Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence最新文献

Optimized Computational Diabetes Prediction with Feature Selection Algorithms 基于特征选择算法的优化计算糖尿病预测

Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2023-04-23 DOI: 10.1145/3596947.3596948

Xi Li, Michele Curiger, Rolf Dornberger, T. Hanne

引用次数: 1

Cuckoo Search Algorithm with Lévy Flights for Surface Reconstruction from Point Clouds with Applications to Reverse Engineering 基于lsamvy飞行的点云表面重构杜鹃搜索算法及其在逆向工程中的应用

Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2023-04-23 DOI: 10.1145/3596947.3596970

A. Gálvez, Iztok Fister, S. Deb, Iztok Fister Jr., Andrés Iglesias

{"title":"Cuckoo Search Algorithm with Lévy Flights for Surface Reconstruction from Point Clouds with Applications to Reverse Engineering","authors":"A. Gálvez, Iztok Fister, S. Deb, Iztok Fister Jr., Andrés Iglesias","doi":"10.1145/3596947.3596970","DOIUrl":"https://doi.org/10.1145/3596947.3596970","url":null,"abstract":"Surface reconstruction is a classical task in industrial engineering and manufacturing, particularly in reverse engineering, where the goal is to obtain a digital model from a physical object. For that purpose, the real object is typically scanned and the resulting point cloud is then fitted through mathematical surfaces via numerical optimization. The choice of the approximating functions is crucial for the accuracy of the process. Unfortunately, real-world objects often require complex nonlinear approximating functions, which are not well suited for standard numerical optimization methods. In this paper, we overcome this limitation by using a cuckoo search algorithm with Lévy flights, a swarm intelligence technique envisioned for global optimization. The method is applied to three illustrative examples of point clouds fitted by using a combination of exponential, polynomial and logarithmic functions. The experimental results show that the method performs well in recovering the shape of the point clouds accurately. We conclude that the method is promising towards its application to manufactured workpieces in real industrial settings.","PeriodicalId":183071,"journal":{"name":"Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114369444","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

Predicting Open Parking Space using Deep Learning and Support Vector Regression 基于深度学习和支持向量回归的开放式停车位预测

Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2023-04-23 DOI: 10.1145/3596947.3596961

Lee - Wei Jun, M. D. Esfahani, Tseu - Kwan Lee

{"title":"Predicting Open Parking Space using Deep Learning and Support Vector Regression","authors":"Lee - Wei Jun, M. D. Esfahani, Tseu - Kwan Lee","doi":"10.1145/3596947.3596961","DOIUrl":"https://doi.org/10.1145/3596947.3596961","url":null,"abstract":"Vehicle parking issues have been one of the biggest problems faced in urban areas, as the supply and demand for vehicles and parking spaces are getting unbalanced year by year. The traditional approach of adding more parking spaces is no longer an effective solution. A practical and intelligent solution is to predict open parking spots using machine learning (ML), which would increase the utilization of available parking spaces and alleviate traffic congestion and decrease emissions from idling vehicles. This study aims to propose a parking prediction model using support vector regression (SVR) to predict available parking spaces. The data used in training the ML model is collected using a custom object detector, which is developed using the YOLOv4 (You Only Look Once) algorithm. The result shows that the custom YOLOv4 model is able to detect and identify empty and occupied parking spaces, and the SVR prediction model can predict the number of empty parking spaces. Two additional ML algorithms, which are linear regression (LR) and decision tree regressor were applied in this project to compare the performance of the SVR prediction model.","PeriodicalId":183071,"journal":{"name":"Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131659021","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

A Meta-heuristic Approach for Strategic Fair Division Problems 策略公平分配问题的元启发式方法

Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2023-04-23 DOI: 10.1145/3596947.3596969

Koosha Samieefar

{"title":"A Meta-heuristic Approach for Strategic Fair Division Problems","authors":"Koosha Samieefar","doi":"10.1145/3596947.3596969","DOIUrl":"https://doi.org/10.1145/3596947.3596969","url":null,"abstract":"Fair division of resources emerges in a variety of different contexts in real-world problems, some of which can be seen through the lens of game theory. Many equilibrium notions for simple fair division problems with indivisible items have been considered, and many of these notions are hard to compute. Strategic fair division is a branch of fair division in which participants may act uncooperatively to maximize their utility. In the presence of participants who have strategic behavior, it is essential to have a suitable algorithm in place to allocate resources in a fair and equitable manner. We propose a new approach to solve strategic fair division problems where fairness is attained by finding a constrained Nash equilibrium in a specific game. We show that computational complexity barriers also hold. More broadly, the theoretical results of this paper could potentially be applied to related general game theory problems and complex fair division problems. Finally, we propose an algorithm for finding a constrained Nash equilibrium in the game that we introduce. Our focus will be on one particular meta-heuristic – the bus transportation algorithm – as an approach to improve the running time of the search.","PeriodicalId":183071,"journal":{"name":"Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114462990","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

Feature Selection using Gravitational Search Algorithm in Customer Churn Prediction 重力搜索算法在客户流失预测中的特征选择

Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2023-04-23 DOI: 10.1145/3596947.3596957

H. Hendro, A. M. Shiddiqi

{"title":"Feature Selection using Gravitational Search Algorithm in Customer Churn Prediction","authors":"H. Hendro, A. M. Shiddiqi","doi":"10.1145/3596947.3596957","DOIUrl":"https://doi.org/10.1145/3596947.3596957","url":null,"abstract":"Customer churn prediction is an essential strategy for companies, especially in telecommunications. Such industries face the challenge that customers frequently switch operators. Due to the higher cost of acquiring new customers compared to retaining existing ones, companies put considerable effort into keeping their current customers. Improving service quality and identifying the point at which customers are likely to terminate their engagement with the company are crucial in retaining customers. Customer Churn Prediction aims to predict potential customer churn by building an effective predictive model. However, the model’s performance is sensitive to unnecessary and irrelevant features. Feature selection is used to eliminate irrelevant features while emphasizing significant ones. This study suggests utilizing a feature selection method to identify significant features and enhance the accuracy of the customer churn prediction model. We propose employing a recently developed evolutionary computation method known as the gravitational search algorithm (GSA) for the feature selection approaches. We elaborate on GSA and the SVM as the classifier to find the optimum features and to improve the prediction accuracy. Our method produced higher precision and AUC scores than the baseline model (without feature selection).","PeriodicalId":183071,"journal":{"name":"Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121581053","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

Concept and Development of a Multi-Agent Digital Twin of Plant Focused on Broccoli 以西兰花为中心的植物多智能体数字孪生的概念与发展

Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2023-04-23 DOI: 10.1145/3596947.3596952

P. Skobelev, E. Simonova, A. Tabachinskiy, E. Kudryakov, A. Strizhakov, O. Goryanin, V. Ermakov, Yung-Kuan Chan, Tzong-Ru Lee, Yu Sung

{"title":"Concept and Development of a Multi-Agent Digital Twin of Plant Focused on Broccoli","authors":"P. Skobelev, E. Simonova, A. Tabachinskiy, E. Kudryakov, A. Strizhakov, O. Goryanin, V. Ermakov, Yung-Kuan Chan, Tzong-Ru Lee, Yu Sung","doi":"10.1145/3596947.3596952","DOIUrl":"https://doi.org/10.1145/3596947.3596952","url":null,"abstract":"The paper discusses the principles of developing a multi-agent digital twin of plants using broccoli as an example of plants. The developed model of the digital twin of plants must meet the following requirements: real-time environmental data acquisition, user feedback collection, continuous adaptation of the plant development plan for each event, individual instance for field or field part. The digital twin of plant is designed as an intelligent cyber-physical system that has a user-defined knowledge bas and a multi-agent system for planning and modeling of plant growth and development, as well as for forecasting crop parameters. For this purpose, a new method for estimate stage duration and yield is proposed, which defines a \"tube\" – a corridor to each of the factors corresponding plant development. The key factors have been determined during consultations with practicing agronomists but can be adjusted by users experience. This concept was originally introduced for wheat digital twin, but now is scaled and modified to simulate broccoli growth process.","PeriodicalId":183071,"journal":{"name":"Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122741785","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

EmbAu: A Novel Technique to Embed Audio Data using Shuffled Frog Leaping Algorithm EmbAu:一种利用青蛙跳跃算法嵌入音频数据的新技术

Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2023-04-23 DOI: 10.1145/3596947.3596967

Sahil Nokhwal, Saurabh Pahune, Ankit Chaudhary

{"title":"EmbAu: A Novel Technique to Embed Audio Data using Shuffled Frog Leaping Algorithm","authors":"Sahil Nokhwal, Saurabh Pahune, Ankit Chaudhary","doi":"10.1145/3596947.3596967","DOIUrl":"https://doi.org/10.1145/3596947.3596967","url":null,"abstract":"The aim of steganographic algorithms is to identify the appropriate pixel positions in the host or cover image, where bits of sensitive information can be concealed for data encryption. Work is being done to improve the capacity to integrate sensitive information and to maintain the visual appearance of the steganographic image. Consequently, steganography is a challenging research area. In our currently proposed image steganographic technique, we used the Shuffled Frog Leaping Algorithm (SFLA) to determine the order of pixels by which sensitive information can be placed in the cover image. To achieve greater embedding capacity, pixels from the spatial domain of the cover image are carefully chosen and used for placing the sensitive data. Bolstered via image steganography, the final image after embedding is resistant to steganalytic attacks. The SFLA algorithm serves in the optimal pixels selection of any colored (RGB) cover image for secret bit embedding. Using the fitness function, the SFLA benefits by reaching a minimum cost value in an acceptable amount of time. The pixels for embedding are meticulously chosen to minimize the host image’s distortion upon embedding. Moreover, an effort has been taken to make the detection of embedded data in the steganographic image a formidable challenge. Due to the enormous need for audio data encryption in the current world, we feel that our suggested method has significant potential in real-world applications. In this paper, we propose and compare our strategy to existing steganographic methods.","PeriodicalId":183071,"journal":{"name":"Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131618813","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

Three-dimensional Super-resolution of X-ray CT Data of Rock Samples by Sparse Representation Learning 基于稀疏表示学习的岩石样品x射线CT三维超分辨率研究

Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2023-04-23 DOI: 10.1145/3596947.3596958

Shoi Suzuki, A. Okamoto, K. Michibayashi, T. Omori

引用次数: 0

Improved Solution Search Performance of Constrained MOEA/D Hybridizing Directional Mating and Local Mating 改进约束MOEA/D杂交定向匹配和局部匹配的解搜索性能

Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2023-04-23 DOI: 10.1145/3596947.3596955

Masahiro Kanazaki, Takeharu Toyoda

{"title":"Improved Solution Search Performance of Constrained MOEA/D Hybridizing Directional Mating and Local Mating","authors":"Masahiro Kanazaki, Takeharu Toyoda","doi":"10.1145/3596947.3596955","DOIUrl":"https://doi.org/10.1145/3596947.3596955","url":null,"abstract":"In this study, we propose an improvement to the direct mating method, a constraint handling approach for multi-objective evolutionary algorithms, by hybridizing it with local mating. Local mating selects another parent from the feasible solution space around the initially selected parent. The direct mating method selects the other parent along the optimal direction in the objective space after the first parent is selected, even if it is infeasible. It shows better exploration performance for constraint optimization problems with coupling NSGA-II, but requires several individuals along the optimal direction. Due to the lack of better solutions dominated by the optimal direction from the first parent, direct mating becomes difficult as the generation proceeds. To address this issue, we propose a hybrid method that uses local mating to select another parent from the neighborhood of the first selected parent, maintaining diversity around good solutions and helping the direct mating process. We evaluate the proposed method on three mathematical problems with unique Pareto fronts and two real-world applications. We use the generation histories of the averages and standard deviations of the hypervolumes as the performance evaluation criteria. Our investigation results show that the proposed method can solve constraint multi-objective problems better than existing methods while maintaining high diversity.","PeriodicalId":183071,"journal":{"name":"Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124073091","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

Analyzing the Computing Time to Solve Single Row Facility Layout Problems by Simulated Annealing in a Python Framework 在Python框架下模拟退火求解单行设施布局问题的计算时间分析

Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2023-04-23 DOI: 10.1145/3596947.3596953

Alexandre Miccoli, T. Hanne, Rolf Dornberger

引用次数: 0