Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions

SN Computer Science, Journal Year: 2021, Volume and Issue: 2(6)

Published: Aug. 18, 2021

Language: Английский

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AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems

SN Computer Science, Journal Year: 2022, Volume and Issue: 3(2)

Published: Feb. 10, 2022

Abstract Artificial intelligence (AI) is a leading technology of the current age Fourth Industrial Revolution (Industry 4.0 or 4IR), with capability incorporating human behavior and into machines systems. Thus, AI-based modeling key to build automated, intelligent, smart systems according today’s needs. To solve real-world issues, various types AI such as analytical, functional, interactive, textual, visual can be applied enhance capabilities an application. However, developing effective model challenging task due dynamic nature variation in problems data. In this paper, we present comprehensive view on “AI-based Modeling” principles potential techniques that play important role intelligent application areas including business, finance, healthcare, agriculture, cities, cybersecurity many more. We also emphasize highlight research issues within scope our study. Overall, goal paper provide broad overview used reference guide by academics industry people well decision-makers scenarios domains.

Language: Английский

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A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

Journal Of Big Data, Journal Year: 2023, Volume and Issue: 10(1)

Published: April 14, 2023

Abstract Data scarcity is a major challenge when training deep learning (DL) models. DL demands large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate train frameworks. Usually, manual labeling needed provide labeled data, which typically involves human annotators with vast background knowledge. This annotation process costly, time-consuming, and error-prone. every framework fed by significant automatically learn representations. Ultimately, larger would generate better model its performance also application dependent. issue the main barrier for dismissing use DL. Having sufficient first step toward any successful trustworthy application. paper presents holistic survey on state-of-the-art techniques deal models overcome three challenges including small, imbalanced datasets, lack generalization. starts listing techniques. Next, types architectures are introduced. After that, solutions address listed, such as Transfer Learning (TL), Self-Supervised (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these were followed some related tips about acquisition prior purposes, well recommendations ensuring trustworthiness dataset. The ends list that suffer from scarcity, several alternatives proposed in order more each Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, Cybersecurity. To best authors’ knowledge, this review offers comprehensive overview strategies tackle

Language: Английский

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Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging

Sensors, Journal Year: 2022, Volume and Issue: 22(5), P. 1960 - 1960

Published: March 2, 2022

Advances in technology have been able to affect all aspects of human life. For example, the use medicine has made significant contributions society. In this article, we focus on assistance for one most common and deadly diseases exist, which is brain tumors. Every year, many people die due tumors; based "braintumor" website estimation U.S., about 700,000 primary tumors, 85,000 are added every year. To solve problem, artificial intelligence come aid humans. Magnetic resonance imaging (MRI) method diagnose Additionally, MRI commonly used medical image processing dissimilarity different parts body. study, conducted a comprehensive review existing efforts applying types deep learning methods data determined challenges domain followed by potential future directions. One branches that very successful images CNN. Therefore, survey, various architectures CNN were reviewed with images, especially images.

Language: Английский

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LungNet: A hybrid deep-CNN model for lung cancer diagnosis using CT and wearable sensor-based medical IoT data

Computers in Biology and Medicine, Journal Year: 2021, Volume and Issue: 139, P. 104961 - 104961

Published: Oct. 28, 2021

Language: Английский

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A CNN-SVM study based on selected deep features for grapevine leaves classification

Measurement, Journal Year: 2021, Volume and Issue: 188, P. 110425 - 110425

Published: Nov. 9, 2021

Language: Английский

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Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions

Published: Aug. 2, 2021

Deep learning (DL), a branch of machine (ML) and artificial intelligence (AI) is nowadays considered as core technology today's Fourth Industrial Revolution (4IR or Industry 4.0). Due to its capabilities from data, DL originated neural network (ANN), has become hot topic in the context computing, widely applied various application areas like healthcare, visual recognition, cybersecurity, many more. However, building an appropriate model challenging task, due dynamic nature variations real-world problems data. Moreover, lack understanding turns methods into black-box machines that hamper development at standard level. This article presents structured comprehensive view on techniques including taxonomy considering types tasks supervised unsupervised. In our taxonomy, we take account deep networks for discriminative learning, unsupervised generative well hybrid relevant others. We also summarize where can be used. Finally, point out ten potential aspects future generation modeling with research directions. Overall, this aims draw big picture used reference guide both academia industry professionals.

Language: Английский

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A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods

Applied Soft Computing, Journal Year: 2021, Volume and Issue: 105, P. 107323 - 107323

Published: March 18, 2021

Language: Английский

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COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare

International Journal of Environmental Research and Public Health, Journal Year: 2021, Volume and Issue: 18(21), P. 11086 - 11086

Published: Oct. 21, 2021

In the recent pandemic, accurate and rapid testing of patients remained a critical task in diagnosis control COVID-19 disease spread healthcare industry. Because sudden increase cases, most countries have faced scarcity low rate testing. Chest X-rays been shown literature to be potential source for patients, but manually checking X-ray reports is time-consuming error-prone. Considering these limitations advancements data science, we proposed Vision Transformer-based deep learning pipeline detection from chest X-ray-based imaging. Due lack large sets, collected three open-source sets images aggregated them form 30 K image set, which largest publicly available collection this domain our knowledge. Our transformer model effectively differentiates normal with an accuracy 98% along AUC score 99% binary classification task. It distinguishes COVID-19, normal, pneumonia patient’s 92% Multi-class For evaluation on fine-tuned some widely used models literature, namely, EfficientNetB0, InceptionV3, Resnet50, MobileNetV3, Xception, DenseNet-121, as baselines. outperformed terms all metrics. addition, Grad-CAM based visualization created makes approach interpretable by radiologists can monitor progression affected lungs, assisting healthcare.

Language: Английский

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COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 142, P. 105244 - 105244

Published: Jan. 20, 2022

Language: Английский

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