Top PDF Anomaly Detection Using Predictive Convolutional Long Short-Term Memory Units

Anomaly Detection Using Predictive Convolutional Long Short-Term Memory Units

Anomaly Detection Using Predictive Convolutional Long Short-Term Memory Units

The cost function of eq. (11) was optimized with RMSProp [31]. It uses a running average over the root mean squared of recent gradients to normalize the gradients and divide the learning rate. A learning rate of 10 −3 and decay rate of 0.9 are used. Adagrad [33] and Adam [34] were both considered and tested as well, but RMSProp was empirically chosen for its smaller resulting loss values. The learning rate is set to .0001. We use a mini- batch of five video sequences and train the models for up to 25,000 iterations. Early stopping is performed based on the validation loss if necessary. The weights are initialized using the Xavier algorithm [35]. The algorithm automatically scales the initialization based on the number of input and output neurons to prevent the weights from starting out too small or large. This is significant, as weights that start too small cause a reduction of variance in the output that propagate a decrease in both the weights and values until they become too small to matter. Similarly, too large of a starting weight will cause the learned weights and outputs to explode in magnitude [31]. It is also an important factor in speeding up and ensuring the convergence of the network. The convolutional filters within the input- to-hidden and hidden-to-hidden states in the Conv-LSTM units all use the same filter size in this thesis. It should be noted that while units share the same parameters at each timestep, the filter sizes of different states within the unit may differ in size if necessary.
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An Implementation of Anomaly Detection in IOT DTA Using A Deep (OC NN) With the Long Short Term Memory Network (LSTM)

An Implementation of Anomaly Detection in IOT DTA Using A Deep (OC NN) With the Long Short Term Memory Network (LSTM)

Figure 5.5 shows OC-NN Training Loss and accuracy VI. CONCLUSION AND FUTURE WORK We show that using known deep neural network algorithms for classifying time series data like IoT sensors generated ECG signals allows for accurate classification of normal, benign, and critical arrhythmias as well as distinguishing artifacts and noise from multi-channel ECG recordings. A layered bidirectional LSTM, as well as a combined LSTM-OCNN architecture, is able to achieve relatively high accuracy and precision without the use of feature engineering or extraction of previously known waveform patterns. We also show that ECG classification greatly benefits from the use of multi-channel data, with nearly all classes and models showing markedly decreased accuracy when only one channel is used.
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Weather Forecasting Using Merged Long Short-term Memory Model

Weather Forecasting Using Merged Long Short-term Memory Model

Isabelle Roesch propose method to a recurrent convolutional neural network that was trained and tested on 25 years of climate data to forecast meteorological attributes, such as temperature, air pressure and wind speed. The presented visualization system helped the user to quickly assess, adjust and improve the network design [17]. Aditya Grover propose a hybrid approach model that combines discriminatively trained predictive models with a deep neural network that models the joint statistics of a set of weather-related variables. The result show how the base model can be enhanced with spatial interpolation that uses learned long-range spatial dependencies [18].
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Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection

Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection

The first layer is the Indonesian tweet input layer which has been changed in vector form. In the LSTM layer, several LSTM units will be tested. The LSTM unit is a memory cell that consists of four main components: input gate, self-recurrent connection, forget gate and output gate. In this study, two layers are used, namely forward and backwards, so that the output layer will get the information of the past and the future simultaneously.

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Detection of Delivery Servers in Online Video Service using Long Short Term Memory Network

Detection of Delivery Servers in Online Video Service using Long Short Term Memory Network

In a conventional feedforward neural network we assume that all inputs (and outputs) are independent. Recurrent neural network is called recurrent because connections between units form a directed cycle, with the output being dependent on the previous computations. Both of these networks names after the way they channel information. Feedforward neural network feeds information straight through while the other cycles it through a loop. So RNNs are like they have a memory capturing information about what has been calculated so far. RNNs are designed to recognize patterns in sequences of data, such as text, genomes, spoken words etc. The typical RNN has the form of Figure 10. The process of carrying memory forward described mathematically as:
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A Long Short Term Memory Framework for Predicting Humor in Dialogues

A Long Short Term Memory Framework for Predicting Humor in Dialogues

Our previous attempts on the same corpus (Bert- ero and Fung, 2016b; Bertero and Fung, 2016a) showed that using a bag-of-ngram representation over a sliding window or a simple RNN to cap- ture the contextual information of the setup was not ideal. For this reason we propose a method based on a Long Short-Term Memory network (Hochre- iter and Schmidhuber, 1997), where we encode each sentence through a Convolutional Neural Network (Collobert et al., 2011). LSTM is successfully used in context-dependent sequential classification tasks such as speech recognition (Graves et al., 2013), de- pendency parsing (Dyer et al., 2015) and conversa- tion modelling (Shang et al., 2015). This is also to our knowledge the first-ever attempt that a LSTM is applied to humor response prediction or general hu- mor detection tasks.
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Improving monsoon precipitation prediction using combined convolutional and long short term memory neural network

Improving monsoon precipitation prediction using combined convolutional and long short term memory neural network

Different from the full-connected networks, CNNs involve two special matrix operators: a convolutional layer, and a pooling layer. Units in convolutional layers are only connected to specific local patches through a set of leant filters. In this way, it greatly reduces the number of parameters in the networks and allows the networks to be deeper and more efficient. Usually a non-linear function (such as rectified linear unit or hyberbolic tangent, etc.) is applied after the convolution operators [46]. Then the pooling layers are used to merge semantically similar local features into one [47]. This is due to the fact that the relative positions of features that make up the motifs may vary somewhat, thus coarsing the positions of each feature can help to detect reliably motifs. Typical pooling layers partition a feature map into a set of non-overlapping rectangles and output the maximum or the average value for each sub-region (Deep Learning Tutorials).
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Multivariate Time Serious Traffic Prediction Using Long Short Term Memory Network

Multivariate Time Serious Traffic Prediction Using Long Short Term Memory Network

such as weather and rainfall should take into consideration. Hybrid models were developed with LSTM network to integrate both spatial and temporal correlation to predict the traffic congestion with high accuracy. X Luo et al [16] developed spatio temporal traffic prediction model with K-nearest neighbour (KNN) and LSTM network. KNN was used to choose the neighbouring stations to capture spatial features and LSTM network was used to capture temporal correlations of traffic data. R fu et al. [7] introduced Long short term memory network (LSTM) with Gated recurrent units (GRU) for short term traffic flow prediction. LSTM and GRU were proposed with forget units to forget some information which provides optimal time lags. H Zhang et al. [4] developed a multivariate short term forecasting method based on wavelet analysis and seasonal time serious (WSARIMAX). Occupancy was taken as an exogenous variable with flow to increase the prediction accuracy. Result shows that multivariate analysis based prediction gives better result than univariate analysis. D Yang et al. [2] introduced convolutional neural network for multi feature fusion based traffic flow prediction. Weather and holidays were taken as external factor with flow data to predict the vehicle congestion. High level spatio temporal features have learned and merged by convolutional neural network (CNN). It increases the efficiency and accuracy of prediction model. Based on the relative study, LSTM has proven to be superior for time serious predictions. In this paper we proposed LSTM model for multivariate time serious based traffic flow prediction.
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Anomaly detection using Long Short Term Memory Networks and its applications in Supply Chain Management

Anomaly detection using Long Short Term Memory Networks and its applications in Supply Chain Management

In this Section, we illustrate the performance of proposed method on a situation of detecting anomalous consumer demands in SCM. Understanding consumer demands is a key factor that ensure the success of each enterprise in nowadays globally competitive market (Thomassey (2010)). Detecting anomalies in customer demands enables suppliers to better manage the supply chain. For example, in some real situations, a manager can recognize the dif- ference between current consumer demands and previous demands (which were assumed to be normal), but he or she is not sure whether this difference is abnormal or not. If the answer is ”yes”, he or she should pay attention on discovering the assignable causes of abnormalities to make reasonable adjustments. Moreover, anomaly detection in customer demands also makes the consumer demand fore- casting more efficient as it eliminates errors and anomalies in the data used for forecast models, avoding ”garbage in, garbage out”.
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PICO Element Detection in Medical Text via Long Short Term Memory Neural Networks

PICO Element Detection in Medical Text via Long Short Term Memory Neural Networks

Types: Randomized Controlled Trial (Search con- ducted on 2017/08/28). Among them, abstracts with structured section headings were selected for automatic annotation of sentence category. Al- though P, I and O headings were our detection targets, we also annotated the other types of sen- tences into one of the AIM (A), METHOD (M), RESULTS (R) and CONCLUSION (C) labels to facilitate the use of our CRF label sequence op- timization method. Note that, although we have 7 labels in total, we only care about the detection ac- curacy of the P, I and O labels and thus mainly dis- cuss their performance in the following sections.
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HIERARCHICAL TEXT CLASSIFICATION USING DICTIONARY BASED APPROACH AND LONG-SHORT TERM MEMORY

HIERARCHICAL TEXT CLASSIFICATION USING DICTIONARY BASED APPROACH AND LONG-SHORT TERM MEMORY

The text classification process has been well studied, but there are still many improvements in the classification and feature preparation, which can optimize the performance of classification for specific applications. In the paper we implemented dictionary based approach and long-short term memory approach. In the first approach, dictionaries will be padded based on field's specific input and use automation technology to expand. The second approach, long short term memory used word2vec technique. This will help us in getting a comprehensive pipeline of end-to-end implementations. This is useful for many applications, such as sorting emails which are spam or ham, classifying news as political or sports-related news, etc.
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Arrhythmia Classification Using Long Short-Term Memory with Adaptive Learning Rate

Arrhythmia Classification Using Long Short-Term Memory with Adaptive Learning Rate

In this research, Long Short-Term Memory (LSTM) method was used to classify the waveform of 3 classes which were normal, Premature Ventricular Contraction, and Premature Atrial Contraction. AdaDelta was used as adaptive learning rate method to boost the performance of the network. The number of layer was added from one to three layers. The best performance was obtained by using three-hidden-layered LSTM with AdaDelta which were 0.98 for train data and 0.97 for test data with 1.42E-04 error rate. The best network failed to classify some of the PAC beats which had high similarity with the normal beats. The performance of LSTM with AdaDelta was far better than the performance of LSTM without AdaDelta. But the result showed that the accuracy trend of the test data was still unstable. Although AdaDelta could still handle it, the network could be leaded to be overfitting.
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Anomaly Detection using a Convolutional Winner-Take-All Autoencoder

Anomaly Detection using a Convolutional Winner-Take-All Autoencoder

We present a framework that use a deep spatial sparsity Conv-WTA autoencoder to learn a motion feature representation for anomaly detection. The temporal fusion on feature space gives a robust feature representation. Moreover, the combination of this motion feature rep- resentation with a local application of one-class SVM gives competitive performance on two challenging datasets in comparison to existing state-of-the-art methods. There is potential to improve results further by adding an appearance channel alongside the optical flow chan- nel, and also capturing longer-term motion patterns using a recurrent convolutional network following on from the Conv-WTA encoding, and replacing our temporal smoothing.
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Sentiment Analysis of Novel Review Using Long Short-Term Memory Method

Sentiment Analysis of Novel Review Using Long Short-Term Memory Method

Berkembang pesatnya internet dan media sosial serta besarnya jumlah data teks, telah menjadi subjek penelitian yang penting dalam memperoleh informasi dari data teks tersebut. Dalam beberapa tahun terakhir, telah terjadi peningkatan penelitian terhadap analisis sentimen pada teks review untuk mengetahui polaritas opini pada media sosial. Namun, masih sedikit penelitian yang menerapkan metode deep learning yaitu Long Short-Term Memory untuk analisis sentimen pada teks berbahasa Indonesia.

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Native Language Recognition using Bidirectional Long Short Term Memory Network

Native Language Recognition using Bidirectional Long Short Term Memory Network

contribution from both in reverse and forward layers. Packs are frequently exceeded on to improve execution and openness over that of a lone laptop, even as commonly being altogether extra canny than unmarried desktops of proportional speed or availability. One of the difficulties inside the usage of a pc organization is the rate of administrating it which could sometimes be as excessive because the expense of administrating N unfastened machines, if the bunch has N language identification. Now and again this gives a favorable role to imparted memory fashions to convey down enterprise charges. This has likewise made virtual machines outstanding, due to the simplicity of corporation. . The starting value of accessible frames to be transfer data and basic of the specification requirements of the structure, and it should be organized once the structure is started. In command to compress the largest nodes of sending, the access method has to be allocated in a method that all the data items in the access are placed by the language identifies in the network structure reliving no presence for commands free time intervals. In pros, at the starting of the structure functions, the nodes of the access scheme should be given equally to the accessible and nodes to get smaller the nodes. After receiving the command nodes response, the sender notes the total interval pleased by each node in a processor slot that determines the access scheme. This processor slot controls the structure and defines the functions numbers of the access scheme.
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Currency movement forecasting using time series analysis and

long short-term memory

Currency movement forecasting using time series analysis and long short-term memory

Foreign exchange (FOREX) is one type of trading activity that trades a country’s currency to others for 24 hours continuously (Nagpure, 2019). FOREX market is the world’s largest financial market. The daily trading volume has been increased six trillion dollars which it’s 45% of the transaction volume comes from terminal retail customers (Ni et al., 2019). There are several techniques in FOREX trading, one of them is forecasting FOREX. Forecasting on FOREX can be done by the method of Statistical Learning (time series analysis), Technical analysis (candle stick), and deep learning (Recurrent Neural Network, LSTM). There are some research about forecasting FOREX with any method such using deep learning (Czekalski et al., 2015; Korczak & Hemes, 2017; Nagpure, 2019; Sezer et al., 2020), ARIMA (Reddy SK, 2015), fuzzy neuron (Reddy SK, 2015) and neuro-fuzzy system (Yong et al., 2018). Forecasting provides factors to be able to predict further whether there will be a bullish or bearish. Bullish symbolizes the optimism of the actors in market conditions whose prices are rising. Bearish symbolizes the pessimism of the actors in market conditions whose prices are falling (Ong, 2019). Forecasting is a technique that can help in minimizing losses on FOREX transactions. Forecasting techniques that have the lowest error rate are the most suitable techniques to use. Another consideration is for ease and A R T I C L E I N F O A B S T R A C T
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Long short term memory networks for body movement estimation

Long short term memory networks for body movement estimation

T HE process of capturing and reconstructing full-body movement with high-quality motion capture (mocap) technologies often requires many sensors or markers to be strapped to each body segment. As a consequence, these mocap technologies are often far too expensive for private usage. Also, they are inconvenient in use as it takes much time to suit up. These problems could possibly be relieved if the number of sensors can be reduced to a small number as long as the ability to reconstruct full-body movement remains without suffering from major reconstruction errors. Although the usage of only a small number of sensors provides insufficient information to capture full-body poses, missing information could be estimated based on pre-recorded full- body mocap data. This is based on the belief that natural human motion is highly coordinated and the existence of de- pendencies between (the positions of) limbs during movement [1] [2] [3]. Modeling these dependencies is difficult however due to the high complexity and dimensionality of human movement. Previous works that have solved this problem include that of Chai and Hodgins [1] and Liu et al. [4][5] who successfully adopted the k-nearest neighbor method. Although these authors have managed to achieve good results, their approach also faces several limitations. First, their approach assumes the knowledge of positional data in order to make predictions. While capturing positional data is no problem for e.g. marker-based mocap systems, accurately capturing this information is much harder for inertial mocap systems due to the occurrence of positional drifts. Since the goal of this work it to obtain an inertial mocap system using only a few sensors, it will be looked into whether positional data can
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Stock Prediction with Random Forests and Long Short-term Memory

Stock Prediction with Random Forests and Long Short-term Memory

To create the time series forecasting model, I used an Artificial Neural Networks (ANN) algorithm. In the past few years, many researchers have used ANNs to analyze traditional classification and regression prediction problems in accounting and finance [5]. Many re- searchers have already proved ANN is an efficient algorithm to predict non-linear paths. ANN also has been proved that it performs very well in feature extraction from raw data [6]. In this report, I used a particular method called Long Short-term Memory (LSTM). LSTM is a type of Recurrent Neural Networks (RNN), and RNN is a type of ANN. ANN has one input layer, one or multiple hidden layers and one output layer, as shown in Figure 3.1. We can see that each input neuron is connecting to each hidden neuron and each hidden neuron is also connecting to the output neurons. There is no connection among neurons in the same layer. Every connection has its own weight which can be learned and adjusted from the training process.
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Top down Tree Long Short Term Memory Networks

Top down Tree Long Short Term Memory Networks

(Bengio et al., 2003; Mnih and Hinton, 2007) and recurrent neural networks (Mikolov et al., 2010; Mikolov, 2012) in order to map the feature vec- tors of the context words to the distribution for the next word. Recently, RNNs with Long Short-Term Memory (LSTM) units (Hochreiter and Schmidhu- ber, 1997; Hochreiter, 1998) have emerged as a pop- ular architecture due to their strong ability to capture long-term dependencies. LSTMs have been success- fully applied to a variety of tasks ranging from ma- chine translation (Sutskever et al., 2014), to speech recognition (Graves et al., 2013), and image descrip- tion generation (Vinyals et al., 2015).
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DAG Structured Long Short Term Memory for Semantic Compositionality

DAG Structured Long Short Term Memory for Semantic Compositionality

Recurrent neural networks, particularly long short-term memory (LSTM), have recently shown to be very effective in a wide range of sequence modeling problems, core to which is effective learning of distributed representation for subsequences as well as the sequences they form. An assumption in almost all the previ- ous models, however, posits that the learned representation (e.g., a distributed representa- tion for a sentence), is fully compositional from the atomic components (e.g., representa- tions for words), while non-compositionality is a basic phenomenon in human languages. In this paper, we relieve the assumption by extending the chain-structured LSTM to di- rected acyclic graphs (DAGs), with the aim to endow linear-chain LSTMs with the capa- bility of considering compositionality together with non-compositionality in the same seman- tic composition framework. From a more general viewpoint, the proposed models in- corporate additional prior knowledge into re- current neural networks, which is interesting to us, considering most NLP tasks have rela- tively small training data and appropriate prior knowledge could be beneficial to help cover missing semantics. Our experiments on sen- timent composition demonstrate that the pro- posed models achieve the state-of-the-art per- formance, outperforming models that lack this ability.
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