKLIB software. After an average ensemble of these two algorithms, our method reaches a mean of the distance errors between the 50th and 95th percentiles 1.802 in meters on the private test set of the Kaggle competition: Google Smartphone Decimeter Challenge 2022. this performance gives us a 12/571 rank and a silver medal in this competition. Our work could help accelerate the research with respect to the improvement of high precision GNSS positioning and navigation accuracy. © 2023 ACM.

关键词： machine learning

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Analysis of Tourist Comments Based on Python Natural Language processing: Take Guilin Lijiang Waterfall Hotel as an example 5

Analysis of Tourist Comments Based on Python Natural Languag...

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5th international conference on machine learning and Natural Language processing (MLNLP)

作者： Sun Wenqiang Guilin Univ Technol Coll Tourism & Landscape Architecture Guilin Peoples R China

ISBN: (纸本)9781450399067

Python is a computer language often used in tourism research, which can solve the problem of natural language processing well. In order to solve the problem of stagnation in the long-term tourism development of high star hotels that have been established for a long time, this study takes the Lijiang Waterfall Hotel in Guilin as a case study, crawls the tourists' comments on the Internet through technical means, and uses the natural language processing function based on Python language to process, analyze and visualize them. this study concluded that such hotels need to upgrade their hardware facilities, improve their staff training system and improving hotel dining experience.

关键词： Natural Language processing Reptiles Tourist comments Hotel

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machine learning-Based Electric Vehicle Charging Demand Prediction Using Origin-Destination Data: A UAE Case Study 5

Machine Learning-Based Electric Vehicle Charging Demand Pred...

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5th international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： ElGhanam, Eiman Hassan, Mohamed Osman, Ahmed Amer Univ Sharjah Dept Elect Engn POB 26666 Sharjah U Arab Emirates

ISBN: (纸本)9781665482370

Optimal prediction and coordination of the energy demand of electric vehicles (EVs) is essential to address the energy availability and range anxiety concerns of current and potential EV users. As a result, different EV demand predictors are developed in the literature based on traffic simulators and/or locally-generated EV charging datasets, to provide the required inputs for EV demand management programs. these predictors, however, may not reliably scale to model the EV energy requirements in different regions, particularly with the scarcity of real-world data on EV driving patterns. this work proposes a data-driven, machine learning (ML)-based EV demand predictor based on vehicular traffic flow data between different origin-destination (OD) pairs. the proposed model incorporates the driving patterns in the regions under consideration to determine the corresponding EV energy consumption and hence, the minimum EV energy requirements per trip. the data used in this work is obtained from TomTom Move O/D Analysis portal for the cities of Dubai and Sharjah, UAE. Different ML models are trained on the dataset to develop the EV demand predictor, namely random forests (RF), extreme gradient boosting (XGBoost), multilayer perceptron (MLP) and linear regression models. Results reveal that the MLP offers a superior performance to all other models, with an R-2 > 0:8 and a symmetric mean absolute percentage error of approximate to 20% on both the training and testing data subsets, and a significantly lower training time compared to RF and XGBoost. this makes it suitable for EV demand predictions to incorporate regular updates in vehicular traffic flow data for further model tuning.

关键词： Electric vehicle energy demand prediction OD matrix machine learning

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SUMAC '23: 5th Workshop on the analySis, Understanding and proMotion of heritAge Contents: Advances in machine learning, signal processing, Multimodal Techniques and Human-machine Interaction 23

SUMAC '23: 5th Workshop on the analySis, Understanding and p...

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31st ACM international conference on Multimedia, MM 2023

作者： Gouet-Brunet, Valérie Kosti, Ronak Weng, Li Lastig Ign Gustave Eiffel University Saint-Mandé France Picsart Ai Research Lab Berlin Germany Zhejiang Financial College Hangzhou China

ISBN: (纸本)9798400701085

SUMAC 2023 is the fifth edition of the workshop on analySis, Understanding and proMotion of heritAge Contents. It is held in Ottawa, Canada on November 2, 2023 and is co-located with the 31st ACM international conference on Multimedia. the workshop's objective is to present and discuss the latest and most significant trends, challenges and advances in the fields of machine learning, signal processing, multimodal techniques and human-machine interaction. the workshop is dedicated to the valorization of cultural heritage, with the emphasis on the unlocking of and access to the big data of the past. A representative scope of Computer Science methodologies dedicated to the processing of multimedia heritage contents and their exploitation is covered by the works presented, with the ambition of advancing and raising awareness about this fully developing research field. the complete SUMAC'23 workshop proceedings are available at: https://***/doi/proceedings/10.1145/3581783.3610949. © 2023 Owner/Author.

关键词： Gesture recognition

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An Efficient Patient-Independent Epileptic Seizure Assistive Integrated Model in Human Brain-Computer Interface Applications 5

An Efficient Patient-Independent Epileptic Seizure Assistive...

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5th international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Halawa, Rowan Ihab Youssef, Sherin M. Elagamy, Mazen Nabil AASTMT Coll Engn & Technol Dept Comp Engn Alexandria Egypt

ISBN: (纸本)9781665482370

Epileptic seizures are brief disruptions in the electrical activity of the brain. Epilepsy is a central nervous system illness in which people have repeated seizures that happen at random times and usually without warning. Seizures are more likely to cause physical harm and even death in people who have them frequently. Intelligent techniques supporting brain-computer interfaces in seizure control can provide efficient epilepsy detection where seizure events can be identified in the early stages triggering electrical stimulations to be sent to the cortex of the brain. In this paper, a non-patient-specific seizure detection model is introduced. the proposed model integrates wavelet-based electroencephalography EEG brain signal processing with feature extraction to extract combined features from both time and frequency domains. Classification has been applied using different machine learning techniques for efficient early detection of epileptic seizures. In addition, channel selection analysis is implemented to reach an accurate generic model. the experimental comparative study demonstrated that the electroencephalography signals from the frontal lobe channels supply more discriminative features than the other channels, which enhanced the classification accuracy and sensitivity in the proposed model. Experiments have been carried out on the Children's Hospital Boston-Massachusetts Institute of Technology dataset to validate the robustness of the proposed model. the experimental results showed that the proposed model achieved 99.792% accuracy and 99.59% sensitivity in detecting epileptic seizures with improvement in accuracy with rates of 7%, 5%, and 8% compared to [14], [11], and [9], respectively. Experiments showed that the proposed system can detect epileptic seizures effectively, which can give remarkable potential in clinical applications.

关键词： Epilepsy Seizure Detection Inter-ictal Ictal EEG signal machine learning

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Stacked Convolutional Autoencoder with Multi-label Extreme learning machine (SCAE-MLELM) for Bangla Regional Language Classification 22

Stacked Convolutional Autoencoder with Multi-label Extreme L...

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5th international conference on signal processing and machine learning, spml 2022

作者： Hossain, Prommy Sultana Chakrabarty, Amitabha Piran, Jalil Suh, Doug Young Department of Computer Science and Engineering Brac University Dhaka Bangladesh Department of Computer Science and Engineering Sejong University Seoul Korea Republic of Department of Electronic Engineering Kyung Hee University Seoul Korea Republic of

ISBN: (纸本)9781450396912

Several studies emphasized the importance of detecting the dialect spoken by the user, to improve customer-agent relationships in call centers operated by telecomunication companies. and to create advanced speech recognizing Artificial Intelligence devices using machine learning (ML) techniques. However, the extensive Bangla speech recognition research performed in the past did not address this issue. therefore, in this paper, we propose a model that uses deep learning (DL) techniques, as a subset of ML, to classify the regional language in the audio signal of the Bangla speech. We build a model based on Stacked Convolutional Autoencoders (SCAE) and Multi-label Extreme learning machines (MLELM). SCAE extracts the spatial and temporal features from the input data and produces a detailed feature vector. In order to provide a soft classification score and hard labels, the information is passed onto a series of MLELM networks. For Bangla speech dataset created during this research, the proposed method produced an accuracy score of 95%. Furthermore, the proposed system performs remarkably well in classifying the age and gender of the speaker in Bangla. © 2022 Association for Computing machinery.

关键词： Speech recognition

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2022 5th international conference on signal processing and Information Security, ICSPIS 2022

2022 5th International Conference on Signal Processing and I...

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5th international conference on signal processing and Information Security, ICSPIS 2022

ISBN: (纸本)9781665492652

the proceedings contain 36 papers. the topics discussed include: federated learning through goal programming: a computational study in cancer detection;design of an automated blockchain-enabled vehicle data management system;smart speed camera based on automatic number plate recognition for residential compounds and institutions inside Qatar;classification of cumin, fennel and carom using transfer learning;identification of robust features for classifying spam and ham images using transfer learning;deep neural networks for transmission impairment mitigation in long-reach 5G access networks;a study on seizure detection performance in an automated process by extracting entropy features;detection of compressed deepfake video drawbacks and technical developments;employing information theoretic metrics with data-driven occupancy detection approaches: a comparative analysis;enhanced extra trees classifier for epileptic seizure prediction;and performance comparison of credit card fraud detection system using machine learning.

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