Artificial Intelligence and Digital Pathology Synergy: For detailed, accurate and predictive analysis of WSIs

Ankit,; Hasija, Yasha

doi:10.1109/icaccs54159.2022.9785034

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…i.e., before AI commercialization (which, according to [3], begun after 1980) and large-scale application of artificial neural networks. Currently, the following major concepts of AI are often distinguished in the literature: (1) artificial general intelligence (or strong AI), and (2) artificial narrow intelligence (or weak AI) [8]. Also, other categories can be found such as generative AI or symbolic AI; however, detailed analysis of this field is outside the scope of this paper.…”

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IIR Shelving Filter, Support Vector Machine and k-Nearest Neighbors Algorithm Application for Voltage Transients and Short-Duration RMS Variations Analysis

Liubčuk,

Kairaitis,

Radziukynas

et al. 2024

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This paper focuses on both voltage transients and short-duration RMS variations, and presents a unique and heterogeneous approach to their assessment by applying AI tools. The database consists of both real (obtained from Lithuanian PQ monitoring campaigns) and synthetic data (obtained from the simulation and literature review). Firstly, this paper investigates the fundamental grid component and its harmonics filtering with an IIR shelving filter. Secondly, in a key part, both SVM and KNN are used to classify PQ events by their primary cause in the voltage–duration plane as well as by the type of short circuit in the three-dimensional voltage space. Thirdly, since it seemed to be difficult to interpret the results in the three-dimensional space, the new method, based on Clarke transformation, is developed to convert it to two-dimensional space. The method shows an outstanding performance by avoiding the loss of important information. In addition, a geometric analysis of the fault voltage in both two-dimensional and three-dimensional spaces revealed certain geometric patterns that are undoubtedly important for PQ classification. Finally, based on the results of a PQ monitoring campaign in the Lithuanian distribution grid, this paper presents a unique discussion regarding PQ assessment gaps that need to be solved in anticipation of a great leap forward and refers them to PQ legislation.

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“…7 The defense of the classifier from an adversarial attack is investigated. 8 Deep features are complex patterns extracted from the intermediate layers of CNN. 9 The distance (location) is expressed in numbers.…”

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confidence: 99%