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Machine Learning in Finance 金融中的机器学习
2018 IEEE Punecon Pub Date : 2018-11-01 DOI: 10.1109/PUNECON.2018.8745424
Dhruvil Trivedi, Ashish Bhagchandani, Rutul Ganatra, M. Mehta
{"title":"Machine Learning in Finance","authors":"Dhruvil Trivedi, Ashish Bhagchandani, Rutul Ganatra, M. Mehta","doi":"10.1109/PUNECON.2018.8745424","DOIUrl":"https://doi.org/10.1109/PUNECON.2018.8745424","url":null,"abstract":"The modern developments in machine learning have increased the diversity of its usage. With the help of machine learning it has become easy for mankind to analyse and decode the proper information. In this paper we have created an algorithm which can predict the analysis of financial market. The method used in algorithm is Logistic Regression to multiple variables. In this method, with the help of previous datasets of financial market we can predict the future analysis, which will surely decrease the human efforts and margin of error. The proposal of this algorithm is to create an impact in the near future and to make easy for humans to study the financial market.","PeriodicalId":166677,"journal":{"name":"2018 IEEE Punecon","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132691965","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
Enhanced Image Classification with Feature Level Fusion of Niblack Thresholding and Thepade’s Sorted N-ary Block Truncation Coding using Ensemble of Machine Learning Algorithms 基于集成机器学习算法的Niblack阈值特征融合和thepage排序N-ary块截断编码增强图像分类
2018 IEEE Punecon Pub Date : 2018-11-01 DOI: 10.1109/PUNECON.2018.8745410
Sudeep D. Thepade, Sanjay R. Sange, Rik Das, Suyash Luniya
{"title":"Enhanced Image Classification with Feature Level Fusion of Niblack Thresholding and Thepade’s Sorted N-ary Block Truncation Coding using Ensemble of Machine Learning Algorithms","authors":"Sudeep D. Thepade, Sanjay R. Sange, Rik Das, Suyash Luniya","doi":"10.1109/PUNECON.2018.8745410","DOIUrl":"https://doi.org/10.1109/PUNECON.2018.8745410","url":null,"abstract":"The paper portrays novel enhanced image classification approach with fusion of Machine Learning Algorithms at Feature Level as well as Decision Level with help of Niblack Thresholding and Thepade’s Sorted N-ary Block Truncation Coding. The proposed fusion based image classification method is experimented with help of a database with total one thousand image samples covering ten assorted image categories with 100 images per category. Classification Accuracy is taken into account for the performance evaluation purpose of existing and the proposed Image Classification Technique. The results of experimental analysis explicitly reveal the performance improvement with proposed TSnBTC than Niblack thresholding, also the fusion of these two methods reveal further better performance with several Classifiers proving the worth of proposed fusion based image classification technique. Overall the higher classification accuracy is given by Random Forest immediately followed by ensemble of Random Forest with SVM.","PeriodicalId":166677,"journal":{"name":"2018 IEEE Punecon","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130410465","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
Optimization of Storm Water Drainage Network using Ant Colony System 基于蚁群系统的暴雨排水网络优化
2018 IEEE Punecon Pub Date : 2018-11-01 DOI: 10.1109/PUNECON.2018.8745434
S. Apte, M. Kshirsagar, K. Khare
{"title":"Optimization of Storm Water Drainage Network using Ant Colony System","authors":"S. Apte, M. Kshirsagar, K. Khare","doi":"10.1109/PUNECON.2018.8745434","DOIUrl":"https://doi.org/10.1109/PUNECON.2018.8745434","url":null,"abstract":"The major objectives of storm water management include protecting the environment; minimize the event of flooding and supporting healthy streams which result in healthier and more sustainable communities. Effective storm water management gives ecological, social, and financial advantages to the community. The present study focuses on optimization of storm water collection system by application of proper routing algorithm. The main aim of the present study is to minimize the total cost associated with construction of storm water drains without compromising its discharge carrying capability. In this study a meta-heuristic approach based on Ant Colony System is used to optimize the multi-objective problem of storm-water drainage system. An illustrative example inspired from real life storm-water management problem has also been solved. The problem has been solved by varying the weightages of the heuristic values for drainage length and slope. The findings indicate that present approach provides a scientifically credible solution for storm-water drainage network optimization in urban areas.","PeriodicalId":166677,"journal":{"name":"2018 IEEE Punecon","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132362244","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
Deep Learning for Cancer Cell Detection and Segmentation: A Survey 肿瘤细胞检测与分割的深度学习研究进展
2018 IEEE Punecon Pub Date : 2018-11-01 DOI: 10.1109/PUNECON.2018.8745420
Priyank Hajela, A. Pawar, Swati Ahirrao
{"title":"Deep Learning for Cancer Cell Detection and Segmentation: A Survey","authors":"Priyank Hajela, A. Pawar, Swati Ahirrao","doi":"10.1109/PUNECON.2018.8745420","DOIUrl":"https://doi.org/10.1109/PUNECON.2018.8745420","url":null,"abstract":"The early stage cancer detection is required to provide proper treatment to the patient and reduce the risk of death due to cancer as detection of these cancer cells at later stages lead to more suffering and increases chances of death. Researchers have been working on and developing various deep learning solutions to produce encouraging results. In this paper, we explore the various techniques and technologies that are already in practice to detect the cancer cells in their early stages and works presently going on in the industry.","PeriodicalId":166677,"journal":{"name":"2018 IEEE Punecon","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124356411","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
Techno-Economic Sizing of Renewable Energy Resources for Energy Improvements in Jaggery Making Process 砂石炼制过程中能源改进的可再生能源技术经济规模
2018 IEEE Punecon Pub Date : 2018-11-01 DOI: 10.1109/PUNECON.2018.8745390
T. Hasarmani, R. Holmukhe, Nilkanth Ganer
{"title":"Techno-Economic Sizing of Renewable Energy Resources for Energy Improvements in Jaggery Making Process","authors":"T. Hasarmani, R. Holmukhe, Nilkanth Ganer","doi":"10.1109/PUNECON.2018.8745390","DOIUrl":"https://doi.org/10.1109/PUNECON.2018.8745390","url":null,"abstract":"Electricity is one of the major infrastructures for economical development of any country. Since last 3–4 years, India is recognized as the top five fastest growing country in the world with an average annual growth rate of 7.3% to 7.6%. Agriculture based small-scale businesses play vital role in economic development of India. Conventional jaggery making is one of the oldest small-scale business enterprises, which promotes local job employment and entrepreneurship opportunities in rural India. Though more than 70 % of worldwide jaggery demand is met by India by exporting 3 Lakh MT of jaggery, worth of Rs. 1500 crores in F. Y 2017-18, almost all jaggery units are located in remote places in rural India. Because of their remote geographical position, grid electricity supply of such conventional jaggery units is characterized by frequent power outages with poor quality of supply. Thus, power quality issues are hampering the productivity and quality of final product of this sector. However, India is located in the equatorial sun belt of the earth, which receives an average solar radiation in the range of 4-7 Kwh/m2/day for 300 clear sunny days in a year in most parts of the country. Therefore, solar energy can be a possible alternative that can provide uninterrupted power supply to farmers and people involved in such agriculture based business enterprises. In this research, several jaggery-manufacturing units in various parts of the country are surveyed to understand ground reality of problems faced by farmers and local people. Thereafter one of the jaggery units located at Chakan, near Pune metropolitan city, India is selected to provide technology up gradation and possible reduction in green house gas (GHG) emission using solar PV system. This research paper also presents, PVsyst simulation tool for design and Performance assessment of proposed solar power plants.","PeriodicalId":166677,"journal":{"name":"2018 IEEE Punecon","volume":"401 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115992087","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
Predictive Modeling Of Jobs Delay In Product Manufacturing Using Multivariate Statistics 基于多元统计的产品制造作业延迟预测模型
2018 IEEE Punecon Pub Date : 2018-11-01 DOI: 10.1109/PUNECON.2018.8745385
M. Bajpai, Parvesh Sharma
{"title":"Predictive Modeling Of Jobs Delay In Product Manufacturing Using Multivariate Statistics","authors":"M. Bajpai, Parvesh Sharma","doi":"10.1109/PUNECON.2018.8745385","DOIUrl":"https://doi.org/10.1109/PUNECON.2018.8745385","url":null,"abstract":"In this paper we have investigated a method of prediction of daily number of job delays in a manufacturing line that would happen based on extraction of useful variables that contribute to the job delays in manufacturing line of products and later apply the same methodology to predict delay jobs over next two years using multivariate statistical approach.","PeriodicalId":166677,"journal":{"name":"2018 IEEE Punecon","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116239179","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
Statistical Analysis of Depot-wise PMPML Data 仓库PMPML数据的统计分析
2018 IEEE Punecon Pub Date : 2018-11-01 DOI: 10.1109/PUNECON.2018.8745427
R. Viswanath, S. Kolse, V. Tiwari
{"title":"Statistical Analysis of Depot-wise PMPML Data","authors":"R. Viswanath, S. Kolse, V. Tiwari","doi":"10.1109/PUNECON.2018.8745427","DOIUrl":"https://doi.org/10.1109/PUNECON.2018.8745427","url":null,"abstract":"The study deals with the Depot-wise assessment of buses for the months of 2017 using the data available on the Pune Municipal Corporation Open Data Portal. Data consists of statistical report of 13 depots on various bus transport related parameters. We rank the bus depots on the basis of efficiency, performance and earnings per bus per day. Further, we also compare the buses under BRT system with the regular buses under PMPML.‘Smart City-Villages track’","PeriodicalId":166677,"journal":{"name":"2018 IEEE Punecon","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126311798","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
Statistical Analysis of Water Pollution Levels at various Locations in Pune 浦那不同地点水污染水平的统计分析
2018 IEEE Punecon Pub Date : 2018-11-01 DOI: 10.1109/PUNECON.2018.8745435
J. Kumari, M. Sanap
{"title":"Statistical Analysis of Water Pollution Levels at various Locations in Pune","authors":"J. Kumari, M. Sanap","doi":"10.1109/PUNECON.2018.8745435","DOIUrl":"https://doi.org/10.1109/PUNECON.2018.8745435","url":null,"abstract":"The current study deals with the water quality data available on the Pune Municipal Corporation (PMC) Open Data Portal. The readings of water quality parameters are collected on every Monday from March 2016 to March 2017 at different locations in Pune. The data consists of observations at twenty one locations and on thirteen water quality parameters like temperature of water, pH level, Biochemical and Chemical Oxygen Demand, Dissolved Oxygen etc.The main purpose of the analysis is comparing the water quality at different locations and identifying the most polluted location using different statistical tools. Techniques such as Principal component analysis, Factor analysis, Multivariate Analysis of Variance, Cluster analysis, Jonckheere’s trend test are used for this analysis. We check if seasonal variation (monsoon and non-monsoon) affects the water quality. We come up with two main factors, viz. oxygen level and salt content in water to summarize the conclusions based on initially recorded 13 parameters. Water quality of Khadakwasla Reservoir is the best among all the locations whereas Nallahs have poor quality of water. Dissolved oxygen is high in the lakes and Khadakwasla Reservoir, but its levels are decreasing with time. Water quality at different locations is not the same and pollution level is maximum in summer and minimum during the monsoon.","PeriodicalId":166677,"journal":{"name":"2018 IEEE Punecon","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130423137","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
Sentiment Analysis using Deep Learning on GPU 基于GPU的深度学习情感分析
2018 IEEE Punecon Pub Date : 2018-11-01 DOI: 10.1109/PUNECON.2018.8745401
S. Kolekar, H. Khanuja
{"title":"Sentiment Analysis using Deep Learning on GPU","authors":"S. Kolekar, H. Khanuja","doi":"10.1109/PUNECON.2018.8745401","DOIUrl":"https://doi.org/10.1109/PUNECON.2018.8745401","url":null,"abstract":"Sentiment analysis is performed using deep learning approach on airline tweet dataset on GPU and CPU. The tweet airline dataset is downloaded from internet. In word embedding technique, we represent the text with word vector by mapping tweet's token to already pre-trained word vector like APNews corpus. We split the word vector airline dataset into training and testing dataset and build the proposed model. We feed such word vectors to deep learning model like convolutional neural network and analyze the given tweet as either positive or negative opinion. The proposed model is trained using training dataset and trained model is used to validate the testing dataset on GPU and CPU. The experiment on GPU is helpful for parallel and speedup computing. We got the training accuracy 98% and testing accuracy 90% of airline tweet dataset on GPU.","PeriodicalId":166677,"journal":{"name":"2018 IEEE Punecon","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126625608","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
Industry 4.0: Opportunities for Analytics 工业4.0:分析的机会
2018 IEEE Punecon Pub Date : 2018-11-01 DOI: 10.1109/PUNECON.2018.8745382
P. Ramdasi, Prasad Ramdasi
{"title":"Industry 4.0: Opportunities for Analytics","authors":"P. Ramdasi, Prasad Ramdasi","doi":"10.1109/PUNECON.2018.8745382","DOIUrl":"https://doi.org/10.1109/PUNECON.2018.8745382","url":null,"abstract":"Industry 4.0 is the digital transformation of industrial markets. The information-intensive transformation demands a connected environment. Apart from people, processes, services and systems, important key contributors are- IoT-enabled industrial assets and data. All these contributors currently operate in silos in a closed environment. The fourth industrial design thinking talks about developing a digital thread to generate actionable information and ecosystems of industrial innovation and collaboration. This paper explains the areas where humongous significant industrial data is generated and variety of meaningful insights that can be extracted. While explaining stepwise evolution of Industry 4.0 different types of Big Data Analytics tools and benefits of Analytics are also discussed.","PeriodicalId":166677,"journal":{"name":"2018 IEEE Punecon","volume":"52 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114228154","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
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