Pub Date : 2017-12-01DOI: 10.1109/ICSOFTCOMP.2017.8280083
P. K. Bharne, S. Prabhune
Swarm intelligence (SI) is powerful, newly emerged domain belongs to the field of Artificial Intelligence. The SI is inspired from the behavior of biological entities such as honey bee, fireflies, bat, cuckoo, ant etc. The basic idea of SI is that, the collective behavior of agents with a very limited set of rules. In recent SI is applied in various kind of application including appropriate stock market price movement. This paper makes survey of the use of SI in a stock market application. The paper initially describes the details of a stock market, SI and its various types of algorithm and finally describes some recent SI algorithm based approaches for stock market prediction. From this survey, we found that to improve the efficiency of SI and make optimized results, SI is combined with other approaches like Artificial Neural Network (ANN), Machine Learning ML etc. We found that the combination of SI and ANN produce more accurate and optimized results for stock price prediction than the combination of SI and machine learning. Finally paper provides the comparative analysis of recent techniques on the basis of a type of SI used, the algorithm with which SI is combined, comparable algorithm, the dataset used for performance evaluation, its advantages and future trend for each technique. Future trend will be used for further research in the field of SI and stock market applications.
{"title":"Survey on combined swarm intelligence and ANN for optimized daily stock market price","authors":"P. K. Bharne, S. Prabhune","doi":"10.1109/ICSOFTCOMP.2017.8280083","DOIUrl":"https://doi.org/10.1109/ICSOFTCOMP.2017.8280083","url":null,"abstract":"Swarm intelligence (SI) is powerful, newly emerged domain belongs to the field of Artificial Intelligence. The SI is inspired from the behavior of biological entities such as honey bee, fireflies, bat, cuckoo, ant etc. The basic idea of SI is that, the collective behavior of agents with a very limited set of rules. In recent SI is applied in various kind of application including appropriate stock market price movement. This paper makes survey of the use of SI in a stock market application. The paper initially describes the details of a stock market, SI and its various types of algorithm and finally describes some recent SI algorithm based approaches for stock market prediction. From this survey, we found that to improve the efficiency of SI and make optimized results, SI is combined with other approaches like Artificial Neural Network (ANN), Machine Learning ML etc. We found that the combination of SI and ANN produce more accurate and optimized results for stock price prediction than the combination of SI and machine learning. Finally paper provides the comparative analysis of recent techniques on the basis of a type of SI used, the algorithm with which SI is combined, comparable algorithm, the dataset used for performance evaluation, its advantages and future trend for each technique. Future trend will be used for further research in the field of SI and stock market applications.","PeriodicalId":118765,"journal":{"name":"2017 International Conference on Soft Computing and its Engineering Applications (icSoftComp)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114282900","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}
Pub Date : 2017-12-01DOI: 10.1109/ICSOFTCOMP.2017.8280081
Ankit Dubey, Mayuri Lakhani, Shivansh Dave, Jignesh J. Patoliya
Traffic congestion and higher average waiting time has been a problem for a very long time. The purpose of this project is to design and implement a traffic system that is adaptive to nature of the traffic in respective lanes. Most of traffic signals are having counters according to which the traffic lights of different lanes get changed one by one. To solve this problem of fixed wait time, counter for any traffic, we proposed this adaptive traffic system which is connected to internet so that different lanes can be monitored constantly. The data obtained from different lanes are examined and controlled by Central Traffic Control Office from one place. Data obtained thus gives value of traffic congestion in particular lane, according to which traffic lights are programmed to work. If the first lane is having less traffic than other lane, then the signal lights will be decided on the basis of less wait time and less pollution. This system also gives idea to drivers to choose the path with less congestion. This system is also useful in emergency and VIP clearance and in traffic survey. This increases the efficiency of traffic clearance. This also reduces pollution and traffic congestion, thus being an Adaptive Traffic Control System using Internet of Things.
{"title":"Internet of Things based adaptive traffic management system as a part of Intelligent Transportation System (ITS)","authors":"Ankit Dubey, Mayuri Lakhani, Shivansh Dave, Jignesh J. Patoliya","doi":"10.1109/ICSOFTCOMP.2017.8280081","DOIUrl":"https://doi.org/10.1109/ICSOFTCOMP.2017.8280081","url":null,"abstract":"Traffic congestion and higher average waiting time has been a problem for a very long time. The purpose of this project is to design and implement a traffic system that is adaptive to nature of the traffic in respective lanes. Most of traffic signals are having counters according to which the traffic lights of different lanes get changed one by one. To solve this problem of fixed wait time, counter for any traffic, we proposed this adaptive traffic system which is connected to internet so that different lanes can be monitored constantly. The data obtained from different lanes are examined and controlled by Central Traffic Control Office from one place. Data obtained thus gives value of traffic congestion in particular lane, according to which traffic lights are programmed to work. If the first lane is having less traffic than other lane, then the signal lights will be decided on the basis of less wait time and less pollution. This system also gives idea to drivers to choose the path with less congestion. This system is also useful in emergency and VIP clearance and in traffic survey. This increases the efficiency of traffic clearance. This also reduces pollution and traffic congestion, thus being an Adaptive Traffic Control System using Internet of Things.","PeriodicalId":118765,"journal":{"name":"2017 International Conference on Soft Computing and its Engineering Applications (icSoftComp)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133981334","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}
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