Proceedings of the Third International Conference on Advanced Informatics for Computing Research - ICAICR '19最新文献_第3页

A fuzzy expert system for coronary artery disease diagnosis 冠状动脉疾病诊断的模糊专家系统

Proceedings of the Third International Conference on Advanced Informatics for Computing Research - ICAICR '19 Pub Date : 2019-06-15 DOI: 10.1145/3339311.3339358

Prerna Jain, Amandeep Kaur

引用次数: 5

Smart agriculture system using IoT 使用物联网的智能农业系统

Proceedings of the Third International Conference on Advanced Informatics for Computing Research - ICAICR '19 Pub Date : 2019-06-15 DOI: 10.1145/3339311.3339350

D. Mishra, Tanuja Pande, K. Agrawal, A. Abbas, A. Pandey, R. S. Yadav

引用次数: 8

Grey wolf optimization for global path planning of autonomous underwater vehicle 自主水下航行器全局路径规划的灰狼优化

Proceedings of the Third International Conference on Advanced Informatics for Computing Research - ICAICR '19 Pub Date : 2019-06-15 DOI: 10.1145/3339311.3339314

Madhusmita Panda, Dr. Bikramaditya Das, B. B. Pati

{"title":"Grey wolf optimization for global path planning of autonomous underwater vehicle","authors":"Madhusmita Panda, Dr. Bikramaditya Das, B. B. Pati","doi":"10.1145/3339311.3339314","DOIUrl":"https://doi.org/10.1145/3339311.3339314","url":null,"abstract":"Path planning problem (PPP) deals with finding an optimized path between a source and a goal point. Global path planning (GPP) for Autonomous underwater vehicle (AUV), provides an optimized predefined path to reach the desired destination of the AUV. AUVs are largely useful in missions involving marine geoscience, scientific research, military warfare, along with commercial sectors of oil and gas industries. A time optimized path that can avoid collision helps in reducing time and energy expenses of such real time missions. Grey Wolf Optimization (GWO) is a nature inspired metaheuristic algorithm based on hunting behavior of the grey wolves. GWO provides better exploration of the solution space and good at avoiding local minima. This research presents an overview of GWO with its mathematical modelling. The research mainly contributes in applying GWO for path planning of an AUV to generate a global path in a two-dimensional underwater environment with static obstacles. Simulation results are obtained using MATLAB. The resultant path is optimized in time, distance travel and requires less processing time as compared to results obtained by applying Ant colony Optimization (ACO) for the same problem.","PeriodicalId":206653,"journal":{"name":"Proceedings of the Third International Conference on Advanced Informatics for Computing Research - ICAICR '19","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134072468","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: 13

Partial face recognition: a survey 部分人脸识别:一项调查

Proceedings of the Third International Conference on Advanced Informatics for Computing Research - ICAICR '19 Pub Date : 2019-06-15 DOI: 10.1145/3339311.3339343

M. Shafin, Rojina Hansda, Ekta Pallavi, D. Kumar, Sumanta Bhattacharyya, Sanjeev Kumar

引用次数: 6

Multi factor user authentication mechanism using internet of things 基于物联网的多因素用户认证机制

Proceedings of the Third International Conference on Advanced Informatics for Computing Research - ICAICR '19 Pub Date : 2019-06-15 DOI: 10.1145/3339311.3339335

M. Rao, S. Santhi, Md. Ali Hussain

引用次数: 5

Comparison of video shot detection methods using higher order local descriptor 基于高阶局部描述符的视频镜头检测方法比较

Proceedings of the Third International Conference on Advanced Informatics for Computing Research - ICAICR '19 Pub Date : 2019-06-15 DOI: 10.1145/3339311.3339324

J. Majumdar, Dhanush M. Adiga, M. M., M. P. Ashray

{"title":"Comparison of video shot detection methods using higher order local descriptor","authors":"J. Majumdar, Dhanush M. Adiga, M. M., M. P. Ashray","doi":"10.1145/3339311.3339324","DOIUrl":"https://doi.org/10.1145/3339311.3339324","url":null,"abstract":"Video Shot Detection plays a vital role in the analysis of the contents in Video. The algorithms and methodologies learnt from Video Shot Detection has a wide range of applications starting from Video Browsing, Content-based Video Retrieval and Storage, Surveillance and many more. Video Shot Detection is the temporal segmentation of Video stream to determine the transitions of Video. Out of the two categories of transition viz., Hard Cut and Soft Transition, in this paper we propose methods to determine Hard Cut. Three Video Shot Detection Methods have been used in this work, they are Mutual Information, Weighted Variance, Likelihood Ratio and Edge Change Ratio. A comparative study has been conducted to find out the best Video Shot Detection method out of the three. The essential factor that drives the above three methods is extraction of appropriate features from the frames of video stream which would be used for the temporal segmentation of video to determine the transition.\u0000 In this proposed paper we are using Higher Order Local Descriptors such as Local Binary Pattern(LBP), Local Derivative Pattern(LDP), Local Tetra Pattern(LTP) and Local Vector Pattern(LVP) and convert the original video into these feature videos. Frames from input video sequence are converted to texture domain and for each video sequence we generate four video corresponding to four Higher Order Local Descriptors. These video sequences are used to determine the `CUT' transition. Using QM Parameters, we found out the best feature among four Higher Order Local Descriptors for a given class of video.","PeriodicalId":206653,"journal":{"name":"Proceedings of the Third International Conference on Advanced Informatics for Computing Research - ICAICR '19","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129119140","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

Accuracy evaluation of prediction using supervised learning techniques 使用监督学习技术评估预测的准确性

Proceedings of the Third International Conference on Advanced Informatics for Computing Research - ICAICR '19 Pub Date : 2019-06-15 DOI: 10.1145/3339311.3339337

S. K., Sajimon Abraham

{"title":"Accuracy evaluation of prediction using supervised learning techniques","authors":"S. K., Sajimon Abraham","doi":"10.1145/3339311.3339337","DOIUrl":"https://doi.org/10.1145/3339311.3339337","url":null,"abstract":"The term Big data is used to refer the huge volume of complex and growing data generated from many distinct electronic gadgets. In the case of Big Data, the commonly used programming methods are not adequate to collect, store and analyze the data within a short period. Statistics as well as machine learning techniques are used for finding patterns and information from large data through data driven decision-making. Big Data analytics gives competitive opportunities in designing business plans for Business Analytics. For analytical purpose, traditionally we use Multiple Linear Regression (MLR) model in the statistical method, a type of Supervised Machine Learning Algorithm. We implemented Cross-Validation Resampling technique with MLR model. The performance of new MLR-Leave-One-Out (MLR-LOOCV) model evaluated using partitioning the whole data set. This technique used to validate the model developed from training data with test data to control the problem like over fitting. The accuracy of such prediction model is very poor. So we propose to build a Multilayer Perceptron Neural Network (MPNN) model with gradient descent learning method to improve the efficiency of prediction model. The new proposed model, MPNN with GD shows accuracy much greater than normal MLR. The data set from UCI machine learning repository is used for simulation methods to check the performance.","PeriodicalId":206653,"journal":{"name":"Proceedings of the Third International Conference on Advanced Informatics for Computing Research - ICAICR '19","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131369156","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

Performance comparison of Apache Hadoop and Apache Spark Apache Hadoop和Apache Spark的性能比较

Proceedings of the Third International Conference on Advanced Informatics for Computing Research - ICAICR '19 Pub Date : 2019-06-15 DOI: 10.1145/3339311.3339329

Amritpal Singh, A. Khamparia, A. K. Luhach

引用次数: 8

Uniform debugging interface for simulators 模拟器的统一调试接口

Proceedings of the Third International Conference on Advanced Informatics for Computing Research - ICAICR '19 Pub Date : 2019-06-15 DOI: 10.1145/3339311.3339340

Natasha Vaish, Cherry Khosla

引用次数: 0

Halftone based face recognition using SVM 基于SVM的半色调人脸识别

Proceedings of the Third International Conference on Advanced Informatics for Computing Research - ICAICR '19 Pub Date : 2019-06-15 DOI: 10.1145/3339311.3339322

Kirani Yumnam, Vanlal Hruaia

{"title":"Halftone based face recognition using SVM","authors":"Kirani Yumnam, Vanlal Hruaia","doi":"10.1145/3339311.3339322","DOIUrl":"https://doi.org/10.1145/3339311.3339322","url":null,"abstract":"We propose a face recognition method based on halftone binary image using SVM classifier. In this method, a training set and a testing set of halftone images are created from a face database of gray images. Then, features are extracted from halftone images and a multi-class SVM model is created. To extract features from a halftone image, the image is divided into non-overlapping regions of equal size. Each region is processed to give a feature value corresponding to the region. This reduces the size of feature vector depending on the size of region considered for a feature. Four different types of features can be generated depending on how the processing of the pixels in each region is done to generate a feature. Recognition rate is computed for each of the four different types of features. Three different types of features give comparatively higher recognition rate for different window sizes. The method has been tested on AT&T face database using different feature types and window sizes. In one of feature types, it gives recognition rate of 95% which much higher than recognition rate 91.25% when using with HoG features on the same face database.","PeriodicalId":206653,"journal":{"name":"Proceedings of the Third International Conference on Advanced Informatics for Computing Research - ICAICR '19","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126208301","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