Deep learning architectures in emerging cloud computing architectures: Recent development, challenges and next research trend

Jauro, Fatsuma; Chiroma, Haruna; Gital, Abdulsalam Ya’u; Almutairi, Mubarak; Abdulhamid, Shafií Muhammad; Abawajy, Jemal

doi:10.1016/j.asoc.2020.106582

Cited by 61 publications

(29 citation statements)

References 66 publications

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“…The increase in the number of training data and studies about machine learning and information processing have also contributed to deep learning. Different deep learning architectures such as artificial neural network (ANN), convolutional neural network (CNN) and recurrent neural network (RNN) have been widely used in many fields such as image classification, natural language processing and speech recognition [13] , [14] . ANN is an information processing approach inspired by the human biological nervous system.…”

Section: Deep Learning and Artificial Bee Colony Algorithmmentioning

confidence: 99%

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A novel classifier architecture based on deep neural network for COVID-19 detection using laboratory findings

Göreke

Sarı

Koçkanat

2021

Applied Soft Computing

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Unfortunately, Coronavirus disease 2019 (COVID-19) is spreading rapidly all over the world. Along with causing many deaths, it has substantially affected the social life, economics, and infrastructure worldwide in a negative manner. Therefore, it is very important to be able to diagnose the COVID-19 quickly and correctly. In this study, a new feature group based on laboratory findings was obtained considering ethnical and genetic differences for interpretation of blood data. Then, using this feature group, a new hybrid classifier architecture based on deep learning was designed and COVID-19 detection was made. Classification performance indicators were obtained as accuracy of 94.95%, F1-score of 94.98%, precision of 94.98%, recall of 94.98% and AUC of 100%. Achieved results were compared with those of the deep learning classifiers suggested in literature. According to these results, proposed method shows superior performance and can provide more convenience and precision to experts for diagnosis of COVID-19 disease.

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Section: Deep Learning and Artificial Bee Colony Algorithmmentioning

confidence: 99%

“…CNN has various layer types fulfilling various duties. These are called convolution layer, activation function layer, pooling layer, fully connected layer, and dropout layer [13] . In RNN structures, the result depends not only on the current inputs but also on the other inputs.…”

Section: Deep Learning and Artificial Bee Colony Algorithmmentioning

confidence: 99%

A novel classifier architecture based on deep neural network for COVID-19 detection using laboratory findings

Göreke

Sarı

Koçkanat

2021

Applied Soft Computing

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“…CNN, farklı görevleri yerine getiren farklı katman türlerine sahiptir. Bunlar "convolution layer", "activation function layer", "pooling layer", "fully connected layer" ve "dropout layer" olarak isimlendirilir [28]. Tekrarlayan sinir ağı (Recurrent Neural Networks, RNN) yapılarda sonuç, sadece o andaki girişe değil, diğer girişlere de bağlı olarak çıkarılır.…”

Section: Deneysel Sonuçlarunclassified

“…Tekrarlayan sinir ağı (Recurrent Neural Networks, RNN) yapılarda sonuç, sadece o andaki girişe değil, diğer girişlere de bağlı olarak çıkarılır. Bu ağlarda girişler şimdiki ve önceki bilgilerin birleştirilmesi ile çıkış üretirler [28].…”

Section: Deneysel Sonuçlarunclassified

Cilt Lezyonlarının Sınıflandırılmasında Derin Öğrenme Tabanlı Bir Yöntem

Göreke

2021

Türk Doğa Ve Fen Dergisi

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Öz: Cilt kanseri kötü huylu tümörlerin kontrolsüz çoğalması nedeni ile başlar. Dünya çapında sık karşılaşılan bir kanser türüdür. Uzman hekimler tarafından çıplak gözle incelemesi ve teşhis konulması güçtür. Bu yüzden bilgisayar destekli teşhis sistemleri hekimlere tanı koymada yardımcı olabilir. Bu sistemler günümüzde yapay zekânın bir türü olan derin sinir ağlarını yaygın olarak kullanır. Pek çok derin sinir ağı içeren çalışmada derin sinir ağının girişinde veri girişi olarak doğrudan medikal görüntüler kullanılır. Bu çalışmada ise görüntü verisinden ön eğitimli derin sinir ağı ile elde edilmiş nümerik veri, ön işlemden geçirilerek sınıflandırıcı girişine uygulanmıştır. Ağ mimarisine bağlı olarak bu sistemler öznitelikleri kendi katmanlarında çıkarırlar. Bu çalışmada VGG16 ön eğitimli derin sinir ağı kullanılarak ilk önce ağ katmanlarından görüntülere ilişkin öznitelikler elde edilmiştir. Daha sonra yüksek miktarda veri içeren bu özniteliklerin boyutu azaltılmıştır. Böylece sınıflandırmada en iyi başarımı sağlayacak öznitelikler elde edilmiştir. Veri artırma algoritması kullanılarak elde edilen nümerik veri artırılmış ve CNN tür derin sinir ağında %96 sınıflandırma doğruluğu ve %100 AUC başarımı elde edilmiştir

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“…From what the name entails, the difference between Deep Neural Networks and a shallow/ or network with a single hidden layer is the enormous number of layers network it has; as such, the original input can be transformed more in the deep neural network than the shallow networks. It is through the deep neural network that one can "learn" rigidly than in the shallow networks [10,11,21].…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Convolutional and Multiclass Neural Network for Learning Style Detection from Facial Images

Gambo¹,

Wajiga²,

Shuib³

et al. 2018

ICST Transactions on Scalable Information Systems

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Improving the accuracy of learning style detection models is a primary concern in the area of automatic detection of learning style, which can be achieved either through, attribute/feature selection or classification algorithm. However, the role of facial expression in improving accuracy has not been fully explored in the research domain. On the other hand, deep learning solutions have become a new approach for solving complex problems using Deep Neural networks (DNNs); these DNNs have deep architectures that are capable of decomposing problems into multiple processing layers, enabling and devising multiple mapping of complex problems functions. In this paper, we investigate and compare the performance of Convolutional Neural Network (CNN) and MultiClass Neural Network (MCNN) for classification of learners into VARK learning-style dimensions (i.e Visual, Aural, Reading Kinaesthetic, including Neutral class) based on facial images. The performances of the two networks were evaluated and compared using square mean error MSE for training and accuracy metric for testing. The results show that MCNN offers better and robust classification performance of VARK learning style based on facial images. Finally, this paper has demonstrated a potential of a new method for automatic classification of VARK LS based on Facial Expressions (FEs). Based on the experimental results of the models, this approach can benefit both researchers and users of adaptive e-learning systems to uncover the potential of using FEs as identifier learning styles for recommendations and personalization of learning environments.

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Deep learning architectures in emerging cloud computing architectures: Recent development, challenges and next research trend

Cited by 61 publications

References 66 publications

A novel classifier architecture based on deep neural network for COVID-19 detection using laboratory findings

A novel classifier architecture based on deep neural network for COVID-19 detection using laboratory findings

Cilt Lezyonlarının Sınıflandırılmasında Derin Öğrenme Tabanlı Bir Yöntem

Performance Comparison of Convolutional and Multiclass Neural Network for Learning Style Detection from Facial Images

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