International Conference on Machine Learning and Soft Computing最新文献

A Proposal of Deep Learning Model for Classifying User Interests on Social Networks 一种用于社交网络用户兴趣分类的深度学习模型

International Conference on Machine Learning and Soft Computing Pub Date : 2020-01-17 DOI: 10.1145/3380688.3380707

Dinh Xuan Truong, H. Pham

引用次数: 4

Stargate: a data source connector based on spark SQL Stargate:一个基于spark SQL的数据源连接器

International Conference on Machine Learning and Soft Computing Pub Date : 2018-02-02 DOI: 10.1145/3184066.3184088

Y. Tao, Gang Wu, Yi Kang

引用次数: 0

Colour, trust, satisfaction, and E-loyalty: the vietnamese experience of website design 色彩、信任、满意度和网络忠诚度:越南人的网站设计经验

International Conference on Machine Learning and Soft Computing Pub Date : 2018-02-02 DOI: 10.1145/3184066.3184070

Tran Trung Khuong, Pham Yen Nhi, Dinh Thanh Nhan, N. Thuan

引用次数: 2

Modelling and power estimation of continuously varying residential loads using a quantized continuous-state hidden markov model 基于量化连续状态隐马尔可夫模型的连续变化住宅负荷建模与功率估计

International Conference on Machine Learning and Soft Computing Pub Date : 2018-02-02 DOI: 10.1145/3184066.3184096

Misbah Aiad, P. Lee

{"title":"Modelling and power estimation of continuously varying residential loads using a quantized continuous-state hidden markov model","authors":"Misbah Aiad, P. Lee","doi":"10.1145/3184066.3184096","DOIUrl":"https://doi.org/10.1145/3184066.3184096","url":null,"abstract":"Hidden Markov Models (HMMs) and their extensions have broad useful applications in several fields. Energy disaggregation, or non-intrusive load monitoring (NILM), is the process of analyzing and decomposing the total aggregate energy consumption of a household into the individual consumptions by respective devices. These details were found informative and can influence occupants in a way that achieves noticeable energy savings. Hidden Markov Models (HMMs) were found efficient in modelling and detection of household devices. In this work, we propose a quantized continuous-state HMM so as to model continuously varying loads which is a challenging problem in the domain of energy disaggregation. Two core enhancements to the standard quantized continuous-state HMM are proposed. First, we propose a method that estimate the transition matrix considering potential probabilities to states neighboring that the model switches to. This method reduces the effect of domination of a state transition and achieve better simulation of switching cases in real variable loads. Second, the consumption of the variable load is estimated from the collective mean resulting from the Viterbi algorithm instead of the assigning the center value of the state with the maximum likelihood. In this way, the effect of quantization can be reduced. The proposed approach was tested on synthetic and real variable loads from the REDD public data set. It was found that the proposed models outperform the reference HMM that applies standard estimation algorithms.","PeriodicalId":109559,"journal":{"name":"International Conference on Machine Learning and Soft Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132479306","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 new generalized knowledge measure in multi-attribute group decision making under interval-valued intuitionistic fuzzy environment 区间值直觉模糊环境下多属性群体决策中一种新的广义知识测度

International Conference on Machine Learning and Soft Computing Pub Date : 2018-02-02 DOI: 10.1145/3184066.3184067

Hoang Nguyen

引用次数: 1

A new soft assignment K-means algorithm 一种新的软分配k均值算法

International Conference on Machine Learning and Soft Computing Pub Date : 2018-02-02 DOI: 10.1145/3184066.3184073

Peng Chen, Yongmei Chen, Beibei Jin

引用次数: 1

Collaborative filtering recommendation with threshold value of the equipotential plane in implication field 蕴涵场中具有等势平面阈值的协同过滤推荐

International Conference on Machine Learning and Soft Computing Pub Date : 2018-02-02 DOI: 10.1145/3184066.3184072

H. T. Nguyen, H. Huynh, H. Huynh

引用次数: 1

SIACO: a novel algorithm based on ant colony optimization and game theory for travelling salesman problem SIACO:一种基于蚁群优化和博弈论的旅行商问题求解算法

International Conference on Machine Learning and Soft Computing Pub Date : 2018-02-02 DOI: 10.1145/3184066.3184077

Demison Rolins de Souza Alves, M. T. R. S. Neto, Fabio dos Santos Ferreira, O. N. Teixeira

引用次数: 3

Design and implementation of intrusion detection system using convolutional neural network for DoS detection 采用卷积神经网络进行DoS检测的入侵检测系统的设计与实现

International Conference on Machine Learning and Soft Computing Pub Date : 2018-02-02 DOI: 10.1145/3184066.3184089

Sinh-Ngoc Nguyen, Van-Quyet Nguyen, Jintae Choi, Kyungbaek Kim

{"title":"Design and implementation of intrusion detection system using convolutional neural network for DoS detection","authors":"Sinh-Ngoc Nguyen, Van-Quyet Nguyen, Jintae Choi, Kyungbaek Kim","doi":"10.1145/3184066.3184089","DOIUrl":"https://doi.org/10.1145/3184066.3184089","url":null,"abstract":"Nowadays, network is one of the essential parts of life, and lots of primary activities are performed by using the network. Also, network security plays an important role in the administrator and monitors the operation of the system. The intrusion detection system (IDS) is a crucial module to detect and defend against the malicious traffics before the system is affected. This system can extract the information from the network system and quickly indicate the reaction which provides real-time protection for the protected system. However, detecting malicious traffics is very complicating because of their large quantity and variants. Also, the accuracy of detection and execution time are the challenges of some detection methods. In this paper, we propose an IDS platform based on convolutional neural network (CNN) called IDS-CNN to detect DoS attack. Experimental results show that our CNN based DoS detection obtains high accuracy at most 99.87%. Moreover, comparisons with other machine learning techniques including KNN, SVM, and Naïve Bayes demonstrate that our proposed method outperforms traditional ones.","PeriodicalId":109559,"journal":{"name":"International Conference on Machine Learning and Soft Computing","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117107061","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: 45

A performance analysis of user's intention classification from EEG signal by a computational intelligence in BCI 脑机接口中基于脑电信号的用户意图分类性能分析

International Conference on Machine Learning and Soft Computing Pub Date : 2018-02-02 DOI: 10.1145/3184066.3184092

C. Lim, Chang Young Lee, Yongmin Kim

{"title":"A performance analysis of user's intention classification from EEG signal by a computational intelligence in BCI","authors":"C. Lim, Chang Young Lee, Yongmin Kim","doi":"10.1145/3184066.3184092","DOIUrl":"https://doi.org/10.1145/3184066.3184092","url":null,"abstract":"Knowing the user's intentions is very important and can be useful in our daily life. It would be a very useful way, especially if people with disabilities can use these functions as a means of self-expression. The user's intension classification is a kind of common time-series problem for detecting human cognitive state. In this paper, we classify user intention by analyzing EEG signal using machine learning in BCI. The performance of the classification accuracy can be achieved by using the proposed approach in terms of the number of neurons in the hidden layer, which also leads types of membership function in fuzzy rules. We prepared training and test data using the Emotive headset for the experiment. Our experimental results show that the proposed approach gives us a quite promising method with 5 fuzzy rules obtained through a fuzzy C-means clustering. It is a simple fuzzy system with neural network structure by tuning GA providing statistically superior solutions. Experimental results show that the best results were obtained using the electrode position {F7, F8, FC5, FC6} of EEG. Experimental results using training data showed an accuracy of 94.2%. However, the result of using the test data after learning shows a slightly lower accuracy of 92.3%. This experiment shows that using training data and test dares can result in more than 90% accuracy. Experimental results show that all 4--action behaviors have similar accuracy.","PeriodicalId":109559,"journal":{"name":"International Conference on Machine Learning and Soft Computing","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115086224","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