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Journal of Network Intelligence 2025年第1期10卷 339-352页

作者： Cui, Xiu-Yan Hwang, David Hebei Software Institute Hebei Province University Intelligent Interconnection Equipment and Multimodal Big Data Application Technology Research and Development Center Baoding071000 China College of Engineering St. Paul University Philippines Tuguegarao3500 Philippines

Aiming at the issue that the jobs predicted by the existing job recommendation methods cannot be precisely matched with the job seekers, this paper designs a personalized intelligent job recommendation method based on deep learning in the Elas-ticsearch environment. Firstly, to deal with the data sparsity and cold start issue of traditional collaborative filtering algorithms, the similarity calculation formula of Pearson’s correlation in the algorithm is modified to enhance the accuracy of similarity calculation. Secondly, on the ground of the history of different job seekers, the keywords of job seekers are constructed in terms of various weights, and input into ElasticSearch to recall the set of jobs that are strongly related to the job seekers. Then adopting Convolutional Neural Network (CNN) to extract the text data features of job seekers and positions, and meanwhile, integrating the attention mechanism to get the auxiliary features of job seekers and positions, and finally, integrating the implicit feedback data with other at-tribute features of job seekers and positions as the input of the improved collaborative filtration algorithm, so that we can realize the intelligent recommendation of jobs for job seekers. Simulation outcome indicates that the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and Root Mean Square Percentage Error (RMSPE) of the suggested method are 0.0681, 0.0519, 0.0834 and 0.0628 respectively, which are lower than those of the comparison method, and the accuracy of the recommendation is effectively enhanced. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Convolutional neural networks

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An Improved Apriori Algorithm Based on Goal Orientation and Double Pruning

An Improved Apriori Algorithm Based on Goal Orientation and ...

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2023 International Conference on Networks, Communications and Intelligent Computing, NCIC 2023

作者： Deng, Shiyu Li, Li Xiong, Yichen Zhang, Jian School of Computer Science Beijing International Science and Technology Cooperation Base of Intelligent Decision and Big Data Application Beijing Information Science and Technology University Laboratory of Big Data Decision Making for Green Development Beijing China School of Economics and Management Beijing International Science and Technology Cooperation Base of Intelligent Decision and Big Data Application Beijing Information Science and Technology University Laboratory of Big Data Decision Making for Green Development Beijing China

ISBN: (纸本)9798350371949

Aiming at solving the problem of high time complexity of association rule mining among item-sets in Apriori algorithm and obtaining the set of goal-association relations of single-to-single point type, this paper improves the Apriori algorithm based on Goal Orientation and Double Pruning (GODP-Apriori). Oriented by the type of goal, the GODP-Apriori uses double pruning to remove the redundant item set during the calculation of the frequency of item set support, which greatly improves the efficiency of obtaining the frequent item set;in addition to this, by sacrificing space for time, it also records the set of transaction items corresponding to the item when calculating the support degree, which avoids the repetitive scanning in the subsequent calculations and then effectively increases the computation efficiency of traditional Apriori algorithm. The effectiveness of GODP-Apriori algorithm in improving the calculation time efficiency of traditional Apriori algorithm was verified by using a real household electricity consumption data at the end of this research. © 2023 IEEE.

关键词： Association rules

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Comprehensive evaluation of PD-1 inhibitors marketed in China based on a Quick Guideline for drug evaluation and selection in Chinese Medical Institutions (second edition)

Intelligent Pharmacy

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Intelligent Pharmacy 2024年第5期2卷 630-637页

作者： Min, Long Yulan, Zhao Wenrui, Li Weilin, Shi Wanyi, Chen Kangzhou Pharmaceutical Big Data Development and Application Research Institute China Department of Medical Insurance Chongqing University Cancer Hospital China Department of Pharmacy Chongqing University Cancer Hospital China

Objective: To conduct a comprehensive evaluation of PD-1 inhibitors for the treatment of advanced non-small cell lung cancer on the market in China, using A Quick Guideline for Drug Evaluation and Selection in Chinese Medical Institutions (Second Edition), and to provide a reference for the selection of and decision-making on clinical drugs in hospitals. Method: The Sanzhi drug evaluation and selection system was used to evaluate the PD-1 inhibitors for the treatment of advanced non-small cell lung cancer on the market in China, according to five characteristics: pharmaceutical characteristics, efficiency, safety, economy, and others. Results: Based on the above evaluation criteria, the scores of all PD-1 inhibitors were above 61 points. The main difference among the five characteristics were efficiency and economy. The difference between the other characteristics was relatively small. Sintilimab injection had the highest score due to the advantages among characteristics except the other attributes. Nivolumab injection (100 mg/10 mL (10 mg/mL)) had the lowest total score due to its low effectiveness and economy scores. Conclusion: PD-1 inhibitors are effective therapeutic drugs for immunotherapy in patients with advanced non-small cell lung cancer. Different PD-1 inhibitors have different advantages in clinical treatment. The comprehensive evaluation of PD-1 inhibitors for the treatment of advanced non-small cell lung cancer on the market in China through A Quick Guide for Drug Evaluation and Selection of Chinese Medical Institutions (Second Edition) can provide a basis for drug selection and scientific, reasonable, and safe drug use in medical institutions. © 2024 The Authors

关键词： Advanced non-small cell lung cancer Drug evaluation PD-1 inhibitors

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Using analytic hierarchy process to evaluate deep learning for infrared target recognition

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Multimedia Tools and applications 2024年第38期83卷 86229-86245页

作者： Xu, Laixiang Cao, Bingxu Wei, Xinhong Li, Bei Su, Jingfeng Ning, Shiyuan Zhao, Junmin School of Computer and Data Science Henan University of Urban Construction Pingdingshan467036 China School of Information Engineering Luohe Vocational Technology College Henan University of Technology Luohe Institute of Technology Luohe462000 China School of Computer and Data Science Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Pingdingshan467036 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Henan University of Urban Construction Pingdingshan467036 China Department of Software Information China Electronics Technology Group Corporation 36-th Research Institute Jiaxing314033 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Research Center of Intelligent Campus Application Engineering of Henan Province Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan467036 China

In order to promote the evaluation performance of deep learning infrared automatic target recognition (ATR) algorithms in the complex environment of air-to-air missile research, we proposed an analytic hierarchy process (AHP) evaluation system consisting of the target recognition ability, the generalization recognition ability, the anti-interference ability, the background suppression ability, and the cost metrics ability. According to detailed quantitative analysis and experimental results, we establish probability evaluation models and get their probability values. Compared with traditional evaluation systems, our proposed method implementation is showing high reliability. Similar superior performances are also achieved, such as quick, comprehensive, and scientific. Through the above research, the key technology of deep learning performance evaluation in automatic target recognition has been broken through, which provides some basis and guidance for the analysis of infrared imaging-guided weapon systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

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Wheat Leaf Disease Identification Based on a Fusion Model Integrating Attention Mechanisms and Channel-Spatial Reconstruction Convolution 5

Wheat Leaf Disease Identification Based on a Fusion Model In...

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5th International Conference on big data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Xie, Haifan Zou, Rongxin Yang, Zicong Jin, Aitong Wang, Junxi Li, Yu Zhuhai College of Science and Technology Department of Computer Zhuhai China Zhuhai College of Science and Technology Department of Big Data Zhuhai China Institute of Intelligent Manufacturing and Aviation Zhuhai College of Science and Technology ZhuHai China Zhuhai College of Science and Technology Alibaba Cloud Big Data Application College Zhuhai China

ISBN: (纸本)9798331506612

With the continuous growth in food demand and the escalating importance of crop health, the challenges posed by the high costs and low efficiency of traditional wheat leaf disease detection methods have become increasingly acute. In light of the intricacies and low discernibility of the morphological and color characteristics of wheat leaf diseases and pests in natural environments, this study introduces a novel and efficient recognition and classification model, SCC2f-CSPDarknet53-CBAM, offering a precise and rapid solution for identifying diseases and pests, thereby supporting early prevention and scientific management. By incorporating the SCC-C2f module into the base of C2f-CSPDarknet53, this model enhances the efficiency of network feature fusion and the capability to extract critical features. Furthermore, the integration of the ResBlock-CBAM module at the terminal end bolsters the generalization and recognition abilities for targets. The study employs the Wheat Leaf Disease dataset, which is constructed through a blend of online and offline methods, with meticulous data processing, for training purposes. Experimental results reveal that the SCC2f-CSPDarknet53-CBAM model achieves an average recognition accuracy of 99.54% in the task of wheat leaf disease detection and classification, surpassing mainstream models in recent years, showcasing remarkable accuracy and adaptability. © 2024 IEEE.

关键词： Convolutional neural networks

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Accelerating local SGD for non-IID data using variance reduction

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Frontiers of Computer Science 2023年第2期17卷 73-89页

作者： Xianfeng LIANG Shuheng SHEN Enhong CHEN Jinchang LIU Qi LIU Yifei CHENG Zhen PAN Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230027China Ant Financial Services Group Hangzhou 310000China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China

Distributed stochastic gradient descent and its variants have been widely adopted in the training of machine learning models,which apply multiple workers in *** them,local-based algorithms,including Local SGD and FedAvg,have gained much attention due to their superior properties,such as low communication cost and ***,when the data distribution on workers is non-identical,local-based algorithms would encounter a significant degradation in the convergence *** this paper,we propose Variance Reduced Local SGD(VRL-SGD)to deal with the heterogeneous *** extra communication cost,VRL-SGD can reduce the gradient variance among workers caused by the heterogeneous data,and thus it prevents local-based algorithms from slow convergence ***,we present VRL-SGD-W with an effectivewarm-up mechanism for the scenarios,where the data among workers are quite *** from eliminating the impact of such heterogeneous data,we theoretically prove that VRL-SGD achieves a linear iteration speedup with lower communication complexity even if workers access non-identical *** conduct experiments on three machine learning *** experimental results demonstrate that VRL-SGD performs impressively better than Local SGD for the heterogeneous data and VRL-SGD-W is much robust under high data variance among workers.

关键词： distributed optimization variance reduction local SGD federated learning non-IID data

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Optimization of Performance Models for Interlayer Meltblown Nonwovens with Random Forest 2

Optimization of Performance Models for Interlayer Meltblown ...

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2nd International Conference on Health big data and Intelligent Healthcare, ICHIH 2023

作者： Wang, Pengjie Li, Ya Liang, Yuheng Zhu, Xiaolin Li, Tongfei Lv, Wei Alibaba Cloud College of Big Data Application Zhuhai College of Science and Technology Zhuhai China YGSOFT INC. Information Technology Application Innovation Platform Department Zhuhai China

ISBN: (纸本)9798350309324

In the context of rapid societal and technological advancements, there has been a pronounced elevation in public health awareness. Among various protective measures, meltblown nonwoven materials have emerged as an indispensable component in protective masks, offering a crucial respiratory barrier for users. This study endeavors to provide an in-depth examination of this pivotal material. By systematically analyzing extensive datasets of meltblown fabric manufacturing processes and taking into account key parameters such as receiving distance, air velocity, and material thickness, this paper, grounded on historical production parameters, harnesses advanced machine learning techniques to meticulously design and optimize the performance control model for interlayer melt-blown nonwoven materials. The central aim of this research is to further enhance the efficacy of this material in health protection applications. © 2023 IEEE.

关键词： Neural networks

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Image inpainting via Smooth Tucker decomposition and Low-rank Hankel constraint

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International Journal of Computers and applications 2023年第6期45卷 421-432页

作者： Cai, Jing Jiang, Jiawei Wang, Yibing Zheng, Jianwei Xu, Honghui Department of Forensic Science Zhejiang Police College Hangzhou China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Hangzhou China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

Image inpainting, aiming at exactly recovering missing pixels from partially observed entries, is typically an ill-posed problem. As a powerful constraint, low-rank priors have been widely applied in image inpainting to transform such problems into well-posed ones. However, the low-rank assumption of original visual data is only in an approximate mode, which in turn results in suboptimal recovery of fine-grained details, particularly when the missing rate is extremely high. Moreover, a single prior cannot faithfully capture the complex texture structure of an image. In this paper, we propose a joint usage of Smooth Tucker decomposition and Low-rank Hankel constraint (STLH) for image inpainting, which enables simultaneous capturing of the global low-rankness and local piecewise smoothness. Specifically, based on the Hankelization operation, the original image is mapped to a high-order structure for capturing more spatial and spectral information. By employing Tucker decomposition for optimizing the Hankel tensor and simultaneously applying Discrete Total Variation (DTV) to the Tucker factors, sharper edges are generated and better isotropic properties are enhanced. Moreover, to approximate the essential rank of the Tucker decomposition and avoid facing the uncertainty problem of the upper-rank limit, a reverse strategy is adopted to approximate the true rank of the Tucker decomposition. Finally, the overall image inpainting model is optimized by the well-known alternate least squares (ALS) algorithm. Extensive experiments show that the proposed method achieves state-of-the-art performance both quantitatively and qualitatively. Particularly, in the extreme case with 99% pixels missed, the results from STLH are averagely ahead of others at least 0.9dB in terms of PSNR values. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pixels

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Research on Mining Association Rules for Classroom Concentration Based on Apriori Algorithm 7

Research on Mining Association Rules for Classroom Concentra...

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7th IEEE Information technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Bai, Jingqiu Kong, Huafeng Hu, Wenhan Wuhan Business University Department of Information Engineering Hubei China Major in Data Science and Big Data Technology China The Main Research Direction Is the Computer Application China

ISBN: (纸本)9798350334197

This paper studies the correlation between students' concentration in class and learning interest, emotional state and other influencing factors. By collecting students' classroom status data, a data set suitable for data mining is constructed. Based on the idea of Apriori algorithm, the Anaconda Navigator programming tool is used to mine the association rules of students' classroom concentration. Through data mining based on Apriori algorithm, and the association rules between students' emotional state, learning interest and other factors and in-class test scores are output. The results of strong association rules show that the better students' emotional state, learning interest and other states are, the better their classroom concentration performance is. Mining the potential influencing factors of students' classroom concentration provides valuable reference theory for students to improve classroom concentration and teachers to improve classroom teaching effect, which is helpful for teachers and students to build a more efficient classroom together in the future. © 2023 IEEE.

关键词： Association rules

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Image super‐resolution via dynamic network

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CAAI Transactions on Intelligence technology 2024年第4期9卷 837-849页

作者： Chunwei Tian Xuanyu Zhang Qi Zhang Mingming Yang Zhaojie Ju School of Software Northwestern Polytechnical UniversityXi'anChina National Engineering Laboratory for Integrated Aero‐Space‐Ground‐Ocean Big Data Application Technology Xi'anChina Research&Development Institute Northwestern Polytechnical UniversityShenzhenChina School of Economics and Management Harbin Institute of Technology at WeihaiWeihaiChina Tencent AI Lab ShenzhenChina School of Computing University of PortsmouthPortsmouthUK

Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐***,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction *** residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐*** enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying *** prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained ***,a residual learning operation is embedded in the refinement block to prevent long‐term dependency ***,a construction block is responsible for reconstructing high‐quality *** heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital *** results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and *** code of DSRNet can be obtained at https://***/hellloxiaotian/DSRNet.

关键词： CNN dynamic network image super‐resolution lightweight network

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