作者机构：
[Xu B.; Zong J.F.] School of Computer Science and Technology, Hubei University of Science and Technology, Xianning, 437100, China;[Xu Z.] School of Educational Information Technology, Central China Normal University, Wuhan, 430072, China;[Shu C.; Xiao J.] School of Electronic Information, Wuhan University, Wuhan, 430072, China;[Ding L.] School of Computer Science and Technology, Hubei University of Science and Technology, Xianning, 437100, China, School of Electronic Information, Wuhan University, Wuhan, 430072, China
会议名称：
1st International Symposium on Geometry and Vision, ISGV 2021
会议时间：
28 January 2021 through 29 January 2021
关键词：
Convolutional neural network;Detection;Dotted line;The lane line;The solid line
摘要：
Image classification plays a significant role in robotic vision. This paper proposes an image classification model: Xception-LightGBM, which combines with Xception and Light Gradient Boosting Machine for hybrid image classification. The proposed algorithm produces the image feature extraction via Xception and classifies these feature vectors using Light Gradient Boosting Machine (LightGBM). The Xception-LightGBM model is compared with five representative image prediction models, such as VGG16, VGG19, InceptionV3, DenseNet121, and Xception. The experiments on six data sets demonstrate this proposed model leads to successful runs and provides optimal performances. It shows this model achieves the best results for all six evaluation metrics: accuracy, precision, recall, F1-Score, loss, and Jaccard. Furthermore, this proposed model acquires the highest accuracy on six image data sets, which has at least 1.1% in accuracy improved to the Xception architecture. It suggests this model may be preferable for robotic vision.
作者机构：
[Liu, Qingtang; Wu, Linjing] School of Educational Information Technology, Central China Normal University, China;Information Systems and Technology, The University of Dodoma, Tanzania, United Republic of;[Swai, Carina Titus] School of Educational Information Technology, Central China Normal University, China<&wdkj&>Information Systems and Technology, The University of Dodoma, Tanzania, United Republic of
会议名称：
13th International Conference on Education Technology and Computers, ICETC 2021
期刊：
PROCEEDINGS OF 2021 IEEE 11TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC 2021),2021年:151-155
通讯作者：
Xiao, Kejiang
作者机构：
[Yang, Maotao; Zou, Wei] State Grid Hunan Elect Power Ltd Co, China Hunan Prov Key Lab Intelligent Elect Measur, Power Supply Serv Ctr, Metrol Ctr, Changsha, Hunan, Peoples R China.;[Xiao, Kejiang] Cent China Normal Univ, Fac Artificial Intelligence Educ, Hubei Res Ctr Educ Informationizat, Wuhan, Hubei, Peoples R China.
通讯机构：
[Xiao, Kejiang] C;Cent China Normal Univ, Fac Artificial Intelligence Educ, Hubei Res Ctr Educ Informationizat, Wuhan, Hubei, Peoples R China.
会议名称：
11th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC)
会议时间：
JUN 18-20, 2021
会议地点：
Beijing, PEOPLES R CHINA
会议主办单位：
[Zou, Wei;Yang, Maotao] State Grid Hunan Elect Power Ltd Co, China Hunan Prov Key Lab Intelligent Elect Measur, Power Supply Serv Ctr, Metrol Ctr, Changsha, Hunan, Peoples R China.^[Xiao, Kejiang] Cent China Normal Univ, Fac Artificial Intelligence Educ, Hubei Res Ctr Educ Informationizat, Wuhan, Hubei, Peoples R China.
关键词：
Data processing;Abnormal data;Load Forecasting
摘要：
Electric load forecasting is a very important task, but there are often many abnormal data in the load data (Burrs). This paper proposes a load forecasting method in view of the large number of burrs existing in load forecasting. We first used the preprocessed load data to cluster the courts and got the 7050 and the 3033 these two categories (7050 and 3033 are the numbers of the two categories respectively, here we use the numbers as their indexes). Next, we use two methods the sliding box filter method and the comparison method to remove burrs. After extracting the features, we use XGBoost and LightGBM for load prediction. Finally, we analyzed the courts with large prediction errors.
期刊：
Advances in Intelligent Systems and Computing,2021年1264:390-399 ISSN：2194-5357
通讯作者：
Zeng, C.
作者机构：
[Duan S.; Wang Z.; Ouyang H.; Xu H.] Department of Digital Media Technology, Central China Normal University, Wuhan, Hubei 430079, China;[Zeng C.] Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, Hubei 430068, China
通讯机构：
[Zeng, C.] H;Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, China
会议名称：
23rd International Conference on Network-Based Information Systems, NBiS 2020
会议时间：
31 August 2020 through 2 September 2020
会议论文集名称：
Advances in Networked-Based Information Systems
作者：
Chunyan Zeng;Dongliang Zhu;Zhifeng Wang;Yao Yang
期刊：
Advances in Intelligent Systems and Computing,2021年 1263: 372-381 ISSN：2194-5357
通讯作者：
Wang, Z.
作者机构：
[Zhu D.; Zeng C.; Yang Y.] Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, Hubei 430068, China;[Wang Z.] Department of Digital Media Technology, Central China Normal University, Wuhan, Hubei 430079, China
通讯机构：
[Wang, Z.] D;Department of Digital Media Technology, China
会议名称：
12th International Conference on Intelligent Networking and Collaborative Systems, INCoS 2020
会议时间：
31 August 2020 through 2 September 2020
会议论文集名称：
Advances in Intelligent Networking and Collaborative Systems
作者机构：
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, 430079, China;School of Computer, Central China Normal University, Wuhan, Hubei, 430079, China;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, Hubei, 430079, China;National Language Resources Monitor and Research Center for Network Media, Central China Normal University, Wuhan, Hubei, 430079, China;[Zhang M.] Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, 430079, China, National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, Hubei, 430079, China, National Language Resources Monitor and Research Center for Network Media, Central China Normal University, Wuhan, Hubei, 430079, China
会议名称：
5th China Conference on Knowledge Graph, and Semantic Computing, CCKS 2020
会议时间：
12 November 2020 through 15 November 2020
关键词：
Deep learning;Dialogue generation;External knowledge
作者机构：
[Long Z.; Luo D.] Huaihua University, Huaihua, 418000, China;[Kiu K.] Bohai University, Jingzhou, 121013, China;University of Memphis, Memphis, TN 38152, United States;Central China Normal University, Wuhan, 430079, China;[Hu X.] University of Memphis, Memphis, TN 38152, United States, Central China Normal University, Wuhan, 430079, China
会议名称：
2021 Challenges and Advances in Team Tutoring Workshop, TTW-AIED 2021
