Towards Large Scale Image Retrieval System Using Parallel Frameworks

Mezzoudj, Saliha

doi:10.5772/intechopen.94910

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Mezzoud et al [17], an approach for disease diagnosis mechanism using C-BIR is introduced for histopathological images. The outcomes of the study yield higher accuracy and better retrieval in support of medical research.…”

Section: Literature Surveymentioning

confidence: 99%

Classification of medical X-ray images using supervised and unsupervised learning approaches

Battur¹,

Narayana²

2023

IJEECS

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Most of the traditional approaches for medical image storage are least capable and scanning of relevant matching images are quite difficult. The existing approaches of content-based image retrieval (C-BIR) are less focused with medical images. The available research works with fuzzy logic approaches are very less and not efficient for medical image retrieval. Thus, there is a need of research work that can address both supervised and unsupervised learning approaches for medical image retrieval. Hence, the C-BIR technique is evolved with overcoming above stated concerns. Hence, this manuscript introduces two different C-BIR techniques using a support vector machine (SVM) and a fuzzy logic-based approach for classification. These approaches work on the classification based on feature extraction, region of Interest (ROI), corner detection, and similarity matching. The proposed approach has been analyzed for image retrieval for accuracy. The outcomes of the proposed study enhance the classification performances with retrieval than existing techniques of C-BIR.

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Section: Literature Surveymentioning

confidence: 99%

Classification of medical X-ray images using supervised and unsupervised learning approaches

Battur¹,

Narayana²

2023

IJEECS

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A Comparative Study of Parallel Processing, Distributed Storage Techniques, and Technologies: A Survey on Big Data Analytics

Mezzoudj,

Khelifa,

Saadna

2024

IJDSA

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The significance of developing Big Data applications has increased in recent years, with numerous organizations across various industries relying more on insights derived from vast amounts of data. However, conventional data techniques and platforms struggle to cope the Big Data, exhibiting sluggish responsiveness and deficiencies in scalability, performance, and accuracy. In response to the intricate challenges posed by Big Data, considerable efforts have been made, leading to the creation of a range of distributions and technologies. This article addresses the critical need for efficient processing and storage solutions in the context of the ever-growing field of big data. It offers a comparative analysis of various parallel processing techniques and distributed storage frameworks, emphasizing their importance in big data analytics. Our study begins with definitions of key concepts, clarifying the roles and interconnections of parallel processing and distributed storage. It further evaluates a range of architectures and technologies, such as MapReduce, CUDA, Storm, Flink, MooseFS, and BeeGFS and others technologies, discussing their advantages and limitations in managing large-scale datasets. Key performance metrics are also examined, providing a comprehensive understanding of their effectiveness in big data scenarios.

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Towards Large Scale Image Retrieval System Using Parallel Frameworks

Cited by 2 publications

References 24 publications

Classification of medical X-ray images using supervised and unsupervised learning approaches

Classification of medical X-ray images using supervised and unsupervised learning approaches

A Comparative Study of Parallel Processing, Distributed Storage Techniques, and Technologies: A Survey on Big Data Analytics

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