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6th International Conference on Intelligent Networks and Intelligent Systems (ICINIS)

作者： Dong Liyan Zhang Sainan Tian Geng Li Yongli Cai Guanyan NE Normal Univ Sch Comp Sci & Informat Technol Changchun 130117 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

ISBN: (纸本)9781479928088

Aim at the clustering result of traditional ant colony clustering algorithm is not accurate and the algorithm operating efficiency lower, many modified algorithm have been proposed. In this paper, we propose an ant colony clustering algorithm based on swarm intelligence. This algorithm not only improved from the method of calculating the similarity measure and enhanced ant memory, and also proposed a new policy of picking and dropping objects, which is picking the objects which have been formation of micro-clustering. Through experiment contrast, this paper presents the ant colony clustering algorithm based on swarm intelligence than the traditional ant colony algorithm in terms of efficiency, the correct rate of the clustering results have significantly improved.

关键词： ant colony clustering algorithm Swarm Intelligence algorithm Data Mining Micro-Cluster

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An empirical study of improved ant colony clustering algorithm in English composition review

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2023年第4期21卷 200-208页

作者： Chang, Xiao Sun, Jianguang Huaiyin Inst Technol Fac Foreign Languages Huaian 223000 Jiangsu Peoples R China

The scoring analysis method of English composition review lacks flexibility. To solve this problem, this paper proposes an analysis method based on the improved ant colony clustering algorithm, where cosine distance and Euclidean distance were combined to determine the conversion function. The empirical results show that compared with the previous standard ant colony clustering algorithm, the traditional k-means algorithm and IGKA algorithm, the improved ant colony clustering algorithm can realise the comprehensive evaluation of English composition review. It can be seen that the proposed method is reasonable and feasible, which can effectively conduct cluster analysis on English composition review, and has a higher accuracy rate of 89.33%. Therefore, in order to achieve the clustering analysis of English composition rating more precisely, the next step is to improve the ant colony clustering algorithm by repeated experiments on experimental data.

关键词： ant colony clustering algorithm cluster analysis English composition review score analysis

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Detection of grapevine leafroll disease based on 11-index imagery and ant colony clustering algorithm

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PRECISION AGRICULTURE 2016年第4期17卷 488-505页

作者： Hou, Jingwei Li, Longtang He, Jie Ningxia Univ Sch Resources & Environm Yinchuan 750021 Peoples R China Ningxia Univ Ningxia Key Lab Intelligent Sensing Desert Inform Yinchuan 750021 Peoples R China

Grapevine leafroll disease (GLD) is a virus disease that quickly propagates through vineyards under appropriate weather conditions and can reduce grape production worldwide by nearly 60 %. Therefore, the accurate diagnosis and reliable evaluation of GLD distribution, particularly at the early stage of GLD infection, is important to prevent the spread of this disease. This study applied the ant colony clustering algorithm (ACCA) to detect GLD spectral anomalies on 4 GLD-infected vineyards according to multi-spectral imagery for precision disease management. GLD was classified into three stages: GLD1, GLD2 and GLD3 according to its infection severity. An 11-index feature vector and its stacked image were generated to enhance the spectral differences and spectral discrimination between diseased and healthy grapevines. ACCA was then designed to solve the fuzziness of the multi-spectral image for GLD-infected grapevines and successfully identify GLD from healthy grapevines. Finally, a field survey with 49 samples and pixel purity index technology were applied to validate the effectiveness, efficiency and accuracy of ACCA. Field results indicated that an early stage of the GLD infection (GLD1) could be successfully discriminated from GLD2-, GLD3- and non-infected grapevines. The classification accuracies of non-, GLD1-, GLD2- and GLD3-infected grapevines were 94.4, 75, 84.6 and 83.3 %, respectively. Hence, the method based on an 11-index image and ACCA may significantly detect GLD at an early stage from healthy grapevines for precision disease management at the field level.

关键词： Grapevine leafroll disease Remote sensing Spectral reflectance Minimum noise fraction Pixel purity index ant colony clustering algorithm

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Textural defect detect using a revised ant colony clustering algorithm

Textural defect detect using a revised ant colony clustering...

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Conference on Pattern Recognition and Computer Vision

作者： Zou, Chao Xiao, Li Wang, Bingwen Huazhong Univ Sci & Technol Dept Control Sci & Engn Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469526

We propose a totally novel method based on a revised ant colony clustering algorithm (ACCA) to explore the topic of textural defect detection. In this algorithm, our efforts are mainly made on the definition of local irregularity measurement and the implementation of the revised ACCA. The local irregular measurement defined evaluates the local textural inconsistency of each pixel against their mini-environment. In our revised ACCA, the behaviors of each ant are divided into two steps: release pheromone and act. The quantity of pheromone released is proportional to the irregularity measurement;the actions of the ants to act next are chosen independently of each other in a stochastic way according to some evaluated heuristic knowledge. The independency of ants implies the inherent parallel computation architecture of this algorithm. We apply the proposed method in some typical textural images with defects. From the series of pheromone distribution map (PDM), it can be clearly seen that the pheromone distribution approaches the textual defects gradually. By some post-processing, the final distribution of pheromone can demonstrate the shape and area of the defects well.

关键词： ant colony clustering algorithm textural defect detection local irregularity measurement

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Quality evaluation of software engineering professional talent training under the background of new engineering

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INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED DEVELOPMENT 2025年第1期15卷 24-41页

作者： Liu, Lijuan Bai, Yong Zhang, Shaowei Neijiang Normal Univ Sch Artificial Intelligence Neijiang 641112 Peoples R China Anhui Wenda Univ Informat Engn Dept Comp Engn Hefei 231201 Peoples R China

In order to address the issues of low recall rate, long clustering time, and low accuracy in the quality assessment of traditional software engineering talent cultivation methods, a new quality evaluation method of software engineering professional talent training under the background of new engineering is proposed. The intrinsic dependency relationship among the evaluation indicators of software engineering talent cultivation quality is analysed in depth using factor analysis, and a talent cultivation quality assessment indicator system is constructed. Indicator data is collected. The ant colony clustering algorithm is used to cluster the collected data, and the processed data is inputted into the talent cultivation quality assessment model based on fuzzy comprehensive evaluation to obtain relevant assessment results. The experimental results showed that the recall rate of this method is between 95% and 99%, the average clustering time of indicators is 7.75 s, and the maximum accuracy rate is 97%.

关键词： new engineering software engineering professional talent training quality evaluation collect index data ant colony clustering algorithm fuzzy comprehensive evaluation

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clustering algorithm Based on the Combination of Genetic algorithm and ant colony algorithm

Clustering Algorithm Based on the Combination of Genetic Alg...

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2011 International Conference on Innovative Computing and Cloud Computing(ICCC 2011)

作者： Xianmin Wei Computer and Communication Engineering School of Weifang University

Genetic algorithm has the ability of making a global quick and good research, while ant colony clustering algorithm has the capacity of processing distributed parallel with good feedback. The combination of the above two algorithms can make full use of own advantages and characteristics, and make clustering analysis better. Some experiments have proved that the new combination algorithm has more obvious advantages in optimization capacity and performance than some other common clustering algorithms.

关键词： Genetic algorithm ant colony clustering algorithm clustering

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Credit Data Classification Based on ant colony algorithm and Random Forest 7

Credit Data Classification Based on Ant Colony Algorithm and...

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7th International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Feng, Ruiqi Han, Lu Chen, Muzi Cent Univ Finance & Econ Sch Management Sci & Engn Beijing 100081 Peoples R China

ISBN: (纸本)9798350385113;9798350385106

Classifying users is the most important issue to the commercial banks. Commercial banks usually classify customers according to their credit reports when making loans. In this study, we put our focus on classifying customers based on their credit reports from the People's Bank of China. Based on the credit data of the People's Bank of China (PBC), this study uses SVM, BP neural network, and random forest to classify users and compare their results. Since there is no target labels of users in the credit report of the People's Bank of China, we put forward the fuzzy clustering method for the initial label, and then use the random forest algorithm optimized by ant colony search to carry out intelligent recognition. The research results indicate that using ant colony clustering algorithm and random forest for classification is the most effective method with the PBC credit reports.

关键词： ant colony clustering algorithm Random Forest User Credit Classification

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Research on the Design of Performance Evaluation System for Physical Education Major Based on Multimedia Information Processing Technology

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INTERNATIONAL JOURNAL OF E-COLLABORATION 2024年第1期20卷 1页

作者： Wang, Yawen Jiaozuo Normal Coll Phys Educ Coll Jiaozuo Peoples R China

The Massive Open Online Course (MOOC) platform, despite its reach and potential, faces significant challenges related to learner diversity, including variations in student backgrounds, work experiences, and learning environments. By integrating the principles of ant colony clustering algorithms with MOOC platform dynamics, our study devises a strategy for grouping students based on shared characteristics, thereby fostering a tailored learning environment. A streamlined MOOC platform design framework is outlined, accompanied by a detailed blueprint for a peer-evaluation information system, aimed at accurately gauging students' academic progress within their disciplines. This platform enables students to effortlessly access and engage with courses aligned with their interests, while the reciprocal assessment mechanism heightens understanding of course materials and alleviates pressure on educators. Our research contributes a pragmatic solution to the challenge of student assignment clustering within MOOCs, enhancing both individual learning outcomes and the broader instructional quality.

关键词： Massive Open Online Courses ant colony clustering algorithm Student Grouping Performance Evaluation

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Customer Segmentation Based on ant clustering algorithm

Customer Segmentation Based on Ant Clustering Algorithm

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2010 Second International Conference on Computational Intelligence and Natural Computing (CINC 2010)

作者： Tian Yuanli1 College of Postgraduate Liaoning Technical University Liaoning Huludao, China Shao Liangshan2 College of Business Administration,Liaoning Technical University Liaoning Huludao, China

In this paper, we introduce the ant clustering algorithm which is based on ant clustering behavior, and apply the ant clustering algorithm to the client type of data mining. On this basis,we propose a customer clustering algorithm which is based on ant colony algorithm and data mining technology, using this method to achieve the classification of customers.

关键词： ant colony clustering algorithm Customer segmentation Data mining

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Predicting the Emergency Logistics Demand for Tropical Cyclone Based on Improved RBF Neural Network

Predicting the Emergency Logistics Demand for Tropical Cyclo...

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The Tenth International Conference on Information and Management Sciences

作者： Bin Yang,Jian Zhang,Lei Zhao Logistics Research Center,Shanghai Maritime University,Shanghai 200135,P.R.China

Considering the advantages and limitations of traditional RBF in prediction,an improved BRF based on ant colony clustering algorithm is introduced in this paper. This method is used to predict the demand of emergency *** of artificial neural network algorithm's advantages,such as strong learning ability and non-linear aproximation of various levels of accuracy,this method is widely adopted in prediction.A RBF network is developed in this paper by adding ant colony clustering algorithm with can search in the local *** the weights of the connections between hidden layer and output layer are desinged by linear approach base on the expected outputs of the hidden and output ***,the approach in predicting the demand of emergency logistics is established. And a case study has been made to show the validity of this approach,where this method was adopted in predicting the demand of emergency logistics in anti-tropical cyclone *** experimental studies were conducted by MATLAB.

关键词： Emergency Logistics Demand prediction ant colony clustering algorithm Radical Basis Function Neural Network Tropical Cyclone Disaster

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