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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Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images

Scientific Reports, Journal Year: 2021, Volume and Issue: 11(1)

Published: May 25, 2021

Abstract Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard important tasks for several applications in the field of medical analysis. As each brain modality gives unique key details related to part tumor, many recent approaches used four modalities T1, T1c, T2, FLAIR. Although them obtained a promising result on BRATS 2018 dataset, they suffer complex structure that needs more time train test. So, this paper, obtain flexible effective system, first, we propose preprocessing approach work only small image rather than whole image. This method leads decrease computing overcomes overfitting problems Cascade Deep Learning model. In second step, as dealing with smaller images slice, simple efficient Convolutional Neural Network (C-ConvNet/C-CNN) is proposed. C-CNN model mines both local global features two different routes. Also, improve accuracy compared state-of-the-art models, novel Distance-Wise Attention (DWA) mechanism introduced. The DWA considers effect center location inside Comprehensive experiments conducted dataset show proposed obtains competitive results: achieves mean enhancing core dice scores 0.9203, 0.9113 0.8726 respectively. Other quantitative qualitative assessments presented discussed.

Language: Английский

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Breast Cancer Prediction: A Comparative Study Using Machine Learning Techniques

SN Computer Science, Journal Year: 2020, Volume and Issue: 1(5)

Published: Sept. 1, 2020

Language: Английский

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A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 30551 - 30572

Published: Jan. 1, 2021

Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and become one of most acute severe ailments in past hundred years. The prevalence rate COVID-19 is rapidly rising every day throughout globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be powerful tool arsenal used by clinicians automatic diagnosis COVID-19. This paper aims overview recently developed systems based on using different medical imaging modalities like Computer Tomography (CT) X-ray. review specifically discusses provides insights well-known data sets train these networks. It also highlights partitioning various performance measures researchers field. A taxonomy drawn categorize recent works proper insight. Finally, we conclude addressing challenges associated with use methods detection probable future trends research area. aim facilitate experts (medical or otherwise) technicians understanding ways are regard how they can potentially further utilized combat outbreak

Language: Английский

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Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge

IEEE Open Journal of the Computer Society, Journal Year: 2022, Volume and Issue: 3, P. 172 - 184

Published: Jan. 1, 2022

Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency). The edge computing trend, along with techniques for distributed machine learning such federated learning, has gained popularity a viable solution settings. In this paper, we leverage capabilities medicine by evaluating potential intelligent processing clinical data at edge. We utilized emerging concept clustered (CFL) an automatic COVID-19 diagnosis. evaluate performance proposed framework under different experimental setups on two benchmark datasets. Promising results are obtained both datasets resulting comparable against central baseline where specialized models (i.e., each specific image modality) trained data, 16% 11% overall F1-Scores have been achieved model (using multi-modal data) CFL setup X-ray Ultrasound datasets, respectively. also discussed associated challenges, technologies, available deploying ML privacy delay-sensitive applications.

Language: Английский

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Applications of artificial intelligence in battling against covid-19: A literature review

Chaos Solitons & Fractals, Journal Year: 2020, Volume and Issue: 142, P. 110338 - 110338

Published: Oct. 3, 2020

Language: Английский

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An Automated System to Limit COVID-19 Using Facial Mask Detection in Smart City Network

2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), Journal Year: 2020, Volume and Issue: unknown, P. 1 - 5

Published: Sept. 1, 2020

COVID-19 pandemic caused by novel coronavirus is continuously spreading until now all over the world. The impact of has been fallen on almost sectors development. healthcare system going through a crisis. Many precautionary measures have taken to reduce spread this disease where wearing mask one them. In paper, we propose that restrict growth finding out people who are not any facial in smart city network public places monitored with Closed-Circuit Television (CCTV) cameras. While person without detected, corresponding authority informed network. A deep learning architecture trained dataset consists images and masks collected from various sources. achieved 98.7% accuracy distinguishing for previously unseen test data. It hoped our study would be useful tool communicable many countries

Language: Английский

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Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models

Renewable Energy, Journal Year: 2021, Volume and Issue: 177, P. 101 - 112

Published: May 22, 2021

Language: Английский

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Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches

Journal of Medical Systems, Journal Year: 2022, Volume and Issue: 46(11)

Published: Oct. 6, 2022

Language: Английский

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