Effect of Successive Convolution Layers to Detect Gender

Ahmed, Hanaa Mohsin; Mahmoud, Halah Hasan

doi:10.24996/ijs.2018.59.3c.17

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…Deep learning refers to a vast number of machine learning approaches and frameworks that have the advantage of employing multiple levels of hierarchical nonlinear data processing. Based on the intended application of the architectures and techniques, such as synthesis/generation or identification/classification [28]. Comparing deep learning and traditional machine methods it could be instructive to suggest parallels to a regression model in learning algorithms.…”

Section: Deep Learning Algorithmsmentioning

confidence: 99%

A Survey on Arabic Text Classification Using Deep and Machine Learning Algorithms

Abdulghani

Abdullah

2022

eijs

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Text categorization refers to the process of grouping text or documents into classes or categories according to their content. Text categorization process consists of three phases which are: preprocessing, feature extraction and classification. In comparison to the English language, just few studies have been done to categorize and classify the Arabic language. For a variety of applications, such as text classification and clustering, Arabic text representation is a difficult task because Arabic language is noted for its richness, diversity, and complicated morphology. This paper presents a comprehensive analysis and a comparison for researchers in the last five years based on the dataset, year, algorithms and the accuracy they got. Deep Learning (DL) and Machine Learning (ML) models were used to enhance text classification for Arabic language. Remarks for future work were concluded.

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Section: Deep Learning Algorithmsmentioning

confidence: 99%

A Survey on Arabic Text Classification Using Deep and Machine Learning Algorithms

Abdulghani

Abdullah

2022

eijs

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“…Before the revolutionary deep learning era, traditional methods were employed in semantic segmentation [14], [15]. The success of deep learning in solving different computer vision problems [16]- [22] encouraged using it with semantic segmentation. Deep learning was used in semantic segmentation were led to a boom in its performance [23].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based on Attention in Semantic Segmentation: An Introductory Survey

2023

IJCCCE

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Semantic segmentation refers to labeling each pixel in the scene to its belonging object. It is a critical task for many computer vision applications that requires scene understanding because It attempts to mimic human perceptual grouping. Despite the unremitting efforts in this field, it is still a challenge and preoccupies of researchers. Semantic segmentation performance improved using deep learning rather than traditional methods. Semantic segmentation based on deep learning models requires capturing local and global context information, where deep learning models usually can extract one of them but is challenging to integrate between them. Deep learning based on attention mechanisms can gather between the capturing of local and glopal information, so it is increasingly employed in semantic segmentation. This paper gives an introductory survey of the rising topic attention mechanisms in semantic segmentation. At first, it will discuss the concept of attention and its integration with semantic segmentation requirements. Then, it will review deep learning based on attention mechanisms in semantic segmentation. Index Terms— attention concept, computer vision, deep learning, semantic segmentation.

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“…When the data is fictitious, simple techniques such as zoom in, zoom out, rotation, etc. are used [1].…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement of Generative Adversarial Networks to Generate Digital Color Images of Skin Diseases

Ahmed,

Kashmola

2023

Iraqi Journal of Science

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The main task of creating new digital images of different skin diseases is to increase the resolution of the specific textures and colors of each skin disease. In this paper, the performance of generative adversarial networks has been optimized to generate multicolor and histological color digital images of a variety of skin diseases (melanoma, birthmarks, and basal cell carcinomas). Two architectures for generative adversarial networks were built using two models: the first is a model for generating new images of dermatology through training processes, and the second is a discrimination model whose main task is to identify the generated digital images as either real or fake. The gray wolf swarm algorithm and the whale swarm algorithm were relied on to generate values that improve the performance of GANs and insert them into the generator instead of random values, which in turn worked to reduce the loss values for the generated images. Loss values were adopted as a measure of optimizations for each epoch, and the fastest access time to actual digital images for each skin disease was adopted. Before the optimization operations, 50% accurate images of skin diseases were obtained; after the optimization operations, 98% accurate images of skin diseases were obtained.

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Effect of Successive Convolution Layers to Detect Gender

Cited by 3 publications

References 20 publications

A Survey on Arabic Text Classification Using Deep and Machine Learning Algorithms

A Survey on Arabic Text Classification Using Deep and Machine Learning Algorithms

Deep Learning Based on Attention in Semantic Segmentation: An Introductory Survey

Performance Improvement of Generative Adversarial Networks to Generate Digital Color Images of Skin Diseases

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