2016 2nd International Conference on Computational Intelligence and Networks (CINE)最新文献

{"title":"A Dynamic Energy-Efficient Chain Formation Scheme for PEGASIS in Wireless Sensor Networks","authors":"Alekha Kumar Mishra, M. K. Samantara","doi":"10.1109/CINE.2016.15","DOIUrl":"https://doi.org/10.1109/CINE.2016.15","url":null,"abstract":"Data aggregation is a promising way to reduce the redundant data communication in wireless sensor networks and hence improves the network lifetime. Among the various data aggregation protocols, it is reported that PEGASIS is better than LEACH and HEED. PEGASIS is a chain based aggregation protocol, where each node communicates only with its closest neighbour and on its turn to become a leader, transmits aggregated data to the base station. An energy-efficient variation of PEGASIS, called concentric clustering scheme divides the network into concentric clusters. The leader node is the extreme-end node of a cluster who is responsible for gathering and aggregating sensed data. However, in the concentric clustering scheme, the leader selection mechanism does not ensure the connectivity between the leaders of each cluster. Moreover, it does not consider energy level of the nodes during leader selection. In this paper, we propose an improvement to concentric clustering scheme by designing a mechanism that ensures the connectivity among the leaders and considers the residual energy level of a node during leader selection process. The simulation result shows that the proposed scheme has significant improvement in terms of network lifetime, connectivity, and number of alive nodes.","PeriodicalId":142174,"journal":{"name":"2016 2nd International Conference on Computational Intelligence and Networks (CINE)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131744332","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}

{"title":"A Review of Software Cost Estimation in Agile Software Development Using Soft Computing Techniques","authors":"Saurabh Bilgaiyan, Samaresh Mishra, M. Das","doi":"10.1109/CINE.2016.27","DOIUrl":"https://doi.org/10.1109/CINE.2016.27","url":null,"abstract":"For a successful software project, accurate prediction of its overall effort and cost estimation is a very much essential task. Software projects have evolved through a number of development models over the last few decades. Hence, to cover an accurate measurement of the effort and cost for different software projects based on different development models having new and innovative phases of software development, is a crucial task to be done. An accurate prediction always leads to a successful software project within the budget with no delay, but any percentage of misconduct in the overall effort and cost estimate may lead to a project failure in terms of delivery time, budget or features. Software industries have adopted various development models based on the project requirements and organization's capabilities. Due to adaptability to changes in a software project, agile software development model has become a much successful and popular framework for development over the last decade. The customer is involved as an active participant in the development using an agile framework. Hence, changes can occur at any phase of development and they can be dynamic in nature. That is why an accurate prediction of effort and cost of such projects is a crucial task to be done as the complexity of overall development structure is increased with the time. Soft computing techniques have proven that they are one of the best problem solving techniques in such scenarios. Such techniques are more flexible and presence of bio-intelligence increases their accuracy. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), Fuzzy Inference Systems (FIS), etc. are applied successfully for estimation of cost and effort of agile based software projects. This paper deals with such soft computing techniques and provides a detailed and analytical overview of such methods. It also provides the future scope and possibilities to explore such techniques on the basis of survey provided by this paper.","PeriodicalId":142174,"journal":{"name":"2016 2nd International Conference on Computational Intelligence and Networks (CINE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134096523","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}

{"title":"Impulse Noise Removal from Gray Scale Images Based on ANN Classification Based Fuzzy Filter","authors":"A. Roy, Salam Shuleenda Devi, R. Laskar","doi":"10.1109/CINE.2016.24","DOIUrl":"https://doi.org/10.1109/CINE.2016.24","url":null,"abstract":"In this paper, artificial neural network (ANN) based fuzzy filter is proposed for removal of impulse noise from gray images. ANN is used for classification of noisy and non-noisy pixels from the image corrupted by impulse noise. Based on the classification, fuzzy filtering is done adjusting the corrupted and non-corrupted pixels. In this method, feature set comprises of predicted error, absolute difference between the median and processing kernel, pixel under operation and median value within the kernel. It has been observed that this proposed method increases peak-signal-to-noise ratio (PSNR) not only for low density of noise but also for high density of noise. This method maintains structural similarity of the original image from that corrupted one to a great extent. It reduces computation time of the removal process while removing noise from the corrupted image. It is shown in this work how this proposed method outperforms other conventional filters.","PeriodicalId":142174,"journal":{"name":"2016 2nd International Conference on Computational Intelligence and Networks (CINE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126983461","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}

{"title":"Multimoving Human Sperm Tracking Using CGM-CS Approach and Comparative Analysis for Proper Diagnosis in Infertility","authors":"Sushil Kumar Mahapatra, Sumant Kumar Mohapatra, Sakuntala Mahapatra, R. Bhojray","doi":"10.1109/CINE.2016.20","DOIUrl":"https://doi.org/10.1109/CINE.2016.20","url":null,"abstract":"In this paper, we introduced an algorithm for tracking and detecting multi moving human sperm using Course Grained Multi Threading Cam Shift (CGM-CS) approach in a microscopic human sperm moving video. This method is fully based on adaptive Cam Shift algorithm using color model. This algorithm is design to track and detect the sperms by using multi threading concept. The multi threading concept is compared continuously to its mean value in the successive frame in the appropriate video. The result obtained by the proposed method is also compared with the Maximum Intensity Region (MIR) algorithm, Lukas-Kanade (LK) algorithm and Kernel Based (KB) algorithm. Experimental results demonstrate that the CGM-CS algorithm is capable of tracking the sperm with high detection rate with minimum time taken as compared to existing approaches.","PeriodicalId":142174,"journal":{"name":"2016 2nd International Conference on Computational Intelligence and Networks (CINE)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124973109","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}

{"title":"A Novel Technique for Identification Control of a Non-linear System","authors":"S. Srivatava, Monika Gupta","doi":"10.1109/CINE.2016.37","DOIUrl":"https://doi.org/10.1109/CINE.2016.37","url":null,"abstract":"In this paper combination of neural network (NN) and Fast Traversal Filters (FTF) is used for system identification and control. The error signal is divided into two parts-linear and non-linear. The linear part of the error signal is input by the FTF algorithm, whereas the non-linear part is input to the NN. The minimized errors from the two and then added and it finally becomes the input to the system or plant. The proposed hybrid controller requires less number of samples for training of weights, thus making the system fast. The system under study is Box and Jerkins furnace. The output of the system is carbon dioxide concentration and input is gas flow rate. First identification is done using the proposed technique and then a controller is designed for the same. Comparative analysis is done between conventional feedforward neural networks and the proposed technique. Simulated results show the superiority of the proposed hybrid technique.","PeriodicalId":142174,"journal":{"name":"2016 2nd International Conference on Computational Intelligence and Networks (CINE)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126318660","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}

{"title":"Dynamic Stability Improvement of PSS and STATCOM Based Power System Using Cuckoo Search Based PI Controller","authors":"N. Nayak, P. Rout, Bijaya Rath","doi":"10.1109/CINE.2016.31","DOIUrl":"https://doi.org/10.1109/CINE.2016.31","url":null,"abstract":"Reactive power compensation is a challenging issue in power system. Static Synchronous Compensator is a device which has capability to solve the stability problems in power system. All the disturbances occur for a very few seconds. STATCOM provides continuous reactive control with quick response due to restriction of time. Optimized PI controller is used here to eliminate or damp out the oscillations produced during disturbance in the system. In this study a simple power system, which consist of a single synchronous generator, transmission line connected to infinite bus is considered. A STACOM is installed at the middle of the line. Different types of disturbances are created and the cuckoo search based PI controller is used to improve the stability of the system. Comprehensive simulations are carried out to show the performance of the optimized PI controller under various operating conditions.","PeriodicalId":142174,"journal":{"name":"2016 2nd International Conference on Computational Intelligence and Networks (CINE)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134147775","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}

{"title":"Research Issues on WSBS Performance Evaluation","authors":"Sipali Pradhan, S. Satpathy, A. Tripathy, J. K. Mantri","doi":"10.1109/CINE.2016.29","DOIUrl":"https://doi.org/10.1109/CINE.2016.29","url":null,"abstract":"Software systems which are dynamically composed from autonomous Web Services, coordinated by some composition processes referred to as Web Service Based Systems (WSBS). These are essentially distributed in nature and in most cases facilitated through third party service providers. Service-based approaches are widely used to integrate the heterogeneous systems. Web Services allow for the design of highly dynamic systems where services can be discovered and QoS parameters negotiated at run-time. This necessitates monitoring of the provisioning of WSBS at run time. Run-time monitoring in a non-intrusive, cross-layer, composition platform independent, multi-layer and decentralized manner is a real challenge. The objective of this article is to overview the research development in WSBS monitoring and highlighting the existing current research challenges.","PeriodicalId":142174,"journal":{"name":"2016 2nd International Conference on Computational Intelligence and Networks (CINE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130392686","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}