Euler Recurrent Neural Network: Tracking the Input Contribution to Prediction on Sequential Data

2019 ◽  
pp. 738-748
Author(s):  
Fengcheng Yuan ◽  
Zheng Lin ◽  
Weiping Wang ◽  
Gang Shi
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Sequential Data

scholarly journals Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Jianlei Zhang ◽  
Yukun Zeng ◽  
Binil Starly
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Machine Tool ◽  
Recurrent Neural Network ◽  
Machine Tools ◽  
Prediction Performance ◽  
Sequential Data ◽  
Diagnosis And Prognosis ◽  
Proposed Model

AbstractData-driven approaches for machine tool wear diagnosis and prognosis are gaining attention in the past few years. The goal of our study is to advance the adaptability, flexibility, prediction performance, and prediction horizon for online monitoring and prediction. This paper proposes the use of a recent deep learning method, based on Gated Recurrent Neural Network architecture, including Long Short Term Memory (LSTM), which try to captures long-term dependencies than regular Recurrent Neural Network method for modeling sequential data, and also the mechanism to realize the online diagnosis and prognosis and remaining useful life (RUL) prediction with indirect measurement collected during the manufacturing process. Existing models are usually tool-specific and can hardly be generalized to other scenarios such as for different tools or operating environments. Different from current methods, the proposed model requires no prior knowledge about the system and thus can be generalized to different scenarios and machine tools. With inherent memory units, the proposed model can also capture long-term dependencies while learning from sequential data such as those collected by condition monitoring sensors, which means it can be accommodated to machine tools with varying life and increase the prediction performance. To prove the validity of the proposed approach, we conducted multiple experiments on a milling machine cutting tool and applied the model for online diagnosis and RUL prediction. Without loss of generality, we incorporate a system transition function and system observation function into the neural net and trained it with signal data from a minimally intrusive vibration sensor. The experiment results showed that our LSTM-based model achieved the best overall accuracy among other methods, with a minimal Mean Square Error (MSE) for tool wear prediction and RUL prediction respectively.

scholarly journals SENTIMENT ANALYSIS OF ELECTRIC CARS USING RECURRENT NEURAL NETWORK METHOD IN INDONESIAN TWEETS

Kursor ◽  
2020 ◽  
Vol 10 (4) ◽  
Author(s):  
Felisia Handayani ◽  
Metty Mustikasari
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Recurrent Neural Network ◽  
Confusion Matrix ◽  
Data Representation ◽  
Sequential Data ◽  
Communication Tool ◽  
Electric Cars

Sentiment analysis is computational research of the opinions of many people who are textually expressed against a particular topic. Twitter is the most popular communication tool among Internet users today to express their opinions. Deep Learning is a solution to allow computers to learn from experience and understand the world in terms of the hierarchy concept. Deep Learning objectives replace manual assignments with learning. The development of deep learning has a set of algorithms that focus on learning data representation. The recurrent Neural Network is one of the machine learning methods included in Deep learning because the data is processed through multi-players. RNN is also an algorithm that can recall the input with internal memory, therefore it is suitable for machine learning problems involving sequential data. The study aims to test models that have been created from tweets that are positive, negative, and neutral sentiment to determine the accuracy of the models. The models have been created using the Recurrent Neural Network when applied to tweet classifications to mark the individual classes of Indonesian-language tweet data sentiment. From the experiments conducted, results on the built system showed that the best test results in the tweet data with the RNN method using Confusion Matrix are with Precision 0.618, Recall 0.507 and Accuracy 0.722 on the data amounted to 3000 data and comparative data training and data testing of ratio data 80:20

scholarly journals Klasifikasi Teks Hadis Bukhari Terjemahan Indonesia Menggunakan Recurrent Convolutional Neural Network (CRNN)

2021 ◽  
Vol 8 (5) ◽  
pp. 907
Author(s):  
Muhammad Yuslan Abu Bakar ◽  
Adiwijaya Adiwijaya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Recurrent Neural Networks ◽  
Spatial Information ◽  
Contextual Information ◽  
Computing Time ◽  
Sequential Data ◽  
Text Data

<p class="Abstrak"><span lang="IN">Hadis merupakan sumber hukum dan pedoman kedua bagi umat Islam setelah Al-Qur’an dan banyak sekali hadis yang telah diriwayatkan oleh para ahli hadis selama ini. Penelitian ini membangun sebuah sistem yang dapat melakukan klasifikasi teks hadis Bukhari terjemahan berbahasa Indonesia. Topik ini diangkat untuk memenuhi kebutuhan umat Islam dalam mengetahui apa saja informasi mengenai anjuran dan larangan yang terdapat dalam suatu hadis. Klasifikasi teks memiliki tantangannya tersendiri terkait dengan jumlah fitur yang sangat banyak (dimensi sangat besar) sehingga waktu komputasi menjadi besar dan mengakibatkan sulitnya mendapatkan hasil yang optimal. Pada penelitian ini, digunakan salah satu metode hibrid dalam dunia <em>deep learning</em> dengan menggabungkan Convolutional Neural Network dan Recurrent Neural Network, yaitu Convolutional Recurrent Neural Network (CRNN). Convolutional Neural Network dipilih sebagai metode seleksi dan reduksi data dikarenakan dapat menangkap informasi spasial yang saling berhubungan dan berkorelasi. Sementara Recurrent Neural Network digunakan sebagai metode klasifikasi dengan mengusung kemampuan utamanya yaitu dapat menangkap informasi kontekstual yang sangat panjang khususnya pada data sekuens seperti data teks dengan mengandalkan ‘memori’ yang dimilikinya. Hasil penelitian menyajikan beberapa hasil klasifikasi menggunakan <em>deep learning</em>, dimana hasil akurasi terbaik diberikan oleh Convolutional Recurrent Neural Network (CRNN), yakni sebesar 80.79%.</span></p><p class="Abstrak"> </p><p class="Abstrak"><strong><em>Abstract</em></strong></p><p class="Judul2"><span lang="IN"> </span></p><p class="Abstract"><em><span lang="IN">Hadith is a source of law and guidance for Muslims after the Qur'an and many hadith have been narrated by hadith experts so far. This research builds a system that can classify Bukhari hadith in Indonesian translations. This topic was raised to meet the needs of Muslims in knowing what information about the suggestions and prohibitions that exist in a hadith. Text classification has its own challenges related to several features whose dimensions are very large so that it increases computing time and causes difficulties in getting optimal results. This research uses a hybrid method in deep learning by combining a Convolutional Neural Network and a Recurrent Neural Network, namely Convolutional Recurrent Neural Network (CRNN). Convolutional Neural Network was chosen as a method of selecting and reducing data that can be determined as spatial information that is interrelated and correlated. While Recurrent Neural Networks are used as a classification method by carrying out capabilities that can be used as very long contextual information specifically on sequential data such as text data by relying on the ‘memory’ it has. This research presents several classification results using deep learning, where the best accuracy results are given by the Convolutional Recurrent Neural Network (CRNN), which is equal to 80.79%.</span></em></p><p class="Abstrak"><strong><em><br /></em></strong></p>

scholarly journals Tackling Graphical Natural Language Processing’s Problems with Recurrent Neural Networks

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Ali Sami Sosa ◽  
Saja Majeed Mohammed ◽  
Haider Hadi Abbas ◽  
Israa Al Barazanchi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Recurrent Neural Network ◽  
Recurrent Neural Networks ◽  
Question Answering ◽  
Structural Information ◽  
Sequential Data

Recent years have witnessed the success of artificial intelligence–based automated systems that use deep learning, especially recurrent neural network-based models, on many natural language processing problems, including machine translation and question answering. Besides, recurrent neural networks and their variations have been extensively studied with respect to several graph problems and have shown preliminary success. Despite these successes, recurrent neural network -based models continue to suffer from several major drawbacks. First, they can only consume sequential data; thus, linearization is required to serialize input graphs, resulting in the loss of important structural information. In particular, graph nodes that are originally located closely to each other can be very far away after linearization, and this introduces great challenges for recurrent neural networks to model their relation. Second, the serialization results are usually very long, so it takes a long time for recurrent neural networks to encode them. In the methodology of this study, we made the resulting graphs more densely connected so that more useful facts could be inferred, and the problem of graphical natural language processing could be easily decoded with graph recurrent neural network. As a result, the performances with single-typed edges were significantly better than the Local baseline, whereas the combination of all types of edges achieved a much better accuracy than just that of the Local using recurrent neural network. In this paper, we propose a novel graph neural network, named graph recurrent network.

scholarly journals What to Do Next: Modeling User Behaviors by Time-LSTM

2017 ◽  
Author(s):  
Yu Zhu ◽  
Hao Li ◽  
Yikang Liao ◽  
Beidou Wang ◽  
Ziyu Guan ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Language Modeling ◽  
Sequential Data ◽  
Sequential Order ◽  
Short Term ◽  
Time Intervals ◽  
User Behaviors ◽  
Real World Datasets

Recently, Recurrent Neural Network (RNN) solutions for recommender systems (RS) are becoming increasingly popular. The insight is that, there exist some intrinsic patterns in the sequence of users' actions, and RNN has been proved to perform excellently when modeling sequential data. In traditional tasks such as language modeling, RNN solutions usually only consider the sequential order of objects without the notion of interval. However, in RS, time intervals between users' actions are of significant importance in capturing the relations of users' actions and the traditional RNN architectures are not good at modeling them. In this paper, we propose a new LSTM variant, i.e. Time-LSTM, to model users' sequential actions. Time-LSTM equips LSTM with time gates to model time intervals. These time gates are specifically designed, so that compared to the traditional RNN solutions, Time-LSTM better captures both of users' short-term and long-term interests, so as to improve the recommendation performance. Experimental results on two real-world datasets show the superiority of the recommendation method using Time-LSTM over the traditional methods.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

Sign in / Sign up

Export Citation Format

Share Document