Image-based soft drink type classification and dietary assessment system using deep convolutional neural network with transfer learning

Hafiz, Rubaiya; Haque, Mohammad Reduanul; Rakshit, Aniruddha; Uddin, Mohammad Shorif

doi:10.1016/j.jksuci.2020.08.015

Cited by 20 publications

(8 citation statements)

References 17 publications

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“…Undoubtedly, ML will develop and expand new methods to reveal the relationship between food composition and nutrition [ 90 ]. Furthermore, the results obtained by a DCNN-based model for nutrient estimation and dietary assessment shed light on potential future improvements [ 164 , 165 ].…”

Section: Discussionmentioning

confidence: 99%

“…Computer vision systems based on ML or DL represented great potentiality in food processing and distribution [ 72 , 84 , 86 , 102 , 103 , 115 , 158 , 160 , 184 ]. In addition, non-destructive detection methods based on ML or DL shed light on nutrient estimation and dietary assessment [ 116 , 153 , 154 , 164 , 165 ].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Liu

Wang

Zhang

et al. 2023

Foods

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Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Liu

Wang

Zhang

et al. 2023

Foods

View full text Add to dashboard Cite

show abstract

“…On the other hand, metamaterials are used in various fields due to their advantages. Their uses include absorbers [ [6] , [7] , [8] ], substrates [ 9 ], reconfigurable antennas [ 10 ], microwave application devices like Wi-Fi, GPS [ 11 , 12 ], imaging [ 13 , 14 ], reduction of specific absorption rate (SAR) [ 15 ], radar cross-section (RCS) [ 16 ], cloaking [ 17 ], metamaterial sensors [ 18 , 19 ], filters [ 20 ], lenses [ [21] , [22] , [23] ], perfect tunnelling [ 24 ], optics [ 25 ], microwave communications radar and satellite [ [26] , [27] , [28] ], coding and information metamaterial [ 29 , 30 ], fractal structured metamaterial [ 31 , 32 ], and terahertz metamaterial [ 33 , 34 ]. Many applications require the design of systems that achieve superior functioning, device compactness, and enhanced data measurement capacity.…”

Section: Introductionmentioning

confidence: 99%

SRR inspired inversion symmetry-shaped left-handed metamaterial with a high effective medium ratio for ASR and WLAN applications

Sifat,

Faruque,

Ramachandran

et al. 2024

Heliyon

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“…Stacking further layers for learning better neural networks and getting higher classification accuracy is a way that always leads to exploding or vanishing gradients. This issue has been extensively addressed in the literature [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Image Classification Method Based on Residual Network, Inception, and Proposed Activation Function

Yahya

Liu

Hawbani

et al. 2023

Sensors

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In deeper layers, ResNet heavily depends on skip connections and Relu. Although skip connections have demonstrated their usefulness in networks, a major issue arises when the dimensions between layers are not consistent. In such cases, it is necessary to use techniques such as zero-padding or projection to match the dimensions between layers. These adjustments increase the complexity of the network architecture, resulting in an increase in parameter number and a rise in computational costs. Another problem is the vanishing gradient caused by utilizing Relu. In our model, after making appropriate adjustments to the inception blocks, we replace the deeper layers of ResNet with modified inception blocks and Relu with our non-monotonic activation function (NMAF). To reduce parameter number, we use symmetric factorization and 1×1 convolutions. Utilizing these two techniques contributed to reducing the parameter number by around 6 M parameters, which has helped reduce the run time by 30 s/epoch. Unlike Relu, NMAF addresses the deactivation problem of the non-positive number by activating the negative values and outputting small negative numbers instead of zero in Relu, which helped in enhancing the convergence speed and increasing the accuracy by 5%, 15%, and 5% for the non-noisy datasets, and 5%, 6%, 21% for non-noisy datasets.

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Image-based soft drink type classification and dietary assessment system using deep convolutional neural network with transfer learning

Cited by 20 publications

References 17 publications

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

SRR inspired inversion symmetry-shaped left-handed metamaterial with a high effective medium ratio for ASR and WLAN applications

A Novel Image Classification Method Based on Residual Network, Inception, and Proposed Activation Function

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