2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)最新文献

Comparative study of Automotive Sensor technologies used for Unmanned Driving 用于无人驾驶的汽车传感器技术比较研究

2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM) Pub Date : 2021-01-19 DOI: 10.1109/iccakm50778.2021.9357731

Kritika Rana, P. Kaur

{"title":"Comparative study of Automotive Sensor technologies used for Unmanned Driving","authors":"Kritika Rana, P. Kaur","doi":"10.1109/iccakm50778.2021.9357731","DOIUrl":"https://doi.org/10.1109/iccakm50778.2021.9357731","url":null,"abstract":"Autonomous vehicles utilize a large amount of data from Machine Learning, Neural networks, Image recognition systems for building the techniques that can drive autonomously. Autonomous vehicles depend on sensors for measuring conditions of roads and for making decisions while driving, and safety depends on the consistency of these sensors. Autonomous vehicles are robotic systems that are not only capable of regulating their motion in response to the sensory data they have obtained, but are also capable of behaving intelligently (or flexibly) in their environment. Autonomous vehicles must have the ability to see the things around it in order to know if they need to drive, to stop and turn, and handle the unexpected situations they come across. Each and every sensor has its own types of strengths and weaknesses in terms of range, recognition and reliability. Moreover, each sensor has its own advantages as well as disadvantages. This paper discusses the features of sensors used in autonomous vehicles and compares different set of sensors. We have used a Kalman filter for the detection and tracking of the car. We have used different parameters to see how tracking quality is affected by the tracker and also adjust the tracking filter to specify a different motion.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122768860","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

Arabic Speech Emotion Recognition Method Based On LPC And PPSD 基于LPC和PPSD的阿拉伯语语音情感识别方法

2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM) Pub Date : 2021-01-19 DOI: 10.1109/ICCAKM50778.2021.9357769

O. A. Mohammad, M. Elhadef

{"title":"Arabic Speech Emotion Recognition Method Based On LPC And PPSD","authors":"O. A. Mohammad, M. Elhadef","doi":"10.1109/ICCAKM50778.2021.9357769","DOIUrl":"https://doi.org/10.1109/ICCAKM50778.2021.9357769","url":null,"abstract":"This research detects and recognize the emotions in Arabic speech audio files that contains records of human voices with different emotion classes (sad, happy, surprised, and questioning). In the area of emotion detection, when a person becomes emotional, his voice is adjusted based on the state of emotion. As the acoustic features like pressure, strength and loudness varies from a state of emotion to another. However, in the detection of feelings, the classification and modeling part of the features gets priority with the extracted features. Therefore, extracting the best features that describes the emotions stats is the most challenging task. This paper proposes an efficient approach to recognize the Arabic speech emotions. The presented method contains three main phases, signal preprocessing phase for noise removal and signal bandwidth reduction, feature extraction phase using a combination of Linear Predictive Codes (LPC) and the 10-degree polynomial Curve fitting Coefficients over the periodogram power spectral density function of the speech signal and machine learning phase using various machine learning algorithms (ANN, KNN, SVM, Decision Tree, Logistic Regression) and compare between their accuracy results to get the best accuracy.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114413798","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

Prediction based Load Balancing and VM Migration in Big Data Cloud Environment 大数据云环境下基于预测的负载均衡与虚拟机迁移

2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM) Pub Date : 2021-01-19 DOI: 10.1109/iccakm50778.2021.9357701

P. Tamilarasi, D. Akila

引用次数: 3

Developing Mapping and allotment in Volunteer Cloud systems using Reliability Profile algorithms in a virtual machine 在虚拟机中使用可靠性概要算法开发志愿者云系统中的映射和分配

2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM) Pub Date : 2021-01-19 DOI: 10.1109/iccakm50778.2021.9357710

S. Jeyalaksshmi, M. S. Nidhya, G. Suseendran, Souvik Pal, D. Akila

引用次数: 7

Credit Card Fraud Detection System based on Operational & Transaction features using SVM and Random Forest Classifiers 基于支持向量机和随机森林分类器的操作和交易特征的信用卡欺诈检测系统

2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM) Pub Date : 2021-01-19 DOI: 10.1109/ICCAKM50778.2021.9357709

C. Sudha, D. Akila

引用次数: 8

[Copyright notice] (版权)

2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM) Pub Date : 2021-01-19 DOI: 10.1109/iccakm50778.2021.9357737

引用次数: 0

Towards Technology Attitude comparison of Hungarian and Indian Student for Real- Time 匈牙利和印度学生对技术的实时态度比较

2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM) Pub Date : 2021-01-19 DOI: 10.1109/ICCAKM50778.2021.9357741

C. Verma, Z. Illés, Veronika Stoffová, Viktoria Bakonvi

{"title":"Towards Technology Attitude comparison of Hungarian and Indian Student for Real- Time","authors":"C. Verma, Z. Illés, Veronika Stoffová, Viktoria Bakonvi","doi":"10.1109/ICCAKM50778.2021.9357741","DOIUrl":"https://doi.org/10.1109/ICCAKM50778.2021.9357741","url":null,"abstract":"This paper used descriptive analysis and nonparametric test to explore the significant difference between Indian and Hungarian students' attitudes. A systematic data analysis was performed with primary samples (314) gathered from Indian and Hungarian universities. We proposed two significant hypotheses to achieve the main objective of the study. On the one hand, we did not find a significant difference for “Informative and quality based study” and “Confidence and motivation”, and another hand, both country students, have dissimilar thinking towards “Independent learning”, “Admission/job placement/examination”, “Future acceptance for 21st century” and “Rapid deliver and share content”. The descriptive analysis also proved that both country students have a positive attitude towards technology. Results of the paper also proved that Hungarian students think more positively as compared to Indian students. We proposed a comparative technique to be implemented as a real-time web module with the university website's integration. This method may overcome the traditional offline differential approaches.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115513731","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

Classification of Different Stages of Diabetic Retinopathy using Convolutional Neural Networks 卷积神经网络在糖尿病视网膜病变分期中的应用

2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM) Pub Date : 2021-01-19 DOI: 10.1109/ICCAKM50778.2021.9357735

P. Saranya, K. Umamaheswari, M. Sivaram, Chirag Jain, Debarpan Bagchi

{"title":"Classification of Different Stages of Diabetic Retinopathy using Convolutional Neural Networks","authors":"P. Saranya, K. Umamaheswari, M. Sivaram, Chirag Jain, Debarpan Bagchi","doi":"10.1109/ICCAKM50778.2021.9357735","DOIUrl":"https://doi.org/10.1109/ICCAKM50778.2021.9357735","url":null,"abstract":"Diabetic Mellitus is the most familiar disease around the globe. Long prevalence of diabetes causes several problems related to health. The most common issue is Diabetic Retinopathy (DR). Diabetic retinopathy is a situation in which the vessels inside the retina are vandalized, leaking harmful substances and fluids in the surrounding tissue resulting in hemorrhages, micro aneurysms in the eye and further into partial or complete vision loss. This disease if treated in the early stage can help to prevent vision loss, but since it takes time for diagnosis and there is a shortage of ophthalmologists' patients suffer vision loss even before diagnosis. Hence, early detection of DR may help in reducing the problem. Therefore, in this paper we investigate various approaches to understand the process of detecting Diabetic Retinopathy as accurately as possible and classifying them into different grades of treatable DR (NPDR) namely LO, L1 DR, L2 DR and Proliferate DR (PDR) using Deep Learning and Image Processing techniques also making some improvisations on the same to enhance the capability of other existing systems.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116041301","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

Design Of Estimator For Level Monitoring Using Data Driven Model 基于数据驱动模型的液位监测估计器设计

2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM) Pub Date : 2021-01-19 DOI: 10.1109/iccakm50778.2021.9357704

Vighnesh Shenoy, K. Santhosh

引用次数: 1

Complexity of Risk Management in Danish Power Transmission Industry: Managing Disregarded and Minor risks 丹麦输电行业风险管理的复杂性:管理被忽视的和次要的风险

2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM) Pub Date : 2021-01-19 DOI: 10.1109/iccakm50778.2021.9357740

L. P. Brasen, Torben Tambo

引用次数: 0