2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI)最新文献_第3页

Integrating data science and R programming at an early stage 在早期阶段整合数据科学和R编程

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-01 DOI: 10.1109/ISCMI.2017.8279587

Soma Datta, Veneela Nagabandi

{"title":"Integrating data science and R programming at an early stage","authors":"Soma Datta, Veneela Nagabandi","doi":"10.1109/ISCMI.2017.8279587","DOIUrl":"https://doi.org/10.1109/ISCMI.2017.8279587","url":null,"abstract":"The use of data has become an integral part of everyday life. Hence, introducing data science with games would generate an interest and prepare students for the rapidly changing world of technology. This study is especially intended to teach data science (DS) and R programming using games for cognitive learning. Tangible learning with real-life examples were used to teach Data Science. A correlation between the typical toys that are played are tied to concepts of DS. The three variables of social cognitive learning are integrated in this study. The data collected from the workshops are different from other studies because of broadly two reasons. First, the games used to teach data science were measured with the elements of cognitive learning. The other, was that the learning data collected from the students that participated in the study had a modified environment. The data that was collected during the experimental study for thirty hours for about 100 students. Students at the end of each session were able to identify the data science terms with respect to the games. They were able to write a small R program that they self-taught themselves due to the environmental factor of cognitive learning.","PeriodicalId":119111,"journal":{"name":"2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132458145","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: 6

Eye-motion detection system for mnd patients 精神病人眼动检测系统

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-01 DOI: 10.1109/ISCMI.2017.8279606

Chu-Lian Xu, Chyi-Yeu Lin

引用次数: 7

Accuracy based weighted aging ensemble (AB-WAE) — Algorithm for data stream classification 基于精度的加权老化集成(AB-WAE) .数据流分类算法

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-01 DOI: 10.1109/ISCMI.2017.8279591

Michal Wozniak

{"title":"Accuracy based weighted aging ensemble (AB-WAE) — Algorithm for data stream classification","authors":"Michal Wozniak","doi":"10.1109/ISCMI.2017.8279591","DOIUrl":"https://doi.org/10.1109/ISCMI.2017.8279591","url":null,"abstract":"Nowadays, most of the data comes continuously and its distribution may change over the time. Unfortunately, most of classifiers assume that statistical characteristics of used predicting model are not being changed. This work presents modification of the previously proposed Weighted Aging Classifier Ensemble (WAE), called Accuracy Based WAE (AB-WAE), which can easily adapt to the probability characteristic changes caused by so-called concept drift. The proposed model does not exploit a drift detection mechanism, but AB-WAE tries to change the line-up of the classifier ensemble and weights assigned to base classifiers. Thus, one individual classifier is trained on the basis of the each incoming data chunk, then AB-WAE chooses the most valuable ensemble taking into consideration: fixed ensemble size, the previously trained models and new trained classifier. The discussed WAE modification uses the ensemble of homogeneous classifiers only, what allows to employ more sophisticated combination rule based on support functions, what should boost the classification accuracy, what was confirmed by the computer experiments.","PeriodicalId":119111,"journal":{"name":"2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126064082","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: 5

Dynamic confidence values selection — Experimental studies 动态置信度值选择。实验研究

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-01 DOI: 10.1109/ISCMI.2017.8279620

R. Burduk

引用次数: 0

Five-elements cycle optimization algorithm for solving continuous optimization problems 求解连续优化问题的五行循环优化算法

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-01 DOI: 10.1109/ISCMI.2017.8279601

Mandan Liu

引用次数: 5

Emotional journey for an emotion provoking cycling exergame 情感之旅为一场激发情感的自行车运动

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-01 DOI: 10.1109/ISCMI.2017.8279607

Larissa Müller, Arne Bernin, A. Kamenz, Sobin Ghose, Kai von Luck, C. Grecos, Qi Wang, Florian Vogt

引用次数: 6

Interactive concepts for shaping generative models of spatial behavior 互动概念塑造空间行为的生成模型

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-01 DOI: 10.1109/ISCMI.2017.8279594

Ronny Hug, W. Hübner, Michael Arens

{"title":"Interactive concepts for shaping generative models of spatial behavior","authors":"Ronny Hug, W. Hübner, Michael Arens","doi":"10.1109/ISCMI.2017.8279594","DOIUrl":"https://doi.org/10.1109/ISCMI.2017.8279594","url":null,"abstract":"A technique widely used in video based situation assessment, and especially in anomaly detection, is the analysis of spatial behavior in terms of motion profiles recorded along trajectories. An intuitive assessment metric is the deviation from normal behavior, where generative models are a natural choice for capturing the underlying statistics. Applying such outlier methods in open world scenarios has the drawback that long observation times are required, in order to fully determine the model, while underdetermined models are very prone to generate non-intuitive or wrong results. In order to address this problem, the usage of interactive concepts for supporting the learning process and refining learned models is proposed. Thereby, the method keeps track of automatically integrated observations and stochastic priors generated by examples provided by the user. Examples can be given in terms of individual labeled samples, or in terms of complex pdfs. The feasibility of the proposed approach is illustrated on the BIWI Walking Pedestrians dataset, using partitioned Gaussian mixture models as the generative model.","PeriodicalId":119111,"journal":{"name":"2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116116522","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

Cuckoo search: State-of-the-art and opportunities 布谷鸟搜索:最新技术和机会

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-01 DOI: 10.1109/ISCMI.2017.8279597

Xin-She Yang, S. Deb

引用次数: 19

Inertia weight control strategies: Particle roaming behavior 惯性权值控制策略:粒子漫游行为

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-01 DOI: 10.1109/ISCMI.2017.8279625

A. Engelbrecht

引用次数: 0

Study on automatic tracking algorithm of neurons in most atlas of mouse brain based on open curve snake 基于开放曲线蛇的小鼠脑大部分图谱神经元自动跟踪算法研究

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2017-11-01 DOI: 10.1109/ISCMI.2017.8279614

Zhenlong Xiao, Jiazuan Ni, Qiong Liu, Liang Jiang, Xin Wang

引用次数: 1