2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM)最新文献_第4页

An Analysis of Financial Fraud through PNB Bank Scam and its technical Implications 通过PNB银行诈骗分析金融欺诈及其技术意义

2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) Pub Date : 2020-01-01 DOI: 10.1109/ICCAKM46823.2020.9051500

Gurinder Singh, S. Srivastav, Anubhuti Gupta, V. Garg

引用次数: 3

An Alternative to Create Fast Conceptual Designs in Mechatronics Engineering 在机电工程中创建快速概念设计的另一种选择

2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) Pub Date : 2020-01-01 DOI: 10.1109/ICCAKM46823.2020.9051465

Hector Morano-Okuno, Guillermo Sandoval-Benitez, Rafael Caltenco-Castillo, L. Villagómez-Guerrero, Emmanuel Garcia-Moran, A. Garcia-Garcia

引用次数: 0

Review of the Machine Learning Techniques in Road Crashes 道路交通事故中的机器学习技术综述

2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) Pub Date : 2020-01-01 DOI: 10.1109/ICCAKM46823.2020.9051506

S. Srivastava, Ashwini Bagga, Rajveer Singh Shekhawat

引用次数: 6

Study on Risk Allocation Mechanism of Cultural PPP in China 中国文化PPP风险分担机制研究

2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) Pub Date : 2020-01-01 DOI: 10.1109/ICCAKM46823.2020.9051524

Jieyao Zhang, Yingting Zhu

引用次数: 2

A Comparative Study of Handwriting Recognition Techniques 笔迹识别技术的比较研究

2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) Pub Date : 2020-01-01 DOI: 10.1109/ICCAKM46823.2020.9051464

P. C. Vashist, Anmol Pandey, A. Tripathi

引用次数: 7

Music Instrument Recognition using Machine Learning Algorithms 使用机器学习算法识别乐器

2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) Pub Date : 2020-01-01 DOI: 10.1109/ICCAKM46823.2020.9051514

P. K. Shreevathsa, M. Harshith, Abhishek Rao M, Ashwini

{"title":"Music Instrument Recognition using Machine Learning Algorithms","authors":"P. K. Shreevathsa, M. Harshith, Abhishek Rao M, Ashwini","doi":"10.1109/ICCAKM46823.2020.9051514","DOIUrl":"https://doi.org/10.1109/ICCAKM46823.2020.9051514","url":null,"abstract":"In this is modern era, everyone listens to and plays music. Music is diverse across the globe. It is a language that speaks by itself and is the fulcrum of all the arts. We can say that rich legacy of this flawless art is infinity and beyond. It has the ability to mesmerise one and all. If there was a way in which we could get to know the instruments that are being played in the music, it would be more interesting. So, we can classify the music based on certain instruments. In last two decades, researchers are actively associated with human perception towards the study of Musical Instruments. Our work is based on designing an application that recognizes the instruments that are present in a given Music. It is no hidden fact anymore that neural network in general are dominating every single aspect and application in this world. The fact that a neural network can do a task that can be done by a human brain efficiently if we train them properly tells us a lot about their advancement in the day to day world. Thus, we make use of neural networks for the recognition of an instrument in a piece of music. Hence, we make use of ANN (Artificial Neural Network) and CNN(Convolutional Neural Network). We've considered eight different music instruments. The models thus built play a major role in finding the music instruments being played. Various features are extracted in the process and the result is found.","PeriodicalId":136544,"journal":{"name":"2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"303 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124400433","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

Classification of Homogeneous Sites using IRS-P5 Satellite Imagery 基于IRS-P5卫星图像的同质站点分类

2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) Pub Date : 2020-01-01 DOI: 10.1109/ICCAKM46823.2020.9051510

R. Schowengerdt, D. Lu, Nur Anis Mahmon, Ajay D. Nagne, Vijay P. Singh

{"title":"Classification of Homogeneous Sites using IRS-P5 Satellite Imagery","authors":"R. Schowengerdt, D. Lu, Nur Anis Mahmon, Ajay D. Nagne, Vijay P. Singh","doi":"10.1109/ICCAKM46823.2020.9051510","DOIUrl":"https://doi.org/10.1109/ICCAKM46823.2020.9051510","url":null,"abstract":"Remotely Sensed data is an important component of Land Use/Land Cover (LU/LC) studies. This paper compares the performance of ISODATA classification with Mahalanobis Distance classification for Arsikere semi-urban study area of Karnataka State, INDIA using IRS-P5 PANF satellite imagery. The Arsikere study area is a homogeneous region comprised primarily of water features mixed with impervious features. During the beginning of experiment, ISODATA unsupervised classification was applied on IRS data. Later, Mahalanobis Distance classification was applied on IRS data with 6000 training sites and 100 validation points for water, vegetation, soil and impervious surface features that were randomly generated using a stratified sampling approach. The LU /LC data associated with these points were then compared with Topographic Maps (Survey of India, No. D43Q3, D43Q7) and Ground Truth Data for performance analysis. Based on the confusion matrices obtained for the sample set, the OCA, Kappa statistics were compared with ISODATA. The experimental analysis shows that unsupervised ISODATA classification provides accuracy of 89% in Arsikere, semi-urban area, however Mahalanobis distance classification give up 93% OCA with TS = 6000 and VS = 100.","PeriodicalId":136544,"journal":{"name":"2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"485 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123405971","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

Analyzing Product Usage Based on Twitter Users Based on Datamining Process 基于数据挖掘过程的Twitter用户产品使用分析

2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) Pub Date : 2020-01-01 DOI: 10.1109/ICCAKM46823.2020.9051488

S. Umamaheswari, K. Harikumar

{"title":"Analyzing Product Usage Based on Twitter Users Based on Datamining Process","authors":"S. Umamaheswari, K. Harikumar","doi":"10.1109/ICCAKM46823.2020.9051488","DOIUrl":"https://doi.org/10.1109/ICCAKM46823.2020.9051488","url":null,"abstract":"This paper further enhances the techniques and chronological methods in order to perform the corresponding manipulation and further prediction analysis. We have acquired a real time dataset based on the twitter user's comments sections. The uniqueness of the dataset is that we have extracted only the particular comments which syntheses a particular word based on the product. Then further repetitive extraction is made in order to complete the dataset. Our dataset has three columns based on the ideology that the particular user or our focused subject has enhanced any detail about the product that we are observing. In this precise dataset we have taken the subject about the products usage by the users that have been manufactured by the companies Google and Apple. Both technology giants have well versed their technology reign in this era and their further focused in their upcoming cyber projects and their products will be more advanced in future. As they are in involved in further optimization in their devices and increasing their specifications. It would be complex task to accomplish their project without the feedback, pros and cons of their predecessor projects. They can extract such features from twitter api dataset and they could further enhance their product. They could analyze the drawback, whether the product has reached the market and came out with success all type of this information can be extracted from social media instead of conducting a survey. Such process would be more hectic classification and we cannot predict any accurate results. So we proceed with the social media Data mining Process.","PeriodicalId":136544,"journal":{"name":"2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131391754","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

Response Surface Methodology based Multi-Objective Optimization of Stock Bridge Damper for Pump Induced Floor Vibration 基于响应面法的泵致底板振动Stock Bridge阻尼器多目标优化

2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) Pub Date : 2020-01-01 DOI: 10.1109/ICCAKM46823.2020.9051456

M. S. Rahman, A. Hasnat, M. A. Saqif

引用次数: 2

Modified indel treatment for accurate Phylogenetic Tree construction 改进indel处理方法，用于准确构建系统发育树

2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) Pub Date : 2020-01-01 DOI: 10.1109/ICCAKM46823.2020.9051493

Shashank Awasthi, Asim Kumar Mahadani, G. Sanyal, P. Bhattacharjee

{"title":"Modified indel treatment for accurate Phylogenetic Tree construction","authors":"Shashank Awasthi, Asim Kumar Mahadani, G. Sanyal, P. Bhattacharjee","doi":"10.1109/ICCAKM46823.2020.9051493","DOIUrl":"https://doi.org/10.1109/ICCAKM46823.2020.9051493","url":null,"abstract":"Phylogenetic tree reconstruction has become very rapid-growing field because of its application in the broad range of biological areas. Phylogenetic reconstruction is enhanced by different statistical approaches. Lots of studies have suggested the acceptance of Gap (indel), in evolutionary tree because it contains huge phylogenetic information. We used simulation studies that explore the relative performance of different Indel (gap) treating approaches in a phylogenetic analysis with respect to the accuracy of the phylogenetic tree. We evaluated the approaches using indel rich highly similar ten Polychilos species DNA sequences. Our results indicate that Jukes-Cantor(J-C) with indel model(Indel as a separate state in J-C nucleotide substitution model) performs better than all other indel treating methods (Completely removing the indels, gaps as fifth state in an evolutionary model, combined indel distance and Nucleotide distance, gaps as missing data,) in phylogenetic tree construction using the selected dataset. We have used bootstrapping method and Robinson-Foulds distance method to test the phylogenetic trees constructed utilizing the gap incorporation techniques. The phylogenetic tree constructed with the framework of J-C with indel model shows 81% accuracy while other shows less accuracy.","PeriodicalId":136544,"journal":{"name":"2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124625649","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