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

Hanumanthappa Jayappa Davanagere,

D. S. Guru

и другие.

Mathematical Problems in Engineering, Год журнала: 2022, Номер 2022, С. 1 - 16

Опубликована: Апрель 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.

Язык: Английский

A Comprehensive Survey of COVID-19 Detection Using Medical Images DOI Creative Commons
Faisal Muhammad Shah,

Sajib Kumar Saha Joy,

Farzad Ahmed

и другие.

SN Computer Science, Год журнала: 2021, Номер 2(6)

Опубликована: Авг. 28, 2021

Язык: Английский

Процитировано

62
Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey DOI Open Access
Yassine Meraihi, Asma Benmessaoud Gabis, Seyedali Mirjalili

и другие.

SN Computer Science, Год журнала: 2022, Номер 3(4)

Опубликована: Май 12, 2022

Язык: Английский

Процитировано

51
Machine Learning Approach for Autonomous Detection and Classification of COVID-19 Virus DOI Open Access
Osama R. Shahin, Hamoud Alshammari, Ahmed I. Taloba

и другие.

Computers & Electrical Engineering, Год журнала: 2022, Номер 101, С. 108055 - 108055

Опубликована: Апрель 29, 2022

Язык: Английский

Процитировано

43
CVD-HNet: Classifying Pneumonia and COVID-19 in Chest X-ray Images Using Deep Network DOI Open Access

S. Suganyadevi,

V. Seethalakshmi

Wireless Personal Communications, Год журнала: 2022, Номер 126(4), С. 3279 - 3303

Опубликована: Июнь 19, 2022

Язык: Английский

Процитировано

42
Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images DOI Creative Commons
Chiagoziem C. Ukwuoma, Dongsheng Cai, Md Belal Bin Heyat

и другие.

Journal of King Saud University - Computer and Information Sciences, Год журнала: 2023, Номер 35(7), С. 101596 - 101596

Опубликована: Май 25, 2023

COVID-19 is a contagious disease that affects the human respiratory system. Infected individuals may develop serious illnesses, and complications result in death. Using medical images to detect from essentially identical thoracic anomalies challenging because it time-consuming, laborious, prone error. This study proposes an end-to-end deep-learning framework based on deep feature concatenation Multi-head Self-attention network. Feature involves fine-tuning pre-trained backbone models of DenseNet, VGG-16, InceptionV3, which are trained large-scale ImageNet, whereas network adopted for performance gain. End-to-end training evaluation procedures conducted using COVID-19_Radiography_Dataset binary multi-classification scenarios. The proposed model achieved overall accuracies (96.33% 98.67%) F1_scores (92.68% multi classification scenarios, respectively. In addition, this highlights difference accuracy (98.0% vs. 96.33%) F_1 score (97.34% 95.10%) when compared with against highest individual performance. Furthermore, virtual representation saliency maps employed attention mechanism focusing abnormal regions presented explainable artificial intelligence (XAI) technology. provided better prediction results outperforming other recent learning same dataset.

Язык: Английский

Процитировано

40
Robust Classification and Detection of Big Medical Data Using Advanced Parallel K-Means Clustering, YOLOv4, and Logistic Regression DOI Creative Commons
Fouad H. Awad, Murtadha M. Hamad, Laith Alzubaidi

и другие.

Life, Год журнала: 2023, Номер 13(3), С. 691 - 691

Опубликована: Март 3, 2023

Big-medical-data classification and image detection are crucial tasks in the field of healthcare, as they can assist with diagnosis, treatment planning, disease monitoring. Logistic regression YOLOv4 popular algorithms that be used for these tasks. However, techniques have limitations performance issue big medical data. In this study, we presented a robust approach big-medical-data using logistic YOLOv4, respectively. To improve algorithms, proposed use advanced parallel k-means pre-processing, clustering technique identified patterns structures Additionally, leveraged acceleration capabilities neural engine processor to further enhance speed efficiency our approach. We evaluated on several large datasets showed it could accurately classify amounts data detect images. Our results demonstrated combination resulted significant improvement making them more reliable applications. This new offers promising solution may implications healthcare.

Язык: Английский

Процитировано

38
Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic DOI Creative Commons
Nora El-Rashidy,

Samir Abdelrazik,

Tamer Abuhmed

и другие.

Diagnostics, Год журнала: 2021, Номер 11(7), С. 1155 - 1155

Опубликована: Июнь 24, 2021

Since December 2019, the global health population has faced rapid spreading of coronavirus disease (COVID-19). With incremental acceleration number infected cases, World Health Organization (WHO) reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential artificial intelligence (AI) this context is difficult to ignore. AI companies have been racing develop innovative tools contribute arm world against pandemic and minimize disruption it may cause. main objective study survey decisive role technology used fight pandemic. Five significant applications for were found, including (1) diagnosis using various data types (e.g., images, sound, text); (2) estimation possible future spread based current confirmed cases; (3) association between infection patient characteristics; (4) vaccine development drug interaction; (5) supporting applications. This also introduces comparison datasets. Based limitations literature, review highlights open research challenges could inspire application COVID-19.

Язык: Английский

Процитировано

57
CovFrameNet: An Enhanced Deep Learning Framework for COVID-19 Detection DOI Creative Commons
Olaide N. Oyelade, Absalom E. Ezugwu, Haruna Chiroma

и другие.

IEEE Access, Год журнала: 2021, Номер 9, С. 77905 - 77919

Опубликована: Янв. 1, 2021

The novel coronavirus, also known as COVID-19, is a pandemic that has weighed heavily on the socio-economic affairs of world. Research into production relevant vaccines progressively being advanced with development Pfizer and BioNTech, AstraZeneca, Moderna, Sputnik V, Janssen, Sinopharm, Valneva, Novavax Sanofi Pasteur vaccines. There is, however, need for computational intelligence solution approach to mediate process facilitating quick detection disease. Different methods, which comprise natural language processing, knowledge engineering, deep learning, have been proposed in literature tackle spread coronavirus More so, application learning models demonstrated an impressive performance compared other methods. This paper aims advance image pre-processing techniques characterise detect infection. Furthermore, study proposes framework named CovFrameNet., consist pipelined method model feature extraction, classification, measurement. novelty this lies design CNN architecture incorporates enhanced mechanism. National Institutes Health (NIH) Chest X-Ray dataset COVID-19 Radiography database were used evaluate validate effectiveness model. Results obtained revealed achieved accuracy 0.1, recall/precision 0.85, F-measure 0.9, specificity 1.0. Thus, study's outcome showed CNN-based capability could be adopted pre-screening suspected cases, confirmation RT-PCR-based detected cases COVID-19.

Язык: Английский

Процитировано

47
Artificial Intelligence-Based Approach for Misogyny and Sarcasm Detection from Arabic Texts DOI Creative Commons
Abdullah Y. Muaad,

Hanumanthappa Jayappa Davanagere,

J. V. Bibal Benifa

и другие.

Computational Intelligence and Neuroscience, Год журнала: 2022, Номер 2022, С. 1 - 9

Опубликована: Март 26, 2022

Social media networking is a prominent topic in real life, particularly at the current moment. The impact of comments has been investigated several studies. Twitter, Facebook, and Instagram are just few social networks that used to broadcast different news worldwide. In this paper, comprehensive AI-based study presented automatically detect Arabic text misogyny sarcasm binary multiclass scenarios. key proposed AI approach distinguish various topics from tweets networks. A achieved for detecting both via adopting seven state-of-the-art NLP classifiers: ARABERT, PAC, LRC, RFC, LSVC, DTC, KNNC. To fine tune, validate, evaluate all these techniques, two datasets (i.e., Abu Farah datasets) used. For experimental study, scenarios each case (misogyny or sarcasm): problems. detection, best accuracy using AraBERT classifier with 91.0% classification scenario 89.0% scenario. as well 88% 77.0% method appears be effective platforms suggesting superior deep learning classifier.

Язык: Английский

Процитировано

37
BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification DOI Creative Commons
Channabasava Chola, Abdullah Y. Muaad, Md Belal Bin Heyat

и другие.

Diagnostics, Год журнала: 2022, Номер 12(11), С. 2815 - 2815

Опубликована: Ноя. 16, 2022

Blood cells carry important information that can be used to represent a person's current state of health. The identification different types blood in timely and precise manner is essential cutting the infection risks people face on daily basis. BCNet an artificial intelligence (AI)-based deep learning (DL) framework was proposed based capability transfer with convolutional neural network rapidly automatically identify eight-class scenario: Basophil, Eosinophil, Erythroblast, Immature Granulocytes, Lymphocyte, Monocyte, Neutrophil, Platelet. For purpose establishing dependability viability BCNet, exhaustive experiments consisting five-fold cross-validation tests are carried out. Using strategy, we conducted in-depth comprehensive BCNet's architecture test it three optimizers ADAM, RMSprop (RMSP), stochastic gradient descent (SGD). Meanwhile, performance directly compared using same dataset state-of-the-art models DensNet, ResNet, Inception, MobileNet. When employing optimizers, demonstrated better classification ADAM RMSP optimizers. best evaluation achieved optimizer terms 98.51% accuracy 96.24% F1-score. Compared baseline model, clearly improved prediction 1.94%, 3.33%, 1.65% RMSP, SGD, respectively. model outperformed AI DenseNet, MobileNet testing time single cell image by 10.98, 4.26, 2.03, 0.21 msec. In comparison most recent models, could able generate encouraging outcomes. It for advancement healthcare facilities have such recognition rate improving detection cells.

Язык: Английский

Процитировано

34