## What is/are Deep Artificial?

Deep Artificial - In recent times, deep artificial neural networks have achieved many successes in pattern recognition.^{[1]}To address this issue, we interrogated fMRI activation elicited as 240 sentences were read by 14 participants (9 female, 5 male), using sentences encoded by a recurrent deep artificial neural-network trained on a sentence inference task (InferSent).

^{[2]}Second, a deep artificial neural network was created.

^{[3]}The proposed approach applies a deep artificial neural network model called an importance-weighted autoencoder (IWAE) for exploratory IFA.

^{[4]}Replay-like mechanisms have been incorporated in deep artificial neural networks that learn over time to avoid catastrophic forgetting of previous knowledge.

^{[5]}The most successful method so far involves training deep artificial neural networks (ANNs) using Gradient-Descent and then converting them to SNNs.

^{[6]}In this paper, a novel approach DeepAMD to defend against real-world Android malware using deep Artificial Neural Network (ANN) has been adopted including an efficiency comparison of DeepAMD with conventional machine learning classifiers and state-of-the-art studies based on performance measures such as accuracy, recall, f-score, and precision.

^{[7]}In the research, it was concluded that CO2 flux from soil to atmosphere can be modeled in high accuracy, and deep artificial neural networks can have higher efficiency in similar works.

^{[8]}Besides, in order to predict the green wall performance in a short time interval, a deep artificial neural network was trained from the experimental data and a 15-day weather dataset was collected and fed into the deep learning model.

^{[9]}This boom originated in 2009, when a new model emerged, that is, the deep artificial neural network, which began to surpass other established mature models on some important benchmarks.

^{[10]}In recent years deep artificial neural networks (DNNs) have been successfully employed in numerical simulations for a multitude of computational problems including, for example, object and face recognition, natural language processing, fraud detection, computational advertisement, and numerical approximations of partial differential equations (PDEs).

^{[11]}In this proof-of-concept study, we present a deep artificial neural network (ANN) capable of computing hemodynamics for patients with aortic coarctation in a centerline aggregated (i.

^{[12]}For the ∼20% of the sky not covered by FIMS observations, predictions from a deep artificial neural network were used.

^{[13]}Recent advances in deep artificial neural networks (DNNs) have transformed the field of machine learning, frequently outperforming traditional signal processing techniques in the domain of acoustic event detection and classification.

^{[14]}Neural machine translation (NMT) is an approach to machine translation (MT) that uses deep learning techniques, a broad area of machine learning based on deep artificial neural networks (NNs).

^{[15]}We propose a novel approach based on modern deep artificial neural networks (DNNs) for understanding how the morpho-electrical complexity of neurons shapes their input/output (I/O) properties at the millisecond resolution in response to massive synaptic input.

^{[16]}These models include: Shallow and Deep Artificial Neural Networks (ANN), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF) and Dimension Reduction using Principal Component Analysis (PCA).

^{[17]}Deep learning is an approach to artificial intelligence (AI) centered on the training of deep artificial neural networks to perform complex tasks.

^{[18]}In 24 our case, (1) changing the environment not suitable for cultivation by increased erosion close to mining 25 area and also draining underground water (2) increasing conflicts and stress on habitation by noise 26 pollution and heavy vehicle traffic (3) trapping sand by forming extensive and deep artificial lakes, causing 27 coastal land loss.

^{[19]}The application of deep learning to robotics over the past decade has led to a wave of research into deep artificial neural networks and to a very specific problems and questions that are not usually addressed by the computer vision and machine learning communities.

^{[20]}In this article, a deep artificial neural network (ANN) model has been proposed to predict the boiling heat transfer in helical coils under high gravity conditions, which is compared with experimental data.

^{[21]}The deep artificial neural network of five hidden layers with 500 neurons, each fed by ten descriptors – three Euler angles and seven various bond lengths – predicts the total energies with an accuracy within the root mean square error of 0.

^{[22]}We also observe the rise in the use of deep artificial neural networks.

^{[23]}Some of the algorithms used include XGBoost, Random Forest, and Deep Artificial Neural Networks.

^{[24]}In recent years, deep artificial neural networks can have better forecasting performance than many other artificial neural networks.

^{[25]}To address this, we introduce a deep learning framework, ‘i-Melt’, which combines a deep artificial neural network with thermodynamic equations.

^{[26]}High values are attributed to deep artificial lakes, which are more than ten times deeper than natural lakes and do not freeze throughout in winter.

^{[27]}Traditionally, Deep Artificial Neural Networks (DNN's) are trained through gradient descent.

^{[28]}Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small gap between training and test performance.

^{[29]}Our findings are robust to using discrete hazard models with either a deep artificial neural network (DNN) or logistic regression (LR) in the estimation and hold across different time periods.

^{[30]}Machine Learning (ML) techniques, including Deep Artificial Neural Networks (DNNs), have shown significant improvement in classification and segmentation tasks.

^{[31]}This paper is aimed at applying deep artificial neural networks for solving system of ordinary differential equations.

^{[32]}The proposed model uses a deep artificial neural network (ANN) as a non-linear hazard function.

^{[33]}Numerous models were utilized including models broadly recognized and used (ridge regression, autoregression with exogenous input, deep artificial neural networks), as well as previously unexplored models (combination of summer and winter linear models with the utilization of fuzzy logic).

^{[34]}Methods To achieve the defined objective, we propose a new family of models based on the EfficientNet family of deep artificial neural networks which are known for their high accuracy and low footprints.

^{[35]}This paper introduces a grammar-based general purpose framework for the automatic search and deployment of potentially Deep Artificial Neural Networks (DANNs).

^{[36]}In this work, we combine quantum renormalization group approaches with deep artificial neural networks for the description of the real-time evolution in strongly disordered quantum matter.

^{[37]}This work aims to develop wind speed forecasting models, using artificial intelligence techniques, including Deep Artificial Neural Networks (DANNs) and recurrent network architectures (LSTM).

^{[38]}Conclusion Deep artificial neural networks enable the accurate detection of definite radiographic sacroiliitis relevant for the diagnosis and classification of axSpA.

^{[39]}This paper describes the implementation of fast state-dependent Riccati equation (SDRE) control algorithms through the use of shallow and deep artificial neural networks (ANN).

^{[40]}In this paper, comparisons based on predictive accuracy, recall, precision, AUC, and F1-score of several supervised learning methods such as decision tree (DT), artificial neural network (ANN), deep artificial neural network (DANN), and AdaBoost are presented and their performances are analyzed.

^{[41]}In this current study, the training is carried out using deep convolutional spiking neural networks with surrogate gradient descent backpropagation in a customized layer architecture similar to deep artificial neural networks.

^{[42]}Deep artificial neural networks are especially popular in image processing.

^{[43]}This work seeks to explore the efficacy of deep artificial neural networks (DNNs) for this problem of parameter recovery from spectra and to also investigate the uncertainty of recovering the fireball parameters from FTIR data.

^{[44]}Deep artificial neural networks apply principles of the brain’s information processing that led to breakthroughs in machine learning spanning many problem domains.

^{[45]}Main stream machine learning research on deep artificial neural networks (ANNs) may even be characterized as being behavioristic.

^{[46]}We sought to use deep artificial intelligence (AI) and develop an unbiased predictive algorithm for all-cause mortality in a cohort of patients hospitalized with a de novo or worsened HF.

^{[47]}RESULTS This study showed that functional remineralization of artificial lesions using PILP-releasing restoratives occurred, indicated by an increase of the elastic modulus in shallow lesions and in the middle zone of deep artificial lesions.

^{[48]}However, their applications have been limited to relatively simple tasks such as image classification, since it is difficult to train SNNs and converting deep artificial neural networks (ANNs) into SNNs directly usually causes large accuracy degradation.

^{[49]}This study proposes an automatic search approach in the form of design framework as Islamic Medicine Engine using Meta-Deep Artificial Intelligence (AI) technology based on optimization techniques that have reliable capabilities in the search process for solutions so that no matter how difficult the search space is, it will still be easy to find the optimal solution.

^{[50]}

## 4 4 4

By combining deep learning and intelligence algorithms, we propose a prediction model based on the chaos-enhanced accelerated particle swarm optimization algorithm and deep artificial neural network (CAPSO BP DNN) that possesses a seven-layer 8-4-4-4-4-4-1 structure.^{[1]}

## Training Deep Artificial

The most successful method so far involves training deep artificial neural networks (ANNs) using Gradient-Descent and then converting them to SNNs.^{[1]}In this work we propose a framework for training deep artificial neural networks for interpreting LUS, which may promote broader use of LUS.

^{[2]}Training deep artificial neural networks for classification problems may benefit from exploiting intrinsic class similarities by way of network regularization that compensates for a drawback in the commonly used target error.

^{[3]}

## Produce Deep Artificial

Materials and methods Sound extracted human premolars were demineralized to produce deep artificial carious lesions.^{[1]}Materials and Methods: In this in vitro study, extracted sound premolars were placed in a demineralizing solution to produce deep artificial carious lesions.

^{[2]}

## deep artificial neural

In recent times, deep artificial neural networks have achieved many successes in pattern recognition.^{[1]}To address this issue, we interrogated fMRI activation elicited as 240 sentences were read by 14 participants (9 female, 5 male), using sentences encoded by a recurrent deep artificial neural-network trained on a sentence inference task (InferSent).

^{[2]}Second, a deep artificial neural network was created.

^{[3]}The proposed approach applies a deep artificial neural network model called an importance-weighted autoencoder (IWAE) for exploratory IFA.

^{[4]}Replay-like mechanisms have been incorporated in deep artificial neural networks that learn over time to avoid catastrophic forgetting of previous knowledge.

^{[5]}The most successful method so far involves training deep artificial neural networks (ANNs) using Gradient-Descent and then converting them to SNNs.

^{[6]}In this paper, a novel approach DeepAMD to defend against real-world Android malware using deep Artificial Neural Network (ANN) has been adopted including an efficiency comparison of DeepAMD with conventional machine learning classifiers and state-of-the-art studies based on performance measures such as accuracy, recall, f-score, and precision.

^{[7]}In the research, it was concluded that CO2 flux from soil to atmosphere can be modeled in high accuracy, and deep artificial neural networks can have higher efficiency in similar works.

^{[8]}Besides, in order to predict the green wall performance in a short time interval, a deep artificial neural network was trained from the experimental data and a 15-day weather dataset was collected and fed into the deep learning model.

^{[9]}This boom originated in 2009, when a new model emerged, that is, the deep artificial neural network, which began to surpass other established mature models on some important benchmarks.

^{[10]}In recent years deep artificial neural networks (DNNs) have been successfully employed in numerical simulations for a multitude of computational problems including, for example, object and face recognition, natural language processing, fraud detection, computational advertisement, and numerical approximations of partial differential equations (PDEs).

^{[11]}In this proof-of-concept study, we present a deep artificial neural network (ANN) capable of computing hemodynamics for patients with aortic coarctation in a centerline aggregated (i.

^{[12]}For the ∼20% of the sky not covered by FIMS observations, predictions from a deep artificial neural network were used.

^{[13]}Recent advances in deep artificial neural networks (DNNs) have transformed the field of machine learning, frequently outperforming traditional signal processing techniques in the domain of acoustic event detection and classification.

^{[14]}Neural machine translation (NMT) is an approach to machine translation (MT) that uses deep learning techniques, a broad area of machine learning based on deep artificial neural networks (NNs).

^{[15]}We propose a novel approach based on modern deep artificial neural networks (DNNs) for understanding how the morpho-electrical complexity of neurons shapes their input/output (I/O) properties at the millisecond resolution in response to massive synaptic input.

^{[16]}These models include: Shallow and Deep Artificial Neural Networks (ANN), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF) and Dimension Reduction using Principal Component Analysis (PCA).

^{[17]}Deep learning is an approach to artificial intelligence (AI) centered on the training of deep artificial neural networks to perform complex tasks.

^{[18]}The application of deep learning to robotics over the past decade has led to a wave of research into deep artificial neural networks and to a very specific problems and questions that are not usually addressed by the computer vision and machine learning communities.

^{[19]}In this article, a deep artificial neural network (ANN) model has been proposed to predict the boiling heat transfer in helical coils under high gravity conditions, which is compared with experimental data.

^{[20]}The deep artificial neural network of five hidden layers with 500 neurons, each fed by ten descriptors – three Euler angles and seven various bond lengths – predicts the total energies with an accuracy within the root mean square error of 0.

^{[21]}We also observe the rise in the use of deep artificial neural networks.

^{[22]}Some of the algorithms used include XGBoost, Random Forest, and Deep Artificial Neural Networks.

^{[23]}In recent years, deep artificial neural networks can have better forecasting performance than many other artificial neural networks.

^{[24]}To address this, we introduce a deep learning framework, ‘i-Melt’, which combines a deep artificial neural network with thermodynamic equations.

^{[25]}Traditionally, Deep Artificial Neural Networks (DNN's) are trained through gradient descent.

^{[26]}Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small gap between training and test performance.

^{[27]}Our findings are robust to using discrete hazard models with either a deep artificial neural network (DNN) or logistic regression (LR) in the estimation and hold across different time periods.

^{[28]}Machine Learning (ML) techniques, including Deep Artificial Neural Networks (DNNs), have shown significant improvement in classification and segmentation tasks.

^{[29]}This paper is aimed at applying deep artificial neural networks for solving system of ordinary differential equations.

^{[30]}The proposed model uses a deep artificial neural network (ANN) as a non-linear hazard function.

^{[31]}Numerous models were utilized including models broadly recognized and used (ridge regression, autoregression with exogenous input, deep artificial neural networks), as well as previously unexplored models (combination of summer and winter linear models with the utilization of fuzzy logic).

^{[32]}Methods To achieve the defined objective, we propose a new family of models based on the EfficientNet family of deep artificial neural networks which are known for their high accuracy and low footprints.

^{[33]}This paper introduces a grammar-based general purpose framework for the automatic search and deployment of potentially Deep Artificial Neural Networks (DANNs).

^{[34]}In this work, we combine quantum renormalization group approaches with deep artificial neural networks for the description of the real-time evolution in strongly disordered quantum matter.

^{[35]}This work aims to develop wind speed forecasting models, using artificial intelligence techniques, including Deep Artificial Neural Networks (DANNs) and recurrent network architectures (LSTM).

^{[36]}Conclusion Deep artificial neural networks enable the accurate detection of definite radiographic sacroiliitis relevant for the diagnosis and classification of axSpA.

^{[37]}This paper describes the implementation of fast state-dependent Riccati equation (SDRE) control algorithms through the use of shallow and deep artificial neural networks (ANN).

^{[38]}In this paper, comparisons based on predictive accuracy, recall, precision, AUC, and F1-score of several supervised learning methods such as decision tree (DT), artificial neural network (ANN), deep artificial neural network (DANN), and AdaBoost are presented and their performances are analyzed.

^{[39]}In this current study, the training is carried out using deep convolutional spiking neural networks with surrogate gradient descent backpropagation in a customized layer architecture similar to deep artificial neural networks.

^{[40]}Deep artificial neural networks are especially popular in image processing.

^{[41]}This work seeks to explore the efficacy of deep artificial neural networks (DNNs) for this problem of parameter recovery from spectra and to also investigate the uncertainty of recovering the fireball parameters from FTIR data.

^{[42]}Deep artificial neural networks apply principles of the brain’s information processing that led to breakthroughs in machine learning spanning many problem domains.

^{[43]}Main stream machine learning research on deep artificial neural networks (ANNs) may even be characterized as being behavioristic.

^{[44]}However, their applications have been limited to relatively simple tasks such as image classification, since it is difficult to train SNNs and converting deep artificial neural networks (ANNs) into SNNs directly usually causes large accuracy degradation.

^{[45]}In this paper, we propose a method of learning representation layers with squashing activation functions within a deep artificial neural network which directly addresses the vanishing gradients problem.

^{[46]}Deep Artificial Neural Networks (ANNs) employ a simplified analog neuron model that mimics the rate transfer function of integrate-and-fire neurons.

^{[47]}In this work we propose a framework for training deep artificial neural networks for interpreting LUS, which may promote broader use of LUS.

^{[48]}Motivated by the success of deep learning techniques in solving several complex estimation and prediction problems, we employ two deep artificial neural network (ANN) models, one based on the multilayer perceptron (MLP) and the second on the convolutional neural network (CNN), that can map efficiently the instantaneous received SNR with the user 3D position and the UE orientation.

^{[49]}Object detection in autonomous cars is commonly based on camera images and Lidar inputs, which are often used to train prediction models such as deep artificial neural networks for decision making for object recognition, adjusting speed, etc.

^{[50]}

## deep artificial intelligence

We sought to use deep artificial intelligence (AI) and develop an unbiased predictive algorithm for all-cause mortality in a cohort of patients hospitalized with a de novo or worsened HF.^{[1]}This study proposes an automatic search approach in the form of design framework as Islamic Medicine Engine using Meta-Deep Artificial Intelligence (AI) technology based on optimization techniques that have reliable capabilities in the search process for solutions so that no matter how difficult the search space is, it will still be easy to find the optimal solution.

^{[2]}Here, we present DeepAISE (Deep Artificial Intelligence Sepsis Expert), a recurrent neural survival model for the early prediction of sepsis.

^{[3]}Recently, deep Artificial Intelligence (AI), also known as Deep Learning (DL), has shown great success in computer vision.

^{[4]}These innovations use AI technologies (definition to follow below) in one of its many forms, such as deep Artificial Intelligence for Innovation in Austria.

^{[5]}

## deep artificial cariou

Materials and methods Sound extracted human premolars were demineralized to produce deep artificial carious lesions.^{[1]}Materials and Methods: In this in vitro study, extracted sound premolars were placed in a demineralizing solution to produce deep artificial carious lesions.

^{[2]}

## deep artificial lake

In 24 our case, (1) changing the environment not suitable for cultivation by increased erosion close to mining 25 area and also draining underground water (2) increasing conflicts and stress on habitation by noise 26 pollution and heavy vehicle traffic (3) trapping sand by forming extensive and deep artificial lakes, causing 27 coastal land loss.^{[1]}High values are attributed to deep artificial lakes, which are more than ten times deeper than natural lakes and do not freeze throughout in winter.

^{[2]}