Arabic Document Classification: Performance Investigation of Preprocessing and Representation Techniques DOI Open Access
Abdullah Y. Muaad,

Hanumanthappa Jayappa Davanagere,

D. S. Guru

et al.

Mathematical Problems in Engineering, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 16

Published: April 30, 2022

With the increasing number of online social posts, review comments, and digital documentations, Arabic text classification (ATC) task has been hugely required for many spontaneous natural language processing (NLP) applications, especially within coronavirus pandemics. The variations in meaning same words could directly affect performance any AI-based framework. This work aims to identify effectiveness machine learning (ML) algorithms through preprocessing representation techniques. is measured via different Basically, ATC process influenced by several factors such as stemming preprocessing, method feature extraction selection, nature datasets, algorithm. To improve overall performance, techniques are mainly used convert each word into its root decrease dimension among datasets. Feature selection always play crucial roles represent a meaningful way accuracy rate. selected classifiers this study performed based on various algorithms. evaluation results compared using multinomial Naive Bayes (MNB), Bernoulli (BNB), Stochastic Gradient Descent (SGD), Support Vector Classifier (SVC), Logistic Regression (LR), Linear SVC. All these AI evaluated five balanced unbalanced benchmark datasets: BBC corpus, CNN Open-Source corpus (OSAc), ArCovidVac, AlKhaleej. show that strongly depends technique, methods datasets used. For considered linear SVC outperformed other when prominent features selected.

Language: Английский

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

Mohammad-H. Tayarani N.

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

Published: Oct. 3, 2020

Language: Английский

Citations

196
Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review DOI Open Access
Sujan Sarker, Lafifa Jamal,

Syeda Faiza Ahmed

et al.

Robotics and Autonomous Systems, Journal Year: 2021, Volume and Issue: 146, P. 103902 - 103902

Published: Oct. 7, 2021

Language: Английский

Citations

155
COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare DOI Open Access
Debaditya Shome, T. Kar, Sachi Nandan Mohanty

et al.

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

Citations

114
A comprehensive review of deep learning-based single image super-resolution DOI Creative Commons
Syed Muhammad Arsalan Bashir, Yi Wang, Mahrukh Khan

et al.

PeerJ Computer Science, Journal Year: 2021, Volume and Issue: 7, P. e621 - e621

Published: July 13, 2021

Image super-resolution (SR) is one of the vital image processing methods that improve resolution an in field computer vision. In last two decades, significant progress has been made super-resolution, especially by utilizing deep learning methods. This survey effort to provide a detailed recent single-image perspective while also informing about initial classical used for super-resolution. The classifies SR into four categories, i.e., methods, supervised learning-based unsupervised and domain-specific We introduce problem intuition quality metrics, available reference datasets, challenges. Deep approaches are evaluated using dataset. Some reviewed state-of-the-art include enhanced network (EDSR), cycle-in-cycle GAN (CinCGAN), multiscale residual (MSRN), meta dense (Meta-RDN), recurrent back-projection (RBPN), second-order attention (SAN), feedback (SRFBN) wavelet-based (WRAN). Finally, this concluded with future directions trends open problems be addressed researchers.

Language: Английский

Citations

113
A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images DOI Creative Commons
Chiagoziem C. Ukwuoma, Zhiguang Qin, Md Belal Bin Heyat

et al.

Journal of Advanced Research, Journal Year: 2022, Volume and Issue: 48, P. 191 - 211

Published: Sept. 7, 2022

Pneumonia is a microorganism infection that causes chronic inflammation of the human lung cells. Chest X-ray imaging most well-known screening approach used for detecting pneumonia in early stages. While chest-Xray images are mostly blurry with low illumination, strong feature extraction required promising identification performance. A new hybrid explainable deep learning framework proposed accurate disease using chest images. The workflow developed by fusing capabilities both ensemble convolutional networks and Transformer Encoder mechanism. backbone to extract features from raw input two different scenarios: (i.e., DenseNet201, VGG16, GoogleNet) B InceptionResNetV2, Xception). Whereas, built based on self-attention mechanism multilayer perceptron (MLP) identification. visual saliency maps derived emphasize crucial predicted regions end-to-end training process models over all scenarios performed binary multi-class classification scenarios. model recorded 99.21% performance terms overall accuracy F1-score task, while it achieved 98.19% 97.29% multi-classification task. For scenario, 97.22% 97.14% F1-score, 96.44% F1-score. multiclass 97.2% 95.8% 96.4% 94.9% could provide encouraging comparing individual, models, or even latest AI literature. code available here: https://github.com/chiagoziemchima/Pneumonia_Identificaton.

Language: Английский

Citations

95
InSiNet: a deep convolutional approach to skin cancer detection and segmentation DOI
Hatice Çatal Reis, Veysel Turk, Kourosh Khoshelham

et al.

Medical & Biological Engineering & Computing, Journal Year: 2022, Volume and Issue: 60(3), P. 643 - 662

Published: Jan. 13, 2022

Language: Английский

Citations

94
VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm DOI Open Access
Abdülkadir Karacı

Neural Computing and Applications, Journal Year: 2022, Volume and Issue: 34(10), P. 8253 - 8274

Published: Jan. 24, 2022

Language: Английский

Citations

73
PLDPNet: End-to-end hybrid deep learning framework for potato leaf disease prediction DOI Creative Commons

Fizzah Arshad,

Muhammad Mateen, Shaukat Hayat

et al.

Alexandria Engineering Journal, Journal Year: 2023, Volume and Issue: 78, P. 406 - 418

Published: Aug. 2, 2023

Agricultural productivity plays a vital role in global economic development and growth. When crops are affected by diseases, it adversely impacts nation's resources agricultural output. Early detection of crop diseases can minimize losses for farmers enhance production. In this study, we propose new hybrid deep learning model, PLDPNet, designed to automatically predict potato leaf diseases. The PLDPNet framework encompasses image collection, pre-processing, segmentation, feature extraction fusion, classification. We employ an ensemble approach combining features from two well-established models (VGG19 Inception-V3) generate more powerful features. leverages the concept vision transformers final prediction. To train evaluate utilize public dataset: early blight, late healthy leaves. Utilizing strength segmentation fusion feature, proposed achieves overall accuracy 98.66%, F1-score 96.33%. A comprehensive validation study is conducted using Apple (4 classes) tomato (10 datasets achieving impressive accuracies 96.42% 94.25%, respectively. These experimental findings confirm that provides effective accurate prediction making promising candidate practical applications.

Language: Английский

Citations

48
Application of artificial intelligence for resilient and sustainable healthcare system: systematic literature review and future research directions DOI
Laxmi Pandit Vishwakarma, Rajesh Kumar Singh, Ruchi Mishra

et al.

International Journal of Production Research, Journal Year: 2023, Volume and Issue: unknown, P. 1 - 23

Published: March 13, 2023

Recent years have witnessed increased pressure across the global healthcare system during COVID-19 pandemic. The pandemic shattered existing operations and taught us importance of a resilient sustainable system. Digitisation, specifically adoption Artificial Intelligence (AI) has positively contributed to developing in recent past. To understand how AI contributes building system, this study based on systematic literature review 89 articles extracted from Scopus Web Science databases is conducted. organised around several key themes such as applications, benefits, challenges using technology sector. It observed that wide applications radiology, surgery, medical, research, development Based analysis, research framework proposed an extended Antecedents, Practices, Outcomes (APO) framework. This comprises applications' antecedents, practices, outcomes for Consequently, three propositions are drawn study. Furthermore, our adopted theory, context methodology (TCM) provide future directions, which can be used reference point studies.

Language: Английский

Citations

47
Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment DOI Creative Commons
Md. Mahadi Hasan, Muhammad Usama Islam, Muhammad Jafar Sadeq

et al.

Sensors, Journal Year: 2023, Volume and Issue: 23(1), P. 527 - 527

Published: Jan. 3, 2023

Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in real world domain. intelligence, driving force current technological revolution, been used many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, most importantly healthcare sector. With rise COVID-19 pandemic, several prediction detection methods using artificial have employed to understand, forecast, handle, curtail ensuing threats. In this study, recent related publications, methodologies medical reports were investigated purpose studying intelligence's role pandemic. This study presents comprehensive review specific attention machine learning, deep image processing, object detection, segmentation, few-shot learning studies that utilized tasks COVID-19. particular, genetic analysis, clinical data sound biomedical classification, socio-demographic anomaly health monitoring, personal protective equipment (PPE) observation, social control, patients' mortality risk approaches forecast threatening factors demonstrates artificial-intelligence-based algorithms integrated into Internet Things wearable devices quite effective efficient forecasting insights which actionable through wide usage. The results produced by prove is promising arena can be applied for disease prognosis, forecasting, drug discovery, development sector on global scale. We indeed played important helping fight against COVID-19, insightful knowledge provided here could extremely beneficial practitioners experts domain implement systems curbing next pandemic or disaster.

Language: Английский

Citations

46