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Using NSGA-Ⅲ for optimising biomedical ontology alignment

CAAI Transactions on Intelligence Technology 2019年第3期4卷 135-141页

作者： Xingsi Xue Jiawei Lu Junfeng Chen College of Information Science and Engineering Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Intelligent Information Processing Research Center Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Fujian Key Laboratory for Automotive Electronics and Electric Drive Fujian University of TechnologyFuzhouFujianPeople’s Republic of China College of IOT Engineering Hohai UniversityChangzhouJiangsuPeople’s Republic of China

To support semantic inter-operability between the biomedical information systems, it is necessary to determine the correspondences between the heterogeneous biomedical concepts, which is commonly known as biomedical ontology matching. Biomedical concepts are usually complex and ambiguous, which makes matching biomedical ontologies a challenge. Since none of the similarity measures can distinguish the heterogeneous biomedical concepts in any context independently, usually several similarity measures are applied together to determine the biomedical concepts mappings. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on the similarity measures significantly reduces the alignment’s quality. In this study, a non-dominated sorting genetic algorithm (NSGA)-III-based biomedical ontology matching technique is proposed to effectively match the biomedical ontologies, which first utilises an ontology partitioning technique to transform the large-scale biomedical ontology matching problem into several ontology segment-matching problems, and then uses NSGA-III to determine the optimal alignment without tuning the aggregating weights. The experiment is conducted on the anatomy track and large biomedic ontologies track which are provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI’s participants show the effectiveness of the authors’ approach.

关键词： ontology ontologies transform together Ontology complex context effects

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Improved small gain conditions for input-to-state stability with respect to measurement functions: Discrete time networked system

Improved small gain conditions for input-to-state stability ...

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IEEE Conference on Decision and Control

作者： Yuanqiu Mo Changbin Yu Soura Dasgupta Westlake Institute for Advanced Study Westlake University Hangzhou China College of Artificial Intelligence and Big Data Shandong First Medical University & Shandong Academy of Medical Sciences Jinan China Institute for Intelligent Robots Fudan University Shanghai China Shandong Computer Science Center Shandong Provincial Key Laboratory of Computer Networks China Faculty of Engineering & the Built Environment University of Johannes-Burg Johannesburg South Africa University of Iowa Iowa City Iowa USA

ISBN: (纸本)9781665436601

In this paper we study input-to-state stability with respect to measurement functions for discrete time networked systems. In such a networked system, the trajectory of each subsystem is affected by another in each time step, and this constraining system may change from step to step. We derive small gain conditions of input-to-state stability with respect to measurement functions for this type of discrete time networked system relying on the construction of dissipative-form finite time Lyapunov function. These conditions loosen those presented in [1] and [2]. Finally, we demonstrate the applicability of our results by applying them to the stability analysis of a distributed graph algorithm which does not satisfy the conditions of [1] and [2].

关键词： Network topology Conferences Gain measurement Stability analysis Time measurement Trajectory Topology

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Erratum to "Multi-View Face Synthesis via Progressive Face Flow"

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2021年 30卷 6700页

作者： Yangyang Xu Xuemiao Xu Jianbo Jiao Keke Li Cheng Xu Shengfeng He School of Computer Science and Engineering South China University of Technology Guangzhou China State Key Laboratory of Subtropical Building Science the Key Laboratory of Big Data and Intelligent Robot Ministry of Education and the Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information South China University of Technology Guangzhou China University of Oxford Oxford U.K.

In the above article [1], unfortunately, Fig. 5 was not displayed correctly with many empty images. The correct version is supplemented here.

关键词： Faces

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Training and inference Time Efficiency Assessment Framework for machine learning algorithms: A case study for hyperspectral image classification

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International Journal of Applied Earth Observation and Geoinformation 2025年 141卷

作者： Xiaorou Zheng Jianxin Jia Shoubin Dong Yawei Wang Runuo Lu Yuwei Chen Yueming Wang Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis School of Computer Science and Engineering South China University of Technology Guangzhou 510006 China Department of Photogrammetry and Remote Sensing Finnish Geospatial Research Institute FI-02150 Espoo Finland Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou 311100 Guangdong China Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou 310024 Zhejiang China The Advanced Laser Technology Laboratory of Anhui Province Hefei 230037 China Key Laboratory of Space Active Opto-Electronics Technology Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai 200083 China

The increasing complexity and scale of remote sensing datasets, coupled with the challenges of accurately estimating algorithmic time efficiency, often lead to significant resource waste or even failure when using machine learning algorithms in urgent or resource-constrained scenarios. Accurate time efficiency estimation is critical for deploying effective algorithms, yet it remains challenging due to the many factors influencing computational performance. Traditional methods of evaluating time efficiency often neglect the effects of core model parameters and complex data scales in spectral and temporal dimensions. In addition, inference time, an essential factor in real-world applications, is often overlooked. To address these limitations, we propose the Time Efficiency Assessment Framework (TEAF), a novel method for evaluating the time efficiency of machine learning algorithms. Through mathematical reasoning, TEAF models the training and inference time as functions ( ψ ) of complex data scales and core model parameters. The strong linear correlation between ψ and the actual runtime allows TEAF to accurately predict the time and cost of machine learning tasks with a low computational overhead before algorithm execution. To validate this framework, we derived TEAF formulations for five classical machine learning algorithms and tested them on state-of-the-art hyperspectral image datasets and Sentinel-2 multispectral datasets. The results demonstrated that TEAF could accurately predict both training and inference time for various algorithms, with a strong linear correlation between ψ and actual runtime ( R 2 > 0 . 942 ). This study offers a practical solution to the challenges posed by the increasing volume and complexity of data in remote sensing image processing. The code is available at https://***/SCUT-CCNL/TEAF .

关键词： Time efficiency Hyperspectral image Inference time Algorithm derivation Time prediction Complexity data scale

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Integrating ARIMA and Bidirectional LSTM to Predict ETA in Multi-Airport Systems

Integrating ARIMA and Bidirectional LSTM to Predict ETA in M...

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Integrated Communications, Navigation and Surveillance Conference, ICNS

作者： Lechen Wang Xuechun Li Jianfeng Mao School of Science and Engineering The Chinese University of Hong Kong Shenzhen Guangdong China School of Science and Engineering Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology The Chinese University of Hong Kong Shenzhen Shenzhen Research Institute of Big Data Shenzhen Guangdong China

ISBN: (数字)9781728172705

ISBN: (纸本)9781728172712

Traffic states prediction in air transportation systems is a challenging problem and has not been fully explored because it is subject to many more highly correlated factors and a more complicated traffic management scheme compared to urban transportation systems. It becomes a more formidable task when facing a multi-airport system (MAS), in which several major airports are closely located and tightly coupled with each other through limited terminal airspace. In this work, we propose a novel method using a time series model and recurrent neural network to make the estimated time of arrival (ETA) for a flight to an MAS, which can be potentially utilized for flight delay prediction and congestion analysis. The experiment utilizes two months of 4D trajectories data from Beijing Capital International Airport (PEK) to Shenzhen Bao'an International airport (ZGSZ). The entire prediction work is decomposed into two sub-problems, en-route travel time prediction which is from flight origin to the entering gate of MAS, defined as the location is 300km from the airport in MAS, and terminal maneuvering area (TMA) travel time prediction which is from the entrance to flight's destination. The auto-regressive integrated moving average (ARIMA), a time series prediction model, is used to predict travel time in en-route under given the flight departure time. Bidirectional long short term memory (LSTM), a recurrent neural network, is developed to forecast travel time in the arrival approach by utilizing spatio-temporal features. To design the input features, we use density-based spatial clustering (DBSCAN) with the help of the Voronoi diagram to extract spatial information from every historical flight trajectory of aircraft operated in an MAS, then select the observation time window to capture the temporal information for each flight. The Multivariate Stacked Fully connected-Bidirectional LSTM (MSFCB-LSTM) model is constructed to make shortterm forecasting using spatio-temporal feature

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A survey of facial expression recognition based on deep learning

A survey of facial expression recognition based on deep lear...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Heng Wei Zhi Zhang Collage of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

Facial expression recognition is the key research direction in many fields such as machine vision, pattern recognition and artificial intelligence. lt has become a research hotspot of many scholars and experts. This paper gives a comprehensive overview of deep expression recognition from image preprocessing, common expression recognition networks, and common expression databases. Then we review the current design and training strategies of deep neural networks based on static images and dynamic images, and discuss their advantages and limitations. Finally, the future challenges and corresponding opportunities in the field are analyzed, as well as the future direction of deep learning expression recognition systems.

关键词： Deep learning Training Image recognition databases Face recognition Neural networks Training data

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Automatic Generation Method of Ancient Poetry Based on LSTM

Automatic Generation Method of Ancient Poetry Based on LSTM

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Hanshuang Zhang Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

This paper mainly focuses on the literary genre of ancient poetry with a certain rhythm and cadence, and proposes a novel automatic generation model of ancient poetry. The model uses about 300,000 Tang poems and Song poems as training data, uses One-hot encoding to process data, and uses long short-term memory networks (LSTMs) to learn the semantics of ancient poetry texts and conduct research across a single RNN structure. According to the user's requirements, the model can automatically calculate the most relevant coherent words in the context of the selected context, and generate common ancient poetry and specified words. The method of BLEU automatic evaluation and manual evaluation finally demonstrates the effectiveness of the experiment.

关键词： Industrial electronics Semantics Training data Manuals Rhythm Encoding Periodic structures

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Study on the Relationships between Utilization Ratios and Conversion Rates of Nitrogen,Phosphorus and Potassium for Wheat

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Meteorological and Environmental Research 2020年第5期11卷 46-54页

作者： Zhiping HUANG Xianda HOU Shutian LIU Shuojin WANG Hongyan ZHENG Jian DING Changhong MI Ling LU Yanlin HOU Agro-Environmental Protection Institute Ministry of Agriculture and Rural AffairsTianjin 300191China Guangxi Geographical Indication Crops Research Center of Big Data Mining and Experimental Engineering Technology Nanning Normal UniversityNanning 530001China Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation Nanning Normal UniversityNanning 530001China Key Laboratory of Environment Change and Resources Use in Beibu Gulf(Nanning Normal University) Nanning 530001China

This paper studied the relationships between utilization ratios and conversion rates of nitrogen,phosphorus and potassium for wheat by data mining method based on data of"3414 fertilizer field trials"of wheat by published papers from national soil testing and recommendation since *** results and conclusions were as follows:①the conceptual model of fertilizer utilization ratio of wheat for plot was that,Y N≈a+b×optimum nitrogen application amount+c×optimum fertilizer amount of K+d×rainfall in growth season,Y P≈a+b×optimum fertilizer amount of K+c×rainfall in growth season,Y K≈a+b×temperature in growth season+c×rainfall in growth season-d×optimum yield-e×optimum nitrogen application amount,respectively.②Fertilizer utilization ratio and nutrient conversion rate had 4 kinds of *** significant relationships:13,of which 8 were soil nutrients,the results showed that soil nutrients had little effect on nutrient conversion rate and fertilizer utilization ratio;consistent significant relationships:4,they were all nitrogen and phosphorus,explaining the importance of nitrogen and phosphorus;contrary significant relationships:6,they were all nitrogen and phosphorus,illustrating that phosphorus and nitrogen had inhibitory effect;complementary significant relationships:31,the cause of the complementary phenomenon was that the fertilizer utilization ratio was the measuring index of fertilizer efficiency in one season,while the nutrient conversion rate was the measuring index of fertilizer efficiency in more *** showed that the fertilizer utilization rate was significantly correlated,or the nutrient conversion rate was significantly correlated.

关键词： Optimum yield Optimum fertilization amount Nutrient conversion rate Fertilizer utilization ratio Relative abundance-deficiency value of soil nutrient

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A study on the uncertainty of convolutional layers in deep neural networks

arXiv

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arXiv 2020年

作者： Shen, Haojing Chen, Sihong Wang, Ran Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University ShenzhenGuangdong518060 China College of Mathematics and Statistics Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

This paper shows a Min-Max property existing in the connection weights of the convolutional layers in a neural network structure, i.e., the LeNet. Specifically, the Min-Max property means that, during the back propagation-based training for LeNet, the weights of the convolutional layers will become far away from their centers of intervals, i.e., decreasing to their minimum or increasing to their maximum. From the perspective of uncertainty, we demonstrate that the Min-Max property corresponds to minimizing the fuzziness of the model parameters through a simplified formulation of convolution. It is experimentally confirmed that the model with the Min-Max property has a stronger adversarial robustness, thus this property can be incorporated into the design of loss function. This paper points out a changing tendency of uncertainty in the convolutional layers of LeNet structure, and gives some insights to the interpretability of convolution. © 2020, CC-BY.

关键词： Convolution

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Discriminative Additive Scale Loss for Deep Imbalanced Classification and Embedding

Discriminative Additive Scale Loss for Deep Imbalanced Class...

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IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Weiming Jiang Yang Wang Qiaolin Ye Mingbo Zhao Mingliang Xu Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China AI team Shanghai Shizhuang Information Technology Co. Ltd Shanghai China College of Information Science and Technology Nanjing Forestry University Nanjing China School of Information Science and Technology Donghua University Shanghai China School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665423991

Real-world data in emerging applications may suffer from highly-skewed class imbalanced distribution, however how to deal with this kind of problem appropriately through deep learning needs further investigation. In this paper, we mainly propose a novel cross-entropy based loss function, referred to as Additive Scale Loss (ASL), for deep representation learning and imbalanced classification. To deal with the class imbalanced problem, ASL aims at increasing the loss in case of misclassification, which can avoid the superimposed loss values caused by the large amount of easily classified data in the unbalanced database to dominate the loss value of misclassified data. Moreover, in real-world applications, one data source may be used for multiple scenarios, such as classification and embedding learning, however training two separable models to handle these problems is costly, especially in deep learning area. To tackle this issue, we present and integrate a discriminative inter-class separation term into ASL, and propose a discriminative ASL (D-ASL), which can not only improve the classification performance, but also obtain discriminative representations simultaneously. The discriminative inter-class separation term is general, and can be easily integrated to other loss functions, such as CE and FL, as the byproducts. Finally, a new deep convolutional neural network equipped with D-ASL and a fully-connected (FC) layer is proposed, which can classify the imbalanced image data and obtain the discriminative representations at the same time. Extensive experimental results verified the superior performance of our method.

关键词： Deep learning Training Representation learning Additives databases Soft sensors Conferences

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