Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (6A): 240700029-7.doi: 10.11896/jsjkx.240700029

• Intelligent Medical Engineering • Previous Articles     Next Articles

Study on Diagnosis Model of Livestock and Poultry Disease Based on Improved TF-IIGM Algorithm

GUO Xiaoli1,2,3, LI Qifeng1,3, LIU Yu1,3, ZHANG Jun1,3, ZHAO Hongtao2, YANG Gan1,3, JIANG Ruixiang1,3, YU Ligen1,3   

  1. 1 Research Center of Information Technology,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China
    2 School of Mathematics and Physics,North China Electric Power University,Beijing 102206,China3 Innovation Center of National Digital Livestock,Beijing 100097,China
  • Online:2025-06-16 Published:2025-06-12
  • About author:GUO Xiaoli,born in 1998,postgra-duate.Her main research interests include NLP and diagnosis of livestock and poultry diseases.
    YU Ligen,born in 1985,Ph.D,professor.His main research interests include intelligent diagnosis of livestock and poultry diseases.
  • Supported by:
    National Key R&D Program of China(2023YFD1300805),Yunnan Province Major Science and Technology Special Program(202102AE090039),Beijing Academy of Agriculture and Forestry Sciences Innovation Capacity Building Project(KJCX20230204) and Research on the Development Strategy of Modern Animal Husbandry in Inner Mongolia(2023NM2N-01).

PDF (PC)

47

Abstract: In order to deal with the problem of low diagnostic accuracy caused by inaccurate weight allocation of feature items in livestock and poultry diseases texts,the improved TF-IIGM-GW algorithm combined with Word2vec word vector is used to rea-lize the text vectorization.On the basis of the TF-IIGM weighting method,the method is normalized and combined with the rule based on the keyword extraction algorithm to further improve the weight of core keywords in the texts.Finally,the text vectorization results obtained by combining the weight with Word2vec word vector are inputted into the support vector machine(SVM) for diagnosis of livestock and poultry diseases.In order to verify the effectiveness of the improved algorithm,based on the self-built text datasets of livestock and poultry diseases,the improved algorithm is compared with the commonly used methods of word vector.Results show that the macro-F1 value and micro-F1 value based on the TF-IIGM-GW algorithm are 96.73% and 96.76%,respectively,which are 2.25% and 2.26% higher than those of the commonly used algorithm TF-IDF,and 0.90% and 0.97% higher than those of TF-IIGM weighting method.The improved algorithm could effectively improve the performance of disease diagnosis.The analysis of the experimental results of SVM on each type of diseases shows that sheep oral aphthae is most easily misjudged.

Key words: TF-IIGM, Weighting, Vectorization, Disease diagnosis, SVM

CLC Number: 

  • TP391
[1]JIANG R X,YU L G,DING L Y,et al.Development Status and Prospect of Intelligent Prevention and Control Technology for Livestock and Poultry Diseases[J].Chinese Journal of Animal Science,2020,56(10):23-28.
[2]WANG H,SHEN W,ZHANG Y,et al.Diagnosis of dairy cow diseases by knowledge-driven deep learning based on the text reports of illness state[J].Computers and Electronics in Agriculture,2023,205:107564.
[3]MUHAMEDIYEVA D T,SAFAROVA L U,TUKHTAMU-RODOV N.Early diagnostics of animal diseases on the basis of modern information technologies[C]//AIP Conference Proceedings.AIP Publishing,2023.
[4]ZHENG S,ZHOU C,JIANG X,et al.Progress on infrared imaging technology in animal production:a review[J].Sensors,2022,22(3):705.
[5]TERRADA O,CHERRADI B,RAIHANI A,et al.A novelmedical diagnosis support system for predicting patients with atherosclerosis diseases[J].Informatics in Medicine Unlocked,2020,21:100483.
[6]ALSMADI I,HOON G K.Term weighting scheme for short-text classification:Twitter corpuses[J].Neural Computing and Applications,2019,31(8):3819-3831.
[7]LI C,LI W,TANG Z,et al.An improved term weighting method based on relevance frequency for text classification[J].Soft Computing,2023,27(7):3563-3579.
[8]AO X,YU X,LIU D,et al.News keywords extraction algorithm based on TextRank and classified TF-IDF[C]//International Wireless Communications and Mobile Computing(IWCMC 2020).IEEE,2020:1364-1369.
[9]LAN X F,LIU Z,XU Z H,et al.A Chinese Text Keyword Extraction Method Based on the Combination of TF-IDF and TextRank——A Case Study of Sports News[J].Software Engineering,2023,26(8):6-10.
[10]ZHAO J S,SONG M X,GAO X,et al,Research on Text Representation in Natural Language Processing[J].Journal of Software,2022,33(1):102-128.
[11]SIEBERS P,JANIESCH C,ZSCHECH P.A survey of text representation methods and their genealogy[J].IEEE Access,2022,10:96492-96513.
[12]SALTON G,WONG A,YANG C S.A vector space model for automatic indexing [J].Communications of the ACM,1974,18(11):613-620.
[13]MAHDI A Y,YUHANIZ S S.Automatic Diagnosis of COVID-19 Patients from Unstructured Data Based on a Novel Weighting Scheme[J].Computers,Materials & Continua,2023,74(1).
[14]DEBOLE F,SEBASTIANI F.Supervised term weighting for automated text categorization[C]//Proceedings of the 2003 ACM Symposium on Applied Computing.2003:784-788.
[15]CHEN K,ZHANG Z,LONG J,et al.Turning from TF-IDF to TF-IGM for Term Weighting in Text Classification[J].Expert Systems with Applications,2016,66:245-260.
[16]DOGAN T,UYSAL A K.Improved Inverse Gravity MomentTerm Weighting for Text Classification[J].Expert Systems with Applications,2019,130:45-59.
[17]XU T H,WU M L.An Improved Naive Bayes Algorithm Based on TF-IDF[J].Computer Technology and Development,2020,30(2):75-79.
[18]XU J.A Natural Language Processing Based Technique for Sentiment Analysis of College English Corpus[J].PeerJ Computer Science,2023,9:e1235.
[19]JING L,HE T T.Chinese Text Classification Model Based onImproved TF-IDF and ABLCNN[J].Computer Science,2021,48(S2):170-175.
[20]TANG Z,LI W,LI Y.An Improved Supervised Term Weighting Scheme for Text Representation and Classification[J].Expert Systems with Applications,2022,189:115985.
[21]TENEVA N,CHENG W.Salience Rank:Efficient KeyphraseExtraction with Topic Modeling[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.2017:530-535.
[22]LIU Z,HUANG W,ZHENG Y,et al.Automatic Keyphrase Extraction via Topic Decomposition[C]//Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing.2010:366-376.
[23]STERCKX L,DEMEESTER T,DELEU J,et al.Topical Word Importance for Fast Keyphrase Extraction[C]//Proceedings of the 24th International Conference on World Wide Web.2015:121-122.
[24]YU L G,GUO X L,ZHAO H T,et al.Text Word Segmentation of Livestock and Poultry Diseases Based on BERT-BiLSTM-CRF Model[J].Transactions of the Chinese Society for Agricultural Machinery,2024,55(2):287-294.
[25]QIU Y,YANG B.Research on micro-blog text presentationmodel based on word2vec and TF-IDF[C]//IEEE Asia-Pacific Conference on Image Processing,Electronics and Computers(IPEC 2021).IEEE,2021:47-51.
[26]CORTES C,VAPNIK V.Support-Vector Networks[J].Ma-chine Learning,1995,20:273-297.
[1] SHI Zhuopeng, KONG Xiangmin, WEI Jiahong, SONG Xiaofan. Self-matching Method of Virtual Terminals of Intelligent Stations Based on K-nearest Neighbor Weighting Algorithm [J]. Computer Science, 2025, 52(6A): 240600039-6.
[2] LIAO Zeming, LIU Guikai, HU Yonghua, XIE Anxing. Research on Efficient Code Generation Techniques for Array Computation for Vector DSPs [J]. Computer Science, 2025, 52(6A): 240300156-7.
[3] JIANG Jun, GU Xiaoyang, XU Kunkun, LYU Yongshuai, HUANG Liangming. Design and Research of SIMD Programming Interface for Sunway [J]. Computer Science, 2025, 52(6): 66-73.
[4] LIU Lili, SHAN Zheng, LI Yingying, WU Wenhao, LIU Wenbo. Research on Function Vectorization Technology Based on Directive Statements [J]. Computer Science, 2025, 52(5): 76-82.
[5] HU Huijuan, QIN Yifeng, XU Heand LI Peng. An Improved YOLOv8 Object Detection Algorithm for UAV Aerial Images [J]. Computer Science, 2025, 52(4): 202-211.
[6] SUN Ziwen, YUAN Guanglin, LI Congli, QIN Xiaoyan, ZHU Hong. Object Tracking of Structured SVM Based on DIoU Loss and Smoothness Constraints [J]. Computer Science, 2024, 51(6A): 230700113-8.
[7] SONG Zhen, WANG Jiqiang, HOU Moyu, ZHAO Lin. Conveyor Belt Defect Detection Network Combining Attention Mechanism with Line Laser Assistance [J]. Computer Science, 2024, 51(6A): 230800115-6.
[8] BAI Jie, TIAN Ruili, REN Yifu, YUAN Jianxia. Low-rank HOG Voice Detection Method for Short-wave Communication [J]. Computer Science, 2024, 51(6A): 230600115-5.
[9] WANG Zhen, NIE Kai, HAN Lin. Auto-vectorization Cost Model Based on Instruction MKS [J]. Computer Science, 2024, 51(4): 78-85.
[10] ZHANG Yinling, SHANG Tao, LI Zhaokun. Radar Emitter Target Dynamic Threat Assessment Based on Combining Weighting-TOPSIS Method [J]. Computer Science, 2024, 51(11A): 231000038-7.
[11] YANG Yufan, YUAN Liming, WANG Ke, LI Hongyi, LI Yixuan, YAO Yujia, WANG Jingyi. Grading Model for Diabetic Retinopathy Based on Graph Convolutional Network [J]. Computer Science, 2024, 51(11A): 231000042-5.
[12] WU Jiangnan, ZHANG Hongmei, ZHAO Yongmei, ZENG Hang, HU Gang. Traffic Data Restoration Method Based on Tensor Weighting and Truncated Nuclear Norm [J]. Computer Science, 2023, 50(8): 45-51.
[13] ZHANG Lianfu, TAN Zuowen. Robust Federated Learning Algorithm Based on Adaptive Weighting [J]. Computer Science, 2023, 50(6A): 230200188-9.
[14] YANG Xing, SONG Lingling, WANG Shihui. Remote Sensing Image Classification Based on Improved ResNeXt Network Structure [J]. Computer Science, 2023, 50(6A): 220100158-6.
[15] SUN Wei, BI Yujiang, CHENG Yaodong. Lattice QCD Calculation and Optimization on ARM Processors [J]. Computer Science, 2023, 50(6): 52-57.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.