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International Conference on Computer Science and Service System (CSSS)

作者： Dengzhou Zhang Yuanyuan Gao Zhanglin Peng School of Management Hefei University of Technology Hefei China finance Department China electronics technology group corporation Beijing China Institute of Computer Network System Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China

As the capacity of independent innovation is a vital measure standard for the success of a city's construction, the goal of this paper is to analyze and comment it. In order to make the research result more objective and precise, a system of evaluating indicator which includes the ability of innovation input, management, guarantee and output has been established. Moreover, by using the method of Principal Component Analysis, the capacity for independent innovation of nine cities in the Wanjiang city-region is clearly realized. Finally, hoping that this paper would be helpful to the construction of Wanjiang city-region for industrial transfer and provide a policy basis for the macroscopic readjustment and control as well as management.

关键词： Technological innovation Cities and towns Security Economic indicators Industries Appraisal Finance

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RS-LSSVM: A new ensemble method for sentiment classification based on random subspace

引用

ICIC Express Letters, Part B: Applications 2014年第6期5卷 1773-1778页

作者： Wang, Gang Yang, Shanlin Ma, Jian School of Management Hefei University of Technology No. 193 Tunxi Road Hefei China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei University of Technology No. 193 Tunxi Road Hefei China Department of Information Systems City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong

As analyzing and predicting the polarity of the sentiment plays an important role in understanding social phenomena and general society trends, sentiment classification problem has become a popular topic in academia and industry in recent years. However, comparing with Bagging and Boosting, another popular ensemble method, i.e., Random Subspace, is paid much less attention to the sentiment classification problem. In this research, we propose a new ensemble method, RS-LSSVM, for sentiment classification based on Random Subspace and LSSVM. Ten public sentiment classification datasets are used to verify the effectiveness of the proposed RS-LSSVM. Experimental results reveal that RS-LSSVM can get the better results than the four base learners, Bagging, and Boosting. All these results indicate that RS-LSSVM can be used as an alternative method for sentiment classification.

关键词： Support vector machines

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Impacts of Cross-Channel Recommendation for Online-Niche Products

SSRN

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SSRN 2022年

作者： Qiu, Ju Zhao, Ju Hu, Xiaojian Zhou, Yongwu Min, Jie School of Management Hefei University of Technology Anhui Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Hefei230009 China School of Business Administration South China University of Technology Guangdong Guangzhou510640 China Department of Mathematics Anhui Jianzhu University Anhui Hefei230601 China

This paper investigates a newly emerging recommendation strategy for online niche products, that is, a multichannel retailer recommends online niche products for instore shoppers. Such cross-channel recommendations make more customers aware of niche products, generating a positive information effect for the online channel. It also increases the interaction between online and offline channels. However, it is unclear how the provision of cross-channel recommendations influences the channel relationship when online and offline channels sell common products. Our analysis reveals that the cross-channel recommendation can increase the offline price and sales of common products, which benefits the offline channel. But the online sales of common products are decreased, causing a cannibalization effect on the online channel. The negative effect on common products can be dominated by the positive information effect on niche products when recommending a high-price niche product. In such case, the online channel benefits from cross-channel recommendations and channel synergy is achieved. From the perspective of a multichannel retailer, recommending high-price niche products to in-store shoppers can increase the retailer’s overall profit although the market for common product might shrink. Focusing on the target market with a high potential market scale of recommended customers and increasing the recommendation precision can make cross-channel recommendations more effective. © 2022, The Authors. All rights reserved.

关键词： Sales

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Impacts of Cross-Channel Recommendation for Online-Niche Products

SSRN

引用

SSRN 2022年

关键词： Sales

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Wrapper feature selection embedded Bagging for financial distress prediction

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ICIC Express Letters, Part B: Applications 2013年第2期4卷 375-380页

作者： Wang, Gang Yang, Shanlin Ma, Jian School of Management Hefei University of Technology No. 193 Tunxi Road Hefei 230009 China Ministry of Education Key Laboratory of Process Optimization and Intelligent Decision-Making Hefei University of Technology No. 193 Tunxi Road Hefei 230009 China Department of Information Systems City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong

The prediction of financial distress for financial institutions has been extensively researched for a long time. Latest studies have shown that such ensemble techniques have performed better than single AI technique in financial distress prediction. In this paper a new wrapper feature selection embedded Bagging, WFS-Bagging, is proposed to predict financial distress. WFS-Bagging utilizes the feature selection, e.g., wrapper feature selection, to enhance the accuracy and diversity of base learners. For the testing and illustration purposes, two real world financial distress data sets are selected to demonstrate the effectiveness and feasibility of proposed method. Experimental results reveal that WFS-Bagging can be used as an alternative technique for the financial distress prediction. © 2013 ICIC International.

关键词： Forecasting

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The green project of data management for industrial integration

International Journal of Database Theory and Application

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International Journal of Database Theory and Application 2015年第6期8卷 333-342页

作者： Tian, Yiming Zhu, keyu Pan, Ruoyu Liang, Changyong School of Computer and Information Hefei University of Technology Anhui Transport Interconnection Management Center Hefei China Institute of Computer Network Systems Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China Key Technologies and Equipment of IOT of Highway Traffic Anhui Engineering Technology Research Center Hefei China

The green project fulfills the requests of industrial management and application by sharing data rather than isolating. The data center with energy-efficient equipment is correspondingly effective in industrial service, management and construction of information. This study suggests a green data center in the two-level framework as a basic factor in industrial integration with potent procedures. In the case of transportation, it processed validating data to resolve logistics and emergency operations. © 2015 SERSC.

关键词： Information management

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Towards Trustworthy Web Attack Detection: An Uncertainty-Aware Ensemble Deep Kernel Learning Model

arXiv

引用

arXiv 2024年

作者： Zhou, Yonghang Zhu, Hongyi Chai, Yidong Jiang, Yuanchun Liu, Yezheng School of Management Hefei University of Technology Anhui Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Anhui Hefei230009 China Department of Information Systems and Cyber Security University of Texas at San Antonio 1 UTSA Circle San AntonioTX78249 United States

Web attacks are one of the major and most persistent forms of cyber threats, which bring huge costs and losses to web application-based businesses. Various detection methods, such as signature-based, machine learning-based, and deep learning-based, have been proposed to identify web attacks. However, these methods either (1) heavily rely on accurate and complete rule design and feature engineering, which may not adapt to fast-evolving attacks, or (2) fail to estimate model uncertainty, which is essential to the trustworthiness of the prediction made by the model. In this study, we proposed an Uncertainty-aware Ensemble Deep Kernel Learning (UEDKL) model to detect web attacks from HTTP request payload data with the model uncertainty captured from the perspective of both data distribution and model parameters. The proposed UEDKL utilizes a deep kernel learning model to distinguish normal HTTP requests from different types of web attacks with model uncertainty estimated from data distribution perspective. Multiple deep kernel learning models were trained as base learners to capture the model uncertainty from model parameters perspective. An attention-based ensemble learning approach was designed to effectively integrate base learners' predictions and model uncertainty. We also proposed a new metric named High Uncertainty Ratio-F Score Curve to evaluate model uncertainty estimation. Experiments on BDCI and SRBH datasets demonstrated that the proposed UEDKL framework yields significant improvement in both web attack detection performance and uncertainty estimation quality compared to benchmark models. © 2024, CC BY.

关键词： Adversarial machine learning

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Collaborative Three-Tier Architecture Non-contact Respiratory Rate Monitoring using Target Tracking and False Peaks Eliminating Algorithms

arXiv

引用

arXiv 2020年

作者： Mo, Haimiao Ding, Shuai Yang, Shanlin Vasilakos, Athanasios V. Zheng, Xi The School of Management Hefei University of Technology Anhui Hefei23009 China The Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education China The National Engineering Laboratory of Big Data Distribution and Exchange Technologies China Grimstad Norway The College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China The School of Computing Macquarie University Sydney Australia

Monitoring the respiratory rate is crucial for helping us identify respiratory disorders. Devices for conventional respiratory monitoring are inconvenient and scarcely available. Recent research has demonstrated the ability of non-contact technologies, such as photoplethysmography and infrared thermography, to gather respiratory signals from the face and monitor breathing. However, the current non-contact respiratory monitoring techniques have poor accuracy because they are sensitive to environmental influences like lighting and motion artifacts. Furthermore, frequent contact between users and the cloud in real-world medical application settings might cause service request delays and potentially the loss of personal data. We proposed a non-contact respiratory rate monitoring system with a cooperative three-layer design to increase the precision of respiratory monitoring and decrease data transmission latency. To reduce data transmission and network latency, our three-tier architecture layer-by-layer decomposes the computing tasks of respiration monitoring. Moreover, we improved the accuracy of respiratory monitoring by designing a target tracking algorithm and an algorithm for eliminating false peaks to extract high-quality respiratory signals. By gathering the data and choosing several regions of interest on the face, we were able to extract the respiration signal and investigate how different regions affected the monitoring of respiration. The results of the experiment indicate that when the nasal region is used to extract the respiratory signal, it performs experimentally best. Our approach performs better than rival approaches while transferring fewer data. © 2020, CC BY.

关键词： Edge computing

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Clustering ensembles algorithm based on fractal dimension

引用

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) 2012年第SUPPL. 1期42卷 364-367页

作者： Wu, Xiao-Xuan Ni, Zhi-Wei Ni, Li-Ping School of Management and Business Intelligence Institute Hefei University of Technology Hefei 230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei University of Technology Hefei 230009 China

The fractal dimension based clustering ensembles algorithm was studied. It introduced clustering algorithm based on fractal dimension at first in order to create partitions for clustering ensembles, then using voting strategy to get ensembles result. Finally, an idea on distributed clustering ensembles under cloud computing environment was brief discussed. Fractal dimension based clustering ensembles algorithm can offer better solutions in terms of robustness, novelty and stability than the single clustering algorithm based on fractal dimension. Combining the approaches based on grid and fractal, the clustering algorithm called grid and fractal dimension based clustering algorithm (GFDC) was presented to create partitions for clustering ensembles instead of clustering algorithm based on fractal dimension. GFDC is able to capture arbitrary shapes and non-neighboring clustering and can be applied to the massive and high-dimension dataset.

关键词： Fractal dimension

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A quantitative pairwise comparison-based constraint handling technique for constrained optimization

arXiv

引用

arXiv 2023年

作者： Huang, Ting Zhang, Qiang Pedrycz, Witold Yang, Shanlin School of Management Hefei University of Technology Hefei Box 270 Anhui Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei Box 270 Anhui Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Anhui Hefei230009 China Department of Electrical & Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada

This study proposes a new constraint handling technique for assisting metaheuristic optimization algorithms to solve constrained optimization problems more effectively and efficiently. Given any two solutions of any constrained optimization problems, they are first mapped into a two-dimensional Cartesian coordinate system with their objective function value differences and constraint violation differences as the two axes. To the best of our knowledge, we are the first to deal with constraints by building such a Cartesian coordinate system. Then, the Cartesian coordinate system is divided into a series of grids by assigning ranks to different intervals of differences. In this way, a pairwise comparison criterion is derived with the use of the fused ranks, which achieves non-hierarchical comparison neither preferring objective function values nor constraint violations, resulting in more accurate evaluation compared with existing techniques. Moreover, an evaluation function that is equivalent to the pairwise comparison criterion is proposed, which further improves computational efficiency. The effectiveness and efficiency of the proposed constraint handling technique are verified on two well-known public datasets, that is, CEC 2006 and CEC 2017. The results demonstrate that metaheuristic optimization algorithms with using the proposed constraint handling technique can converge to a feasible optimal solution faster and more reliably. Experimental analysis on the parameters involved reveal guidance for their optimal *** Codes 90-08 (Primary) Copyright © 2023, The Authors. All rights reserved.

关键词： Constrained optimization

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