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2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA)最新文献

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Comparative Performance Analysis of LQR and MPC for Active Suspension System 主动悬架LQR与MPC性能对比分析
2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Pub Date : 2020-10-30 DOI: 10.1109/ICCCA49541.2020.9250813
Saibal Manna, Raktim Mazumdar
{"title":"Comparative Performance Analysis of LQR and MPC for Active Suspension System","authors":"Saibal Manna, Raktim Mazumdar","doi":"10.1109/ICCCA49541.2020.9250813","DOIUrl":"https://doi.org/10.1109/ICCCA49541.2020.9250813","url":null,"abstract":"This research presents the comparative analysis of two very famous controllers that are now being used to design an active suspension system (ASS). The Linear Quadratic Regulator (LQR) and Model Predictive Controller (MPC) are two controller are used here. Our main aim is to track the system’s actions with its controllers and determine which ones perform better in the same circumstances. For the designing of the ASS a quarter-car model (QCM) is used. The entire experiment is conducted on QUANSER ASS (QASS) connected to MATLAB software. The research is to minimize turbulences by applying various controls and to maximize the ride comfort of the QCM. For this purpose a LQR and MPC is used in the same framework. The outcomes are then compared to the controller that performs more effectively. The outcome reveal that the MPC performs better than the other controller. This fact is confirmed by using various performance indexes like integral absolute error (IAE), integral time absolute error (ITAE) and integral square error (ISE).","PeriodicalId":260353,"journal":{"name":"2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131696551","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
Machine Learning based Learning Disability Detection using LMS 基于机器学习的LMS学习障碍检测
2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Pub Date : 2020-10-30 DOI: 10.1109/ICCCA49541.2020.9250761
Masooda M. Modak, Omkar Warade, G. Saiprasad, Shweta Shekhar
{"title":"Machine Learning based Learning Disability Detection using LMS","authors":"Masooda M. Modak, Omkar Warade, G. Saiprasad, Shweta Shekhar","doi":"10.1109/ICCCA49541.2020.9250761","DOIUrl":"https://doi.org/10.1109/ICCCA49541.2020.9250761","url":null,"abstract":"This paper highlights an E-learning system created using Moodle which is an open-source Learning Management System (LMS) that enables a better learning environment between the tutors and students. This system detects two learner profiles i.e. students with Learning Disability (LD) and without Learning Disability (Non-LD) using dedicated courses designed on the basis of various aspects of an LD student. This work also multiple stages of our approach for informal testing used to capture the learning parameters for Dyslexic students. The first stage i.e. data collection has two approaches where the first approach pertains to a smaller age group of 8-10 years with limited parameters whereas the second approach pertains to the age group 11-13 years i.e. grades 6-8 with more parameters. Natural Language Processing (NLP) has been used to perform Speech-to-Text (STT) conversion on the audio responses of the users. The analysis of these responses have been performed in python language. To detect whether the user has LD (Dyslexia in this case) or not, Machine Learning (ML) is used. Two ML algorithms namely Logistic Regression (LR) and Support Vector Machine (SVM) are used to perform binary classification with LD (1) and Non-LD (0) as the two classes of the dataset. The results are shown for both the approaches and comparative analysis shows that the dataset generated in the final approach for capturing parameters involving NLP is better and more robust. LR algorithm for ML shows better results as compared to SVM for performing detection based on the generated dataset.","PeriodicalId":260353,"journal":{"name":"2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121205314","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
A Systematic Literature Review on Machine Learning in Object Detection Security 机器学习在目标检测安全中的系统文献综述
2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Pub Date : 2020-10-30 DOI: 10.1109/ICCCA49541.2020.9250836
Ali Alzaabi, M. A. Talib, A. B. Nassif, Ali Sajwani, Omar Einea
{"title":"A Systematic Literature Review on Machine Learning in Object Detection Security","authors":"Ali Alzaabi, M. A. Talib, A. B. Nassif, Ali Sajwani, Omar Einea","doi":"10.1109/ICCCA49541.2020.9250836","DOIUrl":"https://doi.org/10.1109/ICCCA49541.2020.9250836","url":null,"abstract":"This paper is a Systematic literature review on the topic of machine learning in object detection security, where the work done covers a number of 73 research papers related to this topic, and answers three different research questions, while providing charts, tables, and statistics to summarize the data and give the reader and easy to read summarized overview of the papers related to this topic.","PeriodicalId":260353,"journal":{"name":"2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114256360","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: 4
Sentiment Analysis of Big-Data in Healthcare: Issue and Challenges 医疗大数据情感分析:问题与挑战
2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Pub Date : 2020-10-30 DOI: 10.1109/ICCCA49541.2020.9250841
Saroj Kushwah, Sanjoy Das
{"title":"Sentiment Analysis of Big-Data in Healthcare: Issue and Challenges","authors":"Saroj Kushwah, Sanjoy Das","doi":"10.1109/ICCCA49541.2020.9250841","DOIUrl":"https://doi.org/10.1109/ICCCA49541.2020.9250841","url":null,"abstract":"For a long time, the vital part of data gathering behavior depends on the opinion of the other people has been solved. Due to the continuous growth of popularity and availability of opinions from the various social sites. These are personal blogs and online feedback/reviews sites when there is a lot of data generated by user’s opinions is available freely; have important information that can be very helpful for any organization and consumer as well. Thus new challenges and opportunities are emerging and data scientists to absorb important information from a large amount of data. Now they are using different types of tools and technologies to made decisions? Thus data science helps us to make decisions very fast and in a better way. Data science is very pure science which gives us all detail in bulk. In this paper, we describe the different needs, issues and challenges, and solutions of the problem of big data (sentiments analysis) which possess the result of data science.","PeriodicalId":260353,"journal":{"name":"2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA)","volume":"63 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131137156","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: 4
Deep Neural Network Trained Punjabi Children Speech Recognition System Using Kaldi Toolkit 基于Kaldi工具包的深度神经网络训练旁遮普语儿童语音识别系统
2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Pub Date : 2020-10-30 DOI: 10.1109/ICCCA49541.2020.9250780
Taniya, Vivek Bhardwaj, Virender Kadyan
{"title":"Deep Neural Network Trained Punjabi Children Speech Recognition System Using Kaldi Toolkit","authors":"Taniya, Vivek Bhardwaj, Virender Kadyan","doi":"10.1109/ICCCA49541.2020.9250780","DOIUrl":"https://doi.org/10.1109/ICCCA49541.2020.9250780","url":null,"abstract":"Despite the number of developed Automatic Speech Recognition (ASR) systems for different languages, still no work has been done on children's speech of Punjabi language. Due to the unavailability of children's speech corpus for Punjabi Language, it is a challenging task to collect speech data. In our current work, efforts have been made to collect Punjabi children's speech corpus and build Children ASR system for Indian regional Punjabi language. The recognition rate of ASR systems is observed to be improved drastically by the emergence of Deep Neural Networks (DNN). In our work, the DNN acoustic model has been implemented by varying number of hidden layers. Approximately four hours of Punjabi children's speech corpus has been collected and several experiments have been performed using the DNN modeling technique. Experimental results have revealed that the system has attained 87% accuracy.","PeriodicalId":260353,"journal":{"name":"2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128015471","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: 7
Enhancement of Performance of K-Nearest Neighbors Classifiers for the Prediction of Diabetes Using Feature Selection Method 利用特征选择方法增强k近邻分类器预测糖尿病的性能
2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Pub Date : 2020-10-30 DOI: 10.1109/ICCCA49541.2020.9250887
S. Gupta, Noopur Goel
{"title":"Enhancement of Performance of K-Nearest Neighbors Classifiers for the Prediction of Diabetes Using Feature Selection Method","authors":"S. Gupta, Noopur Goel","doi":"10.1109/ICCCA49541.2020.9250887","DOIUrl":"https://doi.org/10.1109/ICCCA49541.2020.9250887","url":null,"abstract":"For any classification or prediction problem, machine learning algorithm plays an important role in analysis of data. It enhances the performance capability of classifiers and ease the difficulties. In this research analysis, a prediction model is developed for diabetes disease using K-nearest neighbors classification algorithm. The model is implemented in Python using PIMA indian diabetes dataset.The objective of research work is to enhance the prediction capability of KNN classifier with the help of feature selection and normalization of data. A study is also performed to find the optimal number of neighbors on which KNN returns its best result. The F1 score is considered as the main performance metrics for this work.","PeriodicalId":260353,"journal":{"name":"2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134011012","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
Classification of Exudates for Diabetic Retinopathy Prediction using Machine Learning 机器学习用于糖尿病视网膜病变预测的渗出物分类
2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Pub Date : 2020-10-30 DOI: 10.1109/ICCCA49541.2020.9250858
Smitha S Prem, A. Umesh
{"title":"Classification of Exudates for Diabetic Retinopathy Prediction using Machine Learning","authors":"Smitha S Prem, A. Umesh","doi":"10.1109/ICCCA49541.2020.9250858","DOIUrl":"https://doi.org/10.1109/ICCCA49541.2020.9250858","url":null,"abstract":"These days diabetes is one of the most common disease that affects the working age group which eventually leads to complication in retina termed as diabetic retinopathy (DR). Initial screening and diagnosis of DR is essential to avoid partial or complete optical damage. Manual inspection by ophthalmologist is time consuming and tedious process. So in order to lower the work load and to reduce the chances of complete blindness an automated screening system is necessary. The paper proposes an algorithm that focuses on DR detection based on exudates using features such as local binary pattern (LBP) and wavelet transform approximation coefficient matrix. Images are distinguished as exudate and non exudate by using machine learning classification algorithms such as support vector machine (SVM), k-nearest neighbour (KNN), decision tree, random forest (RF) and artificial neural network (ANN).","PeriodicalId":260353,"journal":{"name":"2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA)","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132198613","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
ICCCA 2020 Contributor Page ICCCA 2020贡献者页面
2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Pub Date : 2020-10-30 DOI: 10.1109/iccca49541.2020.9250853
{"title":"ICCCA 2020 Contributor Page","authors":"","doi":"10.1109/iccca49541.2020.9250853","DOIUrl":"https://doi.org/10.1109/iccca49541.2020.9250853","url":null,"abstract":"","PeriodicalId":260353,"journal":{"name":"2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA)","volume":"110 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134564373","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
A Comparative Analysis of Different Regression Models on Predicting the Spread of Covid-19 in India 不同回归模型预测新冠肺炎在印度传播的比较分析
2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Pub Date : 2020-10-30 DOI: 10.1109/ICCCA49541.2020.9250748
Mrittika Chakraborty, A. Mukhopadhyay, U. Maulik
{"title":"A Comparative Analysis of Different Regression Models on Predicting the Spread of Covid-19 in India","authors":"Mrittika Chakraborty, A. Mukhopadhyay, U. Maulik","doi":"10.1109/ICCCA49541.2020.9250748","DOIUrl":"https://doi.org/10.1109/ICCCA49541.2020.9250748","url":null,"abstract":"According to the World Health Organization (WHO) Situation Reports of Corona Virus Disease(Covid-19), as on 15th May 2020, India has 81,970 totals confirmed cases, 2649 total deaths and is still within the limit of community transmission phase. In this study, we analyze the spread of the disease and the fatalities caused up to 15th May 2020, as per the data obtained. A granular computing based regression model, namely Granular Box Regression is used along with Linear Regression for comparative analysis to study the increase in the number of confirmed cases and deaths based on days and an increase in the number of samples tested per day. A separate analysis is also conducted to evaluate the performance of Polynomial Regression on the same dataset. The performance of the different models has been evaluated using R-squared, Mean Absolute Error, Root Mean Squared Error, and Mean Squared Error values.","PeriodicalId":260353,"journal":{"name":"2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA)","volume":"520 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123061027","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: 5
Identification of Faults in Parallel Transmission Lines Using Discrete Meyer Wavelet 离散Meyer小波在并联输电线路故障识别中的应用
2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Pub Date : 2020-10-30 DOI: 10.1109/ICCCA49541.2020.9250831
Gaurav Kapoor, R. Shaw
{"title":"Identification of Faults in Parallel Transmission Lines Using Discrete Meyer Wavelet","authors":"Gaurav Kapoor, R. Shaw","doi":"10.1109/ICCCA49541.2020.9250831","DOIUrl":"https://doi.org/10.1109/ICCCA49541.2020.9250831","url":null,"abstract":"In this paper, a discrete Meyer wavelet-based quick fault identification scheme is developed for the parallel transmission line (PTL) connected with wind-generation power plant (WGPP). The proposed scheme uses the characteristics extracted from the discrete Meyer wavelet to identify the fault type. The parallel circuit power system has been modeled in MATLAB/Simulink. The outcomes of simulation signify that the proposed scheme perform effectively to give 100% accuracy and authenticate the effectiveness of the proposed scheme.","PeriodicalId":260353,"journal":{"name":"2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134324306","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
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