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تم العثور علي : 5641
 تم العثور علي : 5641
  
 
إعادة البحث

Thesis 2024.
Shafeek, Mona Kareem.
621 الفيزياء التطبيقية   621.3 الهندسة الكهربية   621.38 الهندسة الإلكترونية و هندسة الاتصالات  
Electric filters - Digital

Thesis 2024.
Nassar, Rana Melegy Metwaly Melegy.
522 التقنيات ، الأجهزة والمواد  

Thesis 2024.
عبد الحميد ؛ مها ممدوح محمد .
900 الجغرافيا والرحلات والتراجم والتاريخ   900 الجغرافيا والرحلات والتراجم والتاريخ   900 الجغرافيا والرحلات والتراجم والتاريخ  
عنوان البحث: ”أثر اختلاف كثافة عناصر التعلمالرقمية عبر بيئات التعلم الشخصية في تنمية مهارات
التصميم التعليمي والثقافة البصرية لدى طلبة تكنولوجيا التعليم”
اسم الباحثة: مها ممدوح محمد عبد الحميد.
جهة المنح: كلية التربية، جامعة قناة السويس.
سنة المنح: 1445ه/ 2024م.
لغة البحث: اللغة العربية.
الدرجة العلمية: دكتوراه الفلسفة في التربية.
التخصصالدقيق: مناهج وطرق التدريسوتكنولوجيا التعليم.
هيئة الإشراف: أ. د. زينب محمد أمين؛ أ. د/ إسلام جابرأحمد علام؛ د/ حسين عبد الفتاح.
المستخلص:
هدف البحث الحالي إلىتنمية مهارات التصميم التعليمي والثقافة البصرية لدى طلبة تكنولوجيا التعليممن خلال تعرف أثر اختلاف كثافة عناصر التعلم الرقمية عبر بيئات التعلم الشخصيةفي ذلك. وتحقيقًا لأهداف البحث تم استخدام المنهج التجريبي بتصميمه شبه التجريبي ذو الثلاثةمجموعاتتجريبية، وذلك لدراسة تأثير المُتغيرات: كثافة عناصر التعلم الرقمية عبر بيئات التعلم الشخصية كمتغير مستقل(نص/صورةوصوت/ مقاطع فيديو)؛ ومهارات التصميم التعليمي(تحليل المهمات التعليمية/ صياغة الأهداف التعليمية/ تنظيم المحتوى العلمي ومعالجته/ اختيار استراتيجية التعلم/اختيارأو إنتاج مصادر التعلم/ التقويم التعليمي)، والثقافة البصرية (كمتغيرين تابعين): وتكونت عينة البحث من (90) طالبًا/طالبة من كلية التربية النوعية، قسم تكنولوجيا التعليم. وللإجابة عن أسئلة البحث واختبارفروضه، تم بناء مادة المعالجة التجريبية وإعدادأدوات القياس (اختبار معرفي لمهارات التصميم التعليمي، وبطاقة تقييم المنتج، واختبار الثقافة البصرية).وأوضحت النتائج أن هناك أثر لكثافة عناصر التعلم الرقمية عبر بيئات التعلم الشخصية في تنمية الأداء المهاري لمهارات التصميم التعليمي والثقافة البصرية لدى طلبة تكنولوجيا التعليم، حيث جاء معالجات الفيديو في المرتبة الأولى يليها الصورة والصوت ثم النص،بينمالا يوجد اختلاف بين كثافة عناصر التعلم الرقميةعبر بيئات التعلم الشخصية في تنمية الجوانب المعرفية لمهارات التصميم التعليمي.كما أشارت النتائج إلى وجود علاقة ارتباطية موجبة بين الثقافة البصرية والأداء لمهارات التصميم التعليمي.وتم تحليل النتائج وتفسيرها في ضوء الإطار النظري والدراسات السابقة ذات الصلة.
الكلمات المفتاحية: كثافة عناصر التعلم الرقمية؛ بيئات التعلم الشخصية؛ مهارات التصميم التعليمي؛ مهارات الثقافة البصرية.

Abstract
Research Title: The Effect of the Density of Digital Learning Objects Difference via Personal Learning Environments for Developing some Instructional Design and Visual Literacy Skills for Educational Technology Students
Researcher’s name:Maha Mamduh Mohammad Abdelhamid
Language of the Study: Arabic
Academic Degree: Doctor of Philosophy Degree inCurriculum - Instructionl & Educational Technology
Supervision Committee: Prof. Dr. Zeinab Mohamed Amin;Prof. Dr. Islam Gaber Allam; Dr.Hussein Abdelfattah.
Abstract:
The current research aims to develop educational design and visual culture skills among educational technology students by identifying the impact of the difference in the density of digital learning elements across personal learning environments. To achieve the research objectives - the experimental method with its quasi-experimental design with three experimental groups was used to study the effect of the variables:Density of digital learning elements across personal learning environments as an independent variable (text/image and audio/video); And educational design skills (analyzing educational tasks/formulating educational objectives/organizing and processing scientific content/choosing a learning strategy/selecting or producing learning resources/educational evaluation) - and visual culture (as dependent variables): The research sample consisted of (90) male/female students from a college Specific Education - Department of Educational Technology. To answer the research questions and test its hypotheses - the experimental treatment material was constructed and measurement tools were prepared (a cognitive test for educational design skills - a product evaluation card - and a visual culture test).The results showed that there is an impact of the density of digital learning elements across personal learning environments in developing the skill performance of educational design and visual culture skills among educational technology students - as video processors came in first place - followed by image - sound - then text - while there is no difference between the density of digital learning elements across Personal learning environments in developing the cognitive aspects of educational design skills. The results also indicated that there is a positive correlation between visual culture and performance of educational design skills. The results were analyzed and interpreted in light of the theoretical framework and previous relevant studies.
Keywords: Density Digital Learning Objects; Personal Learning Environment (PLE); Instructional Design Skills; Visual Literacy Skills

Book 1971
Siegel, Paul.
ISBN: 0471790656
Q العلم   QA الرياضيات  
001 المعرفة   001.6 تجهيز و إعداد البيانات  
Electronic digital computers
Wiley

Thesis 2024.
Mohamad, atma Mohamad Ghamry.
771 التقنيات والأجهزة والمواد  
Digital images
تصفح الوعاء  

Thesis 2024.
AL-Halalmeh, Zainab Reda Muhammad.
771 التقنيات والأجهزة والمواد  
Digital images

Thesis 2024
Elshouky ‘ Hadeer Hussein Abdel-Mawla Hassan .
621 الفيزياء التطبيقية   621.3 الهندسة الكهربية  
The use of image classification in medical fields is one of the most important uses - including skin cancer image classification. Skin cancer is a major health problem across the world - and early identification is critical for successful treatment. Skin cancer - which is defined by abnormal skin cell development - is a common and dangerous disease worldwide. Despite advances in digital diagnosis tools - many present skin cancer detection technologies frequently fail to attain adequate levels of accuracy. Disease detection - computer-aided diagnosis - and patient risk identification rely heavily on computer vision. This is particularly true for skin cancer - which may be lethal if not detected early on. Several computer-aided diagnosis and detection systems have already been developed to do this.
In this dissertation - two approaches for classifying skin cancer images were examined and compared with the proposed methods. Machine Learning (ML) and Deep Learning (DL) are these two approaches. ML approaches include Artificial Neural Networks - Support Vector Machines - Naïve Bayes - and Decision Tree. Both Convolutional Neural Networks and Pretrained Deep Neural Networks (PDNN) were employed in the DL approach.
Two methods for detecting and binary classifying dermoscopic skin cancer images into benign and malignant were proposed. The first proposed method employs K-Nearest Neighbor (KNN) as a classifier with several PDNN serving as feature extractors - (KNN-PDNN). These networks include AlexNet - VGG-16 - VGG-19 - EfficientNet-B0 - ResNet-18 - ResNet-50 - ResNet-101 - DenseNet-201 - Inception-v3 - and MobileNet-v2. The second proposed method employs some PDNN with the Improved Grey Wolf Optimizer (I-GWO) - (PDNN-I-GWO). The PDNN used in this technique are AlexNet - ResNet-18 - SqueezeNet - ShuffleNet - and DarkNet-19.
The experiments of KNN-PDNN method used 4000 images from the ISIC archive dataset to train and test images. In certain PDNN - the KNN-PDNN method’s accuracy exceeded 99%. The PDNN-I-GWO method investigated two datasets: MED-NODE and DermIS. The outcomes showed that the proposed methods outperformed the other tested approaches. The highest accuracy achieved by this method is 100% and 97% in the MED-NODE and DermIS datasets - respectively. The highest accuracy achieved with this method is 100% and 97% in the MED-NODE and DermIS datasets - respectively.
The dissertation consists of five chapters as follows:
Chapter 1: Introduction
An introduction to the dissertation is given - explaining the importance of the research point and the goals it seeks to achieve - and an explanation of the problems found in some of the old techniques that we seek to improve in this thesis and the extent of their impact on classifying skin cancer images. This chapter also summarizes what the other chapters contain and the order in which they are reviewed in the thesis.
Chapter 2: Literature Review
This chapter covers background on skin cancer image classification and presents some previous works and methods used and their features and characteristics.
Chapter 3: Proposed System
The third chapter presents the proposed algorithms that were represented and applied in the dissertation. It reviews them in detail and discusses the additions and modifications that were made to achieve high accuracy. This chapter also presents the preprocessing of images before using them in the proposed methods. In addition - it includes different datasets for training and testing images.
Chapter 4: Experimental Results
It reviews all the experiments - their accompanying results - and details of the images that were used in the experiments. This dissertation also includes many comparisons between the proposed and modified algorithms that were used during the image classification process. This included using several methods and methods to evaluate and compare the performance of these algorithms.
Chapter 5: Conclusions and Recommendations for Future Work
It presents a summary of the results reached as well as some recommended points for future work that can be used to develop the work presented in this dissertation or related works - The use of image classification in medical fields is one of the most important uses - including skin cancer image classification. Skin cancer is a major health problem across the world - and early identification is critical for successful treatment. Skin cancer - which is defined by abnormal skin cell development - is a common and dangerous disease worldwide. Despite advances in digital diagnosis tools - many present skin cancer detection technologies frequently fail to attain adequate levels of accuracy. Disease detection - computer-aided diagnosis - and patient risk identification rely heavily on computer vision. This is particularly true for skin cancer - which may be lethal if not detected early on. Several computer-aided diagnosis and detection systems have already been developed to do this.
In this dissertation - two approaches for classifying skin cancer images were examined and compared with the proposed methods. Machine Learning (ML) and Deep Learning (DL) are these two approaches. ML approaches include Artificial Neural Networks - Support Vector Machines - Naïve Bayes - and Decision Tree. Both Convolutional Neural Networks and Pretrained Deep Neural Networks (PDNN) were employed in the DL approach.
Two methods for detecting and binary classifying dermoscopic skin cancer images into benign and malignant were proposed. The first proposed method employs K-Nearest Neighbor (KNN) as a classifier with several PDNN serving as feature extractors - (KNN-PDNN). These networks include AlexNet - VGG-16 - VGG-19 - EfficientNet-B0 - ResNet-18 - ResNet-50 - ResNet-101 - DenseNet-201 - Inception-v3 - and MobileNet-v2. The second proposed method employs some PDNN with the Improved Grey Wolf Optimizer (I-GWO) - (PDNN-I-GWO). The PDNN used in this technique are AlexNet - ResNet-18 - SqueezeNet - ShuffleNet - and DarkNet-19.
The experiments of KNN-PDNN method used 4000 images from the ISIC archive dataset to train and test images. In certain PDNN - the KNN-PDNN method’s accuracy exceeded 99%. The PDNN-I-GWO method investigated two datasets: MED-NODE and DermIS. The outcomes showed that the proposed methods outperformed the other tested approaches. The highest accuracy achieved by this method is 100% and 97% in the MED-NODE and DermIS datasets - respectively. The highest accuracy achieved with this method is 100% and 97% in the MED-NODE and DermIS datasets - respectively.
The dissertation consists of five chapters as follows:
Chapter 1: Introduction
An introduction to the dissertation is given - explaining the importance of the research point and the goals it seeks to achieve - and an explanation of the problems found in some of the old techniques that we seek to improve in this thesis and the extent of their impact on classifying skin cancer images. This chapter also summarizes what the other chapters contain and the order in which they are reviewed in the thesis.
Chapter 2: Literature Review
This chapter covers background on skin cancer image classification and presents some previous works and methods used and their features and characteristics.
Chapter 3: Proposed System
The third chapter presents the proposed algorithms that were represented and applied in the dissertation. It reviews them in detail and discusses the additions and modifications that were made to achieve high accuracy. This chapter also presents the preprocessing of images before using them in the proposed methods. In addition - it includes different datasets for training and testing images.
Chapter 4: Experimental Results
It reviews all the experiments - their accompanying results - and details of the images that were used in the experiments. This dissertation also includes many comparisons between the proposed and modified algorithms that were used during the image classification process. This included using several methods and methods to evaluate and compare the performance of these algorithms.
Chapter 5: Conclusions and Recommendations for Future Work
It presents a summary of the results reached as well as some recommended points for future work that can be used to develop the work presented in this dissertation or related works

Book 2022. ,©2022
Young, Michael,
ISBN: 9781000402100 , ,1000402010 ,9781003149682 ,1003149685 ,9781000402018
N الفنون الجميلة   NA الهندسة المعمارية  
720 العمارة   720 العمارة  
Architecture - Digital images - Architecture - Images numériques - digital images - ARCHITECTURE - ARCHITECTURE - ARCHITECTURE - Architecture - Digital images

Articles 2024
Elberry, Seham A.
618 أمراض النساء والولادة والموضوعات ذات الصلة  
Colposcopy - Cervix uteri - Cervix uteri - Cervix uteri - Uterus - Hysteroscopy - Colpomicroscopy - Gynecologic diseases
Benha medical journal
Vol. 41, Issue 4, special issue gynecology and obstetrics (July and August 2024) /
Benha university, Benha faculty of medicine,

Thesis 2024.
Elshouky‘Hadeer Hussein Abdel-Mawla Hassan.
621 الفيزياء التطبيقية   621.3 الهندسة الكهربية  
The use of image classification in medical fields is one of the most important uses - including skin cancer image classification. Skin cancer is a major health problem across the world - and early identification is critical for successful treatment. Skin cancer - which is defined by abnormal skin cell development - is a common and dangerous disease worldwide. Despite advances in digital diagnosis tools - many present skin cancer detection technologies frequently fail to attain adequate levels of accuracy. Disease detection - computer-aided diagnosis - and patient risk identification rely heavily on computer vision. This is particularly true for skin cancer - which may be lethal if not detected early on. Several computer-aided diagnosis and detection systems have already been developed to do this.
In this dissertation - two approaches for classifying skin cancer images were examined and compared with the proposed methods. Machine Learning (ML) and Deep Learning (DL) are these two approaches. ML approaches include Artificial Neural Networks - Support Vector Machines - Naïve Bayes - and Decision Tree. Both Convolutional Neural Networks and Pretrained Deep Neural Networks (PDNN) were employed in the DL approach.
Two methods for detecting and binary classifying dermoscopic skin cancer images into benign and malignant were proposed. The first proposed method employs K-Nearest Neighbor (KNN) as a classifier with several PDNN serving as feature extractors - (KNN-PDNN). These networks include AlexNet - VGG-16 - VGG-19 - EfficientNet-B0 - ResNet-18 - ResNet-50 - ResNet-101 - DenseNet-201 - Inception-v3 - and MobileNet-v2. The second proposed method employs some PDNN with the Improved Grey Wolf Optimizer (I-GWO) - (PDNN-I-GWO). The PDNN used in this technique are AlexNet - ResNet-18 - SqueezeNet - ShuffleNet - and DarkNet-19.
The experiments of KNN-PDNN method used 4000 images from the ISIC archive dataset to train and test images. In certain PDNN - the KNN-PDNN method’s accuracy exceeded 99%. The PDNN-I-GWO method investigated two datasets: MED-NODE and DermIS. The outcomes showed that the proposed methods outperformed the other tested approaches. The highest accuracy achieved by this method is 100% and 97% in the MED-NODE and DermIS datasets - respectively. The highest accuracy achieved with this method is 100% and 97% in the MED-NODE and DermIS datasets - respectively.
The dissertation consists of five chapters as follows:
Chapter 1: Introduction
An introduction to the dissertation is given - explaining the importance of the research point and the goals it seeks to achieve - and an explanation of the problems found in some of the old techniques that we seek to improve in this thesis and the extent of their impact on classifying skin cancer images. This chapter also summarizes what the other chapters contain and the order in which they are reviewed in the thesis.
Chapter 2: Literature Review
This chapter covers background on skin cancer image classification and presents some previous works and methods used and their features and characteristics.
Chapter 3: Proposed System
The third chapter presents the proposed algorithms that were represented and applied in the dissertation. It reviews them in detail and discusses the additions and modifications that were made to achieve high accuracy. This chapter also presents the preprocessing of images before using them in the proposed methods. In addition - it includes different datasets for training and testing images.
Chapter 4: Experimental Results
It reviews all the experiments - their accompanying results - and details of the images that were used in the experiments. This dissertation also includes many comparisons between the proposed and modified algorithms that were used during the image classification process. This included using several methods and methods to evaluate and compare the performance of these algorithms.
Chapter 5: Conclusions and Recommendations for Future Work
It presents a summary of the results reached as well as some recommended points for future work that can be used to develop the work presented in this dissertation or related works - The use of image classification in medical fields is one of the most important uses - including skin cancer image classification. Skin cancer is a major health problem across the world - and early identification is critical for successful treatment. Skin cancer - which is defined by abnormal skin cell development - is a common and dangerous disease worldwide. Despite advances in digital diagnosis tools - many present skin cancer detection technologies frequently fail to attain adequate levels of accuracy. Disease detection - computer-aided diagnosis - and patient risk identification rely heavily on computer vision. This is particularly true for skin cancer - which may be lethal if not detected early on. Several computer-aided diagnosis and detection systems have already been developed to do this.
In this dissertation - two approaches for classifying skin cancer images were examined and compared with the proposed methods. Machine Learning (ML) and Deep Learning (DL) are these two approaches. ML approaches include Artificial Neural Networks - Support Vector Machines - Naïve Bayes - and Decision Tree. Both Convolutional Neural Networks and Pretrained Deep Neural Networks (PDNN) were employed in the DL approach.
Two methods for detecting and binary classifying dermoscopic skin cancer images into benign and malignant were proposed. The first proposed method employs K-Nearest Neighbor (KNN) as a classifier with several PDNN serving as feature extractors - (KNN-PDNN). These networks include AlexNet - VGG-16 - VGG-19 - EfficientNet-B0 - ResNet-18 - ResNet-50 - ResNet-101 - DenseNet-201 - Inception-v3 - and MobileNet-v2. The second proposed method employs some PDNN with the Improved Grey Wolf Optimizer (I-GWO) - (PDNN-I-GWO). The PDNN used in this technique are AlexNet - ResNet-18 - SqueezeNet - ShuffleNet - and DarkNet-19.
The experiments of KNN-PDNN method used 4000 images from the ISIC archive dataset to train and test images. In certain PDNN - the KNN-PDNN method’s accuracy exceeded 99%. The PDNN-I-GWO method investigated two datasets: MED-NODE and DermIS. The outcomes showed that the proposed methods outperformed the other tested approaches. The highest accuracy achieved by this method is 100% and 97% in the MED-NODE and DermIS datasets - respectively. The highest accuracy achieved with this method is 100% and 97% in the MED-NODE and DermIS datasets - respectively.
The dissertation consists of five chapters as follows:
Chapter 1: Introduction
An introduction to the dissertation is given - explaining the importance of the research point and the goals it seeks to achieve - and an explanation of the problems found in some of the old techniques that we seek to improve in this thesis and the extent of their impact on classifying skin cancer images. This chapter also summarizes what the other chapters contain and the order in which they are reviewed in the thesis.
Chapter 2: Literature Review
This chapter covers background on skin cancer image classification and presents some previous works and methods used and their features and characteristics.
Chapter 3: Proposed System
The third chapter presents the proposed algorithms that were represented and applied in the dissertation. It reviews them in detail and discusses the additions and modifications that were made to achieve high accuracy. This chapter also presents the preprocessing of images before using them in the proposed methods. In addition - it includes different datasets for training and testing images.
Chapter 4: Experimental Results
It reviews all the experiments - their accompanying results - and details of the images that were used in the experiments. This dissertation also includes many comparisons between the proposed and modified algorithms that were used during the image classification process. This included using several methods and methods to evaluate and compare the performance of these algorithms.
Chapter 5: Conclusions and Recommendations for Future Work
It presents a summary of the results reached as well as some recommended points for future work that can be used to develop the work presented in this dissertation or related works


من 565
 







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