Deep Learning: A Comprehensive Overview on Techniques, Taxonomy
Deep Learning: A Comprehensive Overview on ...
Deep learning: systematic review, models, challenges, and research
Deep learning: systematic review, models, challenges, and ...
A Survey of Deep Learning: Platforms, Applications and Emerging
In this paper, we seek to provide a thorough investigation of deep learning in its applications and mechanisms. Specifically, as a categorical collection of state of the art in deep learning research, we hope to provide a broad reference for those seeking a primer on deep learning and its various implementations, platforms, algorithms, and uses ...
Deep learning
Deep learning
Deep learning in computer vision: A critical review of emerging
Deep learning in computer vision: A critical review of ...
The Understanding of Deep Learning: A Comprehensive Review
Deep learning is a computer-based modeling approach, which is made up of many processing layers that are used to understand the representation of data with several levels of abstraction. ... briefed about the recent developments on signal processing and machine learning (SPML) research. Morabito et al. implemented the deep learning concept ...
[1404.7828] Deep Learning in Neural Networks: An Overview
[1404.7828] Deep Learning in Neural Networks: An Overview
PDF Deep Learning: A Comprehensive Overview on Techniques, Taxonomy
This article presents a structured and comprehensive view on deep learning (DL) techniques, taxonomy, applications and research directions. It covers various types of DL networks for supervised, unsupervised and hybrid learning, and summarizes real-world application areas where DL can be used.
Current progress and open challenges for applying deep learning across
Current progress and open challenges for applying deep ...
Review of deep learning: concepts, CNN architectures, challenges
Review of deep learning: concepts, CNN architectures ...
A Comprehensive Overview and Comparative Analysis on Deep Learning
CNN, RNN, LSTM, GRU
Review of Deep Learning Algorithms and Architectures
Deep learning (DL) is playing an increasingly important role in our lives. It has already made a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving cars, predictive forecasting, and speech recognition. The painstakingly handcrafted feature extractors used in traditional learning, classification, and pattern recognition systems are not scalable for large-sized data ...
(PDF) Deep Learning
(PDF) Deep Learning
A survey on deep learning and its applications
A survey on deep learning and its applications
deep learning Latest Research Papers
Browse the latest documents on deep learning from various sources and disciplines. Find papers on topics such as COVID-19 diagnosis, heart rate estimation, earth image segmentation, and more.
Frontiers
Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and machine learning. Recent breakthrough results in image analysis and speech recognition have generated a massive interest in this field because also applications in many other domains providing big data seem possible.
A Comprehensive Survey of Convolutions in Deep Learning: Applications
for their research or development from various perspectives. Additionally, we explore the main research fields of CNN like 6D vision, generative models, and meta-learning. This survey paper provides a comprehensive examination and comparison of various CNN architectures, highlighting their architectural differences
Deep learning for healthcare: review, opportunities and challenges
Deep learning for healthcare: review, opportunities and ...
Recent advances in deep learning models: a systematic ...
In recent years, deep learning has evolved as a rapidly growing and stimulating field of machine learning and has redefined state-of-the-art performances in a variety of applications. There are multiple deep learning models that have distinct architectures and capabilities. Up to the present, a large number of novel variants of these baseline deep learning models is proposed to address the ...
Overview of deep learning
In recent years, deep learning has achieved great success in many fields, such as computer vision and natural language processing. Compared to traditional machine learning methods, deep learning has a strong learning ability and can make better use of datasets for feature extraction. Because of its practicability, deep learning becomes more and more popular for many researchers to do research ...
(PDF) DEEP LEARNING: A REVIEW
R. Vargas, A. Mosavi, L. Ruiz, Deep Learning: A Review Advances in Intelligent Systems and Computing (2017). performed in this study clearly demonstrates the relevance of this technology and gives ...
A comprehensive survey on applications of transformers for deep
The advantages of the Transformer model have inspired deep learning researchers to explore its potential for various tasks in different fields of application (Ren, Li, & Liu, 2023), leading to numerous research papers and the development of Transformer-based models for a range of tasks in the field of artificial intelligence (Reza et al., 2022 ...
Deep Learning Applications
This issue highlights the technical theme on "Deep Learning Applications," one of the most active areas in this new age of AI and machine learning. Eight articles demonstrate new progress made in deep representation learning, deep neural network architectures, and their multidomain applications. Three column articles debate on decentralized AI, autonomous racing, and big AI.
Generative Artificial Intelligence and GPT using Deep Learning: A
This paper discusses recent methodologies adopted by researchers in GAI and machine learning techniques for multimodal applications like image, text and audio-based data generation and identifies techniques and associated limitations. Generative Artificial intelligence is a prominent and recently emerging subdomain in the field of artificial intelligence. It deals with question-answering based ...
How to Read Deep Learning Paper as a Software Engineer
Deep learning papers can look daunting to read.Especially if you don't have a strong theoretical background in machine or deep learning.Some papers can be so...
Deep learning-based optimization of energy utilization in IoT-enabled
Through detailed simulations and realistic case studies done in several smart cities throughout the world, the research paper proves the effectiveness of the deep learning-based strategy. The findings show significant reductions in energy usage, cost savings, and significant contributions to greenhouse gas emissions elimination, eventually ...
Deep Learning Techniques for Automated Dementia Diagnosis Using
Dementia is a condition that often comes with aging and affects how people think, remember, and behave. Diagnosing dementia early is important because it can greatly improve patients' lives. This systematic review looks at how deep learning (DL) techniques have been used to diagnose dementia automatically from 2012 to 2023. We explore how different DL methods like Convolutional Neural ...
Exploring Integration of Multimodal Deep Learning Approaches for
Alzheimer's disease (AD), is the most common form of dementia that affects the nervous system. In the past few years, non-invasive early AD diagnosis has become more popular as a way to improve patient care and treatment results. Imaging methods, electroencephalogram (EEG) tests, and sound evaluations are some of the new ways that researchers have looked into. This review covers 60 papers ...
[2409.03933] A deep learning approach to wall-shear stress
The accurate quantification of wall-shear stress dynamics is of substantial importance for various applications in fundamental and applied research, spanning areas from human health to aircraft design and optimization. Despite significant progress in experimental measurement techniques and post-processing algorithms, temporally resolved wall-shear stress dynamics with adequate spatial ...
Security Assessment Framework for DDOS Attack Detection via Deep Learning
In this paper, a novel Iot based Security Assessment for intrusion using Deep Learning (ISAI-DL) technique has been proposed to identify IoT device vulnerabilities such as DDoS and MitM attacks. Initially, the features are extracted from the API documents using Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) techniques.
COMMENTS
Deep Learning: A Comprehensive Overview on ...
Deep learning: systematic review, models, challenges, and ...
In this paper, we seek to provide a thorough investigation of deep learning in its applications and mechanisms. Specifically, as a categorical collection of state of the art in deep learning research, we hope to provide a broad reference for those seeking a primer on deep learning and its various implementations, platforms, algorithms, and uses ...
Deep learning
Deep learning in computer vision: A critical review of ...
Deep learning is a computer-based modeling approach, which is made up of many processing layers that are used to understand the representation of data with several levels of abstraction. ... briefed about the recent developments on signal processing and machine learning (SPML) research. Morabito et al. implemented the deep learning concept ...
[1404.7828] Deep Learning in Neural Networks: An Overview
This article presents a structured and comprehensive view on deep learning (DL) techniques, taxonomy, applications and research directions. It covers various types of DL networks for supervised, unsupervised and hybrid learning, and summarizes real-world application areas where DL can be used.
Current progress and open challenges for applying deep ...
Review of deep learning: concepts, CNN architectures ...
CNN, RNN, LSTM, GRU
Deep learning (DL) is playing an increasingly important role in our lives. It has already made a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving cars, predictive forecasting, and speech recognition. The painstakingly handcrafted feature extractors used in traditional learning, classification, and pattern recognition systems are not scalable for large-sized data ...
(PDF) Deep Learning
A survey on deep learning and its applications
Browse the latest documents on deep learning from various sources and disciplines. Find papers on topics such as COVID-19 diagnosis, heart rate estimation, earth image segmentation, and more.
Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and machine learning. Recent breakthrough results in image analysis and speech recognition have generated a massive interest in this field because also applications in many other domains providing big data seem possible.
for their research or development from various perspectives. Additionally, we explore the main research fields of CNN like 6D vision, generative models, and meta-learning. This survey paper provides a comprehensive examination and comparison of various CNN architectures, highlighting their architectural differences
Deep learning for healthcare: review, opportunities and ...
In recent years, deep learning has evolved as a rapidly growing and stimulating field of machine learning and has redefined state-of-the-art performances in a variety of applications. There are multiple deep learning models that have distinct architectures and capabilities. Up to the present, a large number of novel variants of these baseline deep learning models is proposed to address the ...
In recent years, deep learning has achieved great success in many fields, such as computer vision and natural language processing. Compared to traditional machine learning methods, deep learning has a strong learning ability and can make better use of datasets for feature extraction. Because of its practicability, deep learning becomes more and more popular for many researchers to do research ...
R. Vargas, A. Mosavi, L. Ruiz, Deep Learning: A Review Advances in Intelligent Systems and Computing (2017). performed in this study clearly demonstrates the relevance of this technology and gives ...
The advantages of the Transformer model have inspired deep learning researchers to explore its potential for various tasks in different fields of application (Ren, Li, & Liu, 2023), leading to numerous research papers and the development of Transformer-based models for a range of tasks in the field of artificial intelligence (Reza et al., 2022 ...
This issue highlights the technical theme on "Deep Learning Applications," one of the most active areas in this new age of AI and machine learning. Eight articles demonstrate new progress made in deep representation learning, deep neural network architectures, and their multidomain applications. Three column articles debate on decentralized AI, autonomous racing, and big AI.
This paper discusses recent methodologies adopted by researchers in GAI and machine learning techniques for multimodal applications like image, text and audio-based data generation and identifies techniques and associated limitations. Generative Artificial intelligence is a prominent and recently emerging subdomain in the field of artificial intelligence. It deals with question-answering based ...
Deep learning papers can look daunting to read.Especially if you don't have a strong theoretical background in machine or deep learning.Some papers can be so...
Through detailed simulations and realistic case studies done in several smart cities throughout the world, the research paper proves the effectiveness of the deep learning-based strategy. The findings show significant reductions in energy usage, cost savings, and significant contributions to greenhouse gas emissions elimination, eventually ...
Dementia is a condition that often comes with aging and affects how people think, remember, and behave. Diagnosing dementia early is important because it can greatly improve patients' lives. This systematic review looks at how deep learning (DL) techniques have been used to diagnose dementia automatically from 2012 to 2023. We explore how different DL methods like Convolutional Neural ...
Alzheimer's disease (AD), is the most common form of dementia that affects the nervous system. In the past few years, non-invasive early AD diagnosis has become more popular as a way to improve patient care and treatment results. Imaging methods, electroencephalogram (EEG) tests, and sound evaluations are some of the new ways that researchers have looked into. This review covers 60 papers ...
The accurate quantification of wall-shear stress dynamics is of substantial importance for various applications in fundamental and applied research, spanning areas from human health to aircraft design and optimization. Despite significant progress in experimental measurement techniques and post-processing algorithms, temporally resolved wall-shear stress dynamics with adequate spatial ...
In this paper, a novel Iot based Security Assessment for intrusion using Deep Learning (ISAI-DL) technique has been proposed to identify IoT device vulnerabilities such as DDoS and MitM attacks. Initially, the features are extracted from the API documents using Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) techniques.