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International Conference on Information, Communication and Engineering (ICICE)

作者： Linqiu Wang Tungchun Shen Chintan Lee School of Software and Microelctronics Peking University National Quemoy University

ISBN: (数字)9781728158396

ISBN: (纸本)9781728158402

Plasma etching processing plays a key role in integrated circuits (IC) fabrication technology. For exploring the optimization scheme of etching process, a novel three-dimensional (3D) cellular model with improved parametric equation group is proposed to study the evolution effect in etching processing. The dimensionality reduction fitting algorithm is also introduced to decompose original 3D cellular space into two 2D planes, then calculate the angle of ion incidence quickly by least squares fitting, and improve time-space efficiency of etching processing. Additionally, the cellular selection and the incident angle calculation algorithms are improved as well. After simulation results are compared with the experimental results, the high accuracy and efficiency of our algorithms in etching processing are verified.

关键词： plasma etching cellular model etching evolution algorithm dimensionality reduction algorithm

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A new validity index of feature subset for evaluating the dimensionality reduction algorithms

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KNOWLEDGE-BASED SYSTEMS 2017年 121卷 83-98页

作者： Liu, Chuan Wang, Wenyong Konan, Martin Wang, Siyang Huang, Lisheng Tang, Yong Zhang, Xiang Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 611731 Peoples R China Univ Calif San Diego Dept Math La Jolla CA 92093 USA

A critical aspect of dimensionality reduction is to assess the quality of selected (or produced) feature subsets properly. Feature subset assessment in machine learning refers to split a given feature subset into a training set, which is used to estimate the parameters of a classification model, and a test set used to estimate the predictive performance of the model. Then, averaging the results of multiple splitting (i.e., Cross-Validation, CV) is commonly used to decrease the variance of the estimator. But in practice, CV scheme is very computationally expensive. In this paper, we propose a new statistics index method called LW-index for evaluation of feature subset and dimensionality reduction algorithms in general. The proposed method is a type of "classical statistics" approach that uses the feature subset to compute an empirical estimate of the quality of feature subset. A large number of performance comparisons with the machine learning approach conducted on fourteen benchmark collections show that the proposed LW index is highly correlated with the external indices (i.e., MacroF(1), MicroF(1)) of SVM and Centroid-Based Classifier (CBC) trained by five-fold CV scheme. Furthermore, the experimental results indicate that LW index has the same performance as the traditional CV scheme for evaluating the dimensionality reduction algorithms and it is more efficient than the traditional methodology. Therefore, one contribution of this paper is to present an alternative methodology, based on an internal index typically used in the unsupervised learning context, that is computationally cheaper than the traditional CV methodology. Another contribution is to propose a new internal index that behaves better than other similar indices widely used in clustering and shows high correlation with the results obtained by the traditional methodology. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： Machine learning Validity index Cross validation dimensionality reduction algorithm

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A fuzzy decision-making model for enhancing sustainable tourism investments in climate-vulnerable destinations

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2025年 380卷 125151页

作者： Shahbaz, Muhammad Yuksel, Serhat Eti, Serkan Dincer, Hasan Khamdamov, Shoh-Jakhon Beijing Inst Technol Sch Econ Beijing Peoples R China Gulf Univ Sci & Technol GUST Ctr Sustainable Dev CSD Hawally Kuwait Istanbul Medipol Univ Sch Business Istanbul Turkiye Khazar Univ Dept Econ & Management Baku Azerbaijan Azerbaijan State Univ Econ UNEC Clin Econ Baku Azerbaijan Istanbul Medipol Univ IMU Vocat Sch Istanbul Turkiye Mamun Univ Fac Econ Khiva City Uzbekistan Alfraganus Univ Tashkent Uzbekistan Tashkent State Univ Econ Tashkent 100002 Uzbekistan

This study aims to determine key investment strategies for the performance improvements of the sustainable tourism investment projects. First, the significance levels of experts are computed via dimensionality reduction algorithm. Next, the indicators for sustainable tourism investments are weighted by spherical fuzzy Markov chain. Finally, strategy alternatives for climate-vulnerable destination selection are ranked with spherical fuzzy combinative distance-based assessment (CODAS). Moreover, our ranking results are also compared by using spherical fuzzy ranking technique by geometric mean of similarity ratio to optimal solution (RATGOS). Our findings denote that the improvement of technological infrastructure is determined as the most important factor in the development of sustainable tourism investments. Another important issue in the development of sustainable tourism investments is the determination of environmental priorities. On other side, the optimal strategy alternatives for climate-vulnerable destination selection are financial supports for climate changing and ecological evaluations for renewable energy sources.

关键词： Sustainable tourism Tourism investment projects dimensionality reduction algorithm Spherical fuzzy sets

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Terahertz spectroscopy combined with data dimensionality reduction algorithms for quantitative analysis of protein content in soybeans

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021年 253卷 119571-119571页

作者： Wei, Xiao Li, Song Zhu, Shiping Zheng, Wanqin Xie, Yong Zhou, Shengling Hu, Miedie Miao, Yujie Ma, Linkai Wu, Weiji Xie, Zhiyong Southwest Univ Coll Engn & Technol Chongqing 400716 Peoples R China Southwest Univ Coll Food Sci Chongqing 400716 Peoples R China China Tianjin Grain & Oil Wholesale Trade Market Tianjin 300171 Peoples R China

Protein content in soybean is a key determinant of its nutritional and economic value. The paper inves-tigated the feasibility of terahertz (THz) spectroscopy and dimensionality reduction algorithms for the determination of protein content in soybean. First of all, the THz sample spectrum was data processed by pre-processing or dimensionality reduction algorithms. Secondly, by calibration set, using partial least squares regression (PLSR), genetic algorithms-support vector regression (GA-SVR), grey wolf optimizer-support vector regression (GWO-SVR) and back propagation neural network (BPNN) were respectively used to model protein content determination. Afterwards, the model was validated by the prediction set. Ultimately, the BPNN model combined with linear discriminant analysis (LDA) for related coefficient of prediction set (Rp), root mean square error of prediction set (RMSEP), relative standard deviation (RSD), the time required for the operation was respectively 0.9677, 1.2467%, 3.3664%, and 53.51 s. The experimental results showed that the rapid and accurate quantitative determination of protein in soy-bean using THz spectroscopy is feasible after a suitable dimensionality reduction algorithm. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Terahertz dimensionality reduction algorithm Pre-processing SVR BPNN

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An efficient nondestructive detection method of rapeseed varieties based on hyperspectral imaging technology

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MICROCHEMICAL JOURNAL 2025年 210卷

作者： Wang, Jian Zhou, Xin Liu, Yang Sun, Jun Guo, Peirui Lv, Weijian Informat Engn Jiangsu Univ Sch Elect Zhenjiang 212013 Peoples R China Jiangsu Univ Key Lab Theory & Technol Intelligent Agr Machinery Zhenjiang 212013 Peoples R China Jiangsu Prov & Educ Minist Cosponsored Synergist Innovat Ctr Modern Agr Equip Zhenjiang 212013 Peoples R China Shenyang Univ Technol Sch Elect Engn Shenyang 110027 Peoples R China

In response to the diverse requirements for rapeseed varieties in different fields and the prevalence of counterfeit seeds, efficient nondestructive detection methods are essential. Hyperspectral imaging (HSI) is widely used for this purpose, but its high dimensionality and redundant information complicate practical applications. This study proposes a dimensionality reduction algorithm that first selects feature wavelength intervals and then extracts features. The modified interval random frog (miRF) conducts supervised training on labeled spectral data to evaluate and select important wavelength intervals, capturing interactions between features while eliminating redundancy. Additionally, kernel principal component analysis (KPCA) addresses the nonlinear relationships among the selected intervals by mapping the data into a high-dimensional space, revealing its intrinsic structure and enhancing model generalization. This integrated approach constructs an optimized, streamlined feature space, improving detection capabilities for rapeseed varieties. The dimensionality reduction results of KPCAmiRF are also analyzed, and a strategy of feature selection followed by extraction is established. Furthermore, nature-inspired optimization algorithms, including hippopotamus optimization (HO), goose optimization (GOOSE), and artificial gorilla troop optimization (GTO), are introduced to refine hyperparameter selection and create a robust framework for efficient nondestructive detection. Ultimately, the GOOSE-SVC model established based on the spectral features extracted by miRF-KPCA demonstrated superior model performance and achieved an accuracy rate of 98.96% on the prediction set. The results validate the potential of HSI in rapeseed variety detection and present an innovative method for dimensionality reduction of hyperspectral data.

关键词： Hyperspectral imaging Rapeseed dimensionality reduction algorithm Model optimization Nondestructive testing

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dimensionality reduction based on determinantal point process and singular spectrum analysis for hyperspectral images

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IET IMAGE PROCESSING 2019年第2期13卷 299-306页

作者： Chen, Weizhao Yang, Zhijing Cao, Faxian Yan, Yijun Wang, Meilin Qing, Chunmei Cheng, Yongqiang Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Guangdong Peoples R China Univ Strathclyde Dept Elect & Elect Engn Glasgow Lanark Scotland South China Univ Technol Sch Elect & Informat Engn Guangzhou Guangdong Peoples R China Univ Hull Sch Engn & Comp Sci Kingston Upon Hull HU6 7RX N Humberside England

dimensionality reduction is of high importance in hyperspectral data processing, which can effectively reduce the data redundancy and computation time for improved classification accuracy. Band selection and feature extraction methods are two widely used dimensionality reduction techniques. By integrating the advantages of the band selection and feature extraction, the authors propose a new method for reducing the dimension of hyperspectral image data. First, a new and fast band selection algorithm is proposed for hyperspectral images based on an improved determinantal point process (DPP). To reduce the amount of calculation, the dual-DPP is used for fast sampling representative pixels, followed by k-nearest neighbour-based local processing to explore more spatial information. These representative pixel points are used to construct multiple adjacency matrices to describe the correlation between bands based on mutual information. To further improve the classification accuracy, two-dimensional singular spectrum analysis is used for feature extraction from the selected bands. Experiments show that the proposed method can select a low-redundancy and representative band subset, where both data dimension and computation time can be reduced. Furthermore, it also shows that the proposed dimensionality reduction algorithm outperforms a number of state-of-the-art methods in terms of classification accuracy.

关键词： spectral analysis hyperspectral imaging image classification geophysical image processing feature extraction image sampling image representation learning (artificial intelligence) feature extraction hyperspectral image data fast band selection algorithm fast sampling representative pixels representative pixel points two-dimensional singular spectrum analysis representative band subset dimensionality reduction algorithm hyperspectral data processing data redundancy classification accuracy determinantal point process dual-DPP k-nearest neighbour

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A computational drug repositioning model based on hybrid similarity side information powered graph neural network

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 125卷 24-31页

作者： Li, Sumin Pan, Xiuqin Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China

Computational drug repositioning technology aims to rediscover the potential use of drugs already on the market and can significantly accelerate the traditional drug development process, reducing significant drug development costs and drug development instability In this work, in order to capture valid and robust hidden feature representations of drugs and diseases, we introduce a new computational drug relocation model, HSSIGNN, based on hybrid similarity side information powered graph neural network, by drawing on the application of graph neural networks and Side information in recommender systems. Its advantage is to utilize the learning capability of graph neural networks to capture the effective hidden feature representation of drugs and diseases, which is used to infer the probability of whether a drug can treat the disease of interest, as a way to improve the generalization capability of the model. In addition, dimensionality reduction algorithms and side information of drugs and diseases are used to overcome the cold start problem encountered by traditional computational drug relocation models. Finally, the experimental results of the proposed model on two real drug-disease association datasets are analyzed to verify its superiority and effectiveness. Comprehensive experimentations on several real-world datasets show the efficiency of HSSIGNN. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Computational drug repositioning Drug-disease association prediction Graph neural networks Side information dimensionality reduction algorithm

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Graph Weighted Subspace Learning Models in Bankruptcy

Graph Weighted Subspace Learning Models in Bankruptcy

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International Joint Conference on Neural Networks (IJCNN)

作者： Ribeiro, Bernardete Chen, Ning Univ Coimbra Dept Informat Engn CISUC P-3000 Coimbra Portugal Inst Politecni Porto Inst Super Engn Porto GECAD Porto Portugal

ISBN: (纸本)9781424496365

MANY dimensionality reduction algorithms have been proposed easing both tasks of visualization and classification in high dimension problems. Despite the different motivations they can be cast in a graph embedding framework. In this paper we address weighted graph subspace learning methods for bankruptcy analysis. The rationale behind re-embedding the data in a lower dimensional space that would be better filled is twofold: to get the most compact representation (visualization) and to make subsequent processing of data more easy (classification). The approaches used, Graph regularized Non-Negative Matrix Factorization (GNMF) and Spatially Smooth Subspace Learning (SSSL), construct an affinity weight graph matrix to encode geometrical information and to learn in the training set the subspace models that enhance visualization and are able to ease the task of bankruptcy prediction. The experimental results on a real problem of French companies show that from the perspective of financial problem analysis the methodology is quite effective.

关键词： Companies Data visualization Kernel Laplace equations Manifolds Predictive models Principal component analysis bankruptcy analysis bankruptcy prediction data classification data visualisation data visualization dimensionality reduction algorithm financial data processing financial problem analysis graph matrix graph regularized nonnegative matrix factorization graph theory graph weighted subspace learning method learning (artificial intelligence) matrix decomposition pattern classification spatially smooth subspace learning

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Visible-NIR spectral characteristics and grade inversion model of skarn-type iron ore

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INFRARED PHYSICS & TECHNOLOGY 2022年第0期123卷

作者： Li, Xiaohua Wen, Jian Fu, Yanhua Mao, Yachun Cao, Wang Huang, Jiaqi Zhao, Zhanguo Yu, Ge Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Sch Resources & Civil Engn Shenyang 110819 Peoples R China Northeastern Univ Sch Architecture Shenyang 110819 Peoples R China China Gold Grp Beijing 100000 Peoples R China

Iron ore grade determination is a key step in mining production, and its efficiency has a significant impact on mining efficiency and profitability. At present, the most commonly used method for ore grade determination is chemical analysis, which not only is expensive but also has a long assay cycle and lag effect relative to the ore allocation process and cannot effectively reduce the loss depletion rate of ore mining. Therefore, in situ iron ore grade data determination technology based on visible and near-infrared hyperspectral data analysis is an effective way to solve the above problems, but the poor quality of raw hyperspectral data and low accuracy of the ore grade inversion model are still one of the bottlenecks in the application of this technology. In this paper, 225 ore samples were collected from Hongling skarn-type iron ore in Chifeng, Inner Mongolia Autonomous Region, China. The visible and near-infrared hyperspectral data of the sample set and the grade data obtained from chemical analysis were used as data sources. The raw spectral data were first smoothed, and then the smoothed spectral data were processed using two algorithms, continuum removal (CR) and standard normal variate transform (SNV). Then, two dimensionality reduction algorithms, principal component analysis (PCA) and genetic algorithm (GA), were used to reduce the dimensionality of the spectral data before and after preprocessing, and the data sources were obtained after the four different preprocessing combination algorithms were processed. Finally, several quantitative inversion models were established based on the random forest algorithm and the backpropagation neural network (BPNN) algorithm. The stability, accuracy and credibility of the models were evaluated by four metrics: the coefficient of determination (R2), root mean square error (RMSE), residual prediction deviation(RPD) and mean relative error (MRE). The results showed that the quantitative inversion model (SNV-PCA-BPNN) es

关键词： Visible and near-infrared spectroscopy Skarn -type iron ore dimensionality reduction algorithm Preprocessing combination algorithm Inversion models

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Neural network-based strategies for automatically diagnosing of COVID-19 from X-ray images utilizing different feature extraction algorithms

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NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2023年第1期12卷 28-28页

作者： Prity, Farida Siddiqi Nath, Nishu Nath, Antara Uddin, K. M. Aslam Noakhali Sci & Technol Univ Dept Informat & Commun Engn Noakhali Bangladesh Montana State Univ Gianforte Sch Comp Bozeman MT 59717 USA Mainamoti Med Coll Cumilla Bangladesh

The COVID-19 pandemic has had an obliterating impact on the health and well-being of the worldwide populace. It has recently become one of the most severe and acute diseases and has spread globally. Therefore, an automated detection system should be implemented as the fastest diagnostic option to control the spread of COVID-19. This study aims to introduce neural network-based strategies for classifying and detecting COVID-19 early through image processing utilizing X-ray images. Despite the extensive use of directly fed X-ray images into the classifier, there is a lack of comparative analysis between the feature-based system and the direct imaging system in the classification of COVID-19 to evaluate the efficiency of feature extraction methods. Therefore, the proposed system represents introductory experiments of feature-based system using image Feature Extraction algorithms [Texture, Grey-Level Co-occurrence Matrix (GLCM), Grey-Level Dependence Matrix (GLDM), Fast Fourier Transform (FFT), and Discrete Wavelet Transform (DWT)], dimensionality reduction algorithms (Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), Sparse Autoencoder, and Stacked Autoencoder), Feature Selection algorithms (Anova F-measure, Chi-square Test, and Random Forest), and Neural Networks [Feed-Forward Neural Network (FFNN) and Convolutional Neural Network (CNN)] on generated datasets consisting of Normal, COVID-19, and Pneumonia-infected chest X-ray images. Tenfold Cross-Validations are implemented during the classification process. A baseline model is created where no Feature Extraction algorithm is implemented. Accuracy, sensitivity, specificity, precision, and F-measure metrics are utilized to assess classification performance. Finally, a comprehensive comparison of the success of the Feature-based system (Feature Extraction algorithms, dimensionality reduction algorithms, and Feature Selection algorithms) in COVID-19 classification from X-ray images is made w

关键词： COVID-19 X-ray image Neural network Feature extraction algorithm dimensionality reduction algorithm Feature selection algorithm

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