Object detection using YOLO: challenges, architectural successors, datasets and applications

Multimedia Tools and Applications, Journal Year: 2022, Volume and Issue: 82(6), P. 9243 - 9275

Published: Aug. 8, 2022

Language: Английский

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Deep Neural Networks for Medical Image Segmentation

Journal of Healthcare Engineering, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 15

Published: March 10, 2022

Image segmentation is a branch of digital image processing which has numerous applications in the field analysis images, augmented reality, machine vision, and many more. The medical growing organs, diseases, or abnormalities images become demanding. helps checking growth disease like tumour, controlling dosage medicine, exposure to radiations. Medical really challenging task due various artefacts present images. Recently, deep neural models have shown application tasks. This significant achievements high performance learning strategies. work presents review literature employing convolutional networks. paper examines widely used datasets, different metrics for evaluating tasks, performances CNN based In comparison existing survey papers, also discusses challenges state-of-the-art solutions available literature.

Language: Английский

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Contributions of Smart City Solutions and Technologies to Resilience against the COVID-19 Pandemic: A Literature Review

Sustainability, Journal Year: 2021, Volume and Issue: 13(14), P. 8018 - 8018

Published: July 18, 2021

Since its emergence in late 2019, the COVID-19 pandemic has swept through many cities around world, claiming millions of lives and causing major socio-economic impacts. The occurred at an important historical juncture when smart solutions technologies have become ubiquitous cities. Against this background, review, we examine how city contributed to resilience by enhancing planning, absorption, recovery, adaptation abilities. For purpose, reviewed 147 studies that discussed issues related use during pandemic. results were synthesized under four themes, namely, planning preparation, adaptation. This review shows investment initiatives can enhance preparation ability. In addition, adoption can, among other things, capacity predict patterns, facilitate integrated timely response, minimize or postpone transmission virus, provide support overstretched sectors, supply chain disruption, ensure continuity basic services, offer for optimizing operations. These are promising demonstrate utility resilience. However, it should be noted realizing potential hinges on careful attention challenges privacy security, access open-source data, technological affordance, legal barriers, feasibility, citizen engagement. Despite this, further development unprecedented opportunities similar future events.

Language: Английский

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Semantic segmentation for multiscale target based on object recognition using the improved Faster-RCNN model

Future Generation Computer Systems, Journal Year: 2021, Volume and Issue: 123, P. 94 - 104

Published: May 4, 2021

Language: Английский

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Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 95730 - 95753

Published: Jan. 1, 2021

The beginning of 2020 has seen the emergence coronavirus outbreak caused by a novel virus called SARS-CoV-2. sudden explosion and uncontrolled worldwide spread COVID-19 show limitations existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies as blockchain Artificial Intelligence (AI) have emerged promising solutions for fighting epidemic. particular, can combat pandemics enabling early detection outbreaks, ensuring ordering medical data, reliable supply chain during tracing. Moreover, AI provides intelligent identifying symptoms treatments supporting drug manufacturing. Therefore, we present an extensive survey on use combating epidemics. First, introduce new conceptual architecture which integrates COVID-19. Then, latest research efforts various applications. newly emerging projects cases enabled these to deal with pandemic are also presented. A case study is provided using federated detection. Finally, point out challenges future directions that motivate more coronavirus-like

Language: Английский

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Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction

Sensors, Journal Year: 2022, Volume and Issue: 22(2), P. 575 - 575

Published: Jan. 12, 2022

Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety considerable loss in production agricultural products. Disease identification on plant essential for long-term agriculture sustainability. Manually monitoring difficult due time limitations diversity diseases. In realm inputs, automatic characterization widely required. Based performance out all image-processing methods, better suited solving this task. This work investigates grapevines. Leaf blight, Black rot, stable, measles are four types found grape plants. Several earlier research proposals using machine learning algorithms were created detect one or two leaves; no offers complete detection The photos taken from village dataset order use transfer retrain EfficientNet B7 deep architecture. Following learning, collected features down-sampled Logistic Regression technique. Finally, most discriminant traits identified with highest constant accuracy 98.7% state-of-the-art classifiers after 92 epochs. simulation findings, an appropriate classifier application also suggested. proposed technique’s effectiveness confirmed by fair comparison existing procedures.

Language: Английский

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Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

Brain Sciences, Journal Year: 2021, Volume and Issue: 11(11), P. 1525 - 1525

Published: Nov. 18, 2021

Electroencephalography (EEG) is a non-invasive technique used to record the brain's evoked and induced electrical activity from scalp. Artificial intelligence, particularly machine learning (ML) deep (DL) algorithms, are increasingly being applied EEG data for pattern analysis, group membership classification, brain-computer interface purposes. This study aimed systematically review recent advances in ML DL supervised models decoding classifying signals. Moreover, this article provides comprehensive of state-of-the-art techniques signal preprocessing feature extraction. To end, several academic databases were searched explore relevant studies year 2000 present. Our results showed that application both mental workload motor imagery tasks has received substantial attention years. A total 75% convolutional neural networks with various 36% achieved competitive accuracy by using support vector algorithm. Wavelet transform was found be most common extraction method all types tasks. We further examined specific methods end classifier recommendations discovered systematic review.

Language: Английский

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Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images

Sensors, Journal Year: 2021, Volume and Issue: 21(21), P. 7116 - 7116

Published: Oct. 27, 2021

COVID-19 frequently provokes pneumonia, which can be diagnosed using imaging exams. Chest X-ray (CXR) is often useful because it cheap, fast, widespread, and uses less radiation. Here, we demonstrate the impact of lung segmentation in identification CXR images evaluate contents image influenced most. Semantic was performed a U-Net CNN architecture, classification three architectures (VGG, ResNet, Inception). Explainable Artificial Intelligence techniques were employed to estimate segmentation. A three-classes database composed: opacity (pneumonia), COVID-19, normal. We assessed creating from different sources, generalization one source another. The achieved Jaccard distance 0.034 Dice coefficient 0.982. segmented an F1-Score 0.88 for multi-class setup, 0.83 identification. In cross-dataset scenario, obtained 0.74 area under ROC curve 0.9 images. Experiments support conclusion that even after segmentation, there strong bias introduced by underlying factors sources.

Language: Английский

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Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives

International Journal of Biological Sciences, Journal Year: 2021, Volume and Issue: 17(6), P. 1581 - 1587

Published: Jan. 1, 2021

Artificial intelligence (AI) is being used to aid in various aspects of the COVID-19 crisis, including epidemiology, molecular research and drug development, medical diagnosis treatment, socioeconomics.The association AI can accelerate rapidly diagnose positive patients.To learn dynamics a pandemic with relevance AI, we search literature using different academic databases (PubMed, PubMed Central, Scopus, Google Scholar) preprint servers (bioRxiv, medRxiv, arXiv).In present review, address clinical applications machine learning deep learning, characteristics, electronic records, images (CT, X-ray, ultrasound images, etc.) diagnosis.The current challenges future perspectives provided this review be direct an ideal deployment technology pandemic.

Language: Английский

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COVID-19 image classification using deep learning: Advances, challenges and opportunities

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 144, P. 105350 - 105350

Published: March 3, 2022

Language: Английский

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