Comparison of CNN Classification Model using Machine Learning with Bayesian Optimizer

Surono, Sugiyarto; Afitian, M. Yahya Firza; Setyawan, Anggi; Eni Arofah, Dyiyah Kresna; Thobirin, Aris

doi:10.28991/hij-2023-04-03-05

Cited by 14 publications

(3 citation statements)

References 32 publications

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“…The image correction and image segmentation modules are gradually removed, and experiments are conducted on the test dataset to verify the method's rationality and the necessity of each module [18]. The experimental results in Table 2 indicate a sharp drop in the retrieval hit rate after removing the image segmentation and image correction modules [19], highlighting the necessity of these modules in the method [20]. The modular combination of the methods in this paper is deemed more reasonable.…”

Section: Figure 6 Part Of the Retrieval Results Of The Book Page Retr...mentioning

confidence: 97%

Automatic Recognition Technology of Library Books Based on Convolutional Neural Network Model

Hu,

Yan,

Xie

2024

HighTech. Innov. J.

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Background: The development of technological devices has changed many facets of our lives, particularly the way we engage with information and learning. The advent of automated technology for identification has had a had a revolutionary effect on how we read and organize books within the context of books and data searches. It starts by solving the difficulties in analyzing photos of book pages by using methods such as distortion rectification and book separation. Objective: The research compares the effectiveness of the suggested method with traditional straight-line identification techniques using real-world testing. Methodology: The Skip-Gram model in Word2Vec is used to accurately represent spoken language, allowing word vectors to be generated and input data to be preprocessed for CNN. The results show that the methodology created regarding the present investigation works better than alternatives concerning accuracy and efficiency during line identification. Result:This work advances the field of book suggestion systems by presenting a strong and effective method that leverages CNNs. The findings demonstrate deep learning techniques may be used to optimize system recommendations and improve customer service and happiness in a variety of contexts. This technique creates a bridge between natural language processing and picture evaluation and opens up new possibilities for suggestion advancement along with user satisfaction. Doi: 10.28991/HIJ-2024-05-01-015 Full Text: PDF

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Section: Figure 6 Part Of the Retrieval Results Of The Book Page Retr...mentioning

confidence: 97%

Automatic Recognition Technology of Library Books Based on Convolutional Neural Network Model

Hu,

Yan,

Xie

2024

HighTech. Innov. J.

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“…Several methods exist for splitting data into training and test sets. Surono et al (27) employed a convolutional neural network to achieve an 80%-20% ratio for training and testing in various machine learning methods to estimate lung disease. In a different study, Kurdthongmee (28) investigated the impact of varying the number of training data points from 100 to 250 to determine the optimal amount for estimating parawood pith.…”

Section: Resultsmentioning

confidence: 99%

Effect of Training Data Ratio and Normalizing on Fatigue Lifetime Prediction of Aluminum Alloys with Machine Learning

Matin,

Azadi

2024

IJE

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It is critical to evaluate the estimation of the fatigue lifetimes for the piston aluminum alloys, particularly in the automotive industry. This paper investigates the effect of different normalization methods on the performance of the fatigue lifetime estimation using Extreme Gradient Boosting (XGBoost), as a supervised machine learning method. For this purpose, the dataset used in this study includes various physical and experimental inputs related to an aluminum alloy and the corresponding fatigue lifetime outputs. Furthermore, before fitting the XGBoost model, different fatigue lifetime preprocessing methods were utilized and evaluated using metrics such as Root Mean Square Error (RMSE), Determination Coefficient (R 2 ), and Scatter Band (SB). The results indicate that modeling fatigue lifetime with logarithmic values as a preprocessing method excels when XGBoost is trained with 100% of the data. However, other normalization methods demonstrate superior accuracy in estimating test data with a 20% test and 80% train set split.

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“…The computer vision is a subfield of Artificial Intelligence (AI) (17,18), which uses some algorithms to detect the details of images by using a computer called image processing (19). Image processing (20,21) is the process by which one can obtain useful information about any image. Image processing was used in engineering application like cyphering (22,23) chemistry (24,25), materials (26), biomedical (27)(28)(29), and nanotechnology (30)(31)(32), Telcomunications (33), Covid19 detection (34).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Particle Size in Composite Materials Using Image Processing

Alabdali,

Alkalid

2024

IJE

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Composite materials are the most important materials in materials science and engineering, which contain two or more materials. In materials engineering, the scanning electron microscopy (SEM) technique is an approach to measure the material's particle size. A new procedure was used instead of SEM is called Artificial Intelligence (AI). Artificial Intelligence (AI) is an interdisciplinary science and branch of computer science that involves solving problems that require human intelligence and capabilities. The computer vision is a subfield of AI, which uses some algorithms to detect the details of images by using computer called image processing. Detecting the particles and measuring the size of materials scanned by SEM is an essential task that helps to describe their feature, traditionally, the size is calculated manually by adding mesh to an SEM image or by drawing a diagonal line in an arbitrary particle. In this paper, a new model based on Artificial Intelligence (AI) is proposed using computer vision to analyze the size of all particles. This model is used to detect the particle size of additives in composite materials like graphene flakes and measure the size of them depending on the reference size fixed on the scanning electron microscope (SEM). The model was used based on the Open-source Computer Vision (OpenCV) library, utilizing multi-layers of canny edge detection, Sobel filter, Brightness and contrast algorithms, using Python 3. The results have achieved very satisfied indication with a very low process time = 0.2 mili-seconds.

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Comparison of CNN Classification Model using Machine Learning with Bayesian Optimizer

Cited by 14 publications

References 32 publications

Automatic Recognition Technology of Library Books Based on Convolutional Neural Network Model

Automatic Recognition Technology of Library Books Based on Convolutional Neural Network Model

Effect of Training Data Ratio and Normalizing on Fatigue Lifetime Prediction of Aluminum Alloys with Machine Learning

Analysis of Particle Size in Composite Materials Using Image Processing

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