Cited by A high-accuracy lightweight network model for X-ray image diagnosis: A case study of COVID detection

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods DOI

Nooshin Ayoobi,

Danial Sharifrazi, Roohallah Alizadehsani

и другие.

Results in Physics, Год журнала: 2021, Номер 27, С. 104495 - 104495

Опубликована: Июнь 26, 2021

The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs many countries. Predicting the number new cases deaths during this period can be a useful step predicting facilities required future. purpose study is predict rate one, three seven-day ahead next 100 days. motivation for every n days (instead just day) investigation possibility computational cost reduction still achieving reasonable performance. Such scenario may encountered real-time forecasting time series. Six different deep learning methods are examined on data adopted from WHO website. Three LSTM, Convolutional GRU. bidirectional extension then considered each method forecast Australia Iran This novel as it carries out comprehensive evaluation aforementioned their extensions perform prediction COVID-19 death To best our knowledge, that Bi-GRU Bi-Conv-LSTM models used presented form graphs Friedman statistical test. results show have lower errors than other models. A several error metrics compare all models, finally, superiority determined. research could organisations working against determining long-term plans.

Язык: Английский

Процитировано

131

An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works DOI

Delaram Sadeghi,

Afshin Shoeibi, Navid Ghassemi

и другие.

Computers in Biology and Medicine, Год журнала: 2022, Номер 146, С. 105554 - 105554

Опубликована: Май 10, 2022

Язык: Английский

Процитировано

127

Enhancing E-Learning Adaptability with Automated Learning Style Identification and Sentiment Analysis: A Hybrid Deep Learning Approach for Smart Education DOI

Tahir Hussain, Lasheng Yu, Muhammad Asim

и другие.

Information, Год журнала: 2024, Номер 15(5), С. 277 - 277

Опубликована: Май 13, 2024

In smart education, adaptive e-learning systems personalize the educational process by tailoring it to individual learning styles. Traditionally, identifying these styles relies on learners completing surveys and questionnaires, which can be tedious may not reflect their true preferences. Additionally, this approach assumes that are fixed, leading a cold-start problem when automatically based platform behaviors. To address challenges, we propose novel annotates unlabeled student feedback using multi-layer topic modeling implements Felder–Silverman Learning Style Model (FSLSM) identify automatically. Our method involves answering four FSLSM-based questions upon logging into providing personal information like age, gender, cognitive characteristics, weighted fuzzy logic. We then analyze learners’ behaviors activities web usage mining techniques, classifying sequences specific with an advanced deep model. textual latent Dirichlet allocation (LDA) for sentiment analysis enhance experience further. The experimental results demonstrate our outperforms existing models in accurately detecting improves overall quality of personalized content delivery.

Язык: Английский

Процитировано

Brain tumor segmentation using neuro-technology enabled intelligence-cascaded U-Net model DOI

Haewon Byeon, Mohannad Al-Kubaisi, Ashit Kumar Dutta

и другие.

Frontiers in Computational Neuroscience, Год журнала: 2024, Номер 18

Опубликована: Апрель 3, 2024

According to experts in neurology, brain tumours pose a serious risk human health. The clinical identification and treatment of rely heavily on accurate segmentation. varied sizes, forms, locations make automated segmentation formidable obstacle the field neuroscience. U-Net, with its computational intelligence concise design, has lately been go-to model for fixing medical picture issues. Problems restricted local receptive fields, lost spatial information, inadequate contextual information are still plaguing artificial intelligence. A convolutional neural network (CNN) Mel-spectrogram basis this cough recognition technique. First, we combine voice variety intricate settings improve audio data. After that, preprocess data sure length is consistent create out it. novel tumor (BTS), Intelligence Cascade U-Net (ICU-Net), proposed address these It built dynamic convolution uses non-local attention mechanism. In order reconstruct more detailed tumours, principal design two-stage cascade 3DU-Net. paper’s objective identify best learnable parameters that will maximize likelihood network’s ability gather long-distance dependencies AI, Expectation–Maximization applied lateral connections, enabling it leverage effectively. Lastly, enhance capture characteristics, convolutions adaptive capabilities used place standard convolutions. We compared our results those other typical methods ran extensive testing utilising publicly available BraTS 2019/2020 datasets. suggested method performs well tasks involving BTS, according experimental Dice scores core (TC), complete tumor, enhanced validation sets 0.897/0.903, 0.826/0.828, 0.781/0.786, respectively, indicating high performance BTS.

Язык: Английский

Процитировано

Deep learning in medicine: advancing healthcare with intelligent solutions and the future of holography imaging in early diagnosis DOI

Asifa Nazir, Ahsan Hussain, Mandeep Singh

и другие.

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Июль 5, 2024

Язык: Английский

Процитировано

Enhancing online education recommendations through clustering-driven deep learning DOI

Jayaprakash Chinnadurai,

A. Karthik, Janjhyam Venkata Naga Ramesh

и другие.

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 97, С. 106669 - 106669

Опубликована: Авг. 10, 2024

Язык: Английский

Процитировано

AI based medical imagery diagnosis for COVID-19 disease examination and remedy DOI

Ashraf Aboshosha

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 10, 2025

Abstract COVID-19, caused by the SARS-CoV-2 coronavirus, has spread to more than 200 countries, affecting millions, costing billions, and claiming nearly 2 million lives since late 2019. This highly contagious disease can easily overwhelm healthcare systems if not managed promptly. The current diagnostic method, Molecular diagnosis, is slow low sensitivity. CXR, an initial imaging tool, provides rapid results, but less sensitive compared CT scans. article focuses on using AI for two main objectives: classifying severity of COVID-19 determining appropriate treatment. Highlights key factors in diagnosis treatment addressing questions such as: 1. For innate immunity important or acquired immunity? 2. Is disorder Acute Respiratory Distress Syndrome(ARDS)? 3. cross mortality due aging dangerous COVID-19? 4. a seasonal deficiency vitamin D winter? 5. it better treat as epidemic pandemic?

Язык: Английский

Процитировано

Accurate detection of COVID-19 using deep features based on X-Ray images and feature selection methods DOI