HPCWMF: A Hybrid Predictive Cloud Workload Management Framework Using Improved LSTM Neural Network

AbstractFor cloud providers, workload prediction is a challenging task due to irregular incoming workloads from users. Accurate workload prediction is essential for scheduling the resources to the cloud applications. Thus, in this paper, the authors propose a predictive cloud workload management framework to estimate the needed resources in advance based on a hybrid approach, which is a combination of an improved Long Short-Term Memory (LSTM) network and a multilayer perceptron network. By improving the traditional LSTM architecture by using opposition-based differential evolution algorithm and dropout technique on recurrent connection without memory loss, the proposed approach has the ability to perform a better prediction process. A novel hybrid predictive approach is aiming at enhancing the prediction performance of the cloud workload. Finally, the authors measure the proposed approach’s effectiveness under benchmark data sets of NASA and Saskatchewan servers. The experimental results proved that the proposed approach outperforms the other conventional methods.

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LSTM-based soft sensor design for oxygen content of flue gas in coal-fired power plant

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220932390 ◽

2020 ◽

pp. 014233122093239

Author(s):

Hongguang Pan ◽

Tao Su ◽

Xiangdong Huang ◽

Zheng Wang

Keyword(s):

Power Plant ◽

Oxygen Content ◽

Flue Gas ◽

Oxygen Sensor ◽

Short Term Memory ◽

Support Vector ◽

Data Sets ◽

Auxiliary Variables ◽

Data Set ◽

Lstm Network

To address problems of high cost, complicated process and low accuracy of oxygen content measurement in flue gas of coal-fired power plant, a method based on long short-term memory (LSTM) network is proposed in this paper to replace oxygen sensor to estimate oxygen content in flue gas of boilers. Specifically, first, the LSTM model was built with the Keras deep learning framework, and the accuracy of the model was further improved by selecting appropriate super-parameters through experiments. Secondly, the flue gas oxygen content, as the leading variable, was combined with the mechanism and boiler process primary auxiliary variables. Based on the actual production data collected from a coal-fired power plant in Yulin, China, the data sets were preprocessed. Moreover, a selection model of auxiliary variables based on grey relational analysis is proposed to construct a new data set and divide the training set and testing set. Finally, this model is compared with the traditional soft-sensing modelling methods (i.e. the methods based on support vector machine and BP neural network). The RMSE of LSTM model is 4.51% lower than that of GA-SVM model and 3.55% lower than that of PSO-BP model. The conclusion shows that the oxygen content model based on LSTM has better generalization and has certain industrial value.

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Performance of Combined Recommendation Modeling Fused With Tagawareness In Mixed Traffic Scenarios

10.21203/rs.3.rs-1120711/v1 ◽

2021 ◽

Author(s):

Xiangyuan Li ◽

Fei Ding ◽

Suju Ren ◽

Jianmin Bao ◽

Ruoyu Su ◽

...

Keyword(s):

High Speed ◽

Recommendation System ◽

Short Term Memory ◽

User Behavior ◽

Data Sets ◽

Mixed Traffic ◽

User Interest ◽

Travel Information ◽

Lstm Network ◽

Behavior Characteristics

Abstract Due to the heterogeneous characteristics of vehicles and user terminals, information in mixed traffic scenarios can be interacted based on the Web protocol of different terminals. The recommendation system can dig users' travel preferences by analyzing historical travel information of different traffic participants, to publish accurate travel information and services for the terminals of traffic participants. The diversification of existing road network users and networking modes, as well as the dynamic changes of user interest distribution caused by high-speed movement of vehicles, traditional collaborative filtering algorithms have limitations in terms of effectiveness. This paper proposes a novel Hybrid Tag-aware Recommender Model (HTRM). The model embedding layer first employs the Word2vec model to represent the tags and ratings of projects and users, respectively. The feature layer then introduces the auto-encoder to extract self-similar features of the item, and a long short-term memory (LSTM) network is used to extract user behavior characteristics to provide higher-quality recommendations. The gating layer combines the features of users and projects and then makes score recommendations based on the Fully Connected Neural Network (FCNN). Finally, Web data sets of different service preferences of traffic participants during the trip are used to evaluate the model recommendation performance in different scenarios. The experimental results show that the HTRM model is reasonable in design and can achieve high recommendation accuracy.

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Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction

Sustainability ◽

10.3390/su10103765 ◽

2018 ◽

Vol 10 (10) ◽

pp. 3765 ◽

Cited By ~ 27

Author(s):

Hyejung Chung ◽

Kyung-shik Shin

Keyword(s):

Genetic Algorithm ◽

Stock Market ◽

Short Term Memory ◽

Time Window ◽

Hybrid Approach ◽

Window Size ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Lstm Network

With recent advances in computing technology, massive amounts of data and information are being constantly accumulated. Especially in the field of finance, we have great opportunities to create useful insights by analyzing that information, because the financial market produces a tremendous amount of real-time data, including transaction records. Accordingly, this study intends to develop a novel stock market prediction model using the available financial data. We adopt deep learning technique because of its excellent learning ability from the massive dataset. In this study, we propose a hybrid approach integrating long short-term memory (LSTM) network and genetic algorithm (GA). Heretofore, trial and error based on heuristics is commonly used to estimate the time window size and architectural factors of LSTM network. This research investigates the temporal property of stock market data by suggesting a systematic method to determine the time window size and topology for the LSTM network using GA. To evaluate the proposed hybrid approach, we have chosen daily Korea Stock Price Index (KOSPI) data. The experimental result demonstrates that the hybrid model of LSTM network and GA outperforms the benchmark model.

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Sarcasm Analysis Using Conversation Context

Computational Linguistics ◽

10.1162/coli_a_00336 ◽

2018 ◽

Vol 44 (4) ◽

pp. 755-792 ◽

Cited By ~ 2

Author(s):

Debanjan Ghosh ◽

Alexander R. Fabbri ◽

Smaranda Muresan

Keyword(s):

Social Media ◽

Computational Models ◽

Human Performance ◽

Short Term Memory ◽

Data Sets ◽

Discussion Forums ◽

Sentence Level ◽

Social Media Platforms ◽

Gold Label ◽

Lstm Network

Computational models for sarcasm detection have often relied on the content of utterances in isolation. However, the speaker’s sarcastic intent is not always apparent without additional context. Focusing on social media discussions, we investigate three issues: (1) does modeling conversation context help in sarcasm detection? (2) can we identify what part of conversation context triggered the sarcastic reply? and (3) given a sarcastic post that contains multiple sentences, can we identify the specific sentence that is sarcastic? To address the first issue, we investigate several types of Long Short-Term Memory (LSTM) networks that can model both the conversation context and the current turn. We show that LSTM networks with sentence-level attention on context and current turn, as well as the conditional LSTM network, outperform the LSTM model that reads only the current turn. As conversation context, we consider the prior turn, the succeeding turn, or both. Our computational models are tested on two types of social media platforms: Twitter and discussion forums. We discuss several differences between these data sets, ranging from their size to the nature of the gold-label annotations. To address the latter two issues, we present a qualitative analysis of the attention weights produced by the LSTM models (with attention) and discuss the results compared with human performance on the two tasks.

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Using Trend Extraction and Spatial Trends to Improve Flood Modeling and Control

10.5772/intechopen.96347 ◽

2021 ◽

Author(s):

Jacob Hale ◽

Suzanna Long ◽

Vinayaka Gude ◽

Steven Corns

Keyword(s):

Data Visualization ◽

Short Term Memory ◽

Decision Makers ◽

Data Sets ◽

Modeling And Control ◽

Trend Extraction ◽

Univariate Time Series ◽

Lstm Network ◽

Visualization Techniques ◽

And Control

Effective management of flood events depends on a thorough understanding of regional geospatial characteristics, yet data visualization is rarely effectively integrated into the planning tools used by decision makers. This chapter considers publicly available data sets and data visualization techniques that can be adapted for use by all community planners and decision makers. A long short-term memory (LSTM) network is created to develop a univariate time series value for river stage prediction that improves the temporal resolution and accuracy of forecasts. This prediction is then tied to a corresponding spatial flood inundation profile in a geographic information system (GIS) setting. The intersection of flood profile and affected road segments can be easily visualized and extracted. Traffic decision makers can use these findings to proactively deploy re-routing measures and warnings to motorists to decrease travel-miles and risks such as loss of property or life.

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Automatic Classification of Myocardial Infarction Using Spline Representation of Single-Lead Derived Vectorcardiography

Sensors ◽

10.3390/s20247246 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7246

Author(s):

Yu-Hung Chuang ◽

Chia-Ling Huang ◽

Wen-Whei Chang ◽

Jen-Tzung Chien

Keyword(s):

Myocardial Infarction ◽

Short Term Memory ◽

Spline Approximation ◽

Three Dimensional ◽

Patient Specific ◽

Reconstruction Method ◽

Ecg Signals ◽

Multilayer Perceptron Network ◽

Lstm Network ◽

Induced Changes

Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases worldwide and most patients suffer from MI without awareness. Therefore, early diagnosis and timely treatment are crucial to guarantee the life safety of MI patients. Most wearable monitoring devices only provide single-lead electrocardiography (ECG), which represents a major limitation for their applicability in diagnosis of MI. Incorporating the derived vectorcardiography (VCG) techniques can help monitor the three-dimensional electrical activities of human hearts. This study presents a patient-specific reconstruction method based on long short-term memory (LSTM) network to exploit both intra- and inter-lead correlations of ECG signals. MI-induced changes in the morphological and temporal wave features are extracted from the derived VCG using spline approximation. After the feature extraction, a classifier based on multilayer perceptron network is used for MI classification. Experiments on PTB diagnostic database demonstrate that the proposed system achieved satisfactory performance to differentiating MI patients from healthy subjects and to localizing the infarcted area.

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Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327908666180727123657 ◽

2019 ◽

Vol 9 (1) ◽

pp. 16-31

Author(s):

Kyungkoo Jun

Keyword(s):

Fourier Transform ◽

Deep Learning ◽

Short Term Memory ◽

Window Size ◽

Sensor Data ◽

Data Sets ◽

Data Set ◽

Proposed Model ◽

Testing Data ◽

Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

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Role of MicroRNA Genes miR-1000 and miR-375 in Forming Olfactory Conditional Memory in Drosophila melanogaster

MicroRNA ◽

10.2174/2211536609666200204113403 ◽

2020 ◽

Vol 09 ◽

Author(s):

Sadniman Rahman ◽

Chaity Modak ◽

Mousumi Akter ◽

Mohammad Shamimul Alam

Keyword(s):

Learning And Memory ◽

Short Term Memory ◽

Memory Loss ◽

Memory Formation ◽

Neural Integration ◽

Significant Difference ◽

Locomotion Speed ◽

Microrna Genes ◽

With Memory

Background: Learning and memory is basic aspects in neurogenetics as most of the neurological disorders start with dementia or memory loss. Several genes associated with memory formation have been discovered. MicroRNA genes miR-1000 and miR-375 were reported to be associated with neural integration and glucose homeostasis in some insects and vertebrates. However, neuronal function of these genes is yet to be established in D. melanogaster. Objective: Possible role of miR-1000 and miR-375 in learning and memory formation in this fly has been explored in the present study. Methods: Both appetitive and aversive olfactory conditional learning were tested in the miR-1000 and miR-375 knockout (KO) strains and compared with wild one. Five days old third instar larvae were trained by allowing them to be associated with an odor with reward (fructose) or punishment (salt). Then, the larvae were tested to calculate their preferences to the odor trained with. Learning index (LI) values and larval locomotion speed were calculated for all strains. Results: No significant difference was observed for larval locomotion speed in mutant strains. Knockout strain of miR-1000 showed significant deficiency in both appetitive and aversive memory formation whereas miR-375 KO strain showed a significantly lower response only in appetitive one. Conclusion: The results of the present study indicate important role played by these two genes in forming short-term memory in D. melanogaster.

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Power System Transient Stability Assessment Based on Snapshot Ensemble LSTM Network

Sustainability ◽

10.3390/su13126953 ◽

2021 ◽

Vol 13 (12) ◽

pp. 6953

Author(s):

Yixing Du ◽

Zhijian Hu

Keyword(s):

Power Systems ◽

Power System ◽

Transient Stability ◽

Short Term Memory ◽

Risk Function ◽

Stability Margin ◽

Risk Level ◽

Stability Assessment ◽

Lstm Network ◽

Hierarchical Assessment

Data-driven methods using synchrophasor measurements have a broad application prospect in Transient Stability Assessment (TSA). Most previous studies only focused on predicting whether the power system is stable or not after disturbance, which lacked a quantitative analysis of the risk of transient stability. Therefore, this paper proposes a two-stage power system TSA method based on snapshot ensemble long short-term memory (LSTM) network. This method can efficiently build an ensemble model through a single training process, and employ the disturbed trajectory measurements as the inputs, which can realize rapid end-to-end TSA. In the first stage, dynamic hierarchical assessment is carried out through the classifier, so as to screen out credible samples step by step. In the second stage, the regressor is used to predict the transient stability margin of the credible stable samples and the undetermined samples, and combined with the built risk function to realize the risk quantification of transient angle stability. Furthermore, by modifying the loss function of the model, it effectively overcomes sample imbalance and overlapping. The simulation results show that the proposed method can not only accurately predict binary information representing transient stability status of samples, but also reasonably reflect the transient safety risk level of power systems, providing reliable reference for the subsequent control.

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A Combined Method for MEMS Gyroscope Error Compensation Using a Long Short-Term Memory Network and Kalman Filter in Random Vibration Environments

Sensors ◽

10.3390/s21041181 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1181

Author(s):

Chenhao Zhu ◽

Sheng Cai ◽

Yifan Yang ◽

Wei Xu ◽

Honghai Shen ◽

...

Keyword(s):

Kalman Filter ◽

Standard Deviation ◽

Error Compensation ◽

Random Vibration ◽

Short Term Memory ◽

Combined Method ◽

Short Term ◽

Mems Gyroscope ◽

Long Short Term Memory ◽

Lstm Network

In applications such as carrier attitude control and mobile device navigation, a micro-electro-mechanical-system (MEMS) gyroscope will inevitably be affected by random vibration, which significantly affects the performance of the MEMS gyroscope. In order to solve the degradation of MEMS gyroscope performance in random vibration environments, in this paper, a combined method of a long short-term memory (LSTM) network and Kalman filter (KF) is proposed for error compensation, where Kalman filter parameters are iteratively optimized using the Kalman smoother and expectation-maximization (EM) algorithm. In order to verify the effectiveness of the proposed method, we performed a linear random vibration test to acquire MEMS gyroscope data. Subsequently, an analysis of the effects of input data step size and network topology on gyroscope error compensation performance is presented. Furthermore, the autoregressive moving average-Kalman filter (ARMA-KF) model, which is commonly used in gyroscope error compensation, was also combined with the LSTM network as a comparison method. The results show that, for the x-axis data, the proposed combined method reduces the standard deviation (STD) by 51.58% and 31.92% compared to the bidirectional LSTM (BiLSTM) network, and EM-KF method, respectively. For the z-axis data, the proposed combined method reduces the standard deviation by 29.19% and 12.75% compared to the BiLSTM network and EM-KF method, respectively. Furthermore, for x-axis data and z-axis data, the proposed combined method reduces the standard deviation by 46.54% and 22.30% compared to the BiLSTM-ARMA-KF method, respectively, and the output is smoother, proving the effectiveness of the proposed method.

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