النشرة المعلوماتية في الحاسبات والمعلومات最新文献

خمسة عقود من نماذج تقدير تكلفة البرمجيات: دراسة استقصائية 五十年来的软件成本计算模式：调查

النشرة المعلوماتية في الحاسبات والمعلومات Pub Date : 2024-07-03 DOI: 10.21608/fcihib.2024.261210.1104

صفا عزام, اسامة امام, معتصم دراز

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A Survey on Advances in Arabic Long-Text Summarization Strategies 阿拉伯语长文本摘要策略进展调查

النشرة المعلوماتية في الحاسبات والمعلومات Pub Date : 2024-07-01 DOI: 10.21608/fcihib.2024.258854.1103

Mostafa Magdy, سلوى أسامة, Ensaf Hussein Mohamed

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تقنيات اكتشاف الأخبار الكاذبة : مراجعة 假新闻检测技术：回顾

النشرة المعلوماتية في الحاسبات والمعلومات Pub Date : 2024-03-10 DOI: 10.21608/fcihib.2024.234205.1094

مصطفى كمال محمود, د. عزت سعد, نرمين عثمان

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Gradient Vanishing Generative Adversial Networks Optimization In Medical Imaging: A Survey 医学成像中的梯度消失生成式逆向网络优化：调查

النشرة المعلوماتية في الحاسبات والمعلومات Pub Date : 2024-02-27 DOI: 10.21608/fcihib.2024.74835.1046

Mustafa AbdulRazek, Ghada Khoriba, Mohamed Belal

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Users Review’s on Software Defect Prediction Utilizing Machine Learning methods 用户对利用机器学习方法进行软件缺陷预测的评论

النشرة المعلوماتية في الحاسبات والمعلومات Pub Date : 2024-01-12 DOI: 10.21608/fcihib.2024.199454.1082

اسامه امام, محمود الصباغ, مني جمال, تامر مدحت

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A Comparative Study Of Artificial Intelligence Techniques For Categorization And Prediction Of Heart Diseases 人工智能技术在心脏病分类和预测方面的比较研究

النشرة المعلوماتية في الحاسبات والمعلومات Pub Date : 2024-01-01 DOI: 10.21608/fcihib.2023.211465.1087

عبدالله رضا رشوان, ليلى الفنجري, صفاء عزام

{"title":"A Comparative Study Of Artificial Intelligence Techniques For Categorization And Prediction Of Heart Diseases","authors":"عبدالله رضا رشوان, ليلى الفنجري, صفاء عزام","doi":"10.21608/fcihib.2023.211465.1087","DOIUrl":"https://doi.org/10.21608/fcihib.2023.211465.1087","url":null,"abstract":"—Heart failure (HF) is one of the most common diseases in recent years, and a large number of people die annually around the world from it. The heart is considered one of the most important organs in the human body, so it requires high accuracy when predicting the presence of heart disease or not, as an error in prediction may cause human death, so it requires a high-accuracy method in predicting HF. Artificial intelligence (AI) plays a large and important role in many fields today, especially in the medical field, as AI helps doctors obtain a quick and accurate diagnosis of the patient’s condition, which contributes to saving time during the diagnosis. It is important to predict HF using AI to help with rapid and accurate diagnosis and thus reduce the number of deaths from this disease. AI techniques increase the accuracy of predicting whether or not HF is present compared to traditional methods. Also, in rural areas where there are fewer physicians, it is very important to provide such technologies to aid in diagnosis. Many studies point to new AI-based HF prediction techniques. These technologies relied on different algorithms and datasets of different sizes and types. Each of these technologies has advantages and limitations. Therefore, this paper presents an illustrative study of the most advanced AI methods for HF prediction. This study also included a comparison between the different methods based on the most famous standards.","PeriodicalId":515131,"journal":{"name":"النشرة المعلوماتية في الحاسبات والمعلومات","volume":"269 19‐23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139636266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning Medical Image Segmentation Methods: A Survey 深度学习医学图像分割方法：调查

النشرة المعلوماتية في الحاسبات والمعلومات Pub Date : 2024-01-01 DOI: 10.21608/fcihib.2024.189094.1079

مى مختار, هالة عبد الجليل, غادة خوريبه

{"title":"Deep Learning Medical Image Segmentation Methods: A Survey","authors":"مى مختار, هالة عبد الجليل, غادة خوريبه","doi":"10.21608/fcihib.2024.189094.1079","DOIUrl":"https://doi.org/10.21608/fcihib.2024.189094.1079","url":null,"abstract":"—Medical image segmentation is essential for detecting and localizing tumors in medical image analysis. Image segmentation involves the identification of anatomical structures in images. Medical image segmentation starts with manual segmentation using Atlas methods, then auto-segmentation, facilitated by deep learning algorithms. Deep learning-based medical image segmentation retains a significant pledge in reducing treatment planning, radiation-related toxicities, and side effects. This study provides a complete overview of deep-learning medical image segmentation models. We review various deep-learning models and architectures applied to medical image segmentation, including fully convolutional networks, U-Net, and attention-based models. This literature review discusses using different loss functions, data augmentation techniques, and transfer learning in deep learning-based medical image segmentation and several types of medical image modality. Evaluation analysis encloses benchmark datasets for human body organs such as the brain, lungs, chest, and liver. Finally, we summarize the challenges and future directions of deep learning for medical image segmentation.","PeriodicalId":515131,"journal":{"name":"النشرة المعلوماتية في الحاسبات والمعلومات","volume":"80 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140522343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0