2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)最新文献_第4页

Anomaly Detection Guidelines for Data Streams in Big Data 大数据中数据流异常检测指南

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.24

Annie Ibrahim Rana, G. Estrada, Marc Solé, Victor Muntés

引用次数: 8

Heterogeneous Ensembles for Software Development Effort Estimation 软件开发工作量评估的异构集成

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.15

Mohamed Hosni, A. Idri, A. B. Nassif, A. Abran

引用次数: 20

Privacy Preserving in Distributed SVM Data Mining on Vertical Partitioned Data 垂直分区数据分布式支持向量机数据挖掘中的隐私保护

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.40

Mohammed Z. Omer, Hui Gao, Faisal Sayed

{"title":"Privacy Preserving in Distributed SVM Data Mining on Vertical Partitioned Data","authors":"Mohammed Z. Omer, Hui Gao, Faisal Sayed","doi":"10.1109/ISCMI.2016.40","DOIUrl":"https://doi.org/10.1109/ISCMI.2016.40","url":null,"abstract":"Data mining algorithms tacitly quite access to the data either at centralized or distributed form. Distributed data becomes a big challenge and cannot handle by a classical analytic tool. Cloud Computing can solve the issues of processing, storing, and analyzing the data at distributing locations within the cloud. However, a significant problem that is preventing free sharing of data is privacy and security issues, therefore obstructing data mining schemes. Lately, there is increasingly hard to find a solution to these problems. Due to the existing knowledge in a more distributed data and better for data mining issues. An important task of data mining and machine learning is classification, a widely used in classification is support vector machine (SVM) algorithms applicable in many various domains. In this paper, we proposes a privacy-preserving solution for SVM classification. Our workaround constructing a global SVM classification model from vertically partitioned distributed data at multi-parties based on Gram matrix, without revealing a party's data. We proposed an efficient and preserve privacy protocol for SVM classification on vertical partitioned data. Our experimental results, the accuracy of distributed SVM using Gram matrix up to 90% and the privacy not compromised.","PeriodicalId":417057,"journal":{"name":"2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116589286","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

A Question Answer System for the Mauritian Judiciary 毛里求斯司法系统的问答系统

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.47

S. Pudaruth, R. Gunputh, K. Soyjaudah, P. Domun

{"title":"A Question Answer System for the Mauritian Judiciary","authors":"S. Pudaruth, R. Gunputh, K. Soyjaudah, P. Domun","doi":"10.1109/ISCMI.2016.47","DOIUrl":"https://doi.org/10.1109/ISCMI.2016.47","url":null,"abstract":"Law is a research-oriented profession and legal research is an activity that costs time and money. Information Technology is now revolutionising the way in which legal research is being done. In this work, we have implemented an online web-based question answer system for the Mauritian Judiciary where users can enter their queries freely using natural language. The system processes the queries by extracting relevant keywords and discards those that do not carry much information and then returns the relevant sections of law which contain these keywords or keyphrases. The system also returns a list of relevant Supreme Court cases. The user can decide on the number of results to be displayed. The user can also wish to have only the name of the relevant acts be displayed for certain keywords or keyphrases. The system does not require the user to know how the law is structured or how the knowledge-base is built in order to benefit from it. The portal can also be accessed via mobile devices without compromising any of its facilities or user-friendliness. It is hoped that the availability of information at the click of a button will help the human resources at the Mauritian Judiciary to become more efficient and this will contribute to the reduction of delays in the disposal of cases.","PeriodicalId":417057,"journal":{"name":"2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"414 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123909698","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 Two Phases Heuristic Based Method for the MinMax Regret Location Problem 最小最大遗憾定位问题的两阶段启发式方法

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.32

S. Ibri

引用次数: 1

Personification of Bag-of-Features Dataset for Real Time Activity Recognition 面向实时活动识别的特征袋数据集人格化

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.27

M. L. Gadebe, Okuthe P. Kogeda

{"title":"Personification of Bag-of-Features Dataset for Real Time Activity Recognition","authors":"M. L. Gadebe, Okuthe P. Kogeda","doi":"10.1109/ISCMI.2016.27","DOIUrl":"https://doi.org/10.1109/ISCMI.2016.27","url":null,"abstract":"Personalization of activity recognition is possible and important, when existing public dataset collected from large group of subjects can be tailored and be used as training and testing dataset for new users (subjects) who have similar personal traits. However, due to shortage of personalized dataset and techniques to tailor public dataset for new users weakens the personalization of human activity. To address shortage of personalized dataset, we propose a personification algorithm that extracts and tailor-make bag-of-features dataset to support new users from publicly available Human Activity Recognition dataset (PAMAP2 and USC-HAD). Studies indicate that BMI can be used to profile user's weight as either normal weight or overweight or obese, which could be used to predict cardiovascular diseases. For that purpose our personification algorithm uses height, weight and BMI to generate human activity bag-of-features. The personification algorithm is implemented in Scala and Java programming languages and is deployed on Apache Spark Server. We validated our algorithm, by running three set trials of experiments for each 5 K threshold values using 2 randomly selected new user's profile against two publicly available Human Activity Recognition dataset PAMAP2 and USC-HAD. The results indicate that it is possible to tailor bag-of-features from public dataset. Overall performance of our algorithm shows precision, recall and F-score of 0.70%, 0.50% and 0.60% respectively.","PeriodicalId":417057,"journal":{"name":"2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134644884","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

Leader-Follower Fixed-Time Consensus for Multi-agent Systems with Heterogeneous Non-linear Inherent Dynamics 具有非均质非线性内在动力学的多智能体系统的领导-随从定时一致性

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.48

A. Sharghi, M. Baradarannia, F. Hashemzadeh

引用次数: 7

Two-Step Feature Selection Methods for Selection of Very Few Features 基于两步特征选择的极少量特征选择方法

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.29

P. Drotár, J. Gazda

引用次数: 2

Swarm Intelligence: Today and Tomorrow 群体智能:今天和明天

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.34

Xin-She Yang, S. Deb, S. Fong, Xingshi He, Yuxin Zhao

引用次数: 8

Comparative Analysis of ECG Classification Using Neuro-Fuzzy Algorithm and Multimodal Decision Learning Algorithm: ECG Classification Algorithm 神经模糊算法与多模态决策学习算法心电分类的比较分析

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.35

G. Naik, K. Reddy

{"title":"Comparative Analysis of ECG Classification Using Neuro-Fuzzy Algorithm and Multimodal Decision Learning Algorithm: ECG Classification Algorithm","authors":"G. Naik, K. Reddy","doi":"10.1109/ISCMI.2016.35","DOIUrl":"https://doi.org/10.1109/ISCMI.2016.35","url":null,"abstract":"Classification of ECGs is an important task for proper identification of the signal which helps in suitable diagnosis of the patient. This paper proposes a new algorithm for ECG basic classification as normal or abnormal. As there are many existing methods for classification like support vector machine, neural networks, neuro-fuzzy algorithms and so on, the main objective of this work is to compare performance analysis of two selected methods, one with adaptive neuro-fuzzy algorithm as the existing method and the other with the proposed method i.e., multimodal decision learning algorithm. The comparative analysis deals with the parameters like true positive (TP), true negative (TN), False positive (FP), False negative (FN), False rejection ratio (FRR), false acceptance ratio (FAR), global acceptance ratio (GAR), confusion matrix (CM), Kappa coefficient (KC), Sensitivity, Specificity and Accuracy. Pre-recorded ECG signals of MIT-BIH database are used for processing, filtering, classification and performance evaluation. Simulation results indicate the ECG signal as normal or abnormal with respect to the above defined parameters.","PeriodicalId":417057,"journal":{"name":"2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116429642","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