Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence最新文献

Analysis of Microscopic Behavior in Ant Traffic to Understand Jam-free Transportation 蚂蚁交通微观行为分析以理解无拥堵交通

Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2020-03-21 DOI: 10.1145/3396474.3396491

P. Kasture, H. Nishimura

引用次数: 1

Healthcare Center IoT Edge Gateway Based on Containerized Microservices 基于容器化微服务的医疗中心物联网边缘网关

Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2020-03-21 DOI: 10.1145/3396474.3396495

Wiroon Sriborrirux, Peeradach Laortum

{"title":"Healthcare Center IoT Edge Gateway Based on Containerized Microservices","authors":"Wiroon Sriborrirux, Peeradach Laortum","doi":"10.1145/3396474.3396495","DOIUrl":"https://doi.org/10.1145/3396474.3396495","url":null,"abstract":"The growth of ubiquitous healthcare systems, particularly for general and residential healthcare, is increasing dramatically. One of the most significant components of such systems is the gateway, which acts as a middleware between Internet of Things (IoT) devices and cloud application services. Here, we propose an IoT edge gateway framework based on docker container technology as the legacy virtualization technology to empower microservice architectures for aiding multiple-device real-time monitoring in locations such as nursing homes and residential care centers. The framework is used to identify IoT devices and the gateway itself in home networks for restricting access only to authorized users and non-manipulated devices. We propose the use of state-of-the-art hardware-based security supporting the mutual authentication process with the Elliptic Curve Digital Signature Algorithm as well as integrity protection to validate the device, gateway, and cloud platform integrity to identify manipulation and detect unauthorized changes by signing these data. This approach can prevent man-in-the-middle attacks. As a result, we can implement each service located in this proposed IoT edge gateway framework to enhance capabilities in edge analytics by adding hardened security with the average latency time at 2.373 ms.","PeriodicalId":408084,"journal":{"name":"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121432482","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: 4

Empirical Analysis of A Partial Dominance Approach to Many-Objective Optimisation 多目标优化的部分优势法实证分析

Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2020-03-21 DOI: 10.1145/3396474.3396483

A. Engelbrecht, Mardé Helbig

引用次数: 3

Identification of Major Depressive Disorder: Using Significant Features of EEG Signals Obtained by Random Forest and Ant Colony Optimization Methods 利用随机森林和蚁群优化方法获得的脑电信号显著特征识别重度抑郁症

Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2020-03-21 DOI: 10.1145/3396474.3396480

Saikat Bandopadhyay, Srijan Nag, Sujay Saha, A. Ghosh

{"title":"Identification of Major Depressive Disorder: Using Significant Features of EEG Signals Obtained by Random Forest and Ant Colony Optimization Methods","authors":"Saikat Bandopadhyay, Srijan Nag, Sujay Saha, A. Ghosh","doi":"10.1145/3396474.3396480","DOIUrl":"https://doi.org/10.1145/3396474.3396480","url":null,"abstract":"Electroencephalogram (EEG) is an electrophysiological monitoring method to record the electrical activity of the brain. EEG is most often used to diagnose epilepsy, which causes abnormalities in EEG readings. It is also used to diagnose sleep disorders, depth of anesthesia, coma, encephalopathy, brain death, and depression. Being one of the prevalent psychiatric disorders, depressive episodes of major depressive disorder (MDD) is often misdiagnosed or overlooked. Therefore, identifying MDD at earlier stages of treatment could help to facilitate efficient and specific treatment. In this article, Random Forest (RF) and Ant Colony Optimization (ACO) algorithm are used to reduce the number of features by removing irrelevant and redundant features. The selected features are then fed into k-nearest neighbors (KNN) and SVM classifiers, a mathematical tool for data classification, regression, function estimation, and modeling processes, in order to classify MDD and non-MDD subjects. The proposed method used Wavelet Transformation (WT) to decompose the EEG data into corresponding frequency bands, like delta, theta, alpha, beta and gamma. A total of 119 participants were recruited by the University of Arizona from introductory psychology classes based on survey scores of the Beck Depression Inventory (BDI). The performance of KNN and SVM classifiers is measured first with all the features and then with selected significant features given by RF and ACO. It is possible to discriminate 44 MDD and 75 non-MDD subjects efficiently using 15 of 65 channels and 3 of 5 frequency bands to improve the performance, where the significant features are obtained by the RF method. It is found that the classification accuracy has been improved from70.21% and76.67% using all the features to the corresponding 91.67% and 83.33% with only significant features using KNN and Support Vector Machine (SVM) respectively.","PeriodicalId":408084,"journal":{"name":"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125227645","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

An Enhanced Grey Wolf Algorithm Based on Equalization Mechanism 一种基于均衡机制的增强灰狼算法

Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2020-03-21 DOI: 10.1145/3396474.3396494

Yun-tao Zhao, Wei Mei, Weigang Li

引用次数: 0

Why Deep Learning Is More Efficient than Support Vector Machines, and How it is Related to Sparsity Techniques in Signal Processing 为什么深度学习比支持向量机更有效，以及它与信号处理中的稀疏性技术有何关系

Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2020-03-21 DOI: 10.1145/3396474.3396478

Laxman Bokati, O. Kosheleva, V. Kreinovich, Uram Anibal Sosa Aguirre

引用次数: 1

Reducing Network Polarization by Edge Additions 通过边缘添加减少网络极化

Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2020-03-21 DOI: 10.1145/3396474.3396486

Ruben Interian, Jorge Moreno, C. Ribeiro

引用次数: 4

Deep Learning (Partly) Demystified 深度学习(部分)揭开神秘面纱

Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2020-03-21 DOI: 10.1145/3396474.3396481

V. Kreinovich, O. Kosheleva

引用次数: 9

A Hybrid Genetic Simulated Annealing Algorithm in the Retardance Optimization of Citrate Coated Ferrofluid 柠檬酸盐包覆铁磁流体缓速优化中的混合遗传模拟退火算法

Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2020-03-21 DOI: 10.1145/3396474.3396477

Jing-Fung Lin

{"title":"A Hybrid Genetic Simulated Annealing Algorithm in the Retardance Optimization of Citrate Coated Ferrofluid","authors":"Jing-Fung Lin","doi":"10.1145/3396474.3396477","DOIUrl":"https://doi.org/10.1145/3396474.3396477","url":null,"abstract":"In this paper, a hybrid of genetic algorithm (GA) and simulated annealing (SA) algorithm (HGSA) is developed to optimize the retardance in citrate (citric acid, CA) coated ferrofluids (FFs). The HGSA not only can overcome the deficiency of GA but also increase the possibility of finding the global solution by using SA. It enhances the ability of local searching by using SA. Initially, two factors that affect the performance of SA as initial temperature and cooling rate are decided. The maximum retardance is found as 42.4058° with a parametric combination of [5.5, 0.12, 40, 90], corresponding to pH of suspension, molar ratio of CA to Fe3O4, CA volume, and coating temperature. Moreover, when executing the HGSA algorithm, two parametric combinations of [5.499, 0.12, 39.369, 90] and [5.496, 0.106, 39.832, 89.976] associated with maximum and minimum retardance obtained by GA are adopted as the start points in the simulation of SA algorithm. Hence, a better solution of 42.4313° with [5.5, 0.12, 38.733, 90] is sought successfully. The hybrid of GA and SA can improve the solving efficiency.","PeriodicalId":408084,"journal":{"name":"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128706561","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: 2

Scheduling Tardiness Constrained Flow Shop with Simultaneously Loaded Stations Using Genetic Algorithm 基于遗传算法的多工位并行调度

Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2020-03-21 DOI: 10.1145/3396474.3396475

D. Davendra, F. Hermann, M. Bialic-Davendra

引用次数: 1