## What is/are Single Deep?

Single Deep - 9% compared to single DeepMove.^{[1]}The performance of FOSSA–DeepAR is compared with other hybrid models and a single DeepAR model.

^{[2]}7) The single deepest pocket should be used for identifying low AFVs.

^{[3]}We virtually generate multiple histological stains through a single deep-neural-network, using at its input autofluorescence images of the unlabeled tissue alongside a user-defined digital-staining-matrix.

^{[4]}Another direction was to construct a single deep-learning framework while keeping the step-by-step translation process.

^{[5]}Points of improvement are amniotic fluid evaluation at term by a single deepest vertical pocket, and the information about induction of labour at term.

^{[6]}Sixteen patients with a single deep-seated, centrally located benign brain lesion treated by CyberKnife (CK, G4 cone-based model) were enrolled.

^{[7]}A single deep and wide Si trench, capped with a hetero-epitaxially grown SiC layer, is employed as the edge termination structure.

^{[8]}Sonographically, it is diagnosed when AFI < 5 cm or single deepest vertical pocket of < 2 cm.

^{[9]}A single DeepSEA program is automatically compiled into a certified ``layer'' consisting of a C program (which is then compiled into assembly by CompCert), a low-level functional Coq specification, and a formal (Coq) proof that the C program satisfies the specification.

^{[10]}During the late Miocene the lateral displacement formed a trishear structure that evolved by the Plio-Pleistocene into the Kinneret-Kinarot-Beit-She'an complex, bounded by longitudinal faults stemming from a single deep-rooted fault.

^{[11]}The purpose of this study is to a) show how to use a single deep-learning-based classifier in conjunction with BCI (brain–computer interface) applications with the CSP (common spatial features) and the Riemannian geometry feature extraction methods in BCI applications and to b) describe one of the wrapper feature-selection algorithms, referred to as the particle swarm optimization, in combination with a decision tree algorithm.

^{[12]}Based on the combination of functional times determined for each element of the system, the system becomes a single deeply integrated structure bound with external and internal time.

^{[13]}We propose a mechanism for high-efficiency collimation of airborne sound through an ultra-thin planar structure perforated with a single deep-subwavelength aperture by combining a zigzag-shaped structure with arrays of subwavelength resonators to increase the equivalent refractive index and eliminate high-order diffractive waves simultaneously.

^{[14]}Our findings link anaerobic methane metabolism and dissimilatory sulfur reduction within a single deeply rooted archaeal population and have implications for the evolution of these traits throughout the Archaea.

^{[15]}Objective: To determine the normal value of amniotic fluid (AF) volume among pregnant women in a Northern Nigerian population and to determine if there is a relationship between AF index (AFI) and single deepest pocket (SDP) with anthropometric variables.

^{[16]}METHODS We performed a nationwide, multicenter, open-label, randomized controlled trial, the PPROM: Expectant Management versus Induction of Labor-III (PPROMEXIL-III) trial, in women with singleton pregnancies and preterm prelabor rupture of membranes at 16 0/7 to 24 0/7 weeks of gestation with oligohydramnios (single deepest pocket less than 20 mm).

^{[17]}Objective: the aim of this study was to evaluate both AFI and single deepest pocket in patients with late severe preeclampsia, and to correlate both markers with different parameters of fetal outcome.

^{[18]}We then created maps based on all wells with total depths below the elevations of their respective pour points in 14and 11-digit hydrologic units (n = 3,203 and 854, respectively), as well as maps based on the single deepest well in the 14and 11-digit hydrologic units (n = 1,420 and 74, respectively).

^{[19]}Hasil : Diagnosis TTTS ditegakkan berdasarkan temuan ultrasonografi; kehamilan monochorionic diamniotic (MCDA) dan polihidramnion pada salah satu kantung (single deepest pocket: 22,64 cm).

^{[20]}Introduction: Single deepest pocket provides quick assessment of AFV, as it consumes less time.

^{[21]}

## convolutional neural network

To overcome these limitations, this paper takes a novel approach of constructing a single deep convolutional neural network (CNN) named as DblurDoseNet that learns to produce dose-rate maps while compensating for the limited resolution of SPECT images.^{[1]}By learning the conditional features that are correlated with the regularization hyperparameter, we demonstrate that optimal solutions with arbitrary hyperparameters can be captured by a single deep convolutional neural network.

^{[2]}YOLO is a single deep convolutional neural network that splits the input image into a set of grid cells, so unlike image classification or face detection, each grid cell in YOLO algorithm will have an associated vector in the output that tells us if an object exists in that grid cell, the class of that object, the predicted bounding box for that object.

^{[3]}The main advantage of using a single Deep Convolutional Neural Network DCNN to detect abnormal positioning of several lines based on chest X-ray image processing is to avoid the complexity caused by using a DCNN (Deep Convolutional Neural Network) network for each type of line.

^{[4]}This paper proposes a single deep convolutional neural network capable of simultaneously predicting objects in a scene, their segmentation mask, and a ranked list of the optimal grasping locations.

^{[5]}The results on synthetic data and real-world data are promising, and they suggest that our approach may have advantages compared to the monolithic approach based on a single deep convolutional neural network.

^{[6]}More specifically, in contrary to the majority of existing data-driven prognostic approaches for RUL estimation, which are developed based on a single deep model and can hardly maintain satisfactory generalization performance across various prognostic scenarios, the proposed HDNN framework consists of two parallel paths (one based on Long Short Term Memory (LSTM) and one based on convolutional neural networks (CNN)) followed by a fully connected multilayer fusion neural network, which acts as the fusion center combining the outputs of the two paths to form the target RUL.

^{[7]}The proposed model is based on single Deep Convolutional Neural Networks (DNNs), which contain convolution layers and deep residual blocks.

^{[8]}Our method separately handles these two sub-tasks within a single deep convolutional neural network (CNN).

^{[9]}We propose a new end-to-end model that jointly computes detection and feature extraction steps through a single deep Convolutional Neural Network architecture.

^{[10]}We present a multi-task algorithm for simultaneous face identification, gender recognition, age estimation, several facial attributes prediction and facial expression classification by using a single deep multi-branch convolutional neural network (CNN) based on MobileFaceNet.

^{[11]}Specifically, we propose a multimodal fusion model based on convolutional neural networks (CNN) and long short-term memory (LSTM) to fuse the ECG, vehicle data and contextual data to jointly learn the highly correlated representation across modalities, after learning each modality, with a single deep network.

^{[12]}

## amniotic fluid index

OBJECTIVE This study sought to determine whether there is a significant difference in amniotic fluid measurements when measuring perpendicular to the floor compared with perpendicular to the uterine contour using both amniotic fluid index and single deepest pocket.^{[1]}Oligohydramnios is defined by USG as an amniotic fluid index 5 cm or less or Single deepest pocket ABSTRACT.

^{[2]}OBJECTIVE Our goal was to develop a United States standard for amniotic fluid volume that is estimated by the amniotic fluid index and single deepest pocket.

^{[3]}Estimated AFV was ultrasonographically assessed, and then actual AFV was directly measured during the cesarean delivery to compare the subjective method (SM), amniotic fluid index (AFI), single deepest pocket (SDP), and 2-diameter pocket.

^{[4]}

## One Single Deep

All variations have been tackled with one single deep learning model to detect surface damages.^{[1]}Only one single deep dip within a record-large waveband (S+C+L band) is observed by appropriately designing a side-coupled Bragg-grating-assisted Fabry–Perot filter, which has been applied as the basic sensing unit for both the refractive index and temperature measurement.

^{[2]}On the one hand, most existing methods focus on using traditional machine learning models for achieving classification accuracy, while ignoring the advantages of deep representation learning; on the other hand, the current deep neural network models only rely on one single deep learning model, and hence fail to improve performance effectively.

^{[3]}By completing this task, it becomes possible to consider the generation of a tremendous amount of synthetic training data using only one single deep learning algorithm.

^{[4]}It is interesting that unlike any other pairwise model structures, we argue that cross-domain retrieval is still possible using only one single deep network trained on real and synthetic data.

^{[5]}To improve the detection process, the proposed solution employs "model generator" and "testing configurator" components where each model is trained using one single deep network.

^{[6]}We integrate vision-based robotic grasping detection and visual manipulation relationship reasoning in one single deep network and build the autonomous robotic grasping system.

^{[7]}

## single deep learning

All variations have been tackled with one single deep learning model to detect surface damages.^{[1]}It has been observed that the proposed architecture outclass the single deep learning architectures, with an average accuracy in detection bleeding regions of 97.

^{[2]}This article proposes a coordinated scheme to analyze the similarity between sentences in two independent domains instead of a single deep learning model.

^{[3]}The model is fully evaluated, and contrasted with the traditional single deep learning model.

^{[4]}The above problems are solved by Traditional Encoder Single Deep Learning (TESDL) method introduced for weather forecasts using deep Learning Techniques.

^{[5]}Experimental results show that the proposed method gives better results for multiple rail surface defects under low contrast than using a single deep learning model.

^{[6]}In this work, we propose an approach with a single deep learning model that is trained on multiple households, which can create hourly energy consumption forecasts for individual households.

^{[7]}The improvement of the novel model is, on average, 55% for single deep learning models and 30% for decomposition-based hybrid models.

^{[8]}With a small number of data points, a single deep learning algorithm may be ineffective at capturing different patterns for intrusive attacks.

^{[9]}Most of the recent research projects have applied single deep learning systems for data fusion and classification.

^{[10]}As such, prior forecasting techniques that use classical modelling and single deep learning models that undertake feature extraction procedures manually were unable to meet the output demands in specific situations with dynamic variability.

^{[11]}In this work we show the versatility of this technique by means of a single deep learning architecture capable of successfully performing segmentation on two very different types of imaging: computed tomography and magnetic resonance.

^{[12]}Specifically, a single deep learning model cannot capture special patterns in intrusive attacks with a few experiments.

^{[13]}Results show our method can effectively solve the domain shift problem caused by condition variation and noise, thus outperforms not only single deep learning or deep ELM classifier, but also other state-of-the-art ensemble methods and domain adaptation methods.

^{[14]}We integrate seven remote sensing datasets into a single deep learning network.

^{[15]}Deep learning algorithms have been used successfully for the recognition of myocardial infarction in recent years, but most of them are based on the segmentation of heartbeat signals and the single deep learning model.

^{[16]}In this article, a combined deep learning prediction (CDLP) model including two paralleled single deep learning models, CNN-LSTM-attention model and CNN-GRU-attention model, is established.

^{[17]}Here, we compare different ways to represent, or embed, the codes based on their textual, structural and statistical characteristics, using a single deep learning baseline model in quantitative evaluations on discharge reports from the MIMIC-III Intensive Care Unit database.

^{[18]}However, medical image analysis algorithms are required to be reliable, robust, and accurate for clinical applications which can be difficult to achieve for some single deep learning methods.

^{[19]}The proposed method solves the problem that the single deep learning network structure is difficult to keep the time sequence characteristics between samples in the training process.

^{[20]}Existing approaches usually adopt a single deep learning mechanism to extract personality information from user data, which leads to semantic loss to some extent.

^{[21]}On the one hand, most existing methods focus on using traditional machine learning models for achieving classification accuracy, while ignoring the advantages of deep representation learning; on the other hand, the current deep neural network models only rely on one single deep learning model, and hence fail to improve performance effectively.

^{[22]}The single deep learning semantic segmentation defect detection method usually can not meet the needs of industrial applications, finally, it is necessary to combine the simple machine vision method to judge and screen all the suspected defect regions detected by the deep learning semantic segmentation method.

^{[23]}However, these models only use a single deep learning network for chest radiograph detection; the accuracy of this approach required further improvement.

^{[24]}Connecting a tracks-to-KDE model to a KDE-to-hists model used to find PVs provides a proof-of-concept that a single deep learning model can use track information to find PVs with high efficiency and high fidelity.

^{[25]}Instead of relying on a single deep learning model, multiple schemes using convolutional (CNN), long short-term memory (LSTM), and recurrent neural networks (RNNs) are investigated.

^{[26]}To account for this, we propose a new approach that unites both tasks within a single deep learning model.

^{[27]}MATERIALS AND METHODS We address these challenges by developing Multitask-Clinical BERT: a single deep learning model that simultaneously performs 8 clinical tasks spanning entity extraction, personal health information identification, language entailment, and similarity by sharing representations among tasks.

^{[28]}Purpose: A computer-aided diagnosis (CADx) system for breast masses is proposed, which incorporates both handcrafted and convolutional radiomic features embedded into a single deep learning model.

^{[29]}Traditionally, researchers build the single deep learning model using the entire dataset.

^{[30]}Compared with the traditional interpolation algorithm and the single deep learning algorithm, the proposed algorithm has higher performance.

^{[31]}In the prediction, single deep learning model is tested firstly, then ensemble of different deep learning models are compared to achieve better performance than that of single model.

^{[32]}The former is specific because it uses a single deep learning network to perform both the segmentation and the classification at the pixel level of the image.

^{[33]}By completing this task, it becomes possible to consider the generation of a tremendous amount of synthetic training data using only one single deep learning algorithm.

^{[34]}The study was conducted to investigate the feasibility of a single deep learning model for all subjects without compromising on information decoding rate for any of the BCI participants.

^{[35]}The experimental results show that our proposed method outperforms other single deep learning methods (i.

^{[36]}Originality/value No previous work has used a single deep learning framework to learn different representations of Web entities for entity resolution.

^{[37]}Together, our findings show that a single deep learning algorithm can predict clinically actionable alterations from routine histology data.

^{[38]}We show that a single deep learning model with a single neural TTS system can generate multiple languages with unique voices and display them in real life environment.

^{[39]}

## single deep neural

However training a single deep neural network for multiple organs is highly sensitive to class imbalance and variability in size between several structures within the head and neck region.^{[1]}Neural machine translation (NMT) employs the prevailing deep learning techniques to build a single deep neural network (DNN) that directly maps the input speech utterances of one language to the corresponding texts of the other language.

^{[2]}The training/recognition using both sEMG signals and object images can be performed with a single deep neural network in an end-to-end manner.

^{[3]}This approach uses a single deep neural network capable of self-learning a policy, and driving the surge speed and yaw dynamics of a vessel.

^{[4]}The first one proved how Principal Component Analysis and Variational Autoencoder models of the proposed method improves the performance of a single deep neural network.

^{[5]}A single deep neural network might not achieve optimum performance due to instability.

^{[6]}The study also includes a simpler architecture where no patches are used at all – a single deep neural network inputs a whole text image and directly provides a writer recognition hypothesis.

^{[7]}Achieving an automatic trade-off between accuracy and efficiency for a single deep neural network is highly desired in time-sensitive computer vision applications.

^{[8]}The proposed method is able to segment any class of objects using a single deep neural network without any assumptions about their shapes and sizes.

^{[9]}Textile pattern design is a challenging task that can be hardly resolved by a single deep neural network, due to the requirements on high resolution, periodic tiling, copyright protection and aesthetic preference of designers.

^{[10]}In this work, we present EyeNet, the first single deep neural network which solves multiple heterogeneous tasks related to eye gaze estimation for an off-axis camera setting.

^{[11]}The solution we present not only allows us to train for multiple application objectives in a single deep neural network architecture, but takes advantage of correlated information in the combination of all training data from each application to generate a unified embedding that outperforms all specialized embeddings previously deployed for each product.

^{[12]}The simple approach of aggregating data from all source domains and training a single deep neural network end-to-end on all the data provides a surprisingly strong baseline that surpasses many prior published methods.

^{[13]}We evaluate this ensemble learning framework on two real-world datasets, Market-1501 for person re-identification and CIFAR for image classification, and show that it outperforms a single deep neural network remarkably.

^{[14]}An automatic trade-off between accuracy and efficiency for a single deep neural network is highly desired in time-sensitive computer vision applications.

^{[15]}In this study, we exploited two specialist models in addition to a baseline model and applied the knowledge distillation framework from those three models into a single deep neural network.

^{[16]}In this work, we propose a single deep neural network for panoptic segmentation, for which the goal is to provide each individual pixel of an input image with a class label, as in semantic segmentation, as well as a unique identifier for specific objects in an image, following instance segmentation.

^{[17]}The end-to-end (E2E) approach to automatic speech recognition (ASR) is a simplified and an elegant approach where a single deep neural network model directly converts the acoustic feature sequence to the text sequence.

^{[18]}With a trained deep neural network, the unseen phase fields of living mouse osteoblasts and dynamic candle flame are successfully unwrapped, demonstrating that the complicated nonlinear phase unwrapping task can be directly fulfilled in one step by a single deep neural network.

^{[19]}A single deep neural network based on a reduced ResNeXt model and Feature Pyramid Networks is proposed in this paper, which is named as Single Shot Feature Pyramid Detector (SSFPD).

^{[20]}In order to solve some defects of single deep neural network in Chinese entity Relationship Extraction task, a hybrid neural network entity relationship extraction model is designed and implemented in this paper.

^{[21]}In overcoming this, TSN first obtains MBPs from a single deep neural network.

^{[22]}Experimental results from our model were compared with other models, including a single deep neural network, an ensemble of SVM models and an ensemble of decision trees.

^{[23]}This paper describes a technique for learning multiple distinct behavioral modes in a single deep neural network through the use of multi-modal multi-task learning.

^{[24]}

## single deep network

The method achieves optimal results than the single deep network and traditional integration method.^{[1]}Because our approach combines the data likelihood and image prior terms into a single deep network, it is computationally tractable and improves performance through an end-to-end training.

^{[2]}First, lung parenchyma segmentation is used as the attention module and is combined with nodule detection in a single deep network.

^{[3]}We propose a solution in the form of a single deep network, tested for three agricultural datasets pertaining to bananas-per-bunch, spikelets-per-wheat-spike, and berries-per-grape-cluster.

^{[4]}The proposed system has faster learning speed and better accuracy than single deep network based approaches do.

^{[5]}It is interesting that unlike any other pairwise model structures, we argue that cross-domain retrieval is still possible using only one single deep network trained on real and synthetic data.

^{[6]}To improve the detection process, the proposed solution employs "model generator" and "testing configurator" components where each model is trained using one single deep network.

^{[7]}We integrate vision-based robotic grasping detection and visual manipulation relationship reasoning in one single deep network and build the autonomous robotic grasping system.

^{[8]}Specifically, we propose a multimodal fusion model based on convolutional neural networks (CNN) and long short-term memory (LSTM) to fuse the ECG, vehicle data and contextual data to jointly learn the highly correlated representation across modalities, after learning each modality, with a single deep network.

^{[9]}

## single deep convolutional

To overcome these limitations, this paper takes a novel approach of constructing a single deep convolutional neural network (CNN) named as DblurDoseNet that learns to produce dose-rate maps while compensating for the limited resolution of SPECT images.^{[1]}By learning the conditional features that are correlated with the regularization hyperparameter, we demonstrate that optimal solutions with arbitrary hyperparameters can be captured by a single deep convolutional neural network.

^{[2]}YOLO is a single deep convolutional neural network that splits the input image into a set of grid cells, so unlike image classification or face detection, each grid cell in YOLO algorithm will have an associated vector in the output that tells us if an object exists in that grid cell, the class of that object, the predicted bounding box for that object.

^{[3]}The main advantage of using a single Deep Convolutional Neural Network DCNN to detect abnormal positioning of several lines based on chest X-ray image processing is to avoid the complexity caused by using a DCNN (Deep Convolutional Neural Network) network for each type of line.

^{[4]}This paper proposes a single deep convolutional neural network capable of simultaneously predicting objects in a scene, their segmentation mask, and a ranked list of the optimal grasping locations.

^{[5]}The results on synthetic data and real-world data are promising, and they suggest that our approach may have advantages compared to the monolithic approach based on a single deep convolutional neural network.

^{[6]}The proposed model is based on single Deep Convolutional Neural Networks (DNNs), which contain convolution layers and deep residual blocks.

^{[7]}Our method separately handles these two sub-tasks within a single deep convolutional neural network (CNN).

^{[8]}We propose a new end-to-end model that jointly computes detection and feature extraction steps through a single deep Convolutional Neural Network architecture.

^{[9]}

## single deep model

More specifically, in contrary to the majority of existing data-driven prognostic approaches for RUL estimation, which are developed based on a single deep model and can hardly maintain satisfactory generalization performance across various prognostic scenarios, the proposed HDNN framework consists of two parallel paths (one based on Long Short Term Memory (LSTM) and one based on convolutional neural networks (CNN)) followed by a fully connected multilayer fusion neural network, which acts as the fusion center combining the outputs of the two paths to form the target RUL.^{[1]}More specifically, in contrary to the majority of existing data-driven prognostic approaches for RUL estimation, which are developed based on a single deep model and can hardly maintain good generalization performance across various prognostic scenarios, the proposed HDNN framework consists of two parallel paths (one LSTM and one CNN) followed by a fully connected multilayer fusion neural network which acts as the fusion centre combining the output of the two paths to form the target RUL.

^{[2]}When deploying two or more well-trained deep-learningmodels on a system, we would hope to unify them into asingle deep model for the execution in the inference stage, sothat the computation time can be increased and the energyconsumption can be saved.

^{[3]}However, it is still difficult to integrate multi-modal imaging data into a single deep model, to gain benefit from complementary datasets as much as possible.

^{[4]}However, considering the wide application scenario of fault diagnosis technology, the application scope of single deep model may have corresponding limitations.

^{[5]}

## single deep cnn

Moreover, finding local minima in the subparameter space of a deep CNN architecture is more affordable at the training stage, and the multiple models at the found local minima can also be selectively fused to achieve better ensemble generalization while limiting the expense to a single deep CNN model at the testing stage.^{[1]}An end-to-end learning system comprising single deep CNN model that directly classified the LI-RADS grade was developed for comparison.

^{[2]}On the one hand, we make full use of the prior knowledge learned from big dataset of four single deep CNNs (VGG, Inception-v3, ResNet and DenseNet).

^{[3]}

## single deep breath

Clinical cases of stroke-like syndromes after single deep breath-hold dives point to possible mechanisms of decompression stress, caused by N2 entering the vasculature upon ascent from these deep dives.^{[1]}Valved holding chambers are now first choice for aerosol drug delivery for young children, who inhale drug by breathing in and out of the device several times and are increasingly used by older children and adults who inhale drug aerosol by taking a single deep breath.

^{[2]}In our opinion, the test, beat to beat variation of heart rate to single deep breath is though easy to perform and more understandable, but lacks the sensitivity of other two tests.

^{[3]}

## single deep framework

Moreover, how to effectively incorporate neuroscience knowledge into multimodal data fusion with a single deep framework is understudied.^{[1]}As far as we know, it is the first work to jointly optimize these two complementary tasks in a single deep framework.

^{[2]}

## single deep q

A single deep Q network (DQN) is trained for each zone.^{[1]}A single Deep Q-Learning Network is trained subject to multi-agent settings.

^{[2]}

## single deep reservoir

By contrast, the volcanic products of the four volcanic provinces in Libya are primarily basalts and fed directly from single deep reservoirs.^{[1]}All kimberlite came from a single deep reservoir in the planet’s mantle.

^{[2]}

## single deep mandibular

CONCLUSION Treatment of single deep mandibular anterior recessions with a combined tunneled CTG approach in addition to frenuloplasty appears to lead to complete long-term root coverage in one surgery with lasting aesthetics results.^{[1]}CONCLUSION This novel surgical approach, based on the combination of laterally displaced pedicle flap and tunneling in addition to CTG, seems to lead to promising results for the treatment of single deep mandibular anterior recessions.

^{[2]}

## single deep snow

We will note that airborne gamma methods are campaign-based and expensive, while point measurement gamma methods are not able, and are not as efficient, in providing detailed measurements across a single deep snow feature in the same way as grounded-in-situ CRNS.^{[1]}We will note that airborne gamma methods are campaign-based and expensive, while point measurement gamma methods are not able, and are not as efficient, in providing detailed measurements across a single deep snow feature in the same way as grounded-in-situ CRNS.

^{[2]}