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5th International Conference on E-Business, Information Management and Computer Science, EBIMCS 2022

作者： Yu, Youhua Xie, Xiaolan Cui, Yunlong School of Information Science and Engineering Guilin University of Technology Guangxi Guilin China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guangxi Guilin China Zhejiang Academy of Surveying and Mapping Zhejiang Hangzhou China

ISBN: (纸本)9781450397834

In recent years, with the continuous innovation of science and technology, users' demand for electronic products gradually presents a diversified trend. However, there are a wide variety of electronic products in the market and the products are updated quickly, which brings great difficulties for users to choose. To solve the above problems, this paper proposes a particle swarm optimization hybrid ant colony optimization algorithm to solve the CTO (Configure To Order) recommendation model of high-end electronic products, so as to provide customers with unique personalized customization services for electronic products. The simulation results show that the algorithm has good performance in solving the CTO recommendation model of electronic products. © 2022 ACM.

关键词： Ant colony optimization

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An improved random forest order review classification method based on Word2vec 7

An improved random forest order review classification method...

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7th International Conference on intelligent Information Processing, ICIIP 2022

作者： Wang, Yuhan Xie, Xiaolan Wang, Bangjin School of Information Science and Engineering Guilin University of Technology China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology China Zhejiang Academy of Surveying and Mapping China

ISBN: (纸本)9781450396714

In recent years, with the continuous development of the Internet, online shopping has gradually become a mainstream shopping method for people. At the same time, people often comment on the purchased goods, which expresses their shopping feelings on the one hand and provides corresponding reference for others' shopping on the other. In order to better mine and analyze the massive review data and extract valuable reviews from them, this paper proposes an improved random forest classification method based on Word2vec, which optimizes the number of decision trees and the number of leaf node variables in the random forest algorithm by incorporating the seagull algorithm to classify the crawled e-mall review data emotionally. The results show that the word2vec?RF classification model is improved more significantly and can effectively enhance the performance of the classification algorithm. © 2022 ACM.

关键词： Decision trees

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One-against-all-based Hellinger distance decision tree for multiclass imbalanced learning

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Frontiers of Information technology & Electronic Engineering 2022年第2期23卷 278-290页

作者： Minggang DONG Ming LIU Chao JING School of Information Science and Engineering Guilin University of TechnologyGuilin 541004China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin 541004China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China

Since traditional machine learning methods are sensitive to skewed distribution and do not consider the characteristics in multiclass imbalance problems,the skewed distribution of multiclass data poses a major challenge to machine learning *** tackle such issues,we propose a new splitting criterion of the decision tree based on the one-against-all-based Hellinger distance(OAHD).Two crucial elements are included in ***,the one-against-all scheme is integrated into the process of computing the Hellinger distance in OAHD,thereby extending the Hellinger distance decision tree to cope with the multiclass imbalance ***,for the multiclass imbalance problem,the distribution and the number of distinct classes are taken into account,and a modified Gini index is ***,we give theoretical proofs for the properties of OAHD,including skew insensitivity and the ability to seek a purer node in the decision ***,we collect 20 public real-world imbalanced data sets from the Knowledge Extraction based on Evolutionary Learning(KEEL)repository and the University of California,Irvine(UCI)*** and statistical results show that OAHD significantly improves the performance compared with the five other well-known decision trees in terms of Precision,F-measure,and multiclass area under the receiver operating characteristic curve(MAUC).Moreover,through statistical analysis,the Friedman and Nemenyi tests are used to prove the advantage of OAHD over the five other decision trees.

关键词： Decision trees Multiclass imbalanced learning Node splitting criterion Hellinger distance One-against-all scheme

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Beyond animal detection: A benchmark for detecting animal age group 5

Beyond animal detection: A benchmark for detecting animal ag...

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5th International Conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Zhou, Hang Wu, You Li, Jiehong Pan, Liangtao Ye, Hengzhou Li, Shuiwang College of Information Science and Engineering Guilin University of Technology Guangxi Guilin541006 China Guangxi Key Laboratory of Embedded Technology and Intelligent Information Processing Guangxi Guilin541006 China

ISBN: (纸本)9781510668621

There are numerous practical applications for animal age estimation, such as age simulation, population structure estimation, wildlife conservation, biometrics, and more. Most of the current methods either concentrate solely on animal detection or animal age estimation. However, animal detection provides limited information, which is not adequate for more sophisticated computer vision tasks. On the other hand, existing methods for estimating animal age are based primarily on facial images, but capturing an accurate image of an animal’s face in a wild environment is extremely difficult and impractical for animals without discriminative facial features. In this paper, we propose a novel task, called Animal Age Group Detection. This task is a combination of animal detection and animal age estimation, with the primary goal of obtaining more information beyond the location of the animal by also providing crude age information, which is simple to accomplish with computer vision methods in a variety of open-world applications. To facilitate the study of this challenging, we present a benchmark for Animal Age Group Detection (AAGD), which contains a diverse range of 22 animal types, comprising a total of 4, 778 images. These images are well-annotated with axis-aligned bounding boxes and include information about the age groups of the animals. In addition, we establish a baseline detector for this task, called AG-YOLOF. The release of AAGD aims to facilitate future research on the identification of animal age groups and enhance awareness of this significant area of study. © 2023 SPIE.

关键词： Animals

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Study on Vibration Monitoring of Railway Seamless Line

Study on Vibration Monitoring of Railway Seamless Line

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2020 IEEE International Conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2020

作者： Yang, Xiaoping Wang, Yang Du, Jixuan Huang, Ran Liu, Guanghui Information Institute Guilin University of Technology Guilin China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin China Guilin Saipu Electronic Technology Co.Ltd Gunlin China

ISBN: (纸本)9781728152240

With the continuous expansion of the scale of high-speed rail network in China, in order to improve the speed, train load and safety performance, seamless rail technology is widely used in Chinese lines. And seamless rail is to weld 25 meters long rail into several hundred meters long or even thousands of meters long. Because highspeed rail cannot freely expand and contract, the rail is affected by temperature stress, vibration stress, at the same time high speed starting and braking of high speed railway, Large volume of transport is easy to make the rail fatigue and fracture. In order to ensure the safety of people's life and property, in this study, a new real-time monitoring method for rail condition is proposed., using the rail vibration signal analysis of rail damage, through acceleration sensor to collect the rail. vibration signal, then reduce the interference through kalman filter, get the rail close to the real vibration amplitude. under laboratory conditions, build vibration signal test system, use bench drill hammer rail model, use MPU6050 acceleration sensor to collect vibration signal of rail model, pass STM32F103CBT6 to computer keil program, and import the result into MATLAB for filtering and amplitude analysis. The experiment shows that the acceleration sensor is effective in monitoring the amplitude of rail. © 2020 IEEE.

关键词： Kalman filters

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A many-objective evolutionary algorithm based on decomposition with dynamic resource allocation for irregular optimization

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Frontiers of Information technology & Electronic Engineering 2020年第8期21卷 1171-1190页

作者： Ming-gang DONG Bao LIU Chao JING College of Information Science and Engineering Guilin University of TechnologyGuilin 541004China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin 541004China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic&TechnologyGuilin 541004China

The multi-objective optimization problem has been encountered in numerous fields such as high-speed train head shape design,overlapping community detection,power dispatch,and unmanned aerial vehicle *** address such issues,current approaches focus mainly on problems with regular Pareto front rather than solving the irregular Pareto *** this situation,we propose a many-objective evolutionary algorithm based on decomposition with dynamic resource allocation(Ma OEA/D-DRA)for irregular *** proposed algorithm can dynamically allocate computing resources to different search areas according to different shapes of the problem’s Pareto *** evolutionary population and an external archive are used in the search process,and information extracted from the external archive is used to guide the evolutionary population to different search *** evolutionary population evolves with the Tchebycheff approach to decompose a problem into several subproblems,and all the subproblems are optimized in a collaborative *** external archive is updated with the method of rithms using a variety of test problems with irregular Pareto *** results show that the proposed algorithèm out-p￡performs these five algorithms with respect to convergence speed and diversity of population *** comparison with the weighted-sum approach and penalty-based boundary intersection approach,there is an improvement in performance after integration of the Tchebycheff approach into the proposed algorithm.

关键词： Many-objective optimization problems Irregular Pareto front External archive Dynamic resource allocation Shift-based density estimation Tchebycheff approach

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Dif-NoBa A differential privacy noise Bayesian gradient descent algorithm in Deep Learning 7

Dif-NoBa A differential privacy noise Bayesian gradient desc...

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7th International Conference on intelligent Information Processing, ICIIP 2022

作者： Zhang, Panfeng Wu, Danhua Yang, Zhiwei Department of Information Science and Technology Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin541006 China Department of Information Science and Technology Guilin University of Technology Guilin541006 China

ISBN: (纸本)9781450396714

In recent years, differential privacy technology has been introduced into deep learning to satisfy the protection of personal privacy data. However, the existing differential privacy technology will weaken the generalization ability of deep learning networks. To address the problem, a differential privacy noise Bayesian gradient descent algorithm (Dif-NoBa) in Deep Learning was proposed. For the method, the privacy protection algorithm acts on a single neuron in the neural network. Therefore, no matter how complex the network is, it only focuses on a single neuron, so that each neuron can satisfy its own differential privacy, and then the whole network can meet the differential privacy, effectively solving the problem of insufficient generalization ability of neural network caused by adding arbitrary noise. © 2022 ACM.

关键词： Deep learning

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Short-term Forecast of Agricultural Prices Using CNN? 7

Short-term Forecast of Agricultural Prices Using CNN?

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7th International Conference on intelligent Information Processing, ICIIP 2022

作者： Chen, Peng Ye, Hengzhou College of Information Science and Engineering Guilin University of Technology China Guangxi Key Laboratory of Embedded Technology and Intelligent Systems Guilin University of Technology China

ISBN: (纸本)9781450396714

Agriculture is one of the important branch of national economy. Accurately predicting the price of agricultural products is conducive to reducing the decision-making risk of agricultural practitioners, businessmen and managers. The price of agricultural products has the characteristics of large fluctuation, non-stationary and nonlinear, which increases the difficulty of forecasting agricultural prices. In this paper, a hybrid prediction method called CNN? is proposed, which takes the comparison of the prediction results of two methods as the final predicted value. One method is to use Convolutional Neural Network (CNN). In the other method, after denoising the price series using the equal-depth bin method and the Ensemble Empirical Mode Decomposition (EEMD) model, the Long Short-Term Memory (LSTM) model is adopted to predict. The effectiveness of our method is demonstrated by taking tomato price series as an example. © 2022 ACM.

关键词： Empirical mode decomposition

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Research on film data preprocessing and visualization

Research on film data preprocessing and visualization

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2020 IEEE International Conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2020

作者： Zhang, Huaxin Liu, Yu Wang, Zituo Li, Tiansong Cao, keyin College of Information Science and Engineering Guilin University of Technology Guilin China The School of Mechanical Engineering Tiangong University Tianjin China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin China

ISBN: (纸本)9781728152240

Data is the core of information, and good data quality is a prerequisite for many data analysis. Data cleaning is to increase the fault tolerance rate by correcting the error value of detected data. This paper aims to solve the problem of data set processing and visualization in the recommendation algorithm, so as to better apply in the field of recommendation algorithm. The recommendation algorithm and data sets Movielens and IMDB are analyzed theoretically. First, data set A was processed from data reading and movie score calculation;Again, the IMDB is processed in four steps to make it more suitable for the recommendation algorithm field;Finally, the plot function is used to visualize the key information. experiment shows: The data set sorted out by the above methods can effectively improve the quality and availability of data and provide relevant basis for better application in the algorithm. © 2020 IEEE.

关键词： Visualization

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Method for Recognition Pneumonia Based on Convolutional Neural Network

Method for Recognition Pneumonia Based on Convolutional Neur...

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2019国际计算机前沿大会

作者： Xin Li Dongdong Gao Haijiang Hao Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology

Pneumonia is one of the most common infectious diseases in clinical practice. In the field of pneumonia recognition, traditional algorithms have limitations in feature extraction and scope of application. To solve this problem,a pneumonia recognition is proposed based on convolutional neural ***, the morphological preprocessing operation was performed on the chest X-ray. Secondly, the convolutional layer containing the 1 * 1 convolution kernel was used instead of a fully connected layer in the convolutional neural network to segment the lung field and obtain the segmentation. The index Dice coefficient can reach 0.948. Finally, a pneumonia recognition model based on convolutional neural network was established. The segmented images were trained and tested. The experimental results show that the average accuracy of the proposed method for pneumonia is up to 96.3%.

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