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The K-Means Algorithm: A Comprehensive Survey And Performance Evaluation, Mohiuddin Ahmed, Raihan Seraj, Syed Mohammed Shamsul Islam
The K-Means Algorithm: A Comprehensive Survey And Performance Evaluation, Mohiuddin Ahmed, Raihan Seraj, Syed Mohammed Shamsul Islam
Research outputs 2014 to 2021
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of …
Structural Similarity Loss For Learning To Fuse Multi-Focus Images, Xiang Yan, Syed Zulqarnain Gilani, Hanlin Qin, Ajmal Mian
Structural Similarity Loss For Learning To Fuse Multi-Focus Images, Xiang Yan, Syed Zulqarnain Gilani, Hanlin Qin, Ajmal Mian
Research outputs 2014 to 2021
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Convolutional neural networks have recently been used for multi-focus image fusion. However, some existing methods have resorted to adding Gaussian blur to focused images, to simulate defocus, thereby generating data (with ground-truth) for supervised learning. Moreover, they classify pixels as ‘focused’ or ‘defocused’, and use the classified results to construct the fusion weight maps. This then necessitates a series of post-processing steps. In this paper, we present an end-to-end learning approach for directly predicting the fully focused output image from multi-focus input image pairs. The suggested approach uses a CNN architecture …
“Notame”: Workflow For Non-Targeted Lc-Ms Metabolic Profiling, Anton Klåvus, Marietta Kokla, Stefania Noerman, Ville M. Koistinen, Marjo Tuomainen, Iman Zarei, Topi Meuronen, Merja R. Häkkinen, Soile Rummukainen, Ambrin Farizah Babu, Taisa Sallinen, Olli Karkkainen, Jussi Paananen, David Broadhurst, Carl Brunius, Kati Hanhineva, Hanhineva
“Notame”: Workflow For Non-Targeted Lc-Ms Metabolic Profiling, Anton Klåvus, Marietta Kokla, Stefania Noerman, Ville M. Koistinen, Marjo Tuomainen, Iman Zarei, Topi Meuronen, Merja R. Häkkinen, Soile Rummukainen, Ambrin Farizah Babu, Taisa Sallinen, Olli Karkkainen, Jussi Paananen, David Broadhurst, Carl Brunius, Kati Hanhineva, Hanhineva
Research outputs 2014 to 2021
Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography-mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research …