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摘要: Approximate computing is a popular field for low power consumption that is used in several applications like image processing, video processing, multimedia and data mining. This Approximate computing is majorly performed with an arithmetic circuit particular with a multiplier. The multiplier is the most essential element used for approximate computing where the power consumption is majorly based on its performance. There are several researchers are worked on the approximate multiplier for power 关键词: Deep learning; approximate multiplier; LSTM; jellyfish
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摘要: Internet of Things (IoT) is the most widespread and fastest growing technology today. Due to the increasing of IoT devices connected to the Internet, the IoT is the most technology under security attacks. The IoT devices are not designed with security because they are resource constrained devices. Therefore, having an accurate IoT security system to detect security attacks is challenging. Intrusion Detection Systems (IDSs) using machine learning and deep learning techniques can detect security a关键词: IoT; IDS; deep learning; machine learning; CNN; LSTM
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摘要: Today, fatalities, physical injuries, and significant economic losses occur due to car accidents. Among the leading causes of car accidents is drowsiness behind the wheel, which can affect any driver. Drowsiness and sleepiness often have associated indicators that researchers can use to identify and promptly warn drowsy drivers to avoid potential accidents. This paper proposes a spatiotemporal model for monitoring drowsiness visual indicators from videos. This model depends on integrating a 3D c关键词: 3D-CNN; deep learning; driver drowsiness detection; LSTM; spatiotemporal features
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摘要: Background(#br)Modern day's society is engaged in commitment-based and time-bound jobs. This invites tension and mental depression among many people who are not able to cope up with this type of working environment. Cases of mental depression are increasing day by day all over the world. Recently, the onset of the COVID-19 pandemic has added further fuel to the fire. In many countries, the ratio between patients with mental depression and psychiatrists or psychologists is remarkably poor. Under 关键词: Multi-layer perceptron (MLP);Convolution neural network with MLP as a classifier (CNN);Recurrent neural network (RNN);RNN with LSTM (long- and short-term memory);Support vector machine (SVM);Logistic regression (LR);Mental depression tracker
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摘要: Anyone involved in designing or finding molecules in the life sciences over the past few years has witnessed a dramatic change in how we now work due to the COVID-19 pandemic. Computational technologies like artificial intelligence (AI) seemed to become ubiquitous in 2020 and have been increasingly applied as scientists worked from home and were separated from the laboratory and their colleagues. This shift may be more permanent as the future of molecule design across different industries will i关键词: R&D;AI;GCNN;RNN;LSTM;Artificial intelligence;Design-make-test;Machine learning;Molecule design;Recurrent neural networks
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摘要: Freshness prediction was a research hotspot in the field of food science. The current microbial kinetic equations could predict the freshness under certain fixed temperature conditions, but they were no longer effective when the temperature was fluctuated. To solve this problem, this paper used deep learning techniques to mine the inherent relation of variable temperature during storage and proposed a novel model named CNN_LSTM (convolutional neural network_ long short-term memory). The model di关键词: Freshness;Temperature fluctuation;CNN_LSTM;Salmon
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摘要: With the inherent volatility of solar radiation, accurate short-term prediction of solar irradiance is essential to cost-efficiently stabilise and operate power grids. Cloud is the major influencing factor for variations in solar irradiance at a minute scale. This study proposes a short-term solar irradiance prediction model based on a deep learning network. Firstly, a new hybrid cloud detection method is proposed to calculate cloud coverage under different sky condition. The relationship betwee关键词: Short-term prediction;Global horizontal irradiance (;GHI;);Clear sky index;Cloud detection;Bayesian Optimisation (BO);Long Short-Term Memory (LSTM) model
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摘要: In this paper, we propose a novel prediction framework (ST-Random Forest) for flight delay prediction from temporal and spatial perspective. We first apply complex network theory to extract the spatial feature of the aviation network at edge-, node-, and network-level. Furthermore, considering the temporal correlation of weather condition and airport crowdedness on flight delays, we create a prediction framework based on LSTM units to extract the temporal property of crowdedness and weather cond关键词: Flight delay prediction;Complex network theory;LSTM approach;ST-Random Forest
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摘要: Forecasting oil production can be very challenging, especially for reservoirs with sparse data or other complexities. If traditional decline curve analysis or time series models fail to capture production rate variabilities, a machine learning model for time series data may be effective. A temporal machine learning model, a long short-term memory network model (LSTM) in specific, may be trained to predict oil, gas, and water production rates. We develop an LSTM for such an application and evalua关键词: Enhanced Oil Recovery (EOR);Long Short-Term Memory (LSTM);SACROC unit;Water Alternating CO2 Injection;Mean Absolute Percentage Error (MAPE);Production Performance
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摘要: Gait recognition is the identification of any person from his/her walking pattern. Walking pattern of each individual is unique and cannot be replicated by others. But, gait recognition is very difficult if any object is carried by any individual. This article proposes a novel computer vision based method of gait recognition both with and without carried objects (COs) using Faster region convolutional neural network (R-CNN) based architecture. To the best of my knowledge, this is the first inves关键词: Gait recognition;With and without CO;Faster R-CNN;RNN;LSTM;BLSTM
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摘要: Modeling and forecasting electricity consumption (EC) help industry managers make better strategic decisions. In this study, a hybrid approach for predicting EC is proposed which first EC is decomposed into approximate and detail parts based on wavelet transfer (WT). Next, the modeling of wavelet components is accomplished based on the adaptive WT (AWT)-long short-term memory (LSTM) and autoregressive integrated moving average with explanatory variable (ARIMAX)-generalized autoregressive conditi关键词: Electricity consumption;Prediction;Wavelet transform;LSTM;ARIMAX-GARCH
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摘要: This paper proposes an improved Long Short-Term Memory neural network (LSTM) for Capacitor Voltage Transformer (CVT) measurement error prediction. The proposed model introduces bidirectional memory, deep feature extraction, and multi-task learning strategies to improve LSTM for high accuracy and high convergence speed. Then, the network parameters are optimized from the multi-source heterogeneous data of CVT, which combines the advantages of prior knowledge and intelligent prediction method. Fin关键词: Capacitive voltage transformer;Measurement error;Bidirectional memory;LSTM
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摘要: Effectively predicting stock prices is critical to reduce investors' decision-making risks This study considers the indirect effects of air pollutants on investor psychology, combined with commonly used financial data, and explores a more comprehensive and effective stock price prediction model. This research takes the SSE Shanghai Enterprises (SSESHE) index as the research object, selects six kinds of air pollutants as the input indexes, constructs the stock closing price prediction model based关键词: Stock market forecast;Deep learning;Long short-term memory (LSTM);Air quality index
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摘要: We applied the multifractal detrended cross-correlation analysis (MF-DCCA) method to investigate the cross-correlation behaviour between the USD/CNY exchange rate (UCER) and the Baidu index (Chines netizens’ behaviour big data, BI). We observed a significant anti-persistent cross-correlation between UCER and BI through MF-DCCA testing. Based on the cross-correlation between UCER and BI, a hybrid deep learning model WOA-STL-BI-LSTM combining network big data, decomposition and integration techniq关键词: USD/CNY exchange rate;Baidu Index;MF-DCCA;STL;LSTM
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摘要: Several machine learning and deep learning models were reported in the literature to forecast COVID-19 but there is no comprehensive report on the comparison between statistical models and deep learning models. The present work reports a comparative time-series analysis of deep learning techniques (Recurrent Neural Networks with GRU and LSTM cells) and statistical techniques (ARIMA and SARIMA) to forecast the country-wise cumulative confirmed, recovered, and deaths. The Gated Recurrent Units (GR关键词: COVID-19 pandemic;Gated Recurrent Units (GRUs);Long Short-Term Memory (LSTM) cells;Recurrent Neural Networks (RNNs);Auto Regressive Integrated Moving Average (ARIMA);Seasonal Auto Regressive Integrated Moving Average (SARIMA)
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摘要: Nowadays, several kinds of attacks exist in cyberspace, and hence comprehensive research has been implemented to overcome these drawbacks. One such method to provide security in WSN (Wireless sensor network) is Intrusion Detection System. However, the determination of unknown attacks remains a major challenge in the intrusion detection system. Hence, the usage of deep learning methodologies remains to be an active area in cyber security. However, prevailing a deep learning algorithm possesses li关键词: Intrusion detection;Wireless sensor networks;LSTM;Empirical mode decomposition;Component analysis
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摘要: Effective system identification is critical to realize the high-performance control of shaking table, however, the common reduced-fidelity models can rarely capture the practical dynamics of the system over a wide frequency range. To address this issue, this study adopts the latest advances in deep learning to develop a physics-guided long short-term memory (PhyLSTM) network for system identification of the shaking table. The basic idea is to embed the physical laws describing the system into th关键词: Shaking table;System identification;Physics-guided deep learning;Long short-term memory (LSTM);Data-driven modelling
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摘要: The technologies related to manufacturing processes monitoring, optimization, and control are becoming prevalent to achieve autonomous operations in Smart Manufacturing. The present work establishes an edge-level system based on the long short-term memory (LSTM) model for monitoring significant variations of cutting depths during end milling of near-net-shaped components. The proposed system consists of a trained LSTM model that decodes force data to identify cutting depths and an edge-level int关键词: end milling;depth of cut monitoring;LSTM model;cutting force;edge-level system;computer aided manufacturing;machine learning for engineering applications;manufacturing automation;physics-based simulations;process modeling for engineering applications
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摘要: Generally, it is difficult to establish an accurate building load forecasting model by using insufficient energy data. Although the transfer of knowledge from similar buildings can effectively solve this problem, there is still a lack of effective methods for both the selection of source domain buildings and the use of transfer knowledge when many candidate buildings are available. In view of this, this paper proposes a multi-source transfer learning guided ensemble LSTM method for building mult关键词: Building load forecasting;Transfer learning;Multi-source;LSTM
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摘要: Within last decade, the investing habits of people is rapidly increasing towards stock market. The nonlinearity and high volatility of stock prices have made it challenging to predict stock prices. Since stock price data contains incomplete, complex and fuzzy information, it is very difficult to capture any nonlinear characteristics of stock price data, which usually may be unknown to the investors. There is a dire need of an accurate stock price prediction model that could offer insights to the关键词: BiCuDNNLSTM-1dCNN;BiCuDNNLSTM;CNN;LSTM;Stock price prediction;Time series data
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摘要: China’s commercial Bank shares have become the backbone of the capital market. The prediction of a bank's stock price has been a hot topic in the investment field. However, the stock price is always unstable and non-linear, challenging the traditional statistical models. Inspired by this problem, a novel hybrid deep learning approach is proposed to improve prediction performance. By modifying the distance measurement algorithm into DTW, an improved K-means clustering algorithm is proposed to clu关键词: Commerical Bank;Stock price prediction;K-means;DTW;LSTM neural network;Hybrid model
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Bhandari Hum Nath;Rimal Binod;Pokhrel Nawa Raj;Rimal Ramchandra;Dahal Keshab R.;Khatri Rajendra K.C.;
Machine Learning with ApplicationsVolume 9, Issue , 2022, PP
摘要: The rapid advancement in artificial intelligence and machine learning techniques, availability of large-scale data, and increased computational capabilities of the machine opens the door to develop sophisticated methods in predicting stock price. In the meantime, easy access to investment opportunities has made the stock market more complex and volatile than ever. The world is looking for an accurate and reliable predictive model which can capture the market’s highly volatile and nonlinear behav关键词: Stock market index;LSTM;Prediction;Machine learning;Deep learning;Denoising
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摘要: Predicting the production behaviors of shale gas wells is of great importance for further developing future unconventional hydrocarbon strategies. An accurate prediction production, as well as reliable shale gas production models, are required to fully understand the shale gas exploitation budget. However, a major problem with classical analytic methods is the insufficient accuracy of the existing models, the time-consuming collection of historical production data, and the costly computational e关键词: Shale gas;Exponential smoothing method;LSTM model;Production prediction;Deep learning
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Semeraro Concetta;Caggiano Mariateresa;Olabi Abdul-Ghani;Dassisti Michele;
EnergyVolume 255, Issue , 2022, PP
摘要: In recent years, many researchers have been conducted on batteries' health monitoring and prognostics, mainly focusing on the batteries' state of charge (SOC). Accurately estimating the state of health (SOH) and predicting the remaining useful life (RUL) of battery components are very important for the prognosis and health management of the overall battery system. However, due to the non-linear dynamics caused by the electrochemical characteristics in batteries, the accurate estimations of SOC, 关键词: Battery;Model-based approaches;Data-driven approaches;Hybrid approaches;Optimization techniques;ACKF;AI;ANFIS;ANN;ARNN;AUKF;BA;BLS;BMO;BMS;BN;BPNN;B-LSTM;CC-CV;CHMM;CKF;CNN;CPE;CWRNN;C;maximum;C;remaining;C;t;C;0;DAE-NN;DBN;DCNN;DE;DL;DOD;ECL;ECM;EDPSO;EIS;EKF;ELM;EMT;EOCV;ERA;ESC;FD;FFNN;FOC;FOTM, SOTM;FUDS;GA-SVR;GM;GNL;GPR;GRU;HPPC;IM;IndRNN;KDE;KF;LCA;LS;LSE;LS-SVM;LSTM;MAE;MAPE;MEM;ML;MONESN;MSVM;NASA;NB;NCA;NN;PA;PBM;PCA;PF;PHM;PNGV;PNN;PRS;RB;RBF;RC;RLM;RLS;RMSE;RNN;ROM;RUL;RUP;RVFL;RVM;RWPF;SOC;SOE;SOH;SPKF;SRCKF;SSL;SVM;TM;UKF;VRLA;ZEBRA
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摘要: Most mean–variance (MV) models construct a portfolio based on nonstationary stocks. This study presents a new MV model constructed using stationary portfolios composed of cointegrated stocks. The expected return of this new model is predicted by using machine learning models, such as support vector machine, random forest, and attention-based long short-term memory (LSTM) network. The proposed model is evaluated using data on stocks in the CSI 300 and the S&P 500, with 42 features over 8 year关键词: Mean–variance portfolio optimization;Pairs trading;Mean-reverting spread prediction;Stationary portfolio;Attention-based LSTM network
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摘要: In recent years, Transformer structures have been widely applied in image captioning with impressive performance. However, previous works often neglect the geometry and position relations of different visual objects. These relations are often thought of as crucial information for good captioning results. Aiming to further promote the image captioning by Transformers, this paper proposes an improved Geometry Attention Transformer (GAT) framework. In order to obtain geometric representation abilit关键词: Image captioning;Transformer framework;Gate-controlled geometry attention;Position-aware LSTM
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摘要: Due to mass uncertain issues affecting health status of aero-engines, their degradation process commonly exhibits multi-stage features. Also, key characteristics underlying degradation process cannot be precisely described by traditional remaining useful life (RUL) prediction methods. Thus, for multi-stage RUL prediction, we develop a novel real-time combined approach that effectively explains the weights of each base model. The degradation process is divided into multiple stages through real-ti关键词: Combined prediction model;Wiener process;LSTM;XGBoost;Real-time clustering
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摘要: In real-world applications, machinery operates under non-stationary conditions such as operating environment, failure modes, and noise where domain shift problems generally arise. Hence, deep learning methods are trained on one working condition cannot generalize effectively on different conditions. Also, the suitability of prognostic features significantly affects the prediction results. To address these issues, this paper proposes a transfer learning-based bi-directional Long Short-Term Memory关键词: Remaining useful life;Domain adaptation;Bi-directional LSTM;Prognostic features;Transfer learning;Maximum mean discrepancy
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摘要: The urban heat island (UHI) phenomenon is a serious concern for urban planners and policymakers, requiring effective and efficient mitigation policies. To develop such policies, accurate and pre-emptive estimations of current and future UHI manifestations are vital elements that help determine efficient policies and mitigation techniques. There are two fundamental approaches for modelling overheating in an urban environment: white-box and black-box based methods. The first one is characterized b关键词: UHI;White-box models;Black-box models;Physical-based modelling;Machine learning;The fusion of white box and black box models;ANN;ANOVA;ARIMA;AWS;BEM;BEP;BH;BRT;CAT;CFD;CRENAU;CWS;DEM;DNN;DT;EDR;EF;ETM +;EVI;GNDVI;GPI;GPS;GRU;GWR;HVAC;KMA;LIME;LST;LSTM;LULC;MAE;MAPE;MESO-NH;ML;MLP-MC;MNDWI;MNUHII;NDBI;NDBSI;NDVI;OLI;OLS;PCM;PD;PID;RD;RF;RH;RL;RMSE;RNN;RPSC;SH;SLUCM;SURFEX;SVF;SINDy;TEB;TF;TIRS;TMY;UCM;UHI;UHII;UTFVI;UWG;WRF
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摘要: Photovoltaic (PV) systems are usually exposed outdoors for a long time, easily lead to various faults. Apart from fault location and classification widely studied, accurate and quantitative fault severity diagnosis is vital for the decision making, given the cost of eliminating faults. However, there are scarce investigations into this problem. Therefore, a novel quantitative fault diagnosis method is proposed based on the deviation between the predicted power of fault states and the actual powe关键词: Photovoltaic (PV) system;Quantitative fault diagnosis;Data-driven;Clustering algorithm;Long short-term memory (LSTM) neural network;Transfer learning
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