2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)最新文献_第6页

Classification Techniques Used in Sentiment Analysis & Prediction of Heart Disease using Data Mining Techniques: Review 使用数据挖掘技术进行情感分析和心脏病预测的分类技术综述

2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2019-09-01 DOI: 10.1109/ICICT46931.2019.8977707

Rahul, Himanshu Bansal, Monika

{"title":"Classification Techniques Used in Sentiment Analysis & Prediction of Heart Disease using Data Mining Techniques: Review","authors":"Rahul, Himanshu Bansal, Monika","doi":"10.1109/ICICT46931.2019.8977707","DOIUrl":"https://doi.org/10.1109/ICICT46931.2019.8977707","url":null,"abstract":"Sentiment analysis uses data mining methods to extract information and data from the web through natural language processing. This consists of emotion artificial intelligent and text analysis. It basically helps in finding out the polarity of word data which is categorized into negative, positive and neutral. Sentiment extraction from data sources is a difficult task because some data sources may have unstructured format of data. In this review paper, we tried to summarize a number of classification techniques used in sentiment analysis stating some of their advantages and disadvantages, performance and their accuracy.In this paper, the various data mining techniques used for the prediction of the heart disease are discussed. With the help of data mining, it is very easy task to make expert system where this plays an important role in the prediction of the health related problems. This helps in solving threat of heart related issues also. Data mining is the extraction of hidden predictive information from large databases which creates enhanced knowledge in the field of pharmaceutical science which helps to predict heart disease. Various data mining techniques are applied here. It produces fast, straightforward assessment of the distinct prediction prototype with the help of Artificial Intelligent techniques.","PeriodicalId":412668,"journal":{"name":"2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122539009","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

Two Hand Indian Sign Language dataset for benchmarking classification models of Machine Learning 用于机器学习基准分类模型的双手印度手语数据集

2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2019-09-01 DOI: 10.1109/ICICT46931.2019.8977713

Leela Surya Teja Mangamuri, Lakshay Jain, Abhishek Sharmay

{"title":"Two Hand Indian Sign Language dataset for benchmarking classification models of Machine Learning","authors":"Leela Surya Teja Mangamuri, Lakshay Jain, Abhishek Sharmay","doi":"10.1109/ICICT46931.2019.8977713","DOIUrl":"https://doi.org/10.1109/ICICT46931.2019.8977713","url":null,"abstract":"Currently, a lot of research is going in the field of sign language recognition. Recognition of gesture poses a serious challenge to the system due to inconsistent illuminance and background conditions, different skin colours of the hand and each person has his/her own trait of making the gesture. It gets even more difficult with Two Hand Indian Sign Language (THISL) due to the representation of gesture with both hands. There is no proper THISL dataset available to the public. So, we present a THISL dataset consisting of 26 gestures each representing the English alphabet. This dataset consists of 50x50 images of total 9100 in which each gesture is made of 350 images and it is divided into two parts, training and test. The training set consists of 7020 images and the test set consists of 2080 images. In this paper, THISL dataset is validated on various classification models of machine learning and overall accuracy of 91.72% is achieved. This dataset serves a very good purpose for benchmarking machine learning algorithms and it is freely available to people on request to authors.","PeriodicalId":412668,"journal":{"name":"2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122734021","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

Algorithm Based on Stockwell Transform for Processing of Communication Signal to Detect Superimposed Harmonics and Transient Disturbances 基于Stockwell变换的通信信号处理中叠加谐波和瞬态干扰检测算法

2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2019-09-01 DOI: 10.1109/ICICT46931.2019.8977642

Monika Mathur, Vivek Upadhyaya, Rahul Srivastava

{"title":"Algorithm Based on Stockwell Transform for Processing of Communication Signal to Detect Superimposed Harmonics and Transient Disturbances","authors":"Monika Mathur, Vivek Upadhyaya, Rahul Srivastava","doi":"10.1109/ICICT46931.2019.8977642","DOIUrl":"https://doi.org/10.1109/ICICT46931.2019.8977642","url":null,"abstract":"An algorithm based on Stockwell Transform focused on processing of communication signals to detect harmonics and transient disturbances superimposed on the signals is presented in this paper. These disturbances are being superimposed on the signals in the communication channel or at the transmitter or the receiver stations. Investigated transient disturbances include impulsive transient and oscillatory transients. Communication signals incorporating harmonics or transient disturbance are decomposed with the help of Stockwell Transform and S-matrix is derived. A summation of absolute values curve, median curve and maximum absolute values plot are proposed to detect disturbances. These curves are obtained from S-matrix. On comparing these plots of signal having harmonics or transient disturbances with respective curves of pure sinusoidal communication signal, superimposed harmonics or transient disturbance have been detected successfully. Effectiveness of the proposed approach is established using the MATLAB software.","PeriodicalId":412668,"journal":{"name":"2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132605663","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

Acquiring and Analyzing Movement Detection through Image Granulation 基于图像粒化的运动检测获取与分析

2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2019-09-01 DOI: 10.1109/ICICT46931.2019.8977703

Neelam Rawat, J. Sodhi, R. Tyagi

引用次数: 1

Comparative Study Indian Electoral Reforms in Indian Context 印度语境下的印度选举改革比较研究

2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2019-09-01 DOI: 10.1109/ICICT46931.2019.8977657

Jayesh R Solanki, Divykant Meva

引用次数: 3

Leukemia Diagnosis using Computational Intelligence 利用计算智能诊断白血病

2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2019-09-01 DOI: 10.1109/ICICT46931.2019.8977709

Sunita Chand, V. P. Vishwakarma

引用次数: 4

Hiding Text In Color Image Using YCbCr Color Model: An Image Steganography approach 使用YCbCr颜色模型隐藏彩色图像中的文本:一种图像隐写方法

2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2019-09-01 DOI: 10.1109/ICICT46931.2019.8977644

Deepak Kumar

引用次数: 3

An Improved Approach for Predicting Drug Target Interactions 预测药物靶标相互作用的改进方法

2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2019-09-01 DOI: 10.1109/ICICT46931.2019.8977697

Kanica Sachdev, M. Gupta

{"title":"An Improved Approach for Predicting Drug Target Interactions","authors":"Kanica Sachdev, M. Gupta","doi":"10.1109/ICICT46931.2019.8977697","DOIUrl":"https://doi.org/10.1109/ICICT46931.2019.8977697","url":null,"abstract":"The identification of drug protein associations assists the exploration of novel drugs, drug repurposing and drug side effect identification. The experimental evaluation of these interactions requires extensive capital and money. Thus, in-silico computational methods are being developed to aid the interaction prediction. These techniques have been broadly grouped into similarity based approaches and feature based approaches. This paper proposes a novel feature based approach to identify the probable drug protein communications. The method is based on Support Vector Machine classifier. Support Vector Machines have shown a satisfactory performance in many applications related to the pharmacology domain. To further improve the accuracy and reduce the computational complexity, dimensionality reduction by PCA has been proposed. The proposed technique achieves an AUC score of 0.822. The method has been compared to various other state of the art methods based on their respective AUC scores. The comparison has shown that the proposed approach has a better performance in contrast to the other techniques.","PeriodicalId":412668,"journal":{"name":"2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"407 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121005750","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: 1

Comparative Analysis of Resampling Techniques under Noisy Imbalanced Datasets 噪声不平衡数据下重采样技术的比较分析

2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2019-09-01 DOI: 10.1109/ICICT46931.2019.8977650

Arjun Puri, M. Gupta

引用次数: 12

Defect Segmentation in Surfaces using Deep Learning 基于深度学习的曲面缺陷分割

2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2019-09-01 DOI: 10.1109/ICICT46931.2019.8977696

Somit Mittal, Chahes Chopra, A. Trivedi, P. Chanak

引用次数: 1