A clustering-optimized segmentation algorithm and application on food quality detection

Wu, QingE; Li, Penglei; Chen, Zhiwu; Zong, Tao

doi:10.1038/s41598-023-36309-8

Cited by 3 publications

(2 citation statements)

References 27 publications

(32 reference statements)

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“…This approach proves effective in enhancing the overall acceptance rate of food quality. This approach proves effective in enhancing the overall acceptance rate of food quality [10].…”

Section: Industrial Engineering Advance Research and Applicationmentioning

confidence: 94%

Using self-organizing map for quality classification on fish processed product

Muharni,

Hartono,

Ulfah

et al. 2023

JISS UNTIRTA

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Assessing the quality of processed fish products stands as a critical factor in ensuring consumer satisfaction, upholding industry standards, and reducing wastage. Traditional methods for quality classification typically involve manual inspection, which is both time-consuming and subjective. In recent years, the utilization of advanced data analysis techniques, such as Self-Organizing Maps (SOMs), has emerged as a promising approach to enhance the accuracy and efficiency of quality assessment in the fish processing industry. SOMs provide a multi-dimensional map capable of representing various quality attributes of processed fish products. This study aims to classify the quality of processed fish products based on four attributes that impact their time to spoilage. The SOMs effectively segmented the dataset into two clusters, with one cluster being more prone to spoilage, while the other demonstrated a longer shelf life.

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“…This approach proves effective in enhancing the overall acceptance rate of food quality. This approach proves effective in enhancing the overall acceptance rate of food quality [10].…”

Section: Industrial Engineering Advance Research and Applicationmentioning

confidence: 94%

Using self-organizing map for quality classification on fish processed product

Muharni,

Hartono,

Ulfah

et al. 2023

JISS UNTIRTA

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“…The above examples show that advanced machine learning algorithmic modeling of LIBS experimental data can reduce and correct the spectral intensity fluctuations caused by emission source noise and matrix effects, thus improving the measurement accuracy. 32 In this regard, this paper designs a multi-dimensional Baijiu high-precision characterization system based on an optimized grid search and cross-validation (OptGSCV) quadratic optimization network combined with LIBS based on high-frequency ultrasonic atomization system (AS-LIBS), which achieves high-precision real-time on-line inspection of the wavelength range of the internal plasma of the Baijiu, the fluid properties, and the compositional constituents.…”

Section: Introductionmentioning

confidence: 99%

High precision and fast classification of different dimensions of Baijiu using an OptGSCV quadratic optimization network combined with AS-LIBS

Jin,

Hao,

et al. 2024

J. Anal. At. Spectrom.

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F-Net: Follicles Net an efficient tool for the diagnosis of polycystic ovarian syndrome using deep learning techniques

S.,

Umapathy,

Alhajlah

et al. 2024

PLoS ONE

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The study’s primary objectives encompass the following: (i) To implement the object detection of ovarian follicles using you only look once (YOLO)v8 and subsequently segment the identified follicles using a hybrid fuzzy c-means-based active contour technique. (ii) To extract statistical features and evaluate the effectiveness of both machine learning (ML) and deep learning (DL) classifiers in detecting polycystic ovary syndrome (PCOS). The research involved a two different dataset in which dataset1 comprising both normal (N = 50) and PCOS (N = 50) subjects, dataset 2 consists of 100 normal and 100 PCOS affected subjects for classification. The YOLOv8 method was employed for follicle detection, whereas statistical features were derived using Gray-level co-occurrence matrices (GLCM). For PCOS classification, various ML models such as Random Forest (RF), k- star, and stochastic gradient descent (SGD) were employed. Additionally, pre-trained models such as MobileNet, ResNet152V2, and DenseNet121 and Vision transformer were applied for the categorization of PCOS and healthy controls. Furthermore, a custom model named Follicles Net (F-Net) was developed to enhance the performance and accuracy in PCOS classification. Remarkably, the F-Net model outperformed among all ML and DL classifiers, achieving an impressive classification accuracy of 95% for dataset1 and 97.5% for dataset2 respectively in detecting PCOS. Consequently, the custom F-Net model holds significant potential as an effective automated diagnostic tool for distinguishing between normal and PCOS.

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A clustering-optimized segmentation algorithm and application on food quality detection

Cited by 3 publications

References 27 publications

Using self-organizing map for quality classification on fish processed product

Using self-organizing map for quality classification on fish processed product

High precision and fast classification of different dimensions of Baijiu using an OptGSCV quadratic optimization network combined with AS-LIBS

F-Net: Follicles Net an efficient tool for the diagnosis of polycystic ovarian syndrome using deep learning techniques

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