Unveiling the frontiers of deep learning: innovations shaping diverse domains

• URL: http://arxiv.org/abs/2309.02712v2
• Date: Sat, 05 Apr 2025 01:29:03 GMT
• Title: Unveiling the frontiers of deep learning: innovations shaping diverse domains
• Authors: Shams Forruque Ahmed, Md. Sakib Bin Alam, Maliha Kabir, Shaila Afrin, Sabiha Jannat Rafa, Aanushka Mehjabin, Amir H. Gandomi,
• Abstract summary: Deep learning (DL) allows computer models to learn, visualize, optimize, refine, and predict data.<n>Prior reviews focused on DL applications in only one or two domains.<n>This review thoroughly investigates the use of DL in four different broad fields.
• Score: 6.66443389693158
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning (DL) allows computer models to learn, visualize, optimize, refine, and predict data. To understand its present state, examining the most recent advancements and applications of deep learning across various domains is essential. However, prior reviews focused on DL applications in only one or two domains. The current review thoroughly investigates the use of DL in four different broad fields due to the plenty of relevant research literature in these domains. This wide range of coverage provides a comprehensive and interconnected understanding of DL's influence and opportunities, which is lacking in other reviews. The study also discusses DL frameworks and addresses the benefits and challenges of utilizing DL in each field, which is only occasionally available in other reviews. DL frameworks like TensorFlow and PyTorch make it easy to develop innovative DL applications across diverse domains by providing model development and deployment platforms. This helps bridge theoretical progress and practical implementation. Deep learning solves complex problems and advances technology in many fields, demonstrating its revolutionary potential and adaptability. CNN LSTM models with attention mechanisms can forecast traffic with 99 percent accuracy. Fungal diseased mango leaves can be classified with 97.13 percent accuracy by the multi layer CNN model. However, deep learning requires rigorous data collection to analyze and process large amounts of data because it is independent of training data. Thus, large scale medical, research, healthcare, and environmental data compilation are challenging, reducing deep learning effectiveness. Future research should address data volume, privacy, domain complexity, and data quality issues in DL datasets.

Related papers

• EndToEndML: An Open-Source End-to-End Pipeline for Machine Learning Applications [0.2826977330147589]
  We propose a web-based end-to-end pipeline that is capable of preprocessing, training, evaluating, and visualizing machine learning models. Our library assists in recognizing, classifying, clustering, and predicting a wide range of multi-modal, multi-sensor datasets.
  arXiv  Detail & Related papers  (2024-03-27T02:24:38Z)
• Incomplete Multimodal Learning for Complex Brain Disorders Prediction [65.95783479249745]
  We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks. We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative cohort.
  arXiv  Detail & Related papers  (2023-05-25T16:29:16Z)
• Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization [3.6393183544320236]
  Speech recognition has become an important challenge when using deep learning (DL) It requires large-scale training datasets and high computational and storage resources. Deep transfer learning (DTL) has been introduced to overcome these issues.
  arXiv  Detail & Related papers  (2023-04-27T21:08:05Z)
• Deep Learning in Healthcare: An In-Depth Analysis [1.892561703051693]
  We provide a review of Deep Learning models and their broad application in bioinformatics and healthcare. We also go over some of the key challenges that still exist and can show up while conducting DL research.
  arXiv  Detail & Related papers  (2023-02-12T20:55:34Z)
• Deep networks for system identification: a Survey [56.34005280792013]
  System identification learns mathematical descriptions of dynamic systems from input-output data. Main aim of the identified model is to predict new data from previous observations. We discuss architectures commonly adopted in the literature, like feedforward, convolutional, and recurrent networks.
  arXiv  Detail & Related papers  (2023-01-30T12:38:31Z)
• An overview of open source Deep Learning-based libraries for Neuroscience [0.0]
  This paper summarizes the main developments in Deep Learning and their relevance to Neuroscience. It then reviews neuroinformatic toolboxes and libraries, collected from the literature and from specific hubs of software projects oriented to neuroscience research.
  arXiv  Detail & Related papers  (2022-12-19T09:09:40Z)
• Deep Active Learning for Computer Vision: Past and Future [50.19394935978135]
  Despite its indispensable role for developing AI models, research on active learning is not as intensive as other research directions. By addressing data automation challenges and coping with automated machine learning systems, active learning will facilitate democratization of AI technologies.
  arXiv  Detail & Related papers  (2022-11-27T13:07:14Z)
• A Survey of Learning on Small Data: Generalization, Optimization, and Challenge [101.27154181792567]
  Learning on small data that approximates the generalization ability of big data is one of the ultimate purposes of AI. This survey follows the active sampling theory under a PAC framework to analyze the generalization error and label complexity of learning on small data. Multiple data applications that may benefit from efficient small data representation are surveyed.
  arXiv  Detail & Related papers  (2022-07-29T02:34:19Z)
• GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation [60.07812405063708]
  3D point cloud semantic segmentation is fundamental for autonomous driving. Most approaches in the literature neglect an important aspect, i.e., how to deal with domain shift when handling dynamic scenes. This paper advances the state of the art in this research field.
  arXiv  Detail & Related papers  (2022-07-20T09:06:07Z)
• Do Deep Neural Networks Always Perform Better When Eating More Data? [82.6459747000664]
  We design experiments from Identically Independent Distribution(IID) and Out of Distribution(OOD) Under IID condition, the amount of information determines the effectivity of each sample, the contribution of samples and difference between classes determine the amount of class information. Under OOD condition, the cross-domain degree of samples determine the contributions, and the bias-fitting caused by irrelevant elements is a significant factor of cross-domain.
  arXiv  Detail & Related papers  (2022-05-30T15:40:33Z)
• A streamable large-scale clinical EEG dataset for Deep Learning [0.0]
  We publish the first large-scale clinical EEG dataset that simplifies data access and management for Deep Learning. This dataset contains eyes-closed EEG data prepared from a collection of 1,574 juvenile participants from the Healthy Brain Network.
  arXiv  Detail & Related papers  (2022-03-04T20:05:50Z)
• BERT WEAVER: Using WEight AVERaging to enable lifelong learning for transformer-based models in biomedical semantic search engines [49.75878234192369]
  We present WEAVER, a simple, yet efficient post-processing method that infuses old knowledge into the new model. We show that applying WEAVER in a sequential manner results in similar word embedding distributions as doing a combined training on all data at once.
  arXiv  Detail & Related papers  (2022-02-21T10:34:41Z)
• Quantum Heterogeneous Distributed Deep Learning Architectures: Models, Discussions, and Applications [13.241451755566365]
  Quantum deep learning (QDL) and distributed deep learning (DDL) are emerging to complement existing deep learning methods. QDL takes computational gains by replacing deep learning computations on local devices and servers with quantum deep learning. It can increase data security by using a quantum secure communication protocol between the server and the client.
  arXiv  Detail & Related papers  (2022-02-19T12:59:11Z)
• Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting [75.80116276369694]
  In crowd counting, due to the problem of laborious labelling, it is perceived intractability of collecting a new large-scale dataset. We resort to the multi-domain joint learning and propose a simple but effective Domain-specific Knowledge Propagating Network (DKPNet) It is mainly achieved by proposing the novel Variational Attention(VA) technique for explicitly modeling the attention distributions for different domains.
  arXiv  Detail & Related papers  (2021-08-18T08:06:37Z)
• Ten Quick Tips for Deep Learning in Biology [116.78436313026478]
  Machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Deep learning has become its own subfield of machine learning. In the context of biological research, deep learning has been increasingly used to derive novel insights from high-dimensional biological data.
  arXiv  Detail & Related papers  (2021-05-29T21:02:44Z)
• A Survey of Deep Active Learning [54.376820959917005]
  Active learning (AL) attempts to maximize the performance gain of the model by marking the fewest samples. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize massive parameters. Deep active learning (DAL) has emerged.
  arXiv  Detail & Related papers  (2020-08-30T04:28:31Z)
• Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey [11.034875974800487]
  This paper presents a first survey of the approaches devised to integrate domain knowledge, expressed in the form of constraints, in Deep Learning (DL) learning models. We identify five categories that encompass the main approaches to inject domain knowledge: 1) acting on the features space, 2) modifications to the hypothesis space, 3) data augmentation, 4) regularization schemes, 5) constrained learning.
  arXiv  Detail & Related papers  (2020-05-19T15:34:09Z)
• Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog [70.79442700890843]
  We propose a novel Dynamic Fusion Network (DF-Net) which automatically exploit the relevance between the target domain and each domain. With little training data, we show its transferability by outperforming prior best model by 13.9% on average.
  arXiv  Detail & Related papers  (2020-04-23T08:17:22Z)
• Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review [62.490310870300746]
  The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals. This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.
  arXiv  Detail & Related papers  (2019-12-28T02:44:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.