Image classification based deep learning: A Review

Hassan, Aya; Refaat, Moatamed; Hemeida, Ashraf Mohamed

doi:10.21608/aujst.2022.259887

Cited by 5 publications

(4 citation statements)

References 59 publications

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“…A histogram analysis may provide insight into an image's pixel value distribution. Original images typically exhibit distinct patterns and biases in their histograms, reflecting features like dominant colors, shapes, and textures [33][34][35][36]. Secure encryption techniques aim to disrupt these patterns and create a more uniform distribution of pixel values in the encrypted image's histogram.…”

Section: Analytical Histogrammentioning

confidence: 99%

Scrambled Encryption Approach for Color Images Based on 9-D Chaotic Systems with 3-D Substitution Bit Levels

Abdel Hameed

2024

Aswan University Journal of Sciences and Technology

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Over the past few decades, the need for robust and secure communication has grown exponentially, particularly in image transmission over networks. This is due to the increasing reliance on digital technologies. This work demonstrates a novel "Scrambled Encryption Approach" that leverages the inherent unpredictability. To this end, nine-dimensional chaotic systems are combined with the innovative technique of 3-D substitution bit levels to verify strengths such as the confidentiality and integrity of visual data while maintaining fast encryption and decryption processes. This has the drawback of being vulnerable to known plaintext attacks and performing more sophisticated calculations than simpler encryption approaches. So, the proposed technique first separates the color input image into 24 bits to be categorized into 8 bits for each color channel (red, green, and blue). Then, 3-bit levels are used with position sequences for substitutions taken from the 9-D Lorenz chaotic system. After that, a multilayer differentiation technique produces three key matrices, which are then used to diffuse the scrambled components and produce the color cipher image. The simulation results show that our method has a high visual quality of decrypted images as well as high protection against brute force attacks because we have a large key space used for encryption data. Moreover, our proposed technique achieves more security against statistical attacks, including histogram analysis, information entropy, and correlation coefficients between pixels. Finally, the proposed encryption technique achieves a more secure transmission than state-of-the-art encryption approaches to guarantee integrity, privacy, and efficiency through global networks.

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Section: Analytical Histogrammentioning

confidence: 99%

Scrambled Encryption Approach for Color Images Based on 9-D Chaotic Systems with 3-D Substitution Bit Levels

Abdel Hameed

2024

Aswan University Journal of Sciences and Technology

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“…47 Lastly, the thermal decomposition of organometallic compounds that decompose compounds by heat is another option for mirror preparation techniques. 47,49 Generally, 3 types of mirrors are used for the PTC: silvered glass mirrors, aluminized reflectors, and silvered polymer reflectors. The summary of these reflectors is shown in Table II.…”

Section: Parabolic Trough Collector (Ptc) Systemmentioning

confidence: 99%

A Review on CSP and CPVT Technologies: Energy, Economic Analysis and Recycling towards Circular Economy

Wong

Walvekar²,

Vaka³

et al. 2023

ECS J. Solid State Sci. Technol.

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Recently, there has been a shift towards renewable energy sources for electricity generation due to rapidly depleting non-renewable energy sources. Solar energy is generally the most promising renewable energy source to be harvested by concentrated solar power (CSP) and solar photovoltaic (PV) technologies. CSP technology can be further classified into a few categories, such as parabolic trough collector (PTC), solar power tower, linear Fresnel reflector, and solar parabolic dish. In contrast, solar PV can be further developed into concentrated photovoltaic and concentrated photovoltaic thermal (CPVT) systems. The modifications (e.g. optical and thermal modifications) done on PTC and parabolic trough based CPVT systems on enhancing system performance are discussed. Next, the economic analyses conducted for both systems are discussed to compare the economic feasibility of the technologies employed in different countries. The appropriate recycling and CE approaches applied for PTC and PV technologies are reviewed and classified based on the key material elements within this industry. Overall, this paper compares PTC and parabolic trough-based CPVT systems from the technical, economic, and environmental aspects to provide insight for the solar energy harvesting field researchers.

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“…This capability enables the successful accomplishment of tasks such as image categorization or prediction. Unlike conventional machine learning, deep learning leverages multi-layered neural networks to autonomously learn from images and extract intricate underlying features present within the images [7], [8]. Among the prevailing deep neural network architectures, (convolutional neural networks (CNNs) or ConvNets) stand as one of the most widely embraced.…”

Section: Introductionmentioning

confidence: 99%

Classification of grapevine leaves images using VGG-16 and VGG-19 deep learning nets

A. Rajab,

Abdullatif,

Sutikno

2024

TELKOMNIKA

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The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classification by adapting VGG-16 net and VGG-19 net models and subsequently identifying the optimal performer between the two nets during the classification process. A publicly available dataset comprising 500 images categorized into 5 distinct classes (100 images per class), was utilized in this work. The obtained empirical outcomes demonstrate a remarkable accuracy rate of 99.6% for the VGG-16 net model, while VGG-19 net achieves a 100% accuracy rate. Based on these findings, it can be inferred that VGG-19 net exhibits superior performance in classifying images of grapevine leaves compared to the VGG-16 net.

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Image classification based deep learning: A Review

Cited by 5 publications

References 59 publications

Scrambled Encryption Approach for Color Images Based on 9-D Chaotic Systems with 3-D Substitution Bit Levels

Scrambled Encryption Approach for Color Images Based on 9-D Chaotic Systems with 3-D Substitution Bit Levels

A Review on CSP and CPVT Technologies: Energy, Economic Analysis and Recycling towards Circular Economy

Classification of grapevine leaves images using VGG-16 and VGG-19 deep learning nets

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