Pub Date : 2016-12-01DOI: 10.1109/IC3I.2016.7917965
Yojna Arora, Dinesh Goyal
In the current scenario, data is considered to be the biggest assets. One who has maximum relevant data is considered to be rich in the information industry. But only the collection of data is not enough, it needs to be analyzed. This huge amount of data which is termed ass Big Data cannot be analyzed by traditional tools and techniques, rather it requires more advanced Techniques which can make data retrieval, management and storage much faster are required. In this paper an introduction to big data is explained along with a detailed comparative study of various Big Data techniques which have already been implemented. At the end various issues which still exist are enlisted.
{"title":"Big data: A review of analytics methods & techniques","authors":"Yojna Arora, Dinesh Goyal","doi":"10.1109/IC3I.2016.7917965","DOIUrl":"https://doi.org/10.1109/IC3I.2016.7917965","url":null,"abstract":"In the current scenario, data is considered to be the biggest assets. One who has maximum relevant data is considered to be rich in the information industry. But only the collection of data is not enough, it needs to be analyzed. This huge amount of data which is termed ass Big Data cannot be analyzed by traditional tools and techniques, rather it requires more advanced Techniques which can make data retrieval, management and storage much faster are required. In this paper an introduction to big data is explained along with a detailed comparative study of various Big Data techniques which have already been implemented. At the end various issues which still exist are enlisted.","PeriodicalId":305971,"journal":{"name":"2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120931378","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.2016.7918006
P. K. A. Kumar, S. Vivekanandan, C. K. Kumar, V. Chinnaiyan
Steadiness of power system is a significant issue in power system operation. In this article design of Neural Network tuned Fuzzy logic power system stabilizer (NNTFLPSS) for single machine infinite bus (SMIB) system is proposed to settle down low frequency swinging that improves small signal stability in power system. The speed deviance and variation in speed deviance of the rotor of synchronous generator from the trained neural network were considered as the feedback to the fuzzy logic power system stabilizer (FLPSS) to recover the power system from small signal stability problem by refining damping oscillations. The comparative reading was noted for rotor speed deviances and rotor angle deviances using conventional PSS (CPSS), Fuzzy logic based power system stabilizer (FLPSS) and NNTFLPSS. The MATLAB simulation results obtained indicates the improved performance of NNTFLPSS over the CPSS and FLPSS.
{"title":"Neural network tuned fuzzy logic power system stabilizer design for SMIB","authors":"P. K. A. Kumar, S. Vivekanandan, C. K. Kumar, V. Chinnaiyan","doi":"10.1109/IC3I.2016.7918006","DOIUrl":"https://doi.org/10.1109/IC3I.2016.7918006","url":null,"abstract":"Steadiness of power system is a significant issue in power system operation. In this article design of Neural Network tuned Fuzzy logic power system stabilizer (NNTFLPSS) for single machine infinite bus (SMIB) system is proposed to settle down low frequency swinging that improves small signal stability in power system. The speed deviance and variation in speed deviance of the rotor of synchronous generator from the trained neural network were considered as the feedback to the fuzzy logic power system stabilizer (FLPSS) to recover the power system from small signal stability problem by refining damping oscillations. The comparative reading was noted for rotor speed deviances and rotor angle deviances using conventional PSS (CPSS), Fuzzy logic based power system stabilizer (FLPSS) and NNTFLPSS. The MATLAB simulation results obtained indicates the improved performance of NNTFLPSS over the CPSS and FLPSS.","PeriodicalId":305971,"journal":{"name":"2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120935860","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.2016.7918007
P. K. A. Kumar, R. Uthirasamy, G. Saravanan, A. Ibrahim
Application of ANN Controller towards the improvement of AGC performance issue in three area system is proposed in this article. To minimize the Area Control Error (ACE) and stabilize the frequency oscillations along with tie line power deviations with conventional PI controller is used initially. The application of ANN is to tune PI gain values. Here multilayer feed forward technique is intended to simulation purpose. The performance enhancement of AGC using of ANN controller results were compared with the PI. From the analogy point the results gained from ANN controller seems to be better than performance of PI.
{"title":"AGC performance enhancement using ANN","authors":"P. K. A. Kumar, R. Uthirasamy, G. Saravanan, A. Ibrahim","doi":"10.1109/IC3I.2016.7918007","DOIUrl":"https://doi.org/10.1109/IC3I.2016.7918007","url":null,"abstract":"Application of ANN Controller towards the improvement of AGC performance issue in three area system is proposed in this article. To minimize the Area Control Error (ACE) and stabilize the frequency oscillations along with tie line power deviations with conventional PI controller is used initially. The application of ANN is to tune PI gain values. Here multilayer feed forward technique is intended to simulation purpose. The performance enhancement of AGC using of ANN controller results were compared with the PI. From the analogy point the results gained from ANN controller seems to be better than performance of PI.","PeriodicalId":305971,"journal":{"name":"2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116451623","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":null,"pages":null},"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.2016.7918046
A. K. Jaiswal, Udai Shanker
In the mobile environment the major issues for commit the transactions are disconnection due to mobile handoff or shut down of devices. Disconnection is frequent problem in mobile environment. This results transaction failure. A traditional two-phase commit protocol is best for the distributed environment. In case of mobile environment where devices are connected wirelessly we should deal with the issues of mobility. The paper presents comparison study of three major commit protocols in the mobile environment. The paper surveys different approaches proposed for mobile transaction and summarize how the conventional commitment are revisited to fit the needs of mobile environment.
{"title":"Comparative study of commit protocols in mobile environment: M2PC, UCM and TCOT","authors":"A. K. Jaiswal, Udai Shanker","doi":"10.1109/IC3I.2016.7918046","DOIUrl":"https://doi.org/10.1109/IC3I.2016.7918046","url":null,"abstract":"In the mobile environment the major issues for commit the transactions are disconnection due to mobile handoff or shut down of devices. Disconnection is frequent problem in mobile environment. This results transaction failure. A traditional two-phase commit protocol is best for the distributed environment. In case of mobile environment where devices are connected wirelessly we should deal with the issues of mobility. The paper presents comparison study of three major commit protocols in the mobile environment. The paper surveys different approaches proposed for mobile transaction and summarize how the conventional commitment are revisited to fit the needs of mobile environment.","PeriodicalId":305971,"journal":{"name":"2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131531665","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.2016.7917996
P. Pushpa
The number of sensors deployed at each and every place is growing at a faster rate to meet the current needs of modern society. The uninterrupted huge amount of data generated from sensors and other devices in Internet of Things (IoT) is to be captured and inferred into meaningful information. Context-aware computing is one of the key research issues in IoT paradigm and it is evident that it is successful in understanding each sensor data. As the physical world is highly dynamic the sensed context information is inherently imperfect or imprecise. Therefore the challenge here is to design a context information modelling and reasoning technique so as to extract meaningful information from raw sensor data. In this paper, we propose a rich and unique classification of context information from the perspective of IoT. Our research findings indicate the importance of the proposed context information classification and modelling process.
{"title":"Context information modelling for Internet of Things (Invited paper)","authors":"P. Pushpa","doi":"10.1109/IC3I.2016.7917996","DOIUrl":"https://doi.org/10.1109/IC3I.2016.7917996","url":null,"abstract":"The number of sensors deployed at each and every place is growing at a faster rate to meet the current needs of modern society. The uninterrupted huge amount of data generated from sensors and other devices in Internet of Things (IoT) is to be captured and inferred into meaningful information. Context-aware computing is one of the key research issues in IoT paradigm and it is evident that it is successful in understanding each sensor data. As the physical world is highly dynamic the sensed context information is inherently imperfect or imprecise. Therefore the challenge here is to design a context information modelling and reasoning technique so as to extract meaningful information from raw sensor data. In this paper, we propose a rich and unique classification of context information from the perspective of IoT. Our research findings indicate the importance of the proposed context information classification and modelling process.","PeriodicalId":305971,"journal":{"name":"2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132776361","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":null,"pages":null},"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.2016.7918011
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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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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