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An Intelligent Tree Extractive Text Summarization Deep Learning

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Computers, Materials & Continua 2022年第11期73卷 4231-4244页

In recent research,deep learning algorithms have presented effective representation learning models for natural *** deep learningbased models create better data representation than classical *** are capable of automated extraction of distributed representation of *** this research,we introduce a new tree Extractive text summarization that is characterized by fitting the text structure representation in knowledge base training module,and also addresses memory issues that were not addresses *** proposed model employs a tree structured mechanism to generate the phrase and text *** proposed architecture mimics the tree configuration of the text-texts and provide better feature *** also incorporates an attention mechanism that offers an additional information source to conduct better summary *** novel model addresses text summarization as a classification process,where the model calculates the probabilities of phrase and text-summary *** model classification is divided into multiple features recognition such as information entropy,significance,redundancy and *** model was assessed on two datasets,on the Multi-Doc Composition Query(MCQ)and Dual Attention Composition dataset(DAC)*** experimental results prove that our proposed model has better summarization precision *** models by a considerable margin.

关键词： Neural network architecture text structure abstractive summarization

Deep Learning Prediction Model for Heart Disease for Elderly Patients

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Intelligent Automation & Soft Computing 2023年第2期35卷 2527-2540页

作者： abeer abdulaziz alarfaj Hanan Ahmed Hosni Mahmoud Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The detection of heart disease is a problematic task in medical *** diagnosis utilizes a thorough analysis of the clinical tests from the patient’s medical *** massive advances in deep learning models pursue the devel-opment of intelligent computerized systems that aid medical professionals to detect the disease type with the internet of things ***,in this paper,we propose a deep learning model for elderly patients to aid and enhance the diag-nosis of heart *** proposed model utilizes a deeper neural architecture with multiple perceptron layers with regularization learning *** mod-el performance is veriﬁed with a full and minimum set of *** features enhance the processing time of the classiﬁcation process while the accuracy is *** performance of classiﬁers with less features has been analyzed with experimental *** proposed system is built on the Internet of Things Platform for medical data for the classiﬁcation process which aids medical profes-sionals to detect heart diseases through cloud *** results accuracy is matched to classical learning models such as Convolutional Neural Network(CNN),Deep CNN,and neural ensemble *** analysis of the proposed diagnostic system can determine the heart disease risks efﬁ*** results demonstrate thatﬂexible modeling and tuning of the hyperparameters can attain an accuracy of up to 97.11%.

关键词： Heart disease internet of things deep learning

Predicting Violence-Induced Stress in an Arabic Social Media Forum

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Intelligent Automation & Soft Computing 2023年第2期35卷 1423-1439页

作者： abeer abdulaziz alarfaj Nada Ali Hakami Hanan Ahmed Hosni Mahmoud Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyJazan UniversityJazanSaudi Arabia

Social Media such as Facebook plays a substantial role in virtual com-munities by sharing ideas and ideologies among different populations over *** interaction analysis aids in deﬁning people’s emotions and aids in assessing public attitudes,towards different issues such as violence against women and *** this paper,we proposed an Arabic language prediction model to identify the issue of Violence-Induced Stress in social *** searched for Arabic posts of many countries through Facebook application programming interface(API).We discovered that the stress state of a battered woman is usually related to her friend’s stress states on *** applied a large real database from Facebook platforms to analytically investigate the correlation of violence-induced stress states and the victim interactions on social *** extracted a set of tex-tual,spatial,and interaction attributes from various ***,we are proposing a hybrid model–an interaction graph model incorporated in a deep learning neural model to leverage post content and interaction data for vio-lence-induced stress *** depict that our proposed hybrid mod-el can enhance the prediction performance by 10%in ***,considering the user interaction information can learn an interesting phenomenon,where,the sparse social interactions of violence-induced stress stressed victims is higher by around 15%percent non-battered users,signifying that the structure of the friends of such victims is less connected than non-stressed users.

关键词： Arabic language analysis violence-induced stress detection hybrid model deep learning

The Awareness of Keratoconus among General Population in Saudi Arabia

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Open Journal of Ophthalmology 2021年第1期11卷 49-59页

作者： Rahaf Mohammed Alruwaili abeer Surihan Alharbi Omar Ali Alghamdi Yahya Abdulrahman Alyahya Nouf Mohammed Albalawi Sameer Sohail Alostaz Omar Ahmed Asiri Khalid alarfaj College of Medicine Jouf University Sakaka Saudi Arabia Ophthalmology Department Ohud Hospital Madina Saudi Arabia Ophthalmology Department King Fahad Hospital Albaha Saudi Arabia Ophthalmology Department Burydah Central Hospital Qassim Saudi Arabia College of Medicine Tabuk University Tabuk Saudi Arabia Batterjee Medical College Jeddah Saudi Arabia Faculty of Medicine Taif University Taif Saudi Arabia Ophthalmology Department Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

Introduction: Keratoconus (KCN) is one of the degenerative corneal disorders, which causes impairment in normal visual acuity with a higher prevalence in Saudi Arabia. The study aimed to assess the awareness and knowledge regarding KCN, its risk factors, and management among the Saudi population. Materials and Methods: A survey was conducted using a pre-tested and validated questionnaire distributed through online platforms. A mixture of convenience and snowball sampling techniques was adopted for sample collection. Our analysis included a final sample size of 837 participant responses. The questionnaire had items related to sociodemographic characteristics (5 items), knowledge (10 items), and source of information (1 item). The mean knowledge scores were calculated and categorized (good, fair, and poor) to assess each participant’s knowledge. Pearson’s chi-square test was used to determine the statistical association between categorical variables. Results: The mean knowledge score was found to be 4.12 ± 2.6, and 67.5% of the participants showed “poor” scores. There was no statistically significant association of knowledge scores observed with any of the sociodemographic details. It was agreed by 76% and 42.5% that genetic predisposition and chronic eye inflammation could predispose to KCN. Only 38.1% (n = 157) agreed that KCN could be treated with optical glasses or contact lenses in the initial stages. Conclusion: The knowledge regarding KCN was not satisfactory among our study population. There is a need to create an increased level of awareness for KCN. Health awareness programs and community campaigns should be organized to raise awareness regarding KCN and better eye health care utilization.

关键词： Ophthalmology Inflammation Keratoconus Blindness