Pub Date : 2016-12-01DOI: 10.1109/IC3I.2016.7918042
Ashwin S. Ravi, Akshay Sarvesh, K. George
Financial market prediction, being a complex problem, has intrigued researchers for a long time. In this paper, we try to address the problem by treating it as a time-series and employing artificial neural networks (ANNs) to forecast the future stock value. Two types of neural network learning algorithms are illustrated for the current application: The backward propagation algorithm and an online sequential learning algorithm. Several training strategies are also proposed. The principle objective of this paper is to demonstrate the improvement in predictive performance using multiple neural networks. Towards this, an attempt is made at predicting the SENSEX value of the Bombay Stock Exchange.
{"title":"Online multiple-model approach to prediction for financial markets","authors":"Ashwin S. Ravi, Akshay Sarvesh, K. George","doi":"10.1109/IC3I.2016.7918042","DOIUrl":"https://doi.org/10.1109/IC3I.2016.7918042","url":null,"abstract":"Financial market prediction, being a complex problem, has intrigued researchers for a long time. In this paper, we try to address the problem by treating it as a time-series and employing artificial neural networks (ANNs) to forecast the future stock value. Two types of neural network learning algorithms are illustrated for the current application: The backward propagation algorithm and an online sequential learning algorithm. Several training strategies are also proposed. The principle objective of this paper is to demonstrate the improvement in predictive performance using multiple neural networks. Towards this, an attempt is made at predicting the SENSEX value of the Bombay Stock Exchange.","PeriodicalId":305971,"journal":{"name":"2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122658586","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 : 2016-12-01DOI: 10.1109/IC3I.2016.7918801
Ancy Gonsalves, M. Narvekar
With the advancements of technology in wireless environment coupled with cheap cost of smart phones, mobile services such as net surfing, chatting, social networking etc, have become an integral part of day to day life. The user wants data as fast as possible. Due to constraints such as limited bandwidth and limited network connectivity it is therefore necessary to address issues of query processing in wireless distributed environment. This paper investigates various approaches for processing queries in mobile environment.
{"title":"A review of approaches for processing of queries in distributed mobile environment","authors":"Ancy Gonsalves, M. Narvekar","doi":"10.1109/IC3I.2016.7918801","DOIUrl":"https://doi.org/10.1109/IC3I.2016.7918801","url":null,"abstract":"With the advancements of technology in wireless environment coupled with cheap cost of smart phones, mobile services such as net surfing, chatting, social networking etc, have become an integral part of day to day life. The user wants data as fast as possible. Due to constraints such as limited bandwidth and limited network connectivity it is therefore necessary to address issues of query processing in wireless distributed environment. This paper investigates various approaches for processing queries in mobile environment.","PeriodicalId":305971,"journal":{"name":"2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129754142","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 : 2016-12-01DOI: 10.1109/IC3I.2016.7918052
Aditya Sharma, Raj Kamal Kapur
In this paper, a new approach for image enhancement of gray-level images using gravitational search algorithm and particle swarm optimization (PSO) is represented. The equation for the updates of velocity and position of particles in PSO is modified. For that, the values of two variables, iteration IT and α are evaluated through a newly developed Fuzzy Inference System. In PSO, each iteration updated the velocity of the particle, the velocity is dependent on the acceleration of the particle, which in turn is dependent on force applied, this force is optimized using Newton's law of gravity and motion. This makes the convergence of the PSO to yield better result as compared to the classical PSO, when applied for the enhancement of the images. The intensity component is enhanced using the unsharp masking technique. A new contrast gain is defined for amplification of the unsharp mask to produce the enhanced image. The saturation component is enhanced using the power-law transformation. A new objective function comprising entropy, image exposure, histogram flatness and histogram spread is introduced and optimized using PSO to learn the parameters used for the enhancement of a given image. The proposed approach is evaluated using different test images. Different performance measures are used for the quantitative analysis of the proposed approach.
{"title":"Image enhancement using hybrid GSA — Particle swarm optimization","authors":"Aditya Sharma, Raj Kamal Kapur","doi":"10.1109/IC3I.2016.7918052","DOIUrl":"https://doi.org/10.1109/IC3I.2016.7918052","url":null,"abstract":"In this paper, a new approach for image enhancement of gray-level images using gravitational search algorithm and particle swarm optimization (PSO) is represented. The equation for the updates of velocity and position of particles in PSO is modified. For that, the values of two variables, iteration IT and α are evaluated through a newly developed Fuzzy Inference System. In PSO, each iteration updated the velocity of the particle, the velocity is dependent on the acceleration of the particle, which in turn is dependent on force applied, this force is optimized using Newton's law of gravity and motion. This makes the convergence of the PSO to yield better result as compared to the classical PSO, when applied for the enhancement of the images. The intensity component is enhanced using the unsharp masking technique. A new contrast gain is defined for amplification of the unsharp mask to produce the enhanced image. The saturation component is enhanced using the power-law transformation. A new objective function comprising entropy, image exposure, histogram flatness and histogram spread is introduced and optimized using PSO to learn the parameters used for the enhancement of a given image. The proposed approach is evaluated using different test images. Different performance measures are used for the quantitative analysis of the proposed approach.","PeriodicalId":305971,"journal":{"name":"2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128751210","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 : 2016-12-01DOI: 10.1109/IC3I.2016.7918018
Seba Susan, Meetu Agarwal, Seetu Agarwal, Anand Kartikeya, Ritu Meena
This paper proposes semantically meaningful binary clustering of color images by a novel fuzzy co-clustering algorithm. The clustering objective function incorporates the non-extensive entropy with Gaussian gain for regularization purpose. The chromatic color components in the CIEL∗A∗B∗ color space form the feature space for clustering. The result is a very good differentiation of the colors in the scene as belonging to the foreground object and the background, which helps in scene understanding and information gathering. One direct application of our tool is salient or foreground object segmentation. Experimentation on images from a benchmark dataset and comparisons with the state of the art clustering and segmentation methods establish the efficiency of our approach.
{"title":"Binary clustering of color images by fuzzy co-clustering with non-extensive entropy regularization","authors":"Seba Susan, Meetu Agarwal, Seetu Agarwal, Anand Kartikeya, Ritu Meena","doi":"10.1109/IC3I.2016.7918018","DOIUrl":"https://doi.org/10.1109/IC3I.2016.7918018","url":null,"abstract":"This paper proposes semantically meaningful binary clustering of color images by a novel fuzzy co-clustering algorithm. The clustering objective function incorporates the non-extensive entropy with Gaussian gain for regularization purpose. The chromatic color components in the CIEL∗A∗B∗ color space form the feature space for clustering. The result is a very good differentiation of the colors in the scene as belonging to the foreground object and the background, which helps in scene understanding and information gathering. One direct application of our tool is salient or foreground object segmentation. Experimentation on images from a benchmark dataset and comparisons with the state of the art clustering and segmentation methods establish the efficiency of our approach.","PeriodicalId":305971,"journal":{"name":"2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128114380","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 : 2016-12-01DOI: 10.1109/IC3I.2016.7918013
R. K. Lenka, Rabindra Kumar Barik, Noopur Gupta, Syed Mohd Ali, A. Rath, Harishchandra Dubey
In this digitalised world where every information is stored, the data a are growing exponentially. It is estimated that data are doubles itself every two years. Geospatial data are one of the prime contributors to the big data scenario. There are numerous tools of the big data analytics. But not all the big data analytics tools are capabilities to handle geospatial big data. In the present paper, it has been discussed about the recent two popular open source geospatial big data analytical tools i.e. SpatialHadoop and GeoSpark which can be used for analysis and process the geospatial big data in efficient manner. It has compared the architectural view of SpatialHadoop and GeoSpark. Through the architectural comparison, it has also summarised the merits and demerits of these tools according the execution times and volume of the data which has been used.
{"title":"Comparative analysis of SpatialHadoop and GeoSpark for geospatial big data analytics","authors":"R. K. Lenka, Rabindra Kumar Barik, Noopur Gupta, Syed Mohd Ali, A. Rath, Harishchandra Dubey","doi":"10.1109/IC3I.2016.7918013","DOIUrl":"https://doi.org/10.1109/IC3I.2016.7918013","url":null,"abstract":"In this digitalised world where every information is stored, the data a are growing exponentially. It is estimated that data are doubles itself every two years. Geospatial data are one of the prime contributors to the big data scenario. There are numerous tools of the big data analytics. But not all the big data analytics tools are capabilities to handle geospatial big data. In the present paper, it has been discussed about the recent two popular open source geospatial big data analytical tools i.e. SpatialHadoop and GeoSpark which can be used for analysis and process the geospatial big data in efficient manner. It has compared the architectural view of SpatialHadoop and GeoSpark. Through the architectural comparison, it has also summarised the merits and demerits of these tools according the execution times and volume of the data which has been used.","PeriodicalId":305971,"journal":{"name":"2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114443992","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 : 2016-12-01DOI: 10.1109/IC3I.2016.7917941
Anand Shanker Tewari, Naina Yadav, A. Barman
Recommender System is a set of programs and techniques used for predicting items or rating of items in fields in which a user may be interested. The objectives of recommendation techniques are to assess and mitigate the problem of information overload where a user is not able to receive a clear result of his search. From these recommendations may help in various decision-making processes such as which items to buy, which music to listen, or which online news to read and which research paper to read etc. In this paper, we introduce a new recommendation model which takes into consideration a user's information based on tagging. The proposed approach offers significant advantages in terms of improving the recommendation quality for movies.
{"title":"Efficient tag based personalised collaborative movie reccommendation system","authors":"Anand Shanker Tewari, Naina Yadav, A. Barman","doi":"10.1109/IC3I.2016.7917941","DOIUrl":"https://doi.org/10.1109/IC3I.2016.7917941","url":null,"abstract":"Recommender System is a set of programs and techniques used for predicting items or rating of items in fields in which a user may be interested. The objectives of recommendation techniques are to assess and mitigate the problem of information overload where a user is not able to receive a clear result of his search. From these recommendations may help in various decision-making processes such as which items to buy, which music to listen, or which online news to read and which research paper to read etc. In this paper, we introduce a new recommendation model which takes into consideration a user's information based on tagging. The proposed approach offers significant advantages in terms of improving the recommendation quality for movies.","PeriodicalId":305971,"journal":{"name":"2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122553796","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 : 2016-12-01DOI: 10.1109/IC3I.2016.7917937
Gaurav Khatwani, Praveen Ranjan Srivastava
As information technology has evolved, digital media has become increasingly fragmented and has started to proliferate multiple information channels. In order to optimize on the various digital channels that are available, organizations are increasingly recognizing the importance of gaining solid insights into consumer behavior and preferences that can be translated into marketing strategies. Specifically, they are keen to identify which information channels they can use to effectively reach and communicate with their target market. This paper describes how fuzzy AHP and TOPSIS can be used to develop a new method of decision making that will enable an effective and systematic decision process. This paper provides a demonstration of the underpinning working methodology of the proposed model by examining an illustrative example that is based on the decision process Internet users employ during their online search for information.
{"title":"Consumer preferences of information search channel and the role of information technology","authors":"Gaurav Khatwani, Praveen Ranjan Srivastava","doi":"10.1109/IC3I.2016.7917937","DOIUrl":"https://doi.org/10.1109/IC3I.2016.7917937","url":null,"abstract":"As information technology has evolved, digital media has become increasingly fragmented and has started to proliferate multiple information channels. In order to optimize on the various digital channels that are available, organizations are increasingly recognizing the importance of gaining solid insights into consumer behavior and preferences that can be translated into marketing strategies. Specifically, they are keen to identify which information channels they can use to effectively reach and communicate with their target market. This paper describes how fuzzy AHP and TOPSIS can be used to develop a new method of decision making that will enable an effective and systematic decision process. This paper provides a demonstration of the underpinning working methodology of the proposed model by examining an illustrative example that is based on the decision process Internet users employ during their online search for information.","PeriodicalId":305971,"journal":{"name":"2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121855215","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 : 2016-12-01DOI: 10.1109/IC3I.2016.7918011
Himanshu Gupta, A. Gupta, S. Gupta, Pranshu Nayak, Tanmay Shrivastava
Metaheuristics have become popular in solving optimization problems. Recently literature has been flooded with lot of “novel” optimization techniques. These techniques are inspired by various natural phenomenons. One such technique is Black Hole Algorithm, which is inspired by the Black Holes. The author of this technique claim it to be better than Particle Swarm Optimization (PSO), but we have found it contrary. In this paper we compare the Black Hole Algorithm and Particle Swarm Optimizaion(PSO) by evaluating them on standard test suite. The results show that BHA performs very poorly as compared to PSO and thus, falsifying the claim made by authors of BHA.
{"title":"How effective is Black Hole Algorithm?","authors":"Himanshu Gupta, A. Gupta, S. Gupta, Pranshu Nayak, Tanmay Shrivastava","doi":"10.1109/IC3I.2016.7918011","DOIUrl":"https://doi.org/10.1109/IC3I.2016.7918011","url":null,"abstract":"Metaheuristics have become popular in solving optimization problems. Recently literature has been flooded with lot of “novel” optimization techniques. These techniques are inspired by various natural phenomenons. One such technique is Black Hole Algorithm, which is inspired by the Black Holes. The author of this technique claim it to be better than Particle Swarm Optimization (PSO), but we have found it contrary. In this paper we compare the Black Hole Algorithm and Particle Swarm Optimizaion(PSO) by evaluating them on standard test suite. The results show that BHA performs very poorly as compared to PSO and thus, falsifying the claim made by authors of BHA.","PeriodicalId":305971,"journal":{"name":"2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)","volume":"10 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114047200","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 : 2016-12-01DOI: 10.1109/IC3I.2016.7917943
Syed Arshad Ali, Mansaf Alam
Cloud Computing is a paradigm of both parallel processing and distributed computing. It offers computing facilities as a utility service in pay as par use manner. Virtualization, self-service provisioning, elasticity and pay per use are the key features of Cloud Computing. It provides different types of resources over the Internet to perform user submitted tasks. In cloud environment, huge number of tasks are executed simultaneously, an effective Task Scheduling is required to gain better performance of the cloud system. Various Cloud-based Task Scheduling algorithms are available that schedule the user's task to resources for execution. Due to the novelty of Cloud Computing, traditional scheduling algorithms cannot satisfy the cloud's needs, the researchers are trying to modify traditional algorithms that can fulfil the cloud requirements like rapid elasticity, resource pooling and on-demand self-service. In this paper the current state of Task Scheduling algorithms has been discussed and compared on the basis of various scheduling parameters like execution time, throughput, makespan, resource utilization, quality of service, energy consumption, response time and cost.
{"title":"A relative study of task scheduling algorithms in cloud computing environment","authors":"Syed Arshad Ali, Mansaf Alam","doi":"10.1109/IC3I.2016.7917943","DOIUrl":"https://doi.org/10.1109/IC3I.2016.7917943","url":null,"abstract":"Cloud Computing is a paradigm of both parallel processing and distributed computing. It offers computing facilities as a utility service in pay as par use manner. Virtualization, self-service provisioning, elasticity and pay per use are the key features of Cloud Computing. It provides different types of resources over the Internet to perform user submitted tasks. In cloud environment, huge number of tasks are executed simultaneously, an effective Task Scheduling is required to gain better performance of the cloud system. Various Cloud-based Task Scheduling algorithms are available that schedule the user's task to resources for execution. Due to the novelty of Cloud Computing, traditional scheduling algorithms cannot satisfy the cloud's needs, the researchers are trying to modify traditional algorithms that can fulfil the cloud requirements like rapid elasticity, resource pooling and on-demand self-service. In this paper the current state of Task Scheduling algorithms has been discussed and compared on the basis of various scheduling parameters like execution time, throughput, makespan, resource utilization, quality of service, energy consumption, response time and cost.","PeriodicalId":305971,"journal":{"name":"2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122347304","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 : 2016-12-01DOI: 10.1109/IC3I.2016.7918031
Saurabh Chaturvedi, M. Božanić, S. Sinha
This paper compares the transient characteristics of aluminum (Al) and copper (Cu) microstrip line structures. Interconnects are represented using distributed resistance inductance capacitance (RLC) transmission line (TL) model. The equivalent RLC-ladder networks for Al and Cu interconnects are first implemented using VHDL-AMS, and their time-domain simulation responses are compared. For the verification of the results obtained from VHDL-AMS implementation, the process is repeated with SPICE modeling. Both the simulation results are in good agreement.
{"title":"Comparison of Al and Cu interconnects using VHDL-AMS and SPICE modeling","authors":"Saurabh Chaturvedi, M. Božanić, S. Sinha","doi":"10.1109/IC3I.2016.7918031","DOIUrl":"https://doi.org/10.1109/IC3I.2016.7918031","url":null,"abstract":"This paper compares the transient characteristics of aluminum (Al) and copper (Cu) microstrip line structures. Interconnects are represented using distributed resistance inductance capacitance (RLC) transmission line (TL) model. The equivalent RLC-ladder networks for Al and Cu interconnects are first implemented using VHDL-AMS, and their time-domain simulation responses are compared. For the verification of the results obtained from VHDL-AMS implementation, the process is repeated with SPICE modeling. Both the simulation results are in good agreement.","PeriodicalId":305971,"journal":{"name":"2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)","volume":"39 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122633796","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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