HOAX DETECTION IN INDONESIA LANGUAGE USING LONG SHORT-TERM MEMORY MODEL

Nowadays, the internet and social media grow fast. This condition has positive and negative effects on society. They become media to communicate and share information without limitation. However, many people use that easiness to broadcast news or information which do not accurate with the facts and gather people's opinions to get benefits or we called a hoax. Therefore, we need to develop a system that can detect hoax. This research uses the neural network method with Long Short-Term Memory (LSTM) model. The process of the LSTM model to identify hoax has several steps, including dataset collection, pre-processing data, word embedding using pre-trained Word2Vec, built the LSTM model. Detection model performance measurement using precision, recall, and f1-measure matrix. This research results the highest average score of precision is 0.819, recall is 0.809, and f1-measure is 0.807. These results obtained from the combination of the following parameters, i.e., Skip-gram Word2Vec Model Architecture, Hierarchical Softmax, 100 as vector dimension, max pooling, 0.5 as dropout value, and 0.001 of learning rate.

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Application of Skeleton Data and Long Short-Term Memory in Action Recognition of Children with Autism Spectrum Disorder

Sensors ◽

10.3390/s21020411 ◽

2021 ◽

Vol 21 (2) ◽

pp. 411

Author(s):

Yunkai Zhang ◽

Yinghong Tian ◽

Pingyi Wu ◽

Dongfan Chen

Keyword(s):

Autism Spectrum Disorder ◽

Action Recognition ◽

Short Term Memory ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Short Term ◽

Term Memory ◽

Clinical Observations ◽

The Neural Network ◽

Long Short Term Memory

The recognition of stereotyped action is one of the core diagnostic criteria of Autism Spectrum Disorder (ASD). However, it mainly relies on parent interviews and clinical observations, which lead to a long diagnosis cycle and prevents the ASD children from timely treatment. To speed up the recognition process of stereotyped actions, a method based on skeleton data and Long Short-Term Memory (LSTM) is proposed in this paper. In the first stage of our method, the OpenPose algorithm is used to obtain the initial skeleton data from the video of ASD children. Furthermore, four denoising methods are proposed to eliminate the noise of the initial skeleton data. In the second stage, we track multiple ASD children in the same scene by matching distance between current skeletons and previous skeletons. In the last stage, the neural network based on LSTM is proposed to classify the ASD children’s actions. The performed experiments show that our proposed method is effective for ASD children’s action recognition. Compared to the previous traditional schemes, our scheme has higher accuracy and is almost non-invasive for ASD children.

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Long Short-Term Memory Neural Network-Based Attack Detection Model for In-Vehicle Network Security

IEEE Sensors Letters ◽

10.1109/lsens.2020.2993522 ◽

2020 ◽

Vol 4 (6) ◽

pp. 1-4

Author(s):

Zadid Khan ◽

Mashrur Chowdhury ◽

Mhafuzul Islam ◽

Chin-Ya Huang ◽

Mizanur Rahman

Keyword(s):

Neural Network ◽

Network Security ◽

Short Term Memory ◽

Attack Detection ◽

Short Term ◽

Term Memory ◽

Detection Model ◽

Long Short Term Memory ◽

Vehicle Network

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HUMAN ROBOT INTERACTIVE INTENTION PREDICTION USING DEEP LEARNING TECHNIQUES

Journal of Military Science and Technology ◽

10.54939/1859-1043.j.mst.72a.2021.1-12 ◽

2021 ◽

pp. 1-12

Author(s):

Thang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Neural Network ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

The Neural Network ◽

Learning Techniques ◽

Long Short Term Memory ◽

Deep Learning Neural Network

In this research, we propose a method of human robot interactive intention prediction. The proposed algorithm makes use of a OpenPose library and a Long-short term memory deep learning neural network. The neural network observes the human posture in a time series, then predicts the human interactive intention. We train the deep neural network using dataset generated by us. The experimental results show that, our proposed method is able to predict the human robot interactive intention, providing 92% the accuracy on the testing set.

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Research on Safety Prediction of Sector Traffic Operation Based on a Long Short Term Memory Model

Applied Sciences ◽

10.3390/app11115141 ◽

2021 ◽

Vol 11 (11) ◽

pp. 5141

Author(s):

Wenying Lyu ◽

Honghai Zhang ◽

Junqiang Wan ◽

Lei Yang

Keyword(s):

Time Series ◽

Traffic Safety ◽

Prediction Models ◽

Short Term Memory ◽

Model Performance ◽

Risk Identification ◽

Civil Aviation ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Traffic safety has been thought of as a basic feature of transportation, recent developments in civil aviation have emphasized the need for risk identification and safety prediction. This study aims to increase en-route flight safety through the development of prediction models for flight conflicts. Firstly, flight conflicts time series and traffic parameters are extracted from historical ADS-B data. In the second step, a Long Short-Term Memory (LSTM) model is trained to make a one-step-ahead prediction on the flight conflict time series. The results show that the LSTM model has the greatest prediction effect (MAE 0.3901) with comparison to other models. Based on that, we add traffic parameters (volume, density, velocity) into the LSTM model as new input variables and issue a comprehensive analysis of the relative predictive power of traffic parameters. The accuracy of prediction model is validated with a mean error of less than 3%. Based on the improvements of model performance brought by traffic parameters, LSTM models with a single traffic parameter are proposed for further discussion. The results illustrate that volume is the most important factor in promoting prediction accuracy and density has an advantage of improvement in the aspect of model stability.

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Deep Learning for Subtyping and Prediction of Diseases: Long-Short Term Memory

10.5772/intechopen.96180 ◽

2021 ◽

Author(s):

Hayrettin Okut

Keyword(s):

Neural Network ◽

Short Term Memory ◽

Short Term ◽

Time Step ◽

Term Memory ◽

The Neural Network ◽

Long Short Term Memory ◽

Sequential Information ◽

Hidden Layer

The long short-term memory neural network (LSTM) is a type of recurrent neural network (RNN). During the training of RNN architecture, sequential information is used and travels through the neural network from input vector to the output neurons, while the error is calculated and propagated back through the network to update the network parameters. Information in these networks incorporates loops into the hidden layer. Loops allow information to flow multi-directionally so that the hidden state signifies past information held at a given time step. Consequently, the output is dependent on the previous predictions which are already known. However, RNNs have limited capacity to bridge more than a certain number of steps. Mainly this is due to the vanishing of gradients which causes the predictions to capture the short-term dependencies as information from earlier steps decays. As more layers in RNN containing activation functions are added, the gradient of the loss function approaches zero. The LSTM neural networks (LSTM-ANNs) enable learning long-term dependencies. LSTM introduces a memory unit and gate mechanism to enable capture of the long dependencies in a sequence. Therefore, LSTM networks can selectively remember or forget information and are capable of learn thousands timesteps by structures called cell states and three gates.

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Deep stair walking detection using wearable inertial sensor via long short-term memory network

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v9i1.1685 ◽

2020 ◽

Vol 9 (1) ◽

pp. 238-246

Author(s):

Gan Wei Nie ◽

Nurul Fathiah Ghazali ◽

Norazman Shahar ◽

Muhammad Amir As'ari

Keyword(s):

Short Term Memory ◽

Inertial Sensors ◽

Inertial Sensor ◽

Window Size ◽

Sensor Data ◽

Short Term ◽

Term Memory ◽

Detection Model ◽

Long Short Term Memory ◽

Stair Walking

This paper proposes a stair walking detection via Long-short Term Memory (LSTM) network to prevent stair fall event happen by alerting caregiver for assistance as soon as possible. The tri-axial accelerometer and gyroscope data of five activities of daily living (ADLs) including stair walking is collected from 20 subjects with wearable inertial sensors on the left heel, right heel, chest, left wrist and right wrist. Several parameters which are window size, sensor deployment, number of hidden cell unit and LSTM architecture were varied in finding an optimized LSTM model for stair walking detection. As the result, the best model in detecting stair walking event that achieve 95.6% testing accuracy is double layered LSTM with 250 hidden cell units that is fed with data from all sensor locations with window size of 2 seconds. The result also shows that with similar detection model but fed with single sensor data, the model can achieve very good performance which is above 83.2%. It should be possible, therefore, to integrate the proposed detection model for fall prevention especially among patients or elderly in helping to alert the caregiver when stair walking event occur.

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Network Intrusion Detection Based on an Improved Long-Short-Term Memory Model in Combination with Multiple Spatiotemporal Structures

Wireless Communications and Mobile Computing ◽

10.1155/2021/6623554 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Xiaolong Huang

Keyword(s):

Intrusion Detection ◽

Short Term Memory ◽

Network Intrusion Detection ◽

Short Term ◽

Detection Model ◽

Existing Problems ◽

Network Intrusion ◽

The Neural Network ◽

Long Short Term Memory ◽

Better Than

Aimed at the existing problems in network intrusion detection, this paper proposes an improved LSTM combined with spatiotemporal structure for intrusion detection. The unsupervised spatiotemporal encoder is used to intelligently extract the spatial characteristics of network traffic data samples. It can not only retain the overall/nonlocal characteristics of the data samples but also extract the most essential deep features of the data samples. Finally, the extracted features are used as input of the LSTM model to realize classification and identification for intrusion samples. Experimental verification shows that the accuracy and false alarm rate of the intrusion detection model based on the neural network are significantly better than those of other traditional models.

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COMPARISON OF OPTIMIZATION ALGORITHMS OF CONNECTIONIST TEMPORAL CLASSIFIER FOR SPEECH RECOGNITION SYSTEM

Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska ◽

10.35784/iapgos.234 ◽

2019 ◽

Vol 9 (3) ◽

pp. 54-57

Author(s):

Yedilkhan Amirgaliyev ◽

Kuanyshbay Kuanyshbay ◽

Aisultan Shoiynbek

Keyword(s):

Neural Network ◽

Speech Recognition ◽

Short Term Memory ◽

Optimization Algorithms ◽

Recognition System ◽

Model Data ◽

Short Term ◽

Term Memory ◽

The Neural Network ◽

Long Short Term Memory

This paper evaluates and compares the performances of three well-known optimization algorithms (Adagrad, Adam, Momentum) for faster training the neural network of CTC algorithm for speech recognition. For CTC algorithms recurrent neural network has been used, specifically Long-Short-Term memory. LSTM is effective and often used model. Data has been downloaded from VCTK corpus of Edinburgh University. The results of optimization algorithms have been evaluated by the Label error rate and CTC loss.

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Application of a long short-term memory for deconvoluting conductance contributions at charged ferroelectric domain walls

npj Computational Materials ◽

10.1038/s41524-020-00426-z ◽

2020 ◽

Vol 6 (1) ◽

Author(s):

Theodor S. Holstad ◽

Trygve M. Ræder ◽

Donald M. Evans ◽

Didirk R. Småbråten ◽

Stephan Krohns ◽

...

Keyword(s):

Neural Network ◽

Domain Walls ◽

Short Term Memory ◽

Ferroelectric Domain ◽

Short Term ◽

Accurate Analysis ◽

Term Memory ◽

The Neural Network ◽

Long Short Term Memory ◽

Using Data

Abstract Ferroelectric domain walls are promising quasi-2D structures that can be leveraged for miniaturization of electronics components and new mechanisms to control electronic signals at the nanoscale. Despite the significant progress in experiment and theory, however, most investigations on ferroelectric domain walls are still on a fundamental level, and reliable characterization of emergent transport phenomena remains a challenging task. Here, we apply a neural-network-based approach to regularize local I(V)-spectroscopy measurements and improve the information extraction, using data recorded at charged domain walls in hexagonal (Er0.99,Zr0.01)MnO3 as an instructive example. Using a sparse long short-term memory autoencoder, we disentangle competing conductivity signals both spatially and as a function of voltage, facilitating a less biased, unconstrained and more accurate analysis compared to a standard evaluation of conductance maps. The neural-network-based analysis allows us to isolate extrinsic signals that relate to the tip-sample contact and separating them from the intrinsic transport behavior associated with the ferroelectric domain walls in (Er0.99,Zr0.01)MnO3. Our work expands machine-learning-assisted scanning probe microscopy studies into the realm of local conductance measurements, improving the extraction of physical conduction mechanisms and separation of interfering current signals.

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Attention-Based BiLSTM for Negation Handling in Sentimen Analysis

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ◽

10.22146/ijccs.60733 ◽

2020 ◽

Vol 14 (4) ◽

pp. 397

Author(s):

Riszki Wijayatun Pratiwi ◽

Yunita Sari ◽

Yohanes Suyanto

Keyword(s):

Feature Extraction ◽

Sentiment Analysis ◽

Extraction Method ◽

Short Term Memory ◽

Model Performance ◽

Short Term ◽

Term Memory ◽

Feature Extraction Method ◽

Long Short Term Memory

Research on sentiment analysis in recent years has increased. However, in sentiment analysis research there are still few ideas about the handling of negation, one of which is in the Indonesian sentence. This results in sentences that contain elements of the word negation have not found the exact polarity.The purpose of this research is to analyze the effect of the negation word in Indonesian. Based on positive, neutral and negative classes, using attention-based Long Short Term Memory and word2vec feature extraction method with continuous bag-of-word (CBOW) architecture. The dataset used is data from Twitter. Model performance is seen in the accuracy value.The use of word2vec with CBOW architecture and the addition of layer attention to the Long Short Term Memory (LSTM) and Bidirectional Long Short Term Memory (BiLSTM) methods obtained an accuracy of 78.16% and for BiLSTM resulted in an accuracy of 79.68%. whereas in the FSW algorithm is 73.50% and FWL 73.79%. It can be concluded that attention based BiLSTM has the highest accuracy, but the addition of layer attention in the Long Short Term Memory method is not too significant for negation handling. because the addition of the attention layer cannot determine the words that you want to pay attention to.

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