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Assessment of EMG Benchmark data for Gesture Recognition Using the NinaPro database

Assessment of EMG Benchmark Data for Gesture Recognition Usi...

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Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jason Chang Angkoon Phinyomark Erik Scheme Institute of Biomedical Engineering University of New Brunswick Fredericton NB Canada

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

In recent years, many electromyography (EMG) benchmark databases have been made publicly available to the myoelectric control research community. Many small laboratories that lack the instrumentation, access, and experience needed to collect quality EMG data have used these benchmark datasets to explore and propose new signal processing and pattern recognition algorithms. It is widely accepted that noise contamination can affect the performance of myoelectric control systems, and so useful datasets should maintain good signal quality to ensure accurate results for proposed EMG-based gesture recognition systems. Despite the availability and adoption of benchmarks datasets, however, the quality of the EMG signals in these benchmarks has not yet been examined. In this study, the signal quality of the Non-Invasive Adaptive Prosthetics (NinaPro) dataset, the most widely known publicly available benchmark database to date, was comprehensively investigated with the goals of: 1) reporting the level of noise contamination in each NinaPro sub-dataset, 2) proposing signal quality criteria for assessing EMG datasets, 3) analyzing the effect of signal quality on classification performance, and 4) examining the quality of the data labels.

关键词： Electromyography Signal to noise ratio Benchmark testing databases Contamination Electrodes

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Automating Multi-element Subspace Exploration via Reinforcement Learning

Automating Multi-element Subspace Exploration via Reinforcem...

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international conference on Computers, Communications, and Systems (ICCCS)

作者： Yi Sun YinXiao Liu ZhongYao Wang BaoLong Niu School of Software Xi’an Jiaotong University Xi’an China Data Technology and Product Department Alibaba Group Hangzhou China

ISBN: (数字)9781728161365

ISBN: (纸本)9781728161372

Machine learning enters many aspects of our lives and brings us great convenience. However, building an effective machine learning model for a specific task requires not only expertise but also a lot of time and resources. In order to solve this problem, more and more research projects focus on automated machine learning (AutoML). In this paper, we propose an algorithm that can simultaneously optimize the space of multiple datasets, multiple models, and multiple hyperparameters. We call this an automating multi-element subspace exploration algorithm. We first formalize this problem as a reinforcement learning problem and then we define the state, action and well-designed reward function in reinforcement learning system. In addition, we use some skills and experience to accelerate the entire optimization process. Finally, our experimental results on multiple tasks demonstrate that our method is effective.

关键词： Reinforcement learning Task analysis Optimization Training Prediction algorithms Feature extraction

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K-Nearest Neighbor and Naive Bayes Classifier Comparison for Individual Character Classification on Twitter

K-Nearest Neighbor and Naive Bayes Classifier Comparison for...

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international conference on Cybernetics and Intelligent System (ICORIS)

作者： Ema Utami Suwanto Raharjo Anggit Dwi Hartanto Sumarni Adi Aminudin Noor Ichsan Magister of Informatics Engineering Universitas Amikom Yogyakarta Yogyakarta Indonesia Informatics IST AKPRIND Yogyakarta Indonesia Faculty of Computer Science Universitas Amikom Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9781728172576

ISBN: (纸本)9781728172583

Twitter is one of the most commonly used social media, especially in Indonesia. People use Twitter social media to express their opinions every day. This allows the DISC method to be applied to find out Twitter user's character. By knowing their characters using the DISC method and Twitter, this can help parties like the HRD without spending more effort in selecting employees. Three main processes are performed in this study, data acquisition, pre-processing, and calculation process. Classification methods, namely Text Naïve Bayes Classifier algorithm and K - Nearest Neighbor, are used to classified and mapping tweet data to the DISC method. As a result, the accuracy of the Naïve Bayes Classifier algorithm is 34.16%, while the K - Nearest Neighbor is 28.33%. So it can be concluded that the Naïve Bayes Classifier algorithm has a higher accuracy of 5.83% compared to K - Nearest Neighbor in classifying a Twitter account with TF-IDF Weighting into DISC method.

关键词： Social networking (online) Blogs Classification algorithms Mathematical model Training data Psychology Naive Bayes methods

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Sent2Vec: A New Sentence Embedding Representation With Sentimental Semantic

Sent2Vec: A New Sentence Embedding Representation With Senti...

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IEEE international conference on Big data

作者： Mahdi Naser Moghadasi Yu Zhuang Texas Tech University Lubbock TX USA

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Text classification is considered as one of the primary task in many Natural Language processing (NLP) applications. In industrial applications of NLP, sentimental analysis is a task to understand how satisfied a user is after receiving a service or buying a product. The traditional approach is to convert a text into a format of numeric vector before feeding into machine learning algorithm. This representation of a word refers to word embedding. However the traditional embedding methods often model the syntactic context of words but ignore the sentiment information of text [1]. This can impact on the accuracy of a classification model to predict the correct sentimental score for a text. In this paper, we present Sent2Vec, an alternative embedding representation that includes the sentimental semantic of a sentence in its embedding vector. We utilized the unsupervised Smoothed Inverse Frequency (uSIF) sentence embedding method in the Sent2Vec neural network over a multi million samples dataset. The new sentence embedding presented, can be used as features in downstream (un)supervised tasks, which also leads to better or comparable results compared to sophisticated methods. Furthermore, with a simple logistic regression classifier, Sent2Vec reaches competitive performance to state-of-the-art results on several datasets when combined with GloVe(6B).

关键词： Vocabulary Semantics Neural networks Big data Natural language processing Numerical models Task analysis

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Prediction of Battlefield Target Trajectory Based on LSTM

Prediction of Battlefield Target Trajectory Based on LSTM

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international conference on Control and Automation (ICCA)

作者： Yan Zhu Jieling Liu Cao Guo Penglei Song Jiandong Zhang Jianmin Zhu Northwestern Polytechnical University Xi’an China Xi'an North Electro-optic science and technology defense Co. Ltd Xi’an China Shenyang Aircraft Design Institute shenyan China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

Battlefield situation prediction is an indispensable part of battlefield situational awareness, and refers to one of the important references for tactical decision-making by warfighters. The battlefield situation prediction is to use the information obtained in the two phases of situation awareness and situation recognition in battlefield situation awareness to predict the battlefield situation, and obtain the changing trend of the enemy's target state, position, and intention. Aiming at the temporal correlation of Long Short Team Memory Network (LSTM), this paper proposes to build a target trajectory prediction model based on LSTM network. First, the motion trajectory information obtained in the two stages of situation awareness and situation recognition is normalized and time-series processed to construct a time-series sample data set; then, the time-series data set is pushed to a target trajectory prediction model based on the LSTM network. Training; Finally, use the trained model to complete target trajectory prediction. The experimental results show that the prediction method using the LSTM model has the characteristics of high accuracy and easy implementation, and it has obvious advantages in processing sequence data.

关键词： Trajectory Predictive models Logic gates data models Time series analysis Atmospheric modeling Training

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TSV Extrusion Morphology Classification Using Deep Convolutional Neural Networks

TSV Extrusion Morphology Classification Using Deep Convoluti...

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international conference on Machine Learning and Applications (ICMLA)

作者： Brendan Reidy Golareh Jalilvand Tengfei Jiang Ramtin Zand University of South Carolina Columbia SC USA University of Central Florida Orlando FL USA Advanced Materials Processing and Analysis Center University of Central Florida Orlando FL USA

ISBN: (数字)9781728184708

ISBN: (纸本)9781728184715

In this paper, we utilize deep convolutional neural networks (CNNs) to classify the morphology of through-silicon via (TSV) extrusion in three dimensional (3D) integrated circuits (ICs). TSV extrusion is a crucial reliability concern which can deform and crack interconnect layers in 3D ICs and cause device failures. Herein, the white light interferometry (WLI) technique is used to obtain the surface profile of the extruded TSVs. We have developed a program that uses raw data obtained from WLI to create a TSV extrusion morphology dataset, including TSV images with 54 × 54 pixels that are labeled and categorized into three morphology classes. Four CNN architectures with different network complexities are implemented and trained for TSV extrusion morphology classification application. data augmentation and dropout approaches are utilized to realize a balance between overfitting and underfitting in the CNN models. Results obtained show that the CNN model with optimized complexity, dropout, and data augmentation can achieve a classification accuracy comparable to that of a human expert.

关键词： Three-dimensional displays Morphology Surface morphology data models Complexity theory Through-silicon vias Integrated circuit modeling

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Soil organic matter estimation by using Landsat-8 pansharpened image and machine learning

Soil organic matter estimation by using Landsat-8 pansharpen...

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international conference on Intelligent Computing in data Sciences (ICDS)

作者： Abdelkrim Bouasria Khalid Ibno Namr Abdelmejid Rahimi El Mostafa Ettachfini Faculty of Sciences Chouaib Doukkali University El Jadida Morocco

ISBN: (数字)9781728180847

ISBN: (纸本)9781728180854

Considering the significant position of soil organic matter (SOM) in soil quality and maintenance, and its role in the functioning of soil physicochemical and biological processes, it is essential to monitor frequently the SOM status and its dynamics. It is a time-consuming and expensive task if we depend exclusively on chemical analysis, particularly in a semi-arid irrigated zone and with intensive agricultural activities and a very fragmented landscape. It is the Sidi Bennour region, which is situated in Doukkala Irrigated Perimeter in Morocco. data from satellites could be a good alternative to conventional methods and fill this void with low costs. There has been a great deal of interest in satellite image prediction models, especially with free and abundant availability of satellite data. This work intends to predict SOM using Decision Trees (DT), k-Nearest Neighbors (k-NN), and Artificial Neural Networks (ANN). The soil samples (369 points) were collected at 0-30 cm of depth and the laboratory analysis was carried out. A multispectral Landsat-8 image was acquired and then calibrated. An image pansharpening processing was applied to produce a PAN image with 15m of resolution from 30m image resolution (MS). The obtained results indicate that the ANN model outperformed the other predictive models for both images (MS and PAN) with R 2 = 0.6553 and R 2 =0.6985, respectively. The statistical RMSE of predictive models was 0.2153 and 0.2014, and MAE was 0.1682 and 0.1573 for both images, MS and PAN respectively. For this predictive model, the image pansharpening could increase the prediction accuracy of R 2 by 4.32%and reduce the RMSE by 1.39%.

关键词： Soil Predictive models Artificial neural networks Remote sensing Neurons data models Earth

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Deep Learning Using Genetic Algorithm Optimization for Short Term Solar Irradiance Forecasting

Deep Learning Using Genetic Algorithm Optimization for Short...

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international conference on Intelligent Computing in data Sciences (ICDS)

作者： Wadie Bendali Ikram Saber Bensalem Bourachdi Mohammed Boussetta Youssef Mourad Inovative Technologies Laboratory Univérsité Sidi Mohamed Ben Abdellah Fez Morocco PERE Laboratory Univérsité Sidi Mohamed Ben Abdellah Fez Morocco 2EMI 2EMI Team L2MC laboratory ENSAMMoulay Ismail University Mekenes Morocco

ISBN: (数字)9781728180847

ISBN: (纸本)9781728180854

The increase in the use of renewable energy sources (RES) has been remarkable in recent years, especially photovoltaic energy, which is one of the most widely used renewable energy sources for electricity generation. In fact, the world has known the installation of a large number of autonomous or grid-connected photovoltaic systems. However the problem with the introduction of PV is the improvised nature of solar energy that can influence the stability of electricity grids and reliability of the grid, Accurate solar forecasting makes it easier to integrate solar generation into the grid by reducing the integration and operating costs associated with intermittent solar *** this context, the objective of this work is to improve the development of appropriate forecasting models for the prediction of photovoltaic energy production. For that reason, this article presents new hybrid methods to optimize deep learning forecasting by using genetic algorithm based Deep Neural Network. The model is employed to forecast time series of solar irradiation output. Comparisons are made between the performances of three types of neural networks: long short-term memory (LSTM), Gate recurrent unit (GRU), and Recurrent Neural Network (RNN). GA is exploited to find the most appropriate number of window size, and number of units (neurons) in each and all three hidden layers.

关键词： Time series analysis Genetic algorithms Predictive models Forecasting data models Iron Recurrent neural networks

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LSTM-based Alarm Prediction in the Mobile Communication Network

LSTM-based Alarm Prediction in the Mobile Communication Netw...

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international conference on Computer and Communications (ICCC)

作者： Xiaoxue Wang Dong Liang School of Information and Telecommunications Engineering Key Laboratory of Universal Wireless Communication Ministry of Education Beijing University of Post and Telecommunications Beijing China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

With the development of mobile communication network technology, the network scale is expanding, the network complexity is also improved. Once the network fails, it will cause a series of devices to generate numerous alarms. Therefore, it is particularly important to accurately predict the alarms, which can prevent the upcoming network alarms in advance or take other measures to reduce losses. Traditional network diagnosis mainly depends on network administrators, which has many subjective factors and low efficiency, and needs to train experienced technicians, which leads to long cycle and high cost. In addition, the manual method is difficult to use a large amount of historical data. In order to solve the problem of network alarm prediction more intelligently, data mining technology is an effective solution, which can mine valuable information from a large number of historical network alarm data. In this paper, combined with the actual network alarm log data of operators and considering the time correlation of alarms, a two-class network alarm prediction (LAB-NAP) algorithm based on LSTM and association algorithm is proposed, which adopts the method of combining LSTM-based two-class model with network alarm association mode. The LAB-NAP algorithm is compared with LSTM-based two-class network alarm prediction (LB-NAP) algorithm and LM-NAP, and the performance of three network alarm prediction modeling methods on the same data set is compared. It is verified that the LAB-NAP method proposed in this paper balances the prediction effect and training time in the large-scale dataset.

关键词： Visualization Correlation Predictive models Prediction algorithms Mobile communication data models Loss measurement

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Detection of Fake News Using Transformer Model

Detection of Fake News Using Transformer Model

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international conference on Computing, Mathematics and Engineering Technologies (iCoMET)

作者： Momina Qazi Muhammad U.S. Khan Mazhar Ali Department of Computer Science Comsats University Islamabad Abbottabad Pakistan

ISBN: (数字)9781728149707

ISBN: (纸本)9781728149714

Social media is one of the major platforms to get news and information. However, it also provides convenience for widespread of fake news. The reason at the back of fake news is to create hype in order to get the audience's attention and build negative impact on society. The fake news detection is necessary to purify the Internet environment. Various machine learning based detection algorithms are designed to detect fake news. We use attention-based transformer model on publically available dataset for detection of fake and real news. This research aims to test and compare state-of-the-art algorithm and our proposed technique in detection of fake and real news. Our result shows that 15% of the accuracy in fake news detection is improved by transformer model as compare to Hybrid CNN.

关键词： Training data models Decoding Feature extraction Testing Machine learning Recurrent neural networks

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