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IEEE Global Engineering Education Conference (IEEE EDUCON)

作者： Abou El-Seoud, M. Samir Taj-Eddin, Islam A. T. F. British Univ Egypt BUE Fac Informat & Comp Sci Cairo Egypt Assiut Univ Fac Comp & Informat Assiut Egypt

ISBN: (纸本)9781728184784

This paper will compare between two frameworks to identify vehicles. The first is a framework that identify malicious vehicles through license plate recognition and communication protocol. The second is a framework that recognize vehicles in foggy weather based on two-step recognition algorithm. The goal is to produce a combined framework of malicious vehicles recognition in foggy weather. The paper will compare between them and extracts conclusions for better combined framework. That framework will combine the positive features of both frameworks in order to recognize malicious vehicles in real time using a camera and algorithms without any human contact. That combination is needed since the outbreak of COVID-19 and the need of least human exposition and contact.

关键词： Vehicle recognition image defogging convolutional neural network model VANET AlexNet Image Processing Number Plate Recognition Feature Extraction

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Customer preference identification from hotel online reviews: A neural network based fine-grained sentiment analysis

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年第PartA期172卷

作者： Bian, Yiwen Ye, Rongsheng Zhang, Jing Yan, Xin Shanghai Univ SILC Business Sch Shanghai 201899 Peoples R China Dongbei Univ Finance & Econ Sch Data Sci & Artificial Intelligence Dalian 116024 Peoples R China Shanghai Univ Sch Management Shanghai 200444 Peoples R China Dongbei Univ Finance & Econ Sch Data Sci & Artificial Intelligence Dalian 116024 Liaoning Provin Peoples R China

As a kind of user-generated information, online reviews contain customers' preferences for different aspects of hotels, which not only influence customers' booking decisions but also help hotel managers to improve service quality of hotels timely. The key of deriving customers' preferences from hotels' online reviews is to identify fine-grained sentiment towards hotel attributes. In general, fine-grained sentiment analysis involves multiple fundamental tasks such as sentiment element extraction, aspect-opinion pair (i.e., AOP) identification and sentiment orientation analysis. However, existing fine-grained sentiment analysis methods cannot efficiently identify AOPs, especially when dealing with Chinese reviews. To this end, we construct an improved convolu-tional neural network (i.e., CNN) model, which can comprehensively utilize unstructured features and structured features, to improve the performance of AOP identification. We further propose a refined fine-grained sentiment analysis methodology to calculate accurate customer sentiment intensity value for each evaluated aspect rather than positive or negative sentiment, integrated with aspect term clustering algorithm, to identify customers' specific preferences for different hotel attributes. Finally, to illustrate the reasonability and advantages of the proposed methodology, we conduct an empirical study with hotels' online reviews crawled from ***. Empirical results indicate that our proposed method can indeed improve the performance of AOP identification, and can effectively identify customer preferences from hotels' online reviews. Furthermore, we find that cus-tomers show different preferences for different hotel attributes, and these vary across the types of customers.

关键词： Customer preferences Online reviews Fine-grained sentiment analysis convolutional neural network model

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DYNAMIC NONLINEAR EXPRESSION RECOGNITION TECHNOLOGY USING neural network AND ATTENTION MECHANISM

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FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2022年第2期30卷 2240097-2240097页

作者： Zhang, Guanhong Lv, Gang Binsawad, Muhammad Odbal, H. Hefei Univ Sch Artificial Intelligence & Big Data Hefei Peoples R China King Abdulaziz Univ Fac Comp & Informat Technol Dept Comp Informat Syst Jeddah Saudi Arabia Anhui Vocat & Tech Coll Hefei Peoples R China

The purpose is to apply deep learning to facial expression recognition and improve the efficiency of facial expression recognition in real scenes: first, convolutional neural network (CNN) theoretical model is proposed. On this basis, a new CNN model is proposed, which has six learning layers, including three convolutional layers and three fully connected layers. Then, based on the shortcomings of the model, weight decay and dropout optimization methods are proposed. Moreover, the attention module is designed based on the theory of attention mechanism. After each convolutional layer, a hybrid attention module (channel domain attention and spatial domain attention) is added. Finally, the frame error rate (FER) 2013 dataset and Cohn-Kanade (CK)+ dataset are used to test and compare the performance of the model. The results show that after 50Epoch, the accuracy rate of the original model fluctuates greatly in the process of convergence;however, after attention mechanism is added, the fluctuation of the accuracy of the model is obviously reduced. In the CK+ dataset, the accuracy of each model is maintained at about 95%, while the accuracy of FER2013 dataset is about 71%. After attention mechanism is added, the recognition rate of dynamic nonlinear expression is higher than that of basic model. The recognition rate of the original model and the optimized model with attention mechanism for the dynamic nonlinear expression with occlusion decreases in varying degrees, but the reduction of the recognition rate of the latter is alleviated.

关键词： Deep Learning convolutional neural network model Attention Mechanism Dynamic Nonlinear Expression model Recognition Rate

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Analysis of power system load forecasting based on neural networks

Analysis of power system load forecasting based on neural ne...

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作者： Xuhan Lang Shien-Ming Wu School of Intelligent Engineering South China University of Technology

In this research,our main goal was to improve power load forecasting accuracy by considering the impact of meteorological factors on the total power of the electrical system,examining existing load data,local weather,wind direction,and other parameters affecting total power *** divided the data from the past three years into a training dataset,comprising 75% of the data,and a testing dataset with the remaining 25%.We employed a basic machine learning technique(Support Vector Machine) and three distinct neural network approaches(Artificial neural network,convolutional neural network,and Long-Short Term Memory network) to develop analytical *** experimentation,the LSTM model achieved a loss value of 0.0034 and required 1426.78 seconds of training time across *** the time expense and model complexity,we chose the LSTM model to forecast power load at 15-minute intervals for the subsequent ten days,achieving a satisfactory prediction and fitting *** results suggest that the LSTM model is a promising method for optimizing performance and reliability in electrical power systems.

关键词： power system load prediction Support Vector Machine model Artificial neural network model convolutional neural network model Long-Short Term Memory model

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Deep learning model to detect various synchrophasor data anomalies

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第24期14卷 5739-5745页

作者： Deng, Xianda Bian, Desong Wang, Weikang Jiang, Zhihao Yao, Wenxuan Qiu, Wei Tong, Ning Shi, Di Liu, Yilu Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Global Energy Interconnect Res Inst North Amer San Jose CA 95134 USA Oak Ridge Natl Lab Oak Ridge TN 37830 USA

High-density synchrophasors provide valuable information for power grid situational awareness, operation and control. Unfortunately, due to factors including communication instability and hardware failure, their data quality can be greatly deteriorated by anomalies. Since the anomalies can impact the performance of the synchrophasor applications, it is of paramount significance to propose a model to detect anomalies in synchrophasor. In this study, a convolutional neural network model is established to detect and classify the anomalies in the synchrophasor measurements. Four types of anomalies observed in actual synchrophasors including erroneous patterns, random spikes, missing points and high-frequency interferences are considered in this study. The proposed model is extensively evaluated via field-collected measurements from the synchrophasor network in Jiangsu grid, China. The superior performance of the proposed model indicates the great potential of using deep learning for the detection of abnormal synchrophasor measurements.

关键词： learning (artificial intelligence) phase measurement power system measurement phasor measurement power grids power engineering computing pattern classification convolutional neural nets synchrophasor data anomaly detection high-density synchrophasors communication instability hardware failure data quality convolutional neural network model synchrophasor network abnormal synchrophasor measurements deep learning Jiangsu grid China synchrophasor data anomaly classification

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Risk forecasting in the crude oil market: A multiscale convolutional neural network approach

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2020年 541卷 123360-123360页

作者： Zou, Yingchao Yu, Lean Tso, Geoffrey K. F. He, Kaijian Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Beijing Univ Chem Technol Sch Econ & Management Beijing 100029 Peoples R China City Univ Hong Kong Dept Management Sci Kowloon Tong Tat Chee Ave Hong Kong Peoples R China Hunan Univ Sci & Technol Hunan Engn Res Ctr Ind Big Data & Intelligent Dec Xiangtan 411201 Peoples R China Hunan Univ Sci & Technol Sch Business Xiangtan 411201 Peoples R China

As the crude oil price movement is influenced by increasingly diverse range of risk factors in the crude oil markets, the crude oil price exhibits more complex nonlinear behavior and poses higher level of risk for investors than ever before. To model the crude oil risk changes at higher level of accuracy, we proposed a new multiscale approach to estimate Value at Risk. It takes advantage of Variational Mode Decomposition (VMD) model to extract and model the main risk factors in the multiscale domain, where the individual characteristics of these risk factors are modeled using ARMA-GARCH models. The convolutional neural network (CNN) based nonlinear ensemble model is employed to aggregate these risk forecasts as the ensemble members and produce the optimal ensemble forecasts. Empirical evaluation of the performance of the proposed model has been conducted using the extensive dataset, constructed with daily price observations in the major crude oil markets, Experiment results confirm that the proposed risk forecasting models produce an improved forecasting accuracy for the typical risk measures such as Value at Risk. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Crude oil risk forecasting Variational mode decomposition Deep learning model convolutional neural network model Value at risk

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CNN-SVM: a classification method for fruit fly image with the complex background

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IET CYBER-PHYSICAL SYSTEMS: THEORY & APPLICATIONS 2020年第2期5卷 181-185页

作者： Peng, Yingqiong Liao, Muxin Deng, Hong Ao, Ling Song, Yuxia Huang, Weiji Hua, Jing Jiangxi Agr Univ Key Lab Informat Technol Agr Coll & Univ Jiangxi Nanchang 330045 Jiangxi Peoples R China Jiangxi Agr Univ Software Inst Nanchang 330045 Jiangxi Peoples R China Jiangxi Agr Univ Coll Comp & Informat Engn Nanchang 330045 Jiangxi Peoples R China Jiangxi Agr Univ Acad Off Nanchang 330045 Jiangxi Peoples R China

On the basis of the problem that the image background is simple and the traditional shooting equipment of fruit flies is too high, this study improved the convolutional neural network model. First, the authors changed Softmax classifier to support vector machine (SVM). Moreover, then used convolution layers for extracting features of fruit fly images. Finally, they fed features into SVM for training. Experiments show that the model has been classifying the Bactrocera dorsalis Hendel, Bactrocera cucurbitae, Bactrocera tau and Bactrocera scutellata with accuracy over 92.04%, accordingly making the effective classification of the complex background fruit fly images possible. Moreover, it also provides a good practical application prospect.

关键词： feature extraction pest control learning (artificial intelligence) agricultural products support vector machines image classification convolutional neural nets bactrocera tau bactrocera scutellata complex background fruit CNN-SVM classification method fruit fly image image background traditional shooting equipment fruit flies convolutional neural network model Softmax classifier convolution layers bactrocera dorsalis Hendel bactrocera cucurbitae

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Pose-based deep gait recognition

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IET BIOMETRICS 2019年第2期8卷 134-143页

作者： Sokolova, Anna Konushin, Anton Natl Res Univ Higher Sch Econ 20 Myasnitskaya Str Moscow 101000 Russia Lomonosov Moscow State Univ GSP 1 Moscow 119991 Russia

Human gait or walking manner is a biometric feature that allows identification of a person when other biometric features such as the face or iris are not visible. In this study, the authors present a new pose-based convolutional neural network model for gait recognition. Unlike many methods that consider the full-height silhouette of a moving person, they consider the motion of points in the areas around human joints. To extract motion information, they estimate the optical flow between consecutive frames. They propose a deep convolutional model that computes pose-based gait descriptors. They compare different network architectures and aggregation methods and experimentally assess various body parts to determine which are the most important for gait recognition. In addition, they investigate the generalisation ability of the developed algorithms by transferring them between datasets. The results of these experiments show that their approach outperforms state-of-the-art methods.

关键词： image recognition feature extraction neural nets biometrics (access control) image sequences pose estimation gait analysis image motion analysis pose-based deep gait recognition human gait walking manner biometric feature face convolutional neural network model full-height silhouette moving person human joints motion information deep convolutional model computes pose-based gait descriptors different network architectures aggregation methods

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Deep-learning-based power distribution network switch action identification leveraging dynamic features of distributed energy resources

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第14期13卷 3139-3147页

作者： Duan, Nan Stewart, Emma M. Lawrence Livermore Natl Lab Computat Engn Div Energy Delivery & Utilizat Grp Livermore CA 94550 USA

This study proposes a data-driven approach for identifying switch actions in power distribution networks. Simulated micro-phasor measurement unit data is utilised to train a convolutional neural network (CNN) model. The trained CNN model can identify multi-phase multi-switch actions. Instead of working as a blackbox, the proposed approach extracts the features from the hidden layers of the trained CNN for engineering interpretation and error check. In addition, a random-forest-based feature ranking algorithm is proposed to identify the most important features. The proposed approach is validated on the IEEE 123-node feeder modelled in GridLAB-D. The CNN model is built and trained using TensorFlow. The proposed approach achieves 96.57% identification accuracy.

关键词： distribution networks distributed power generation phasor measurement power engineering computing random forests convolutional neural nets distributed energy resources data-driven approach switch actions power distribution networks simulated microphasor measurement unit data convolutional neural network model trained CNN model multiswitch actions random-forest-based feature ranking algorithm deep-learning-based power distribution network switch action identification dynamic features

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Twitter Sentiment Analysis About Public Opinion on 4G Smartfren network Services Using convolutional neural network 3

Twitter Sentiment Analysis About Public Opinion on 4G Smartf...

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3rd International Conference on Informatics and Computational Sciences (ICICoS)

作者： Aldiansyah, Muhammad Radifan Sasongko, Priyo Sidik Univ Diponegoro Dept Informat Fac Sci & Math Semarang Indonesia

ISBN: (纸本)9781728146102

Sentiment Analysis was a process for identifying whether a source of text contains certain opinions, emotions, and polarity. Twitter Sentiment Analysis was a process for identifying sentiment and polarity on tweet. Twitter Sentiment Analysis provided a way for did survey about public sentiment to product, or particular even through collections of tweet. Main problem in sentiment identifying was how to determine classification model that gave high accuracy to classifying sentiment of tweet. One of the method for classifying sentiment of tweet was Deep Learning. convolutional neural network (CNN) was special type of architecture from Deep Learning that its architecture had convolution layer. Convolution layer was important for extract relevant feature from text for classifying sentiment. The objective of this research was for found out the best CNN model for classifying sentiment of tweet. By using a dataset of tweets about public opinion on the Smartfren 4G network service, we searched the best CNN model using 6 combination parameters, that is the computational eficiency method, window size, and dimension of word embedding for parameters in Word2Vec Skip-gram model, then activation function in convolution layer, dropout rate, and pool size in pooling layer for parameters in CNN. The test is done using 10-fold cross validation for each search for the best parameter value and produced the best CNN model with an accuracy value of 88,21%.

关键词： sentiment analysis twitter convolutional neural network model smartfren 4g word embedding

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