الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Object recognition is a subject that is becoming more important in both
industry and academia. The ability to recognize objects is a key problem for many
intelligent applications. Developing an effective feature extraction method is one
of the most difficult issues in object recognition. For this process, a variety of
algorithms were developed, including self-organizing maps (SOMs), support
vector machines (SVM), principal component analysis (PCA), and modern deep
learning (DL) techniques, particularly convolutional neural networks (CNNs) and
U-net. In this thesis, DL was employed to recognize faces, Arabic and English
digits, as well as a variety of other objects. The use of DL for health sciences
applications like COVID-19 infection prediction is also discussed.
The main objective of this thesis is to propose a new optimized technique to
improve the performance of the CNNs in recognizing objects. This objective is a
achieved by four proposed techniques. In the first technique, Cyclic SOMs is
proposed to improve the SOMs’ work as feature extractors. In the second
technique, CNNs were enhanced using the self-organizing maps (SOMs) topology
space in the convolution layer and the KNN classifier instead of the conventional
fully connected layer. The third technique employs the KNN classifier in the fully
connected layer and Cyclic-SOMs in the convolution layer of the CNNs. The
fourth technique involves using DL in the healthcare sector to predict COVID-19
infections using U-net.
The efficiency of the first three techniques has been evaluated on four wide
benchmark datasets: AHDBase for Arabic digits, MNIST for English digits, CMUPIE
for faces, and CIFAR-10 for objects. The experiment results of the proposed
techniques on the four mentioned datasets provide the following findings in
comparison to other techniques in terms of the recognition rate accuracy: the first
technique produced accuracy 80.9%, 90.35%, 96.42%, and 85.77%. The second
technique produced 96.57%, 95.4%, 97%, and 89.23%; while the third technique
produced accuracy 97.7%, 98.2%, 98.51%, and 93.8%. Regarding the fourth
technique, several evaluation metrics using CT scan dataset for the lung were used
to measure the performance of our proposed algorithm in comparison with other
state-of-the-art methods in terms of accuracy, sensitivity, precision, and dice
coefficient. The experimental results of the proposed technique reached 99.71%,
0.83, 0.87, and 0.85, respectively, in comparison with existing techniques.