2020 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第6页

Many-to-Many Path Planning for Emergency Material Transportation in Dynamic Environment 动态环境下应急物资运输的多对多路径规划

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308496

Xiang-Zhi Meng, Hang Zhou, Xiao-Bing Hu

{"title":"Many-to-Many Path Planning for Emergency Material Transportation in Dynamic Environment","authors":"Xiang-Zhi Meng, Hang Zhou, Xiao-Bing Hu","doi":"10.1109/SSCI47803.2020.9308496","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308496","url":null,"abstract":"The problem of emergency material transportation in dynamic environment requires to find optimal path between multiple emergency material storage nodes and distribution nodes in a changing routing environment, so as to guarantee the supply of materials within the shortest time. It corresponds to a many-to-many path planning problem in dynamic routing network. The existing static plan optimization and dynamic path optimization method are difficult to ensure the theoretical optimality of the solution in a dynamic disaster environment, and may lead to the failure of emergency material transportation. In this paper, a method of co-evolutionary path optimization is proposed and improved to resolve the many-to-many path planning problems. The ripple diffusion algorithm completes the search process in the form of a ripple diffusion relay race in a given routing environment. Furthermore, the coevolutionary path optimization method combines the ripple diffusion process with the routing environment change process. When different ripples compete with each other, the routing environment changes dynamically at the same time. Finally, the theoretical optimal solution is obtained in just a single off-line operation. The experimental results show that the coevolutionary path optimization method has advantages over the traditional method in success rate, solving time, optimality, and flexibility.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124738141","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

Data Donations for Mapping Risk in Google Search of Health Queries: A case study of unproven stem cell treatments in SEM 在谷歌健康查询中绘制风险的数据捐赠:扫描电镜中未经证实的干细胞治疗的案例研究

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308420

Martin Reber, Tobias D. Krafft, Roman Krafft, K. Zweig, Anna Couturier

{"title":"Data Donations for Mapping Risk in Google Search of Health Queries: A case study of unproven stem cell treatments in SEM","authors":"Martin Reber, Tobias D. Krafft, Roman Krafft, K. Zweig, Anna Couturier","doi":"10.1109/SSCI47803.2020.9308420","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308420","url":null,"abstract":"On October 1st, 2019 in response to critique from patient advocates and the medical community, Google explicitly prohibited promotion of unproven stem cell and gene therapy treatments on their platform in order to protect users from rising direct-to-consumer marketing of unproven medical interventions. This project aims to record the efficacy of that prohibition as it was enforced and track the impact of Google’s AI-based advertising modalities on end-user results. In particular, this study gives special consideration to the risk potential for vulnerable patient communities navigating health information through Google search. Utilising a crowd-sourced ‘Black Box’ audit with a browser plugin, we captured the continued presence of prohibited and problematic advertisements returned by stem cell-related queries in the months following Google’s ban. In the domain of Search Engine Marketing (SEM), emerging stem cell treatments are situated in a critical juncture between advertisers and potentially vulnerable users with Google Search as an unobserved mediator. Addressing the issues raised by this data collection is of utmost importance in the protection of patient populations online. This project aims to draw attention to the need for transparency and accountability of advertising intermediaries engaged in the targeted promotion of potentially problematic treatments to vulnerable audiences.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124743037","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: 1

A machine-learning framework for a novel 3-step approach for real-time taxi dispatching 一种新型出租车实时调度三步算法的机器学习框架

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308436

Sparsh Agrawal

{"title":"A machine-learning framework for a novel 3-step approach for real-time taxi dispatching","authors":"Sparsh Agrawal","doi":"10.1109/SSCI47803.2020.9308436","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308436","url":null,"abstract":"In the status quo, taxi dispatching is not fully optimized. Low taxi capacity utilization rates along with high passenger wait times suggest inefficiency with dispatching. Computationally, the taxi dispatch problem (TDP) faces key constraints: the problem is very dynamic with information about trips unknown beforehand and must be computed in real-time. These constraints force quick, simple, intuitive, but inefficient solutions like local greedy approaches to be applied. This research study presents a novel solution for TDP. Through TaxiNet, future taxi demand is predicted in four components: the number of passengers picked up, the number of passengers dropped off, the distribution of passengers picked up and the distribution of passengers dropped off for a 15-minute time-step. The predicted demand is inputted into a proposed Monte Carlo algorithm which can link the pickup demand with the drop-off demand to generate a series of predicted trips that will occur shortly. Not only do these predictions allow for a clearer idea of where passengers and taxis will be in the future, but it also extends the window of computation time provided to calculate and find optimal dispatching solutions. A proposed ACO algorithm inputs in the predicted passenger and taxi locations and outputs an optimal dispatching solution. Simulations were run to compare the performance of a taxi fleet operating under existing systems versus the developed algorithm. The results show that the algorithm increased fleet profitability and lowered passenger wait times.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129494374","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

A Novel Algorithm to Detect Brain Tumor using Staged-Type-II Fuzzy Classifier 一种基于ii期模糊分类器的脑肿瘤检测新算法

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308426

Ananya Das, S. Chatterjee

{"title":"A Novel Algorithm to Detect Brain Tumor using Staged-Type-II Fuzzy Classifier","authors":"Ananya Das, S. Chatterjee","doi":"10.1109/SSCI47803.2020.9308426","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308426","url":null,"abstract":"Automatic detection of brain tumor is a crucial step in the domain of medical image processing. Classification of the tumor is a vital part in brain tumor diagnosis to aid accurate treatment. However, manual detection with the help of human interpretation is time taking and also subject to inaccurate diagnosis. Based on these facts, an automated brain tumor classification algorithm is proposed in this work. The present work is divided into the following stages, viz. preprocessing, segmentation, feature extraction, feature selection, ranking of the selected features and finally classification of the segmented tumor. Gray-Level-Co-Occurrence Matrix (GLCM), Law’s Texture and Mass Effect features are extracted from the brain tumor and feature selection is carried out for each individual type followed by the ranking of the individual feature types. The final step comprises of the classification algorithm where a three stage classifier using Interval Type-II Fuzzy Logic System is designed in order to classify the segmented tumor into benign or malignant class. Finally, the work is validated with the help of BRATS 12 dataset and the superiority of the model is showcased in comparison with Type-I Fuzzy Inference System.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129875559","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

Autonomous decision making by the self-generated priority under multi-task 多任务下由自生成优先级进行自主决策

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/ssci47803.2020.9308486

Takuma Kambayashi, K. Kurashige

{"title":"Autonomous decision making by the self-generated priority under multi-task","authors":"Takuma Kambayashi, K. Kurashige","doi":"10.1109/ssci47803.2020.9308486","DOIUrl":"https://doi.org/10.1109/ssci47803.2020.9308486","url":null,"abstract":"In recent years, a robot is required to perform multitask autonomously in human living space. It needs to take actions according to situations. We proposed a method which does decision making on a robot with multi-task according to a situation by using an importance of each task. To respond to changes in importance of task, the robot learned each task independently by using reinforcement learning. An action is selected uniquely using action values and importance of each task in this system. The parameters are designed according to the value that represents the status of each task as an index for evaluating the importance. Therefore, it is necessary to design parameters suitable for the environment for each task. If the environment changes, the parameters must be designed accordingly. Therefore, in this research, we propose an autonomous decision-making method based on priority self-generation. The robot self-generates the priority of each task based on the experience gained by the robot, and realizes an action selection system with the priority matching the environment. We carried out an experiment which set three tasks to the robot applied proposal method. From experimental results, we confirmed the usefulness of proposed method.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129892906","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

Influences of Artificial Speciation on Morphological Robot Evolution 人工物种形成对形态机器人进化的影响

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308433

Matteo De Carlo, Daan Zeeuwe, E. Ferrante, G. Meynen, J. Ellers, A. Eiben

引用次数: 11

A Tailored NSGA-III for Multi-objective Flexible Job Shop Scheduling 多目标柔性作业车间调度的定制NSGA-III

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308373

Yali Wang, Bas van Stein, Thomas Bäck, M. Emmerich

{"title":"A Tailored NSGA-III for Multi-objective Flexible Job Shop Scheduling","authors":"Yali Wang, Bas van Stein, Thomas Bäck, M. Emmerich","doi":"10.1109/SSCI47803.2020.9308373","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308373","url":null,"abstract":"A customized multi-objective evolutionary algorithm (MOEA) is proposed for the flexible job shop scheduling problem (FJSP) with three objectives: makespan, total workload, critical workload. In general, the algorithm can be integrated with any standard MOEA. In this paper, it has been combined with NSGA-III to solve the state-of-the-art benchmark FJSPs, whereas an off-the-shelf implementation of NSGA-III is not capable of solving them. Most importantly, we use the various algorithm adaptations to enhance the performance of our algorithm. To be specific, it uses smart initialization approaches to enrich the first-generation population, and proposes new crossover operator to create a better diversity on the Pareto front approximation. The MIP-EGO configurator is adopted to automatically tune the mutation probabilities, which are important hyper-parameters of the algorithm. Furthermore, different local search strategies are employed to explore the neighborhood for better solutions. The experimental results from the combination of these techniques show the good performance as compared to classical evolutionary scheduling algorithms and it requires less computing budget. Even some previously unknown non-dominated solutions for the BRdata benchmark problems could be discovered.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127153889","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

Online System Identification for Nonlinear Uncertain Dynamical Systems Using Recursive Interval Type-2 TS Fuzzy C-means Clustering 基于递推区间2型TS模糊c均值聚类的非线性不确定动力系统在线辨识

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308202

Ayad Al-Mahturi, Fendy Santoso, M. Garratt, S. Anavatti

引用次数: 3

Scalable Partial-ACO Applied to Fleet Optimisation: Sampling and Multi-Colony Approaches 可扩展部分蚁群算法在舰队优化中的应用:采样和多群体方法

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308283

D. Chitty

引用次数: 0

Detecting Communities in Networks: a Decentralized Approach Based on Multiagent Reinforcement Learning 网络社区检测:一种基于多智能体强化学习的分散方法

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308197

E. C. Paim, A. Bazzan, Camelia Chira

引用次数: 5