Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

By D. García-Pérez, Juan Angel Lorenzo del Castillo, Yahya Al-Hazmi, Josep Martrat, K. Kavoussanakis, Alastair C. Hume, Celia Velayos López, G. Landi, T. Wauters, Michael Gienger and 1 more. International Conference on Parallel Processing, 2014

1,077 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d6425d11904920cf6fafe895e6073e2131978e60

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Citations in the corpus

A Survey on ensemble learning under the era of deep learning (2021, methodology): “DFE5 U-Net (Ronneberger et al. 2015) pretrained on lesion segmentation task (Codella et al.”
A systematic review of the smart home literature: A user perspective (2019, background): “User-centric research 1 User perception of smart home technology [1, 8, 9, 12, 14, 17, 18, 27, 28, 46, 47, 54-56, 61, 62, 73, 84, 92, 93, 106, 111, 113, 119, 123, 130, 133, 141, 163, 164] 30”
A survey of video datasets for human action and activity recognition (2013, neutral): “gesture recognition [138], person identity [181].”
Attitudes towards the Use of Educational Robotics: Exploring Pre-Service and In-Service Early Childhood Teacher Profiles (2021, background): “They are unwilling to use robots in diverse learning settings, and these attitudes are independent of age or gender [38].”
No Explainability without Accountability: An Empirical Study of Explanations and Feedback in Interactive ML (2020, methodology): “This “human-in-the-loop” approach has been applied across machine learning, including in supervised ML [21, 56], unsupervised ML [8], and reinforcement learning [31, 53].”
A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction (2022, methodology): “…family of filter algorithms is the Relief-based algorithm (RBA) family (e.g., Relief (Kira and Rendell, 1992), ReliefF (Kononenko, 1994), TURF (Moore and White, 2007), SURF (Greene et al., 2009), SURF* (Greene et al., 2010), MultiSURF (Urbanowicz et al., 2018a), MultiSURF* (Granizo-Mackenzie…”
Microalgae Water Bioremediation: Trends and Hot Topics (2020, methodology): “Here, the size of the spheres (Figure 2) represents the quantity of publications, while the thickness of the line represents the relationship between the themes [50].”
A survey of the recent architectures of deep convolutional neural networks (2019, methodology): “Another object detection technique is reported by Gidaris and Komodakis (2015), which is based on multi-region based deep CNN that helps to learn the semantic aware features.”
Accurate brain age prediction with lightweight deep neural networks (2019, methodology): “, 2018) as input instead of the 3D full brain, or using a reversible architecture (Brügger et al., 2019), yet involving trade-offs between the GPU memory restriction and the information/performance loss.”