Results 81 to 90 of about 355,362 (266)
A sequence-based multiple kernel model for identifying DNA-binding proteins
BMC Bioinformatics, 2021 Background DNA-Binding Proteins (DBP) plays a pivotal role in biological system. A mounting number of researchers are studying the mechanism and detection methods. To detect DBP, the tradition experimental method is time-consuming and resource-consuming.
Yuqing Qian +4 more
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Advanced Materials, EarlyView.The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj +8 more
wiley +1 more source
International Journal of Distributed Sensor Networks, 2017 Sparse representation–based classification and kernel methods have emerged as important methods for pattern recognition. In this work, we study the problem of vehicle recognition using acoustic sensor networks in real-world applications.
Rui Wang, Wenming Cao, Zhihai He
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Genetics Selection Evolution, 2021 Background Over the past decade, Fourier transform infrared (FTIR) spectroscopy has been used to predict novel milk protein phenotypes. Genomic data might help predict these phenotypes when integrated with milk FTIR spectra.
Toshimi Baba +6 more
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Multiple Random Subset-Kernel Learning [PDF]
, 2011 In this paper, the multiple random subset-kernel learning (MRSKL) algorithm is proposed. In MRSKL, a subset of training samples is randomly selected for each kernel with randomly set parameters, and the kernels with optimal weights are combined for classification. A linear support vector machine (SVM) is adopted to determine the optimal kernel weights;
Kenji Nishida, Jun Fujiki, Takio Kurita
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Multiple kernel learning for speaker verification [PDF]
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008 Many speaker verification (SV) systems combine multiple classifiers using score-fusion to improve system performance. For SVM classifiers, an alternative strategy is to combine at the kernel level. This involves finding a suitable kernel weighting, known as multiple kernel learning (MKL).
Chris Longworth, Mark J. F. Gales
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AI–Guided 4D Printing of Carnivorous Plants–Inspired Microneedles for Accelerated Wound Healing
Advanced Materials, EarlyView.This work presents an artificial intelligence (AI)‐guided 4D‐printed microneedle platform inspired by carnivorous plants for wound healing. A thermo‐responsive shape memory polymer enables body temperature–triggered self‐coiling for autonomous wound closure.
Hyun Lee +21 more
wiley +1 more source
Multiple-Instance Learning via an RBF Kernel-Based Extreme Learning Machine
Journal of Intelligent Systems, 2017 As we are usually confronted with a large instance space for real-word data sets, it is significant to develop a useful and efficient multiple-instance learning (MIL) algorithm.
Wang Jie, Cai Liangjian, Zhao Xin
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Remote Sensing, 2020 In this paper, superpixel features and extended multi-attribute profiles (EMAPs) are embedded in a multiple kernel learning framework to simultaneously exploit the local and multiscale information in both spatial and spectral dimensions for hyperspectral
Lei Pan, Chengxun He, Yang Xiang, Le Sun
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Advanced Robotics Research, EarlyView.Embedded flexible sensing technologies advance underwater soft robotics, yet most systems still suffer from hysteresis and limited perceptiveness. Instead, vision‐based tactile sensors provide reliable and rapid feedback essential for complex underwater tasks.
Qiyi Zhang +5 more
wiley +1 more source