2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)最新文献_第3页

Using AHP to Choose the Best Logistics Distribution Model 运用层次分析法选择最佳物流配送模式

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2018-11-01 DOI: 10.1109/TAAI.2018.00045

M. Lai, Wu-Der Tsay, Zhen Wang

引用次数: 1

Examination of Indicators for Estimating Players' Strength by Using Computer Go 用计算机围棋评估棋手实力指标的检验

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2018-11-01 DOI: 10.1109/TAAI.2018.00030

Yuuto Kosaka, Takeshi Ito

引用次数: 1

Machine Learning Based Path Prediction System - Adapting One Model for All Intersections 基于机器学习的路径预测系统-适用于所有路口的一个模型

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2018-11-01 DOI: 10.1109/TAAI.2018.00023

Kai-Qi Huang, Min-Te Sun

引用次数: 3

On the Effect of Data Imbalance for Multi-Label Pedestrian Attribute Recognition 数据不平衡对多标签行人属性识别的影响

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2018-11-01 DOI: 10.1109/TAAI.2018.00025

T. Wang, Kai-Chen Shu, Chia-Hao Chang, Yi-Fu Chen

引用次数: 2

Using Machine Learning Algorithms in Medication for Cardiac Arrest Early Warning System Construction and Forecasting 机器学习算法在心脏骤停用药预警系统构建与预测中的应用

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2018-11-01 DOI: 10.1109/TAAI.2018.00010

Hsiao-ko Chang, Cheng-Tse Wu, Ji-Han Liu, J. Jang

{"title":"Using Machine Learning Algorithms in Medication for Cardiac Arrest Early Warning System Construction and Forecasting","authors":"Hsiao-ko Chang, Cheng-Tse Wu, Ji-Han Liu, J. Jang","doi":"10.1109/TAAI.2018.00010","DOIUrl":"https://doi.org/10.1109/TAAI.2018.00010","url":null,"abstract":"Target-In this paper, we focus on using medicine for patients who have cardiac arrest then must have to do Cardiopulmonary Resuscitation (CPR). We want to know the medicine influence in predicting state of an illness deterioration. Therefore, we proposes a Medication for Cardiac Arrest Early Warning System (MCAEWS). It's not only assist physicians to early diagnose of an illness and immediately warning, but also increase sensitivity, decrease false positive rate and mortality rate. The most important role is greatly improve medical quality. Methods-In this study, the data is from the emergency department of National Taiwan University Hospital (NTUH). It is from January 2014 to December 2015. The patients who stayed in the emergency detention area for more than six hours during this two years. The patients were included in the retrospective cohort study. To comparative measures for the machine learning models, we used such as the Area Under the Receiver Operating Characteristic Curve (AUROC) and the Area under the Precision-Recall Curve (AUPRC). Results-The data were analyzed for CPR and non-CPR groups respectively. Furthermore, we evaluated sensitivity and specificity. The Random Forest Algorithm (AUC: 0.98; AUP: 0.23) compare with others such as Logistic Regression Algorithm (AUC: 0.94; AUP: 0.13), Decision Tree (AUC: 0.97; AUP: 0.05), and Extreme Random Tree (AUC: 0.91; AUP: 0.08), it was significantly high performance. Conclusion-Increasing the drug factors in vital signs, that it effectively improved the accuracy of predicting cardiac arrest. The results of this study, it's help for emergency clinical Physicians and hospital quality management will validly solve clinical medical resource allocation issues and improve medical quality through decision support systems.","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115323145","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: 3

A Fast PM2.5 Forecast Approach Based on Time-Series Data Analysis, Regression and Regularization 基于时间序列数据分析、回归和正则化的PM2.5快速预报方法

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2018-11-01 DOI: 10.1109/TAAI.2018.00026

Cyuan-Heng Luo, Hsuan Yang, Li-Pang Huang, Sachit Mahajan, Ling-Jyh Chen

引用次数: 14

Alternative Multitask Training for Evaluation Functions in Game of Go 围棋评估函数的可选多任务训练

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2018-11-01 DOI: 10.1109/TAAI.2018.00037

Yusaku Mandai, Tomoyuki Kaneko

{"title":"Alternative Multitask Training for Evaluation Functions in Game of Go","authors":"Yusaku Mandai, Tomoyuki Kaneko","doi":"10.1109/TAAI.2018.00037","DOIUrl":"https://doi.org/10.1109/TAAI.2018.00037","url":null,"abstract":"For the game of Go, Chess, and Shogi (Japanese Chess), deep neural networks (DNNs) have contributed to building accurate evaluation functions, and many studies have attempted to create the so-called value network, which predicts the reward of a given state. A recent study of the value network for the game of Go has shown that a two-headed neural network with two different objectives can be trained effectively and performs better than a single-headed network. One of the two heads is called a value head and the other head, the policy head, predicts the next move at a given state. This multitask training makes the network more robust and improves the generalization performance. In this paper, we show that a simple discriminator network is an alternative target of multitask learning. Compared to the existing deep neural network, our proposed network can be designed more easily because of its simple output. Our experimental results showed that our discriminative target also makes the learning stable and the evaluation function trained by our method is comparable to the training of existing studies in terms of predicting the next move and playing strength.","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122689271","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

AlphaZero for a Non-Deterministic Game 用于非确定性游戏的AlphaZero

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2018-11-01 DOI: 10.1109/TAAI.2018.00034

Chu-Hsuan Hsueh, I-Chen Wu, Jr-Chang Chen, T. Hsu

引用次数: 4

Privacy-Preserving SRS Data Anonymization by Incorporating Missing Values 结合缺失值的SRS数据匿名化保护隐私

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2018-11-01 DOI: 10.1109/TAAI.2018.00032

Wen-Yang Lin, Kuang-Yung Hsu, Zih-Xun Shen

引用次数: 3

Ant Colony Optimization with Negative Feedback for Solving Constraint Satisfaction Problems 求解约束满足问题的负反馈蚁群算法

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2018-11-01 DOI: 10.1109/TAAI.2018.00041

Takuya Masukane, Kazunori Mizuno, Hirotoshi Shinohara

{"title":"Ant Colony Optimization with Negative Feedback for Solving Constraint Satisfaction Problems","authors":"Takuya Masukane, Kazunori Mizuno, Hirotoshi Shinohara","doi":"10.1109/TAAI.2018.00041","DOIUrl":"https://doi.org/10.1109/TAAI.2018.00041","url":null,"abstract":"As meta-heuristics to solve large-scale constraint satisfaction problems (CSPs), ant colony optimization (ACO) has recently been drawing attentions. In most of algorithms based on ACO, candidate assignments are constructed by taking account of data called pheromone graph. The pheromone graph is updated getting positive feedbacks from candidate assignments with the least number of constraint violations. However, it might be easy to get stuck in locally optimal solutions considering only a single perspective. In this paper, we propose a method that adopting new pheromone graph in addition to the original pheromone graph. This new pheromone graph is updated getting negative feedback from candidate assignments with the greatest number of constraint violations. This new pheromone graph, called a negative pheromone graph, is updated getting negative feedback from candidate assignments with the largest number of constraint violations. Also, the standard pheromone graph is updated by considering negative pheromones as well. By using pheromones updated from two perspectives, more effective search can be conducted. Moreover, in this paper, we conducted experiments on graph coloring problems. Graph coloring problem is one of CSPs. We demnonstrated that our model, which is applied to the cunning ant system, can be effective than other ACO-based methods for large-scale and hard graph coloring problems whose instance appears in the phase transition region.","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114176045","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