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Wang X, Shi L, Liu J, Zhang M. Cosine 2DPCA With Weighted Projection Maximization. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:9643-9656. [PMID: 35389871 DOI: 10.1109/tnnls.2022.3159011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Two-dimensional principal component analysis (2DPCA), known to be very sensitive to outliers, employs the square F -norm as the distance metric and only satisfies the optimal objective of maximizing projection variance. However, the objective of minimizing reconstruction errors for all samples is not optimized as much as possible. To handle the problem, a novel cosine objective function is first presented for maximizing weighted projection, in which the 2-norm of vectors with an adjustable power parameter is employed as the distance metric. Not only the objective with the maximum projection distance is accomplished in the cosine objective function, but also the objective with the minimum sum of reconstruction errors is also optimized indirectly. Then, the cosine 2DPCA (Cos-2DPCA) method is proposed, and the greedy iterative algorithm to solve Cos-2DPCA is also developed. The convergence and correlation of solutions are proved theoretically and discussed in detail. Finally, the series of experiments are carried out on the artificial dataset and eight standard datasets, respectively. The results demonstrate that the performances of Cos-2DPCA are significantly improved on the reconstruction, correlation, complexity, and classification, and it outperforms most of the existing robust 2DPCA methods.
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Zgheib R, Chahbandarian G, Kamalov F, Messiry HE, Al-Gindy A. Towards an ML-based semantic IoT for pandemic management: A survey of enabling technologies for COVID-19. Neurocomputing 2023; 528:160-177. [PMID: 36647510 PMCID: PMC9833856 DOI: 10.1016/j.neucom.2023.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 12/03/2022] [Accepted: 01/08/2023] [Indexed: 01/13/2023]
Abstract
The connection between humans and digital technologies has been documented extensively in the past decades but needs to be evaluated through the current global pandemic. Artificial Intelligence(AI), with its two strands, Machine Learning (ML) and Semantic Reasoning, has proven to be a great solution to provide efficient ways to prevent, diagnose and limit the spread of COVID-19. IoT solutions have been widely proposed for COVID-19 disease monitoring, infection geolocation, and social applications. In this paper, we investigate the usage of the three technologies for handling the COVID-19 pandemic. For this purpose, we surveyed the existing ML applications and algorithms proposed during the pandemic to detect COVID-19 disease using symptom factors and image processing. The survey includes existing approaches including semantic technologies and IoT systems for COVID-19. Based on the survey result, we classified the main challenges and the solutions that could solve them. The study proposes a conceptual framework for pandemic management and discusses challenges and trends for future research.
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Affiliation(s)
- Rita Zgheib
- Department of Computer Engineering, Canadian University Dubai, Dubai, United Arab Emirates,Corresponding author at: Canadian University Dubai,City Walk, Dubai, UAE
| | | | - Firuz Kamalov
- Department of Electrical Engineering, Canadian University Dubai, Dubai, United Arab Emirates
| | - Haythem El Messiry
- University of Science and Technology of Fujairah, Fujairah, United Arab Emirates,University of Ain Shams, Cairo, Egypt
| | - Ahmed Al-Gindy
- Department of Electrical Engineering, Canadian University Dubai, Dubai, United Arab Emirates
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Khatamifard SK, Chowdhury Z, Pande N, Razaviyayn M, Kim C, Karpuzcu UR. GeNVoM: Read Mapping Near Non-Volatile Memory. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3482-3496. [PMID: 34613917 DOI: 10.1109/tcbb.2021.3118018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
DNA sequencing is the physical/biochemical process of identifying the location of the four bases (Adenine, Guanine, Cytosine, Thymine) in a DNA strand. As semiconductor technology revolutionized computing, modern DNA sequencing technology (termed Next Generation Sequencing, NGS) revolutionized genomic research. As a result, modern NGS platforms can sequence hundreds of millions of short DNA fragments in parallel. The sequenced DNA fragments, representing the output of NGS platforms, are termed reads. Besides genomic variations, NGS imperfections induce noise in reads. Mapping each read to (the most similar portion of) a reference genome of the same species, i.e., read mapping, is a common critical first step in a diverse set of emerging bioinformatics applications. Mapping represents a search-heavy memory-intensive similarity matching problem, therefore, can greatly benefit from near-memory processing. Intuition suggests using fast associative search enabled by Ternary Content Addressable Memory (TCAM) by construction. However, the excessive energy consumption and lack of support for similarity matching (under NGS and genomic variation induced noise) renders direct application of TCAM infeasible, irrespective of volatility, where only non-volatile TCAM can accommodate the large memory footprint in an area-efficient way. This paper introduces GeNVoM, a scalable, energy-efficient and high-throughput solution. Instead of optimizing an algorithm developed for general-purpose computers or GPUs, GeNVoM rethinks the algorithm and non-volatile TCAM-based accelerator design together from the ground up. Thereby GeNVoM can improve the throughput by up to 3.67×; the energy consumption, by up to 1.36×, when compared to an ASIC baseline, which represents one of the highest-throughput implementations known.
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Mi JX, Wang XD, Zhou LF, Cheng K. Adversarial Examples based on Object Detection tasks: A Survey. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.10.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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5
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Huang C, Wang J, Wang SH, Zhang YD. Applicable artificial intelligence for brain disease: A survey. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Zou F, Chen D, Liu H, Cao S, Ji X, Zhang Y. A survey of fitness landscape analysis for optimization. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.06.084] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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7
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Vo XT, Jo KH. A review on anchor assignment and sampling heuristics in deep learning-based object detection. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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8
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Distance regularization energy terms in level set image segment model: A survey. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.09.080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Guo ZH, Chen ZH, You ZH, Wang YB, Yi HC, Wang MN. A learning-based method to predict LncRNA-disease associations by combining CNN and ELM. BMC Bioinformatics 2022; 22:622. [PMID: 35317723 PMCID: PMC8941737 DOI: 10.1186/s12859-022-04611-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 10/07/2021] [Indexed: 11/10/2022] Open
Abstract
Background lncRNAs play a critical role in numerous biological processes and life activities, especially diseases. Considering that traditional wet experiments for identifying uncovered lncRNA-disease associations is limited in terms of time consumption and labor cost. It is imperative to construct reliable and efficient computational models as addition for practice. Deep learning technologies have been proved to make impressive contributions in many areas, but the feasibility of it in bioinformatics has not been adequately verified. Results In this paper, a machine learning-based model called LDACE was proposed to predict potential lncRNA-disease associations by combining Extreme Learning Machine (ELM) and Convolutional Neural Network (CNN). Specifically, the representation vectors are constructed by integrating multiple types of biology information including functional similarity and semantic similarity. Then, CNN is applied to mine both local and global features. Finally, ELM is chosen to carry out the prediction task to detect the potential lncRNA-disease associations. The proposed method achieved remarkable Area Under Receiver Operating Characteristic Curve of 0.9086 in Leave-one-out cross-validation and 0.8994 in fivefold cross-validation, respectively. In addition, 2 kinds of case studies based on lung cancer and endometrial cancer indicate the robustness and efficiency of LDACE even in a real environment. Conclusions Substantial results demonstrated that the proposed model is expected to be an auxiliary tool to guide and assist biomedical research, and the close integration of deep learning and biology big data will provide life sciences with novel insights.
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Affiliation(s)
- Zhen-Hao Guo
- School of Electronics and Information Engineering, Tongji University, No. 4800 Cao'an Road, Shanghai, 201804, China
| | - Zhan-Heng Chen
- College of Computer Science and Engineering, Shenzhen University, Shenzhen, 518060, China.
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, China
| | - Yan-Bin Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277100, Shandong, China.
| | - Hai-Cheng Yi
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Mei-Neng Wang
- School of Mathematics and Computer Science, Yichun University, Yichun, 336000, Jiangxi, China
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10
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Symmetric positive definite manifold learning and its application in fault diagnosis. Neural Netw 2021; 147:163-174. [PMID: 35038622 DOI: 10.1016/j.neunet.2021.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 10/07/2021] [Accepted: 12/20/2021] [Indexed: 11/24/2022]
Abstract
Locally linear embedding (LLE) is an effective tool to extract the significant features from a dataset. However, most of the relevant existing algorithms assume that the original dataset resides on a Euclidean space, unfortunately nearly all the original data space is non-Euclidean. In addition, the original LLE does not use the discriminant information of the dataset, which will degrade its performance in feature extraction. To address these problems raised in the conventional LLE, we first employ the original dataset to construct a symmetric positive definite manifold, and then estimate the tangent space of this manifold. Furthermore, the local and global discriminant information are integrated into the LLE, and the improved LLE is operated in the tangent space to extract the important features. We introduce Iris dataset to analyze the capability of the proposed method to extract features. Finally, several experiments are performed on five machinery datasets, and experimental results indicate that our proposed method can extract the excellent low-dimensional representations of the original dataset. Compared with the state-of-the-art methods, the proposed algorithm shows a strong capability for fault diagnosis.
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11
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Avola D, Bigdello MJ, Cinque L, Fagioli A, Marini MR. R-SigNet: Reduced space writer-independent feature learning for offline writer-dependent signature verification. Pattern Recognit Lett 2021. [DOI: 10.1016/j.patrec.2021.06.033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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12
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Combing modified Grabcut, K-means clustering and sparse representation classification for weed recognition in wheat field. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.06.140] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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13
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Liu X, Wang S, Zhang Y, Liu D, Hu W. Automatic fluid segmentation in retinal optical coherence tomography images using attention based deep learning. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.07.143] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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15
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A survey on regional level set image segmentation models based on the energy functional similarity measure. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.07.141] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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16
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Chandrasekar KS, Geetha P. A new formation of supervised dimensionality reduction method for moving vehicle classification. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05524-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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17
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Recognition and counting of wheat mites in wheat fields by a three-step deep learning method. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.07.140] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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18
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Yu F, Wei C, Deng P, Peng T, Hu X. Deep exploration of random forest model boosts the interpretability of machine learning studies of complicated immune responses and lung burden of nanoparticles. SCIENCE ADVANCES 2021; 7:7/22/eabf4130. [PMID: 34039604 PMCID: PMC8153727 DOI: 10.1126/sciadv.abf4130] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 04/05/2021] [Indexed: 05/22/2023]
Abstract
The development of machine learning provides solutions for predicting the complicated immune responses and pharmacokinetics of nanoparticles (NPs) in vivo. However, highly heterogeneous data in NP studies remain challenging because of the low interpretability of machine learning. Here, we propose a tree-based random forest feature importance and feature interaction network analysis framework (TBRFA) and accurately predict the pulmonary immune responses and lung burden of NPs, with the correlation coefficient of all training sets >0.9 and half of the test sets >0.75. This framework overcomes the feature importance bias brought by small datasets through a multiway importance analysis. TBRFA also builds feature interaction networks, boosts model interpretability, and reveals hidden interactional factors (e.g., various NP properties and exposure conditions). TBRFA provides guidance for the design and application of ideal NPs and discovers the feature interaction networks that contribute to complex systems with small-size data in various fields.
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Affiliation(s)
- Fubo Yu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Changhong Wei
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Peng Deng
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Ting Peng
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Xiangang Hu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
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Zhang B, Qiang Q, Wang F, Nie F. Flexible Multi-View Unsupervised Graph Embedding. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2021; 30:4143-4156. [PMID: 33667162 DOI: 10.1109/tip.2021.3062692] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Faced with the increasing data diversity and dimensionality, multi-view dimensionality reduction has been an important technique in computer vision, data mining and multi-media applications. Since collecting labeled data is difficult and costly, unsupervised learning is of great significance. Generally, it is crucial to explore the complementarity or independence of different feature spaces in multi-view learning. How to find a low-dimensional subspace to preserve the intrinsic structure of original unlabeled high-dimensional multi-view data is still challenging. In addition, noises and outliers always appear in real data. In this study, we propose a novel model called flexible multi-view unsupervised graph embedding (FMUGE). A flexible regression residual term is introduced so that the strict linear mapping is relaxed, new-coming data and noises are better handled, and the raw data negotiate with the learned low-dimensional representation in the procedure. To ensure the consistency among multiple views, FMUGE adaptively weights different features and fuses them to get an optimal multi-view consensus similarity graph, which assists high-quality graph embedding. We propose an efficient alternating iterative algorithm to optimize the proposed model. Finally, experimental results on synthetic and benchmark datasets show the significant improvement of FMUGE over the state-of-the-art methods.
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20
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Miao KC, Han TT, Yao YQ, Lu H, Chen P, Wang B, Zhang J. Application of LSTM for short term fog forecasting based on meteorological elements. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.12.129] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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21
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Zhang J, Liu L, Zhen L, Jing L. A unified robust framework for multi-view feature extraction with L2,1-norm constraint. Neural Netw 2020; 128:126-141. [PMID: 32446190 DOI: 10.1016/j.neunet.2020.04.024] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 03/28/2020] [Accepted: 04/27/2020] [Indexed: 11/19/2022]
Abstract
Multi-view feature extraction methods mainly focus on exploiting the consistency and complementary information between multi-view samples, and most of the current methods apply the F-norm or L2-norm as the metric, which are sensitive to the outliers or noises. In this paper, based on L2,1-norm, we propose a unified robust feature extraction framework, which includes four special multi-view feature extraction methods, and extends the state-of-art methods to a more generalized form. The proposed methods are less sensitive to outliers or noises. An efficient iterative algorithm is designed to solve L2,1-norm based methods. Comprehensive analyses, such as convergence analysis, rotational invariance analysis and relationship between our methods and previous F-norm based methods illustrate the effectiveness of our proposed methods. Experiments on two artificial datasets and six real datasets demonstrate that the proposed L2,1-norm based methods have better performance than the related methods.
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Affiliation(s)
- Jinxin Zhang
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
| | - Liming Liu
- School of Statistics, Capital University of Economics and Business, Beijing, 100070, China.
| | - Ling Zhen
- College of Science, China Agricultural University, Beijing, 100083, China.
| | - Ling Jing
- College of Science, China Agricultural University, Beijing, 100083, China.
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22
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Yao T, Yan L, Ma Y, Yu H, Su Q, Wang G, Tian Q. Fast discrete cross-modal hashing with semantic consistency. Neural Netw 2020; 125:142-152. [PMID: 32088568 DOI: 10.1016/j.neunet.2020.01.035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 12/17/2019] [Accepted: 01/28/2020] [Indexed: 11/25/2022]
Abstract
Supervised cross-modal hashing has attracted widespread concentrations for large-scale retrieval task due to its promising retrieval performance. However, most existing works suffer from some of following issues. Firstly, most of them only leverage the pair-wise similarity matrix to learn hash codes, which may result in class information loss. Secondly, the pair-wise similarity matrix generally lead to high computing complexity and memory cost. Thirdly, most of them relax the discrete constraints during optimization, which generally results in large cumulative quantization error and consequent inferior hash codes. To address above problems, we present a Fast Discrete Cross-modal Hashing method in this paper, FDCH for short. Specifically, it firstly leverages both class labels and the pair-wise similarity matrix to learn a sharing Hamming space where the semantic consistency can be better preserved. Then we propose an asymmetric hash codes learning model to avoid the challenging issue of symmetric matrix factorization. Finally, an effective and efficient discrete optimal scheme is designed to generate discrete hash codes directly, and the computing complexity and memory cost caused by the pair-wise similarity matrix are reduced from O(n2) to O(n), where n denotes the size of training set. Extensive experiments conducted on three real world datasets highlight the superiority of FDCH compared with several cross-modal hashing methods and demonstrate its effectiveness and efficiency.
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Affiliation(s)
- Tao Yao
- Department of Information and Electrical Engineering, Ludong University, Yantai, 264000, China; Yantai Research Institute of New Generation Information Technology, Southwest Jiaotong University, 264000, China.
| | - Lianshan Yan
- Yantai Research Institute of New Generation Information Technology, Southwest Jiaotong University, 264000, China
| | - Yilan Ma
- Department of Information and Electrical Engineering, Ludong University, Yantai, 264000, China
| | - Hong Yu
- Department of Information and Electrical Engineering, Ludong University, Yantai, 264000, China
| | - Qingtang Su
- Department of Information and Electrical Engineering, Ludong University, Yantai, 264000, China
| | - Gang Wang
- Department of Information and Electrical Engineering, Ludong University, Yantai, 264000, China
| | - Qi Tian
- Huawei Noah's Ark Lab, 518129, China
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Matsuzaka Y, Uesawa Y. DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance. Front Bioeng Biotechnol 2020; 7:485. [PMID: 32039185 PMCID: PMC6987043 DOI: 10.3389/fbioe.2019.00485] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 12/30/2019] [Indexed: 12/16/2022] Open
Abstract
The progesterone receptor (PR) is important therapeutic target for many malignancies and endocrine disorders due to its role in controlling ovulation and pregnancy via the reproductive cycle. Therefore, the modulation of PR activity using its agonists and antagonists is receiving increasing interest as novel treatment strategy. However, clinical trials using the PR modulators have not yet been found conclusive evidences. Recently, increasing evidence from several fields shows that the classification of chemical compounds, including agonists and antagonists, can be done with recent improvements in deep learning (DL) using deep neural network. Therefore, we recently proposed a novel DL-based quantitative structure-activity relationship (QSAR) strategy using transfer learning to build prediction models for agonists and antagonists. By employing this novel approach, referred as DeepSnap-DL method, which uses images captured from 3-dimension (3D) chemical structure with multiple angles as input data into the DL classification, we constructed prediction models of the PR antagonists in this study. Here, the DeepSnap-DL method showed a high performance prediction of the PR antagonists by optimization of some parameters and image adjustment from 3D-structures. Furthermore, comparison of the prediction models from this approach with conventional machine learnings (MLs) indicated the DeepSnap-DL method outperformed these MLs. Therefore, the models predicted by DeepSnap-DL would be powerful tool for not only QSAR field in predicting physiological and agonist/antagonist activities, toxicity, and molecular bindings; but also for identifying biological or pathological phenomena.
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Affiliation(s)
| | - Yoshihiro Uesawa
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo, Japan
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K-Means Clustering-Based Electrical Equipment Identification for Smart Building Application. INFORMATION 2020. [DOI: 10.3390/info11010027] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
With the development and popular application of Building Internet of Things (BIoT) systems, numerous types of equipment are connected, and a large volume of equipment data is collected. For convenient equipment management, the equipment should be identified and labeled. Traditionally, this process is performed manually, which not only is time consuming but also causes unavoidable omissions. In this paper, we propose a k-means clustering-based electrical equipment identification toward smart building application that can automatically identify the unknown equipment connected to BIoT systems. First, load characteristics are analyzed and electrical features for equipment identification are extracted from the collected data. Second, k-means clustering is used twice to construct the identification model. Preliminary clustering adopts traditional k-means algorithm to the total harmonic current distortion data and separates equipment data into two to three clusters on the basis of their electrical characteristics. Later clustering uses an improved k-means algorithm, which weighs Euclidean distance and uses the elbow method to determine the number of clusters and analyze the results of preliminary clustering. Then, the equipment identification model is constructed by selecting the cluster centroid vector and distance threshold. Finally, identification results are obtained online on the basis of the model outputs by using the newly collected data. Successful applications to BIoT system verify the validity of the proposed identification method.
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