Image Classification Approach Using Machine Learning and an Industrial Hadoop Based Data Pipeline

Koulali, Rim; Zaidani, Hajar; Zaim, Maryeme

doi:10.1016/j.bdr.2021.100184

Cited by 12 publications

(3 citation statements)

References 18 publications

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“…This indicates that the method proposed in this paper can effectively improve the efficiency of aerospace image classification and provide a strong theoretical basis for subsequent image processing work. However, there are also some shortcomings in indicates that this method has the shortest image classification time and improves the classification efficiency of aerospace images [19][20][21][22][23]. However, there are also some shortcomings in this study, including the following aspects: limited time and energy: due to time and energy limitations, this study may not be able to cover all possible situations and details.…”

Section: Discussionmentioning

confidence: 89%

“…This 3, it can be seen that the average classification time of this method for aerospace images is 3.5 minutes, which is 14 minutes and 29 minutes less than traditional method 1 and traditional method 2, respectively. This indicates that this method has the shortest image classification time and improves the classification efficiency of aerospace images [19,20,21,22,23].…”

Section: Space Image Classification Timementioning

confidence: 79%

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Research on Space Image Fast Classification Based on Big Data

Wang,

Chen

2023

SCPE

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In order to improve the accuracy and effect of space image classification, the author proposes a space image classification method based on Big data analysis, aiming at the shortcomings of low accuracy and long time of current image classification. First, analyze the current research progress of image classification, find out the shortcomings of different classification methods, then collect aerospace images, preprocess the images, and use big data analysis technology to establish image classifiers, image classification was performed using an image classifier, and finally simulation experiments were conducted with other methods for image classification. The results indicate that: The average classification time of this method for aerospace images is 3.5 minutes, which saves 14 minutes and 29 minutes compared to traditional method 1 and traditional method 2, respectively. This indicates that this method has the shortest image classification time and improves the classification efficiency of aerospace images. This method has been proven to have high accuracy in image classification, the shortest classification time, and significant advantages compared to other image classification methods.

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Section: Discussionmentioning

confidence: 89%

Section: Space Image Classification Timementioning

confidence: 79%

Research on Space Image Fast Classification Based on Big Data

Wang,

Chen

2023

SCPE

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“…The paper discusses the dual challenges in industrial and commercial Big Data systems: one must develop analytics that can help us find value in huge amounts of data and, at the same time, provide ways of handling large amounts of data in an efficient way. In [25], Koulali et al discussed a smart city scenario where citizens take active parts in improving the overall quality of life by taking pictures and videos of different infrastructure problems when they encounter them in their daily lives. These images and videos are uploaded using smartphones, thus allowing city authorities to make appropriate incident responses.…”

Section: Application Areas For Big Datamentioning

confidence: 99%

Research Trends, Enabling Technologies and Application Areas for Big Data

Lundberg¹,

Grahn²

2022

Algorithms

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The availability of large amounts of data in combination with Big Data analytics has transformed many application domains. In this paper, we provide insights into how the area has developed in the last decade. First, we identify seven major application areas and six groups of important enabling technologies for Big Data applications and systems. Then, using bibliometrics and an extensive literature review of more than 80 papers, we identify the most important research trends in these areas. In addition, our bibliometric analysis also includes trends in different geographical regions. Our results indicate that manufacturing and agriculture or forestry are the two application areas with the fastest growth. Furthermore, our bibliometric study shows that deep learning and edge or fog computing are the enabling technologies increasing the most. We believe that the data presented in this paper provide a good overview of the current research trends in Big Data and that this kind of information is very useful when setting strategic agendas for Big Data research.

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