Some wafer fabrication processes are repeated processes, e.g. atomic layer deposition (ALD) process. For such processes, the wafers need to visit some processing modules for a number of times, which complicates the cy...
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Propinquity between Australian Indigenous communities’ social structures and ICT purposed for cultural preservation is a modern area of research;hindered by the ‘digital divide’ thus limiting plentiful literature i...
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The book presents the proceedings of the 12th International Conference on Frontiers of Intelligent computing: Theory and Applications (FICTA 2024), held at Intelligent Systems Research Group (ISRG), London Metropolita...
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ISBN:
(数字)9789819601394
ISBN:
(纸本)9789819601387;9789819601417
The book presents the proceedings of the 12th International Conference on Frontiers of Intelligent computing: Theory and Applications (FICTA 2024), held at Intelligent Systems Research Group (ISRG), London Metropolitan University, London, United Kingdom, during June 6–7, 2024. Researchers, scientists, engineers and practitioners exchange new ideas and experiences in the domain of intelligent computing theories with prospective applications in various engineering disciplines in the book. This book is divided into four volumes. It covers broad areas of information and decision sciences, with papers exploring both the theoretical and practical aspects of data-intensive computing, data mining, evolutionary computation, knowledge management and networks, sensor networks, signal processing, wireless networks, protocols and architectures. This book is a valuable resource for postgraduate students in various engineering disciplines.
Wireless sensor networks employ a variety of techniques such as data recycling, power management, and energy-aware routing protocols to reduce power consumption. Clustering is a well-known approach for extending the l...
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In this paper, some improvements, including the pyramid frame in image scale space, key point locating method for the SIFT (scale invariant feature transform) algorithm, are developed. In view of the characteristic of...
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Photovoltaic panel used in solar power generation is an environmentally beneficial and sustainable energy source that has been used to transform sunlight into electrical power. Arranged in large solar facilities, thes...
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Malware variants refer to all the new malwares manually or automatically produced from any existing malware. However, such simple approach to produce malwares can change signatures of the original malware to confuse a...
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Web-based social systems enable new community-based opportunities for participants to engage, share, and interact. This community value and related services like search and advertising are threatened by spammers, cont...
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Individual movement analysis is becoming more open and common as sensors incorporated in mobile devices and machine learning algorithms progress. In the proposed framework, different classification techniques of machi...
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Hepatocellular carcinoma (HCC) is graded mainly based on the characteristics of liver cell nuclei. This paper proposes a textural feature descriptor and a novel computational method for classifying liver cell nuclei a...
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ISBN:
(纸本)9780889869530
Hepatocellular carcinoma (HCC) is graded mainly based on the characteristics of liver cell nuclei. This paper proposes a textural feature descriptor and a novel computational method for classifying liver cell nuclei and grading the HCC histological images. The proposed textural feature descriptor observes local and spatial characteristics of the texture patterns by using multifractal computation. The textural features are utilized for nuclear segmentation, fiber region detection, and liver cell nuclei classification. Four categories of nuclear features are computed such as texture, geometry, spatial distribution, and surrounding texture, for HCC classification. Significance of liver cell nuclei classification method is evaluated by classifying non-neoplastic and tumor tissues. Furthermore, characteristics of the liver cell nuclei were utilized for grading a set of HCC images into four classes and obtained 97.77% classification accuracy.
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