ICTACT Journal on Soft Computing最新文献

COMPARATIVE ANALYSIS OF HUMAN INTERACTION PATTERN MINING APPROACHES 人际互动模式挖掘方法的比较分析

ICTACT Journal on Soft Computing Pub Date : 2020-01-01 DOI: 10.21917/ijsc.2020.0293

S. Uma, J. Suguna

引用次数: 0

DEEP LEARNING APPROACHES FOR ANSWER SELECTION IN QUESTION ANSWERING SYSTEM FOR CONVERSATION AGENTS 会话主体问答系统中的深度学习方法

ICTACT Journal on Soft Computing Pub Date : 2020-01-01 DOI: 10.21917/ijsc.2020.0289

K. Karpagam, K. Madusudanan, A. Saradha

{"title":"DEEP LEARNING APPROACHES FOR ANSWER SELECTION IN QUESTION ANSWERING SYSTEM FOR CONVERSATION AGENTS","authors":"K. Karpagam, K. Madusudanan, A. Saradha","doi":"10.21917/ijsc.2020.0289","DOIUrl":"https://doi.org/10.21917/ijsc.2020.0289","url":null,"abstract":"The conversation agent acts as core interfaces between a system and user in answering users queries with proper responses. Question answering system acquires an important role in the information retrieval field. The deep learning approach enhances the accuracy in answering complex questions. As outcome, the user is receiving the precise answer instead of large document collections. The aim of this paper is to develop a model with deep learning approach for improving answer selection process which supports more relevant answer displaying by conversation agents. To achieve this, word2vector used for word representation and biLSTM attentive model is used for training, testing and disclosure play precise answer. Question type is identified using POS-tagger based Question Pattern analysis (T-QPA) model. The knowledgebase is created from the bench mark datasets bAbI Facebook (simple QA tasks), TREC QA, Yahoo! Answer, Insurance QA dataset. The proposed framework is built by embedding of questions and answers based on bidirectional long short-term memory (biLSTM) attentive models. The similarity between questions and answers has been measured by semantic and cosine similarity. The proposed model reduces the search gap in extracting among user queries and answer sentences in the education domain. The system results are evaluated with the standard metrics MAP, Top 1 accuracy, F1- Score for the answer selection.","PeriodicalId":30616,"journal":{"name":"ICTACT Journal on Soft Computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44934889","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

COLLABORATIVE NETWORK SECURITY MANAGEMENT SYSTEM BASED ON ASSOCIATION MINING RULE 基于关联挖掘规则的协同网络安全管理系统

ICTACT Journal on Soft Computing Pub Date : 2014-07-01 DOI: 10.21917/IJSC.2014.0112

Nisha Mariam Varughese

{"title":"COLLABORATIVE NETWORK SECURITY MANAGEMENT SYSTEM BASED ON ASSOCIATION MINING RULE","authors":"Nisha Mariam Varughese","doi":"10.21917/IJSC.2014.0112","DOIUrl":"https://doi.org/10.21917/IJSC.2014.0112","url":null,"abstract":"Security is one of the major challenges in open network. There are so many types of attacks which follow fixed patterns or frequently change their patterns. It is difficult to find the malicious attack which does not have any fixed patterns. The Distributed Denial of Service (DDoS) attacks like Botnets are used to slow down the system performance. To address such problems Collaborative Network Security Management System (CNSMS) is proposed along with the association mining rule. CNSMS system is consists of collaborative Unified Threat Management (UTM), cloud based security centre and traffic prober. The traffic prober captures the internet traffic and given to the collaborative UTM. Traffic is analysed by the Collaborative UTM, to determine whether it contains any malicious attack or not. If any security event occurs, it will reports to the cloud based security centre. The security centre generates security rules based on association mining rule and distributes to the network. The cloud based security centre is used to store the huge amount of tragic, their logs and the security rule generated. The feedback is evaluated and the invalid rules are eliminated to improve the system efficiency.","PeriodicalId":30616,"journal":{"name":"ICTACT Journal on Soft Computing","volume":"4 1","pages":"787-790"},"PeriodicalIF":0.0,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68392189","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

OPINION MINING AND SENTIMENT CLASSIFICATION: A SURVEY 意见挖掘与情感分类:一项调查

ICTACT Journal on Soft Computing Pub Date : 2012-10-01 DOI: 10.21917/ijsc.2012.0065

ChandraKala S., S. C.

引用次数: 69

CATEGORIZATION OF LUNG CARCINOMA USING MULTILAYER PERCEPTRON IN OUTPUT LAYER 输出层基于多层感知器的肺癌分类

ICTACT Journal on Soft Computing Pub Date : 1900-01-01 DOI: 10.21917/ijsc.2020.0288

S. Karthigai, K. Sundaram

引用次数: 0