2019 International Conference on Computational Intelligence in Data Science (ICCIDS)最新文献_第2页

A Comparative Study of Different Features for Vehicle Classification 车辆分类不同特征的比较研究

2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Pub Date : 2019-02-01 DOI: 10.1109/ICCIDS.2019.8862136

Anuja Prasad, L. Mary

{"title":"A Comparative Study of Different Features for Vehicle Classification","authors":"Anuja Prasad, L. Mary","doi":"10.1109/ICCIDS.2019.8862136","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862136","url":null,"abstract":"This paper presents a comparative study of different features for vehicle classification. Real-time vehicle classification system using computer vision is relatively cheaper and easy to install. As traffic is heterogeneous in India, road planning and traffic management is challenging. So an automated vehicle detection and classification system is useful for traffic survey, planning, signal time optimization and surveillance. In this work, traffic video data is collected using a camera placed on the top of a vehicle parking on the side of a road at an angle of approximately 45°. Both audio and video are used for vehicle detection. The presence of a vehicle is detected from frames corresponding to the peaks in the short time energy of audio. The process of adaptive background subtraction is performed on the selected frames to separate the vehicle from the background. After background subtraction, morphological processes such as erosion, dilation and closing are applied to get the region of interest. There may be mulitiple frames with the same vehicle are detected at this stage. To reduce the multiple occurrences of the same vehicle in selected frames, Speeded-Up Robust Feature (SURF) matching algorithm is used. Different features like Histogram Oriented Gradient (HOG), Local Binary Pattern (LBP), KAZE, Binary Robust Invariant Scale Keypoint (BRISK) features of selected frames are extracted and Support Vector Machine (SVM) models are developed. Vehicle classification accuracy of various features are compared using a 20 minutes traffic video. It is observed that HOG gives the best result compared to KAZE, LBP and BRISK, with an accuracy of 85.50%.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131211009","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

An Elaborate Comprehensive Survey on Recent Developments in Behaviour Based Intrusion Detection Systems 基于行为的入侵检测系统最新发展综述

2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Pub Date : 2019-02-01 DOI: 10.1109/ICCIDS.2019.8862119

A. M. V. Bharathy, N. Umapathi, S. Prabaharan

引用次数: 4

ICCIDS 2019 Author Index ICCIDS 2019作者索引

2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Pub Date : 2019-02-01 DOI: 10.1109/iccids.2019.8862106

引用次数: 0

Formation of SQL from Natural Language Query using NLP 使用NLP从自然语言查询生成SQL

2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Pub Date : 2019-02-01 DOI: 10.1109/ICCIDS.2019.8862080

M. Uma, V. Sneha, G. Sneha, J. Bhuvana, B. Bharathi

{"title":"Formation of SQL from Natural Language Query using NLP","authors":"M. Uma, V. Sneha, G. Sneha, J. Bhuvana, B. Bharathi","doi":"10.1109/ICCIDS.2019.8862080","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862080","url":null,"abstract":"Today, everyone has their own personal devices that connects to the internet. Every user tries to get the information that they require through internet. Most of the information is in the form of a database. A user who wants to access a database but having limited or no knowledge of database languages faces a challenging and difficult situation. Hence, there is a need for a system that enables the users to access the information in the database. This paper aims to develop such a system using NLP by giving structured natural language question as input and receiving SQL query as the output, to access the related information from the railways reservation database with ease. The steps involved in this process are tokenization, lemmatization, parts of speech tagging, parsing and mapping. The dataset used for the proposed system has a set of 2880 structured natural language queries on train fare and seats available. We have achieved 98.89 per cent accuracy. The paper would give an overall view of the usage of Natural Language Processing (NLP) and use of regular expressions to map the query in English language to SQL.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130158743","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: 18

Crowdsensing-based WiFi Indoor Localization using Feed-forward Multilayer Perceptron Regressor 基于人群感知的WiFi室内定位前馈多层感知器回归

2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Pub Date : 2019-02-01 DOI: 10.1109/ICCIDS.2019.8862117

Simran Barnwal, Wei-Jan Peng

引用次数: 5

Grape Leaf Disease Identification using Machine Learning Techniques 利用机器学习技术识别葡萄叶病

2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Pub Date : 2019-02-01 DOI: 10.1109/ICCIDS.2019.8862084

S. M. Jaisakthi, P. Mirunalini, D. Thenmozhi, Vatsala

{"title":"Grape Leaf Disease Identification using Machine Learning Techniques","authors":"S. M. Jaisakthi, P. Mirunalini, D. Thenmozhi, Vatsala","doi":"10.1109/ICCIDS.2019.8862084","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862084","url":null,"abstract":"Having diseases is quite natural in crops due to changing climatic and environmental conditions. Diseases affect the growth and produce of the crops and often difficult to control. To ensure good quality and high production, it is necessary to have accurate disease diagnosis and control actions to prevent them in time. Grape which is widely grown crop in India and it may be affected by different types of diseases on leaf, stem and fruit. Leaf diseases which are the early symptoms caused due to fungi, bacteria and virus. So, there is a need to have an automatic system that can be used to detect the type of diseases and to take appropriate actions. We have proposed an automatic system for detecting the diseases in the grape vines using image processing and machine learning technique. The system segments the leaf (Region of Interest) from the background image using grab cut segmentation method. From the segmented leaf part the diseased region is fruther segmented based on two different methods such as global thresholding and using semi-supervised technique. The features are extracted from the segmented diseased part and it has been classified as healthy, rot, esca, and leaf blight using different machine learning techniques such as Support Vector Machine (SVM), adaboost and Random Forest tree. Using SVM we have obtained a better testing accuracy of 93%.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121965465","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: 39

Continuous learning mechanism of NLU-ML models boosted by human feedback 人类反馈促进NLU-ML模型的持续学习机制

2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Pub Date : 2019-02-01 DOI: 10.1109/ICCIDS.2019.8862102

G. Abinaya, Gyan Ranjan, P. Aswin Karthik

引用次数: 3

Comparing the Wrapper Feature Selection Evaluators on Twitter Sentiment Classification Twitter情感分类中包装器特征选择评估器的比较

2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Pub Date : 2019-02-01 DOI: 10.1109/ICCIDS.2019.8862033

N. Suchetha, Anupama Nikhil, P. Hrudya

{"title":"Comparing the Wrapper Feature Selection Evaluators on Twitter Sentiment Classification","authors":"N. Suchetha, Anupama Nikhil, P. Hrudya","doi":"10.1109/ICCIDS.2019.8862033","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862033","url":null,"abstract":"The application of machine learning algorithms on text data is challenging in several ways, the greatest being the presence of sparse, high dimensional feature set. Feature selection methods are effective in reducing the dimensionality of the data and helps in improving the computational efficiency and the performance of the learned model. Recently, evolutionary computation (EC) methods have shown success in solving the feature selection problem. However, due to the requirement of a large number of evaluations, EC based feature selection methods on text data are computationally expensive. This paper examines the different evaluation classifiers used for EC based wrapper feature selection methods. A two-stage feature selection method is applied to twitter data for sentiment classification. In the first stage, a filter feature selection method based on Information Gain (IG) is applied. During the second stage, a comparison is made between 4 different EC feature selection methods, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Cuckoo Search (CS) and Firefly Search, with different classifiers as subset evaluators. LibLinear, K Nearest neighbours (KNN) and Naive Bayes (NB) are the classifiers used for wrapper feature subset evaluation. Also, the time required for evaluating the feature subset for the chosen classifiers is computed. Finally, the effect of the application of this combined feature selection approach is evaluated using six different learners. Results demonstrate that LibLinear is computationally efficient and achieves the best performance.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124423635","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: 25

ICCIDS 2019 Photos

2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Pub Date : 2019-02-01 DOI: 10.1109/iccids.2019.8862086

引用次数: 0

Swift Imbalance Data Classification using SMOTE and Extreme Learning Machine 基于SMOTE和极限学习机的快速失衡数据分类

2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Pub Date : 2019-02-01 DOI: 10.1109/ICCIDS.2019.8862112

Rishabh Rustogi, Ayush Prasad

{"title":"Swift Imbalance Data Classification using SMOTE and Extreme Learning Machine","authors":"Rishabh Rustogi, Ayush Prasad","doi":"10.1109/ICCIDS.2019.8862112","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862112","url":null,"abstract":"Continuous expansion in the fields of science and technology has led to the immense availability and attainability of data in every field. Fundamentally understanding and analyzing this data is a critical job in the decision-making process. Although, great success has been achieved by the prevailing data engineering and mining techniques, the problem of swift classification of the imbalanced data still exists in academia and industry. A potential solution to the problem of skewness in data can be resolved by data upsampling or downsampling. There exists a few techniques that firstly remove skewness and then perform classification, however, these methods suffer from hurdles like abortive precision or slower learning rate. In this paper, a hybrid method to classify binary imbalanced data using Synthetic Minority Over-sampling Technique followed by Extreme Learning Machine is proposed. Our method along with swift learning rate is efficacious to predict the desired class. We verified our model using five standard imbalance dataset and obtained higher F-measure, G-mean and ROC score for all the dataset.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123712949","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: 14