Development of machine vision-based ore classification model using support vector machine (SVM) algorithm

Patel, Ashok Kumar; Chatterjee, Snehamoy; Gorai, Amit Kumar

doi:10.1007/s12517-017-2909-0

Cited by 52 publications

(17 citation statements)

References 48 publications

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“…In works by Chatterjee et al (Chatterjee, 2013;Chatterjee et al, 2010Chatterjee et al, , 2008Patel et al, 2017aPatel et al, , 2017bPatel et al, , 2016 crushed rocks from different mines were analyzed. Each rock was separately segmented from the image.…”

Section: Introductionmentioning

confidence: 99%

Deep convolutions for in-depth automated rock typing

Baraboshkin

Ismailova

Orlov

et al. 2020

Computers & Geosciences

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The description of rocks is one of the most time-consuming tasks in the everyday work of a geologist, especially when very accurate description is required. We here present a method that reduces the time needed for accurate description of rocks, enabling the geologist to work more efficiently. We describe the application of methods based on color distribution analysis and feature extraction. Then we focus on a new approach, used by us, which is based on convolutional neural networks. We used several well-known neural network architectures (AlexNet, VGG, GoogLeNet, ResNet) and made a comparison of their performance. The precision of the algorithms is up to 95% on the validation set with GoogLeNet architecture. The best of the proposed algorithms can describe 50 m of full-size core in one minute.

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Section: Introductionmentioning

confidence: 99%

Deep convolutions for in-depth automated rock typing

Baraboshkin

Ismailova

Orlov

et al. 2020

Computers & Geosciences

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“…It separates the classes with a hyper-plane, which maximizes the margin between classes. From the given data, the binary linear classification model is developed through which the boundaries of the classification data is derived [129], [136], [137]. 2) C4.5: It is used to classify the data using a decision tree.…”

Section: A Privacy-preserving Artificial Intelligence Techniquesmentioning

confidence: 99%

Smart Contract Privacy Protection Using AI in Cyber-Physical Systems: Tools, Techniques and Challenges

et al. 2020

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Applications of Blockchain (BC) technology and Cyber-Physical Systems (CPS) are increasing exponentially. However, framing resilient and correct smart contracts (SCs) for these smart application is a quite challenging task because of the complexity associated with them. SC is modernizing the traditional industrial, technical, and business processes. It is self-executable, self-verifiable, and embedded into the BC that eliminates the need for trusted third-party systems, which ultimately saves administration as well as service costs. It also improves system efficiency and reduces the associated security risks. However, SCs are well encouraging the new technological reforms in Industry 4.0, but still, various security and privacy challenges need to be addressed. In this paper, a survey on SC security vulnerabilities in the software code that can be easily hacked by a malicious user or may compromise the entire BC network is presented. As per the literature, the challenges related to SC security and privacy are not explored much by the authors around the world. From the existing proposals, it has been observed that designing a complex SCs cannot mitigate its privacy and security issues. So, this paper investigates various Artificial Intelligence (AI) techniques and tools for SC privacy protection. Then, open issues and challenges for AI-based SC are analyzed. Finally, a case study of retail marketing is presented, which uses AI and SC to preserve its security and privacy.

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“…Producer's accuracy (PA), user's accuracy (UA), overall accuracy (OA), and k are widely used in the field of remote sensing and pattern recognition because of high universality. [38][39][40] PA, UA, OA, and k are thus used as the evaluation indexes.…”

Section: Analysis Of Detection Accuracymentioning

confidence: 99%

Fast road obstacle detection method based on maximally stable extremal regions

Gao

Tan

et al. 2018

International Journal of Advanced Robotic Systems

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Road obstacle detection is an important component of the advanced driver assistance system, and to improve the speed and accuracy of road obstacle detection method is a vital task. In this article, fast image region-matching method based on the maximally stable extremal regions method is proposed to improve the speed of image matching. The theoretical feasibility of detection method combining monocular camera with inertial measurement unit (IMU) is clarified. The fast road obstacle detection method based on maximally stable extremal regions combining fast image region-matching method based on maximally stable extremal regions and the vision-IMU-based obstacle detection method is proposed to bypass obstacle classification and to reduce time and space complexity for road environment perception. The Ada-Boost cascade detector, the speeded-up robust features-based obstacle detection method, and the proposed method are used to detect obstacles in outdoor contrast tests. Test results show that the proposed method has higher accuracy, and the reason of high accuracy is analyzed. The processing time of AdaBoost cascade detector, speeded-up robust featuresbased obstacle detection method, and proposed method are compared, and the results show that the proposed method has faster processing speed, and the reason of faster processing speed is analyzed.

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Development of machine vision-based ore classification model using support vector machine (SVM) algorithm

Cited by 52 publications

References 48 publications

Deep convolutions for in-depth automated rock typing

Deep convolutions for in-depth automated rock typing

Smart Contract Privacy Protection Using AI in Cyber-Physical Systems: Tools, Techniques and Challenges

Fast road obstacle detection method based on maximally stable extremal regions

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