## What is/are Deep Long?

Deep Long - A number of principles for evaluating water resources decisions under deep long-run uncertainty have been proposed in the literature.^{[1]}The basitarsus of the mid- and/or hindlegs of several Amblyoponinae ants shows a deep longitudinal groove or sulcus on its anterior face in workers and queens.

^{[2]}Currently, the deep Longmaxi shale with a burial depth of more than 3500 m has been receiving increasing interest in the Sichuan Basin.

^{[3]}Moreover, machine-learning techniques are used to identify patterns and anomalies in electronic health records and to perform ad-hoc evaluations of gathered data from wearable health tracking devices for deep longitudinal phenotyping.

^{[4]}This trial aims to optimize management of ALK + NSCLC by analyzing the efficacy of second-generation ALK inhibitors in conjunction with deep longitudinal phenotyping across two treatment lines.

^{[5]}In the Sichuan Basin, the deep Longmaxi shale is a major target formation that has attracted increasing interest.

^{[6]}We discuss the uses of DPSleep in relation to other available sleep estimation approaches and provide example use cases that include multi-dimensional, deep longitudinal phenotyping, extended measurement of dynamics associated with mental illness, and the possibility of combining wearable actigraphy and personal electronic device data (e.

^{[7]}The core–shell composite structure is a result of the growth of the SiC layer inward the fiber, which can give rise to deep longitudinal–radial cracks when the SiC shell thickness exceeds 0.

^{[8]}CONCLUSIONS We discuss the use of DPSleep in relation to other available sleep estimation approaches and provide example use cases that include multi-dimensional, deep longitudinal phenotyping, extended measurement of dynamics associated with mental illness, and the possibility of combining wearable actigraphy and personal electronic device data (eg, smartphones and tablets) to measure individual differences across a wide range of behavioral variations in health and disease.

^{[9]}In this paper, the deformation and failure behavior at floor area in deep longwall mining site were analyzed.

^{[10]}The mucosa of the intestinal bulb shows numerous, deep longitudinal folds arranged in zigzagging-like patterns.

^{[11]}Transcriptional dynamics and plasticity were examined by means of scRNA-sequencing with CRISPR based perturbation, spatial transcriptomics and deep long-read RNA-sequencing.

^{[12]}Furthermore, we compare our framework to several popular algorithms including Artificial Neural Networks, Deep Long–Short Term Memory, Random Forest, Naive Bayes and Feed Forward Deep Neural Networks.

^{[13]}Drawing from deep longitudinal and ethnographic work, this article interrogates a set of key relationships between bodies, gender and infrastructure in the context of understanding cities such as Bharatpur and Dhangadhi in Nepal as well as Delhi, India.

^{[14]}The stability of coal wall in deep longwall face has always been a research hotspot.

^{[15]}We explored the ability of deep longitudinal profiling to make health-related discoveries, identify clinically relevant molecular pathways and affect behavior in a prospective longitudinal cohort (n = 109) enriched for risk of type 2 diabetes mellitus.

^{[16]}Deep longitudinal omics profiling can lead to prediction models of insulin resistance with increased acceptance of diet and exercise changes in research participants 8.

^{[17]}, deep longitudinal bulk acoustic waves (DLBAWs) reveals some similarities and important differences in the process of thermal generation of the waves.

^{[18]}This family possesses a greatly specialized head with tylus extremely broad, compound eyes produced, postclypeus almost transversely rectangular in the middle, with basal half having a deep longitudinal groove medially, and rostrum stout.

^{[19]}Deep longwall mining of coal seams is made in the Upper Silesian Coal Basin (USCB) under complicated and mostly unfavourable geological and mining conditions.

^{[20]}In order to make full use of the effective information in historical data to further improve the prediction accuracy of wind power generation, a prediction method combining empirical mode decomposition(EMD) and deep long-term memory(DLSTM) is proposed, constructing a multi-scale combined prediction model(EMD-DLSTM).

^{[21]}Children expressed a deep longing for cure and a recognition that their lives were altered by having the condition that led to limitations in sport and wearing fashionable clothes and shoes.

^{[22]}The frontal region had a deep longitudinal depression dorsally.

^{[23]}The findings illustrated that youth can speak eloquently about their lives and their deep longing to be seen and heard.

^{[24]}Furthermore, the ROP increase mechanism of torsional impact drilling technology is that the ratio of brittle energy consumption under the TIC condition is larger than that under a steady load; the degree of repeated fragmentation of rock chips under the TIC condition is lower than that under the steady load, and the TIC load promotes the formation of a transverse cracking network near the free surface and inhibits the formation of a deep longitudinal cracking network.

^{[25]}The study is based on a deep longitudinal case study of a SME manufacturer and focuses on continuous development capability as one of the core system supplier capabilities.

^{[26]}Based on a unique, broad and deep longitudinal profiling of autoantibody reactivities, our results demonstrate a unique autoreactive profile in each analyzed healthy individual.

^{[27]}

## short term memory

In this study, the geopolymerization process of fly ash-based geopolymer was estimated using the deep long short-term memory (LSTM) and machine learning models.^{[1]}In this paper, an effective technique, named as Fractional Rider Deep Long Short Term Memory (LSTM) network is developed for workload prediction in cloud gaming.

^{[2]}The excellent empirical performance of our Deep Long Short-Term Memory (DLSTM) approach on various forecasting tasks motivated us to extend it to solve the forecasting problem through hierarchical architectures.

^{[3]}This paper proposes an Online Autoregression with Deep Long-Short-Term Memory (OAR-DLSTM) method for the hybrid recurring concept drifts of process industry streaming data.

^{[4]}To effectively learn the spatiotemporal dependencies of the fused feature, deep long short-term memory (LSTM), two-dimensional convolutional neural network (2D-CNN), and hybrid models composed of a combination of LSTM and CNN were used.

^{[5]}In this work, we develop a novel fire detection method using deep Long-Short Term Memory (LSTM) neural networks and variational autoencoder (VAE) to meet the increasingly stringent requirements of fire detection for future space habitats in terms of sensitivity and reliability.

^{[6]}In this paper, a deep long short term memory (DeepLSTM) network to classify personality traits using the electroencephalogram (EEG) signals is implemented.

^{[7]}In the first scheme, raw sensor sequences are directly fed to a deep Long/Short-Term Memory (LSTM) learner.

^{[8]}In order to make full use of multi-sensor monitoring data and enhance prediction accuracy, a feature selection method is developed based on Relief algorithm, then a practical RUL prediction approach is proposed based on deep long and short term memory (LSTM) network in this paper.

^{[9]}Then, Siamese deep long short-term memory model is introduced to calculate the similarity between the question asked and the questions in the database to find the best-matched answer.

^{[10]}Experiments are carried out on an IBM supercomputer for training deep long short-term memory (LSTM) acoustic models on the 2000-hour Switchboard dataset.

^{[11]}First of all, with the concept of dynamic rolling prediction and deep long short term memory (LSTM) algorithm, a novel V2G capacity modeling and prediction method is developed.

^{[12]}The NCM uses a deep long short-term memory recurrent neural network for a global approximation of an optimal contraction metric, the existence of which is a necessary and sufficient condition for exponential stability of nonlinear systems.

^{[13]}A deep long short-term memory (LSTM) is used to discover dependencies between samples of processed electroencephalogram (EEG) signal at different time instances.

^{[14]}The selected forecast is next passed onto a hybrid deep long short-term memory (HD-LSTM) network, which looks back and learns the relationship of the selected forecast with corresponding rainfall and temperature observations to produce the next-day rainfall forecast.

^{[15]}To enable the system to focus on the most salient parts of the learned multimodal representations, we propose an architecture composed of a capsule attention mechanism following a deep Long Short-Term Memory (LSTM) network.

^{[16]}To improve the accuracy and reliability of load identification model, a hierarchical load classification mechanism is constructed, and the deep Long Short-Term Memory (LSTM) structure as the classifier.

^{[17]}In Phase-II, the H-GANs employed Deep Long Short Term Memory (LSTM) as generator and LSTM with 3D Convolutional Neural Network (3D-CNN) as a discriminator.

^{[18]}Thus, we introduce a novel enhanced version of the grasshopper optimization algorithm called EGOA to optimize the deep long short-term memory (LSTM) neural network architecture, which optimally evolves four of its key hyperparameters.

^{[19]}The proposed approach uses deep long short-term memory (DLSTM) network for training and testing.

^{[20]}In this work, we propose Epi-LSTM, a deep long short-term memory (LSTM) recurrent neural network autoencoder to capture the long-term dependencies in the epigenomic data.

^{[21]}We propose a deep Long Short Term Memory (LSTM) autoencoder model which can exploit temporal relations in contrast to the commonly used shallow learning methods, such as Uniform Manifold Approximation and Projection (UMAP).

^{[22]}This paper proposes an online tuning approach for the hyperparameters of deep long short-term memory (DLSTM) model in a dynamic fashion.

^{[23]}5 concentration prediction model is proposed in this study by combining complete ensemble empirical mode decomposition (CEEMD) method, Pearson’s correlation analysis, and a deep long short-term memory (LSTM) method.

^{[24]}So, we developed a new model which is a combination of neural attention mechanism and LSTM called NA-DLSTM (Neural Attention based Deep Long Short-Term Memory) for Context-aware Aspect based Sentiment Analysis.

^{[25]}In this work, we develop a novel fire detection method using deep Long-Short Term Memory (LSTM) neural networks and variational autoencoder (VAE) to meet these increasingly stringent requirements and outperform existing fire detection methods.

^{[26]}This study uses AIoT with deep long short-term memory (LSTM) networks for biosensing application.

^{[27]}In this paper, we aim to estimate mechanical power output by employing a time-sequential information-based deep Long Short-Term Memory (LSTM) neural network from multiple inertial measurement units (IMUs).

^{[28]}This work presents the proposal of a new approaching for applications using a deep Long-Short Term Memory (LSTM) architecture, to assist the driving activities in mobile robots, using data extracted from an expert pilot as the main source of learning data.

^{[29]}We propose the deep long short-term memory neural network (LSTM) with embedded layer and the long short-term memory neural network with automatic encoder to predict the stock market.

^{[30]}OILog builds models by using a deep Long Short-Term Memory network (LSTM) for capturing both high-frequency log keywords in real-time and new log keywords generated by the system, which can transform unstructured raw logs into structured logs quickly.

^{[31]}Accordingly, the proposed deep RNN-TCS is implemented using a deep long short-term memory system.

^{[32]}In this paper, the Deep Long-short term memory Autoencoder (DLAE), a regularized deep learning model, is proposed for the automatic severity assessment of phonological deviations which are crucial.

^{[33]}Non-anomalous data is passed to train a deep Long Short-Term Memory (LSTM) autoencoder that distinguishes anomalies when the reconstruction error exceeds a threshold.

^{[34]}Second, deep long-short term memory is explored to fully learn extracted data attributes and identify the dynamic model.

^{[35]}In this work, we propose Epi-LSTM, a deep long short-term memory (LSTM) recurrent neural network autoencoder to capture the long-term dependencies in the epigenomic data.

^{[36]}We evaluated this approached by training deep long short-term memory (LSTM) recurrent neural network for automatic speech recognition (ASR) problem on a 2000 hour audio dataset, and comparing to BMUF training with 128 GPUs, the proposed approach delivers 1.

^{[37]}In this paper, we design and establish dual-task deep long short-term memory networks for joint learning of degradation assessment and remaining useful life prediction of aeroengines.

^{[38]}First, we present a Deep Long Short Term Memory (DLSTM) network with dropout at multiple layers for RUL prediction.

^{[39]}In order to identify the attack type, we proposed Deep Long Short Term Memory-Recurrent Neural Network (DLSTM-RNN) method with seven optimizers and 500 epochs to train and test a dataset.

^{[40]}In this paper, an automatic sleep staging method using deep long short-term memory was proposed.

^{[41]}In this paper, we present MonoDCell: a novel cellular-based indoor localization system based on a deep long short-term memory (LSTM) network.

^{[42]}A deep Long Short-Term Memory (LSTM) model has been trained and many different experimentation scenarios were set to deal with embeddings, TFIDF, N-grams, GloVe vocabulary and so on.

^{[43]}Then, a deep long short-term memory subnetwork is utilized effectively to capture the complex long-range temporal dynamics, naturally avoiding the conventional sliding window design and thus ensuring high computational efficiency.

^{[44]}Moreover, the performance of a deep long short-term memory (LSTM) network was analyzed on the selected dataset.

^{[45]}In this paper, we present a deep long short term memory (LSTM) neural network to recognize human intention.

^{[46]}METHODS A deep Long Short-Term Memory (LSTM) network is first used to learn the high-level representations of different EEG patterns.

^{[47]}Then the deep long short-term memory (LSTM) network (a variant of recurrent neural network) is introduced to establish the accurate non-linear mapping between the extracted feature image and phase aberrations.

^{[48]}Our approach adopts deep long short term memory (LSTM) as the main model architecture and predicts futures market movement using augmented market trading data.

^{[49]}In this paper, we propose a semisupervised deep learning approach, using temporal ensembling of deep long short-term memory, to recognize human activities with smartphone inertial sensors.

^{[50]}

## recurrent neural network

This paper focuses on the corresponding early warning research on air quality in the mining area of Wuhai, and constructs Deep Recurrent Neural Network (DRNN) and Deep Long Short Time Memory Neural Network (DLSTM) air quality prediction models based on the filtered weather factors.^{[1]}To address the issue of a vanishing gradient and exploding gradient problem in conventional recurrent neural networks, this paper proposed deep long short term memory (LSTM) recurrent neural network to handle the sequence of the input audio data.

^{[2]}The research summarizes the performance of processing telemetry data using autoregressive integrated moving average (ARIMA), Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory Recurrent Neural Network (LSTM RNN), Deep Long Short-Term Memory Recurrent Neural Networks (DLSTM RNNs), Gated Recurrent Unit Recurrent Neural Network (GRU RNN), and Deep Gated Recurrent Unit Recurrent Neural Networks (DGRU RNNs).

^{[3]}The Recurrent Neural Network based deep long short-term memory model is built over the obtained new dataset for prediction model.

^{[4]}

## New Deep Long

We analyse new deep long-slit optical spectroscopic observations, archival optical images, and published Hi and optical spectroscopic data for a sample of seven gLSBGs, for which we performed mass modelling and estimated the parameters of dark matter haloes assuming the Burkert dark matter density profile.^{[1]}The valley was previously characterized by DERT measurements crossing the valley, and now we present a new deep longitudinal acquisition.

^{[2]}To recognize the noise-digital modulation signals automatically, a new deep long short-term memory networks (LSTMs) model has been proposed and then applied to these signals successfully.

^{[3]}

## Training Deep Long

Experiments are carried out on an IBM supercomputer for training deep long short-term memory (LSTM) acoustic models on the 2000-hour Switchboard dataset.^{[1]}We evaluated this approached by training deep long short-term memory (LSTM) recurrent neural network for automatic speech recognition (ASR) problem on a 2000 hour audio dataset, and comparing to BMUF training with 128 GPUs, the proposed approach delivers 1.

^{[2]}

## Adopt Deep Long

Our approach adopts deep long short term memory (LSTM) as the main model architecture and predicts futures market movement using augmented market trading data.^{[1]}Existing approaches to Chinese semantic role labeling (SRL) mainly adopt deep long short-term memory (LSTM) neural networks to address the long-term dependencies problem.

^{[2]}

## deep long short

In this study, the geopolymerization process of fly ash-based geopolymer was estimated using the deep long short-term memory (LSTM) and machine learning models.^{[1]}This paper devises a novel method, namely, Taylor–Harris Hawks Optimization driven deep long short‐term memory (Taylor–HHO‐based Deep LSTM) for malicious JavaScript discovery.

^{[2]}In this paper, an effective technique, named as Fractional Rider Deep Long Short Term Memory (LSTM) network is developed for workload prediction in cloud gaming.

^{[3]}The excellent empirical performance of our Deep Long Short-Term Memory (DLSTM) approach on various forecasting tasks motivated us to extend it to solve the forecasting problem through hierarchical architectures.

^{[4]}To effectively learn the spatiotemporal dependencies of the fused feature, deep long short-term memory (LSTM), two-dimensional convolutional neural network (2D-CNN), and hybrid models composed of a combination of LSTM and CNN were used.

^{[5]}In this paper, a deep long short term memory (DeepLSTM) network to classify personality traits using the electroencephalogram (EEG) signals is implemented.

^{[6]}Then, Siamese deep long short-term memory model is introduced to calculate the similarity between the question asked and the questions in the database to find the best-matched answer.

^{[7]}Experiments are carried out on an IBM supercomputer for training deep long short-term memory (LSTM) acoustic models on the 2000-hour Switchboard dataset.

^{[8]}First of all, with the concept of dynamic rolling prediction and deep long short term memory (LSTM) algorithm, a novel V2G capacity modeling and prediction method is developed.

^{[9]}The NCM uses a deep long short-term memory recurrent neural network for a global approximation of an optimal contraction metric, the existence of which is a necessary and sufficient condition for exponential stability of nonlinear systems.

^{[10]}A deep long short-term memory (LSTM) is used to discover dependencies between samples of processed electroencephalogram (EEG) signal at different time instances.

^{[11]}The selected forecast is next passed onto a hybrid deep long short-term memory (HD-LSTM) network, which looks back and learns the relationship of the selected forecast with corresponding rainfall and temperature observations to produce the next-day rainfall forecast.

^{[12]}To enable the system to focus on the most salient parts of the learned multimodal representations, we propose an architecture composed of a capsule attention mechanism following a deep Long Short-Term Memory (LSTM) network.

^{[13]}To improve the accuracy and reliability of load identification model, a hierarchical load classification mechanism is constructed, and the deep Long Short-Term Memory (LSTM) structure as the classifier.

^{[14]}In Phase-II, the H-GANs employed Deep Long Short Term Memory (LSTM) as generator and LSTM with 3D Convolutional Neural Network (3D-CNN) as a discriminator.

^{[15]}This paper focuses on the corresponding early warning research on air quality in the mining area of Wuhai, and constructs Deep Recurrent Neural Network (DRNN) and Deep Long Short Time Memory Neural Network (DLSTM) air quality prediction models based on the filtered weather factors.

^{[16]}Thus, we introduce a novel enhanced version of the grasshopper optimization algorithm called EGOA to optimize the deep long short-term memory (LSTM) neural network architecture, which optimally evolves four of its key hyperparameters.

^{[17]}The proposed approach uses deep long short-term memory (DLSTM) network for training and testing.

^{[18]}In this work, we propose Epi-LSTM, a deep long short-term memory (LSTM) recurrent neural network autoencoder to capture the long-term dependencies in the epigenomic data.

^{[19]}We propose a deep Long Short Term Memory (LSTM) autoencoder model which can exploit temporal relations in contrast to the commonly used shallow learning methods, such as Uniform Manifold Approximation and Projection (UMAP).

^{[20]}This paper proposes an online tuning approach for the hyperparameters of deep long short-term memory (DLSTM) model in a dynamic fashion.

^{[21]}5 concentration prediction model is proposed in this study by combining complete ensemble empirical mode decomposition (CEEMD) method, Pearson’s correlation analysis, and a deep long short-term memory (LSTM) method.

^{[22]}So, we developed a new model which is a combination of neural attention mechanism and LSTM called NA-DLSTM (Neural Attention based Deep Long Short-Term Memory) for Context-aware Aspect based Sentiment Analysis.

^{[23]}This study uses AIoT with deep long short-term memory (LSTM) networks for biosensing application.

^{[24]}In this paper, we aim to estimate mechanical power output by employing a time-sequential information-based deep Long Short-Term Memory (LSTM) neural network from multiple inertial measurement units (IMUs).

^{[25]}We propose the deep long short-term memory neural network (LSTM) with embedded layer and the long short-term memory neural network with automatic encoder to predict the stock market.

^{[26]}OILog builds models by using a deep Long Short-Term Memory network (LSTM) for capturing both high-frequency log keywords in real-time and new log keywords generated by the system, which can transform unstructured raw logs into structured logs quickly.

^{[27]}Accordingly, the proposed deep RNN-TCS is implemented using a deep long short-term memory system.

^{[28]}Non-anomalous data is passed to train a deep Long Short-Term Memory (LSTM) autoencoder that distinguishes anomalies when the reconstruction error exceeds a threshold.

^{[29]}In this work, we propose Epi-LSTM, a deep long short-term memory (LSTM) recurrent neural network autoencoder to capture the long-term dependencies in the epigenomic data.

^{[30]}We evaluated this approached by training deep long short-term memory (LSTM) recurrent neural network for automatic speech recognition (ASR) problem on a 2000 hour audio dataset, and comparing to BMUF training with 128 GPUs, the proposed approach delivers 1.

^{[31]}In this paper, we design and establish dual-task deep long short-term memory networks for joint learning of degradation assessment and remaining useful life prediction of aeroengines.

^{[32]}To address the issue of a vanishing gradient and exploding gradient problem in conventional recurrent neural networks, this paper proposed deep long short term memory (LSTM) recurrent neural network to handle the sequence of the input audio data.

^{[33]}First, we present a Deep Long Short Term Memory (DLSTM) network with dropout at multiple layers for RUL prediction.

^{[34]}In order to identify the attack type, we proposed Deep Long Short Term Memory-Recurrent Neural Network (DLSTM-RNN) method with seven optimizers and 500 epochs to train and test a dataset.

^{[35]}In this paper, an automatic sleep staging method using deep long short-term memory was proposed.

^{[36]}In this paper, we present MonoDCell: a novel cellular-based indoor localization system based on a deep long short-term memory (LSTM) network.

^{[37]}A deep Long Short-Term Memory (LSTM) model has been trained and many different experimentation scenarios were set to deal with embeddings, TFIDF, N-grams, GloVe vocabulary and so on.

^{[38]}Then, a deep long short-term memory subnetwork is utilized effectively to capture the complex long-range temporal dynamics, naturally avoiding the conventional sliding window design and thus ensuring high computational efficiency.

^{[39]}Moreover, the performance of a deep long short-term memory (LSTM) network was analyzed on the selected dataset.

^{[40]}In this paper, we present a deep long short term memory (LSTM) neural network to recognize human intention.

^{[41]}METHODS A deep Long Short-Term Memory (LSTM) network is first used to learn the high-level representations of different EEG patterns.

^{[42]}Then the deep long short-term memory (LSTM) network (a variant of recurrent neural network) is introduced to establish the accurate non-linear mapping between the extracted feature image and phase aberrations.

^{[43]}Our approach adopts deep long short term memory (LSTM) as the main model architecture and predicts futures market movement using augmented market trading data.

^{[44]}In this paper, we propose a semisupervised deep learning approach, using temporal ensembling of deep long short-term memory, to recognize human activities with smartphone inertial sensors.

^{[45]}This paper proposes a deep learning-based model for the forecast of price and demand for big data using Deep Long Short-Term Memory (DLSTM).

^{[46]}In this paper, a memory-controlled deep long short-term memory (LSTM) neural network post-equalizer is proposed to mitigate both linear and nonlinear impairments in pulse amplitude modulation (PAM) based visible light communication (VLC) systems.

^{[47]}In this paper, a novel data-driven machine health monitoring method is proposed using adaptive kernel spectral clustering (AKSC) and deep long short-term memory recurrent neural networks (LSTM-RNN).

^{[48]}To recognize the noise-digital modulation signals automatically, a new deep long short-term memory networks (LSTMs) model has been proposed and then applied to these signals successfully.

^{[49]}The research summarizes the performance of processing telemetry data using autoregressive integrated moving average (ARIMA), Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory Recurrent Neural Network (LSTM RNN), Deep Long Short-Term Memory Recurrent Neural Networks (DLSTM RNNs), Gated Recurrent Unit Recurrent Neural Network (GRU RNN), and Deep Gated Recurrent Unit Recurrent Neural Networks (DGRU RNNs).

^{[50]}