Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence最新文献

Stability-Guided Multi-Guide Particle Swarm Optimization 稳定性导向的多导粒子群优化

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2022-04-09 DOI: 10.1145/3533050.3533059

Weka Steyn, A. Engelbrecht

引用次数: 0

Genetic Algorithm with Machine Learning to Estimate the Optimal Objective Function Values of Subproblems 基于机器学习的遗传算法估计子问题的最优目标函数值

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2022-04-09 DOI: 10.1145/3533050.3533051

H. Iima, Yohei Hazama

引用次数: 1

Resource Prediction of Virtual Network Function Based on Traffic Feature Extraction 基于流量特征提取的虚拟网络功能资源预测

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2022-04-09 DOI: 10.1145/3533050.3533068

Chang Su, Ya Tan, Xianzhong Xie, Yong Liu

{"title":"Resource Prediction of Virtual Network Function Based on Traffic Feature Extraction","authors":"Chang Su, Ya Tan, Xianzhong Xie, Yong Liu","doi":"10.1145/3533050.3533068","DOIUrl":"https://doi.org/10.1145/3533050.3533068","url":null,"abstract":"∗ With the continuous innovation of the Internet, the development of Cloud Computing technology and standard server promotes the development of Network Function Virtualization (NFV). Although NFV solves the shortcomings of traditional network function equip-ment such as high cost and difficult operation, it also brings certain challenges. Resource management in NFV is a complex problem because the resource requirements of Virtual Network Function (VNF) vary with the dynamic traffic, so it is necessary to under-stand the resource requirements of VNF. Due to the limited physical network resources, it is very important to find an effective resource prediction method. Based on Heterogeneous Information Network (HIN) and Multilayer Perceptron (MLP), we propose VNF-RPHIN, a method of the VNF resource requirement prediction based on traffic feature extraction. Firstly, we construct the HIN by the correlation between traffic features. Secondly, we use the HIN2Vec model to obtain the feature representation of each traffic feature. Finally, the attention mechanism is used to measure the importance of each feature, and different weights are assigned to each feature, and then they are input into the MLP model. The hidden relationship between traffic features is mined by HIN to predict the resource requirement of the VNF. The experimental results show that the proposed method has good performance and is superior to the traditional machine learning model and common deep learning model.","PeriodicalId":109214,"journal":{"name":"Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"346 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133584883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automation of Fabric Pattern Construction using Genetic Algorithms 基于遗传算法的织物图案构建自动化

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2022-04-09 DOI: 10.1145/3533050.3533055

Omema Ahmed, M. S. Abid, Aiman Junaid, S. S. Raza

引用次数: 0

A Hybrid Multi-Objective Teaching Learning-Based Optimization Using Reference Points and R2 Indicator 基于参考点和R2指标的混合型多目标教学优化

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2022-04-09 DOI: 10.1145/3533050.3533053

Farajollah Tahernezhad-Javazm, Debbie Rankin, Damien Coyle

{"title":"A Hybrid Multi-Objective Teaching Learning-Based Optimization Using Reference Points and R2 Indicator","authors":"Farajollah Tahernezhad-Javazm, Debbie Rankin, Damien Coyle","doi":"10.1145/3533050.3533053","DOIUrl":"https://doi.org/10.1145/3533050.3533053","url":null,"abstract":"Hybrid multi-objective evolutionary algorithms have recently become a hot topic in the domain of metaheuristics. Introducing new algorithms that inherit other algorithms’ operators and structures can improve the performance of the algorithm. Here, we proposed a hybrid multi-objective algorithm based on the operators of the genetic algorithm (GA) and teaching learning-based optimization (TLBO) and the structures of reference point-based (from NSGA-III) and R2 indicators methods. The new algorithm (R2-HMTLBO) improves diversity and convergence by using NSGA-III and R2-based TLBO, respectively. Also, to enhance the algorithm performance, an elite archive is proposed. The proposed multi-objective algorithm is evaluated on 19 benchmark test problems and compared to four state-of-the-art algorithms. IGD metric is applied for comparison, and the results reveal that the proposed R2-HMTLBO outperforms MOEA/D, MOMBI-II, and MOEA/IGD-NS significantly in 16/19 tests, 14/19 tests and 13/19 tests, respectively. Furthermore, R2-HMTLBO obtained considerably better results compared to all other algorithms in 4 test problems, although it does not outperform NSGA-III on a number of tests.","PeriodicalId":109214,"journal":{"name":"Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121793650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SemanTV: A Content-Based Video Retrieval Framework SemanTV:基于内容的视频检索框架

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2022-04-09 DOI: 10.1145/3533050.3533067

Juan Miguel A. Mendoza, China Marie G. Lao, Antolin J. Alipio, Dan Michael A. Cortez, Anne Camille M. Maupay, Charito M. Molina, C. Centeno, Jonathan C. Morano

{"title":"SemanTV: A Content-Based Video Retrieval Framework","authors":"Juan Miguel A. Mendoza, China Marie G. Lao, Antolin J. Alipio, Dan Michael A. Cortez, Anne Camille M. Maupay, Charito M. Molina, C. Centeno, Jonathan C. Morano","doi":"10.1145/3533050.3533067","DOIUrl":"https://doi.org/10.1145/3533050.3533067","url":null,"abstract":"With the increased adaption of CCTV for surveillance, challenges in terms of retrieval have recently gained attention. Most Surveillance Video Systems can only retrieve footage based on its metadata, (date, time, camera location, etc.) which limits the diversity of meaningful footage intended to be retrieved by the user. To solve this, a content-based video retrieval framework was proposed to retrieve relevant videos based on their content and match it to the user's query. This framework composes of two (2) methods: A method for Video Content Extraction that utilizes Google's Video Intelligence API for Optical Character Recognition and Label Detection, and a method for Video Retrieval. Various setups for the Video Retrieval method are explored; this includes the usage of SBERT and Okapi BM25. Each setup was tested against various text queries with equivalent test video results based on the MSVD dataset. To measure each setup's performance in terms of relevance, Recall and Precision at K and Median and Mean Rank were used. It was concluded that the framework composed of the Video Intelligence API along with SBERT alone performed better than the other proposed setup for returning videos relevant to the user's text query more accurately than the other setups of the method.","PeriodicalId":109214,"journal":{"name":"Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122449638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

N-Gram-Based Machine Learning Approach for Bot or Human Detection from Text Messages 基于n - gram的机器人或人类文本信息检测的机器学习方法

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2022-04-09 DOI: 10.1145/3533050.3533063

Durga Prasad Kavadi, Chandra Sekhar Sanaboina, Rizwan Patan, A. Gandomi

{"title":"N-Gram-Based Machine Learning Approach for Bot or Human Detection from Text Messages","authors":"Durga Prasad Kavadi, Chandra Sekhar Sanaboina, Rizwan Patan, A. Gandomi","doi":"10.1145/3533050.3533063","DOIUrl":"https://doi.org/10.1145/3533050.3533063","url":null,"abstract":"Social bots are computer programs created for automating general human activities like the generation of messages. The rise of bots in social network platforms has led to malicious activities such as content pollution like spammers or malware dissemination of misinformation. Most of the researchers focused on detecting bot accounts in social media platforms to avoid the damages done to the opinions of users. In this work, n-gram based approach is proposed for a bot or human detection. The content-based features of character n-grams and word n-grams are used. The character and word n-grams are successfully proved in various authorship analysis tasks to improve accuracy. A huge number of n-grams is identified after applying different pre-processing techniques. The high dimensionality of features is reduced by using a feature selection technique of the Relevant Discrimination Criterion. The text is represented as vectors by using a reduced set of features. Different term weight measures are used in the experiment to compute the weight of n-grams features in the document vector representation. Two classification algorithms, Support Vector Machine, and Random Forest are used to train the model using document vectors. The proposed approach was applied to the dataset provided in PAN 2019 competition bot detection task. The Random Forest classifier obtained the best accuracy of 0.9456 for bot/human detection.","PeriodicalId":109214,"journal":{"name":"Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116691848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing the Quality of Car Racing Controllers in a Virtual Setting under Changed Conditions 变化条件下虚拟环境中赛车控制器的质量评估

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2022-04-09 DOI: 10.1145/3533050.3533062

Sebastian Minder, Marc Funken, Rolf Dornberger, T. Hanne

引用次数: 1

Set-based Particle Swarm Optimization for Data Clustering 基于集合的粒子群数据聚类算法

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2022-04-09 DOI: 10.1145/3533050.3533057

Lienke Brown, A. Engelbrecht

引用次数: 2

Structured Pruning with Automatic Pruning Rate Derivation for Image Processing Neural Networks 图像处理神经网络的结构化剪枝与自动剪枝率推导

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2022-04-09 DOI: 10.1145/3533050.3533066

Yasufumi Sakai, Akinori Iwakawa, T. Tabaru

引用次数: 0