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Vimal Kumar, D.
- Efficient Way to Control Road Traffic Using Fuzzy Logic
Abstract Views :153 |
PDF Views:4
Authors
I. Gobi
1,
D. Vimal Kumar
1
Affiliations
1 Nehru Arts & Science College, Thirumalayampalayam, Coimbatore, Tamilnadu, IN
1 Nehru Arts & Science College, Thirumalayampalayam, Coimbatore, Tamilnadu, IN
Source
Digital Signal Processing, Vol 10, No 1 (2018), Pagination: 17-20Abstract
Traffic through road travel makes congestion in roads causes’ major problem to road users. This is due to regular use of vehicles through the cities and waits in traffic signal for a long time which makes traffic impasse. To manage this problem many traffic control system have been developed. Due to increase in vehicles the traffic controlling demand are high. To control these traffic new techniques was proposed and named as Efficient Road Traffic Controller (ERTC) which efficiently reduces congestion in traffic signal. The research model will control the traffic by the adjustment of time and phase of the traffic lights by the situation of traffic intersection and controlled by indication to the applying model.
This paper gives a brief discussion of the procedures we adopted to develop an intelligent fuzzy control system for dealing with the road traffic congestion problem. Specialized node is used in the congestion road traffic which is known as Local Cognitive Node (LCN) implements the learning components and decision making. Fuzzy logic technology is used to develop the system with Cognitive sensor node where these nodes use learning mechanism to take decisions at LCN. The result of the Simulation of proposed system shows that problem of traffic congestion is efficiently reduced in the traffic network.
Keywords
Wireless Sensor Networks, Traffic Congestion, Fuzzy Logic, Fuzzy Rules, Cognitive Node.
References
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- J. Niittymäki & M. Pursula, Signal Control using Fuzzy Logic, Fuzzy Sets and Systems, Vol. 116, 2000, pp. 11-22.
- C. P. Pappis & E. H. Mamdani, A Fuzzy Logic Controller for a Traffic Junction, IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-7, No. 10, 1977, pp. 707-717.
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- G. K. Mann & R.G. Gosine, “Adaptive hierarchical tuning of fuzzy controllers,” Expert Systems, 19(1), 34-45, 2002.
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- K. Tan, M. Khalid & R. Yusof, Intelligent traffic lights control by fuzzy logic. Malaysian Journal of Computer Science, 9(2): 29-35, 1996.
- U. C. Osigwe, F. O. Oladipo, E. A. Onibere, Design and Simulation of an Intelligent Traffic Control System. International Journal of Advances in Engineering & Technology Vol. 1, Issue 5, 2011, pp. 47-57.
- Avhad Kalyani B. "Congestion Control in Wireless Sensor Network-A Survey”. International Journal of Computer & organization Trends (IJCOT), V2 (4):99-101 2012.ISSN 2249-2593.www.ijcotjournal.org. Published by Seventh Sense Research Group.
- Pouya Bolourchi and Sener Uysal , Forest Fire Detection in Wireless Sensor Network Using Fuzzy Logic , 2013 fifth international conference on computational intelligence communication systems and networks.
- A Survey on Effective Relevance Feedback Methods for Web Information Retrieval
Abstract Views :197 |
PDF Views:4
Authors
Affiliations
1 Nehru Arts and Science College, T.M. Palayam, Coimbatore, IN
2 Department of Computer Science, Nehru Arts and Science College, T.M. Palayam, Coimbatore, IN
1 Nehru Arts and Science College, T.M. Palayam, Coimbatore, IN
2 Department of Computer Science, Nehru Arts and Science College, T.M. Palayam, Coimbatore, IN
Source
Data Mining and Knowledge Engineering, Vol 10, No 1 (2018), Pagination: 7-8Abstract
In current scenario, information retrieval is the tedious process due to its vast collection of interconnected hyperlinked documents available in the web. In general, there are several tools and search engines are available to retrieve information’s from web repositories. According to the users query, the search engines are retrieving hundreds and thousands of web links. Several contents are not useful and irrelevant to the user query. However, there are some popular search engines providing appropriate results to the user query, the user need to give the query in a proper manner. This leads to several information retrieval problems. In order to improve the retrieval efficiency, RF (Relevance Feedback) methods are introduced. The relevance feedbacks are categorized into three types, one is implicit, explicit and pseudo feedback. With the use of Relevance feedback and user query management, the information retrieval can be performed effectively. This paper provides a detailed summary of relevance feedback techniques and gives several future directions.Keywords
Data Mining, Information’s Retrieval, Relevance Feedback.References
- Buttcher, Stefan, Charles LA Clarke, and Gordon V. Cormack. Information retrieval: Implementing and evaluating search engines. Mit Press, 2016.
- Salton, Gerard, and Chris Buckley. "Improving retrieval performance by relevance feedback." Readings in information retrieval 24, no. 5 (1997): 355-363.
- Hiemstra, Djoerd. "Information retrieval models." Information Retrieval: searching in the 21st Century (2009): 2-19.
- Aji, Ablimit, Yu Wang, Eugene Agichtein, and Evgeniy Gabrilovich. "Using the past to score the present: Extending term weighting models through revision history analysis." In Proceedings of the 19th ACM international conference on Information and knowledge management, pp. 629-638. ACM, 2010.
- Blanco, Roi, and Paolo Boldi. "Extending BM25 with multiple query operators." In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, pp. 921-930. ACM, 2012.
- Yang, Yi, Feiping Nie, Dong Xu, Jiebo Luo, Yueting Zhuang, and Yunhe Pan. "A multimedia retrieval framework based on semi-supervised ranking and relevance feedback." IEEE Transactions on Pattern Analysis and Machine Intelligence 34, no. 4 (2012): 723-742.
- Raman, Karthik, Paul N. Bennett, and Kevyn Collins-Thompson. "Toward whole-session relevance: exploring intrinsic diversity in web search." In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, pp. 463-472. ACM, 2013.
- Mbarek, Rabeb, Mohamed Tmar, and Hawete Hattab. "A new relevance feedback algorithm based on vector space basis change." In International Conference on Intelligent Text Processing and Computational Linguistics, pp. 355-366. Springer, Berlin, Heidelberg, 2014.
- Melucci, Massimo. "Relevance Feedback Algorithms Inspired By Quantum Detection." IEEE Transactions on Knowledge and Data Engineering 28, no. 4 (2016): 1022-1034.
- Survey on Algorithms Applied in Pattern Mining
Abstract Views :236 |
PDF Views:2
Authors
Affiliations
1 Nehru Arts and Science College, T.M.Palayam, Coimbatore, IN
2 Department of Computer Science, Nehru Arts and Science College, T.M.Palayam, Coimbatore, IN
1 Nehru Arts and Science College, T.M.Palayam, Coimbatore, IN
2 Department of Computer Science, Nehru Arts and Science College, T.M.Palayam, Coimbatore, IN
Source
Data Mining and Knowledge Engineering, Vol 10, No 1 (2018), Pagination: 8-11Abstract
Data mining is a collection of techniques to extract hidden and potentially useful information from large databases of various business domains. For identifying the interesting patterns and co-relation and to get benefits from the repository data, Association Rule Mining (ARM) methods are used. Pattern recognition is a major challenge within the field of data mining and knowledge discovery. In this paper, a range of widely used algorithms are analyzed for finding frequent patterns with the purpose of discovering how these algorithms can be used to obtain frequent patterns over large transactional databases. This has been presented in the form of a comparative study of the following algorithms: Apriori algorithm, Frequent Pattern (FP) Growth algorithm, Rapid Association Rule Mining (RARM), ECLAT algorithm and Associated Sensor Pattern Mining of Data Stream (ASPMS) frequent pattern mining algorithms. The paper also focuses on each of the algorithm’s strengths and weaknesses for finding patterns in different transactional dataset.References
- Sourav S. Bhowmick Qiankun Zhao, "Association Rule Mining: A Survey," Nanyang Technological University, Singapore.
- Jiawei Han • Hong Cheng • Dong Xin • Xifeng Yan, "Frequent pattern mining: current status and future Directions," Data Mining Knowl Discov, vol. 15, no. I, p. 32, 2007.
- Iqbal Gondal and Joarder Kamruzzaman Md. Mamunur Rashid, "Mining Associated Sensor Pattern for data stream of wireless networks," in PM2HW2N '13, Spain, 2013.
- Chistopher.T, PhD Saravanan Suba, "A Study on Milestones of Association Rule Mining," International Journal of Computer Applications, p. 7, June 2012.
- WeeKeong, YewKwong Amitabha Das, "Rapid Association Rule Mining," in Information and Knowledge Management, Atlanta, Georgia, 2001, pp. 474-481.
- Data Mining Techniques for Healthcare
Abstract Views :167 |
PDF Views:4
Authors
Affiliations
1 Nehru Arts and Science College, T.M.Palayam, Coimbatore, IN
1 Nehru Arts and Science College, T.M.Palayam, Coimbatore, IN
Source
Biometrics and Bioinformatics, Vol 10, No 1 (2018), Pagination: 8-12Abstract
The healthcare environment is generally perceived as being “rich information yet “Poor knowledge”. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. Knowledge discovery and data mining have found numerous applications in business and scientific domain. Valuable knowledge can be discovered from application of data mining techniques in healthcare system. We briefly examined the potential use of classification based data mining techniques such as rule based, decision tree and Artificial Neural Network to massive volume of healthcare data.Keywords
Healthcare, Medical Diagnosis, Artificial Neural Network, Knowledge Discovery in Databases (KDD).References
- Frawley and Piatetsky – Shapiro, 1996, Knowledge Discovery in Database. An Overview, The AAAI/MIT Press, Menlo Park, C.A
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- Itchhaporia.D, P.B.Snow, R.J. Almassy and W.J.Oetgen, 1996. Artificial Neural Network: Current status in cardiovascular medicine.
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- K-Means Clustering for Asthma Endotypes
Abstract Views :145 |
PDF Views:4
Authors
Affiliations
1 Department of Computer Technology, Kongu Engineering College, Perundurai, IN
2 Department of Computer Technology, Kongu Engineering College, Perundurai, IN
3 Department of Computer Science, Nehru Arts and Science College, Coimbatore, IN
1 Department of Computer Technology, Kongu Engineering College, Perundurai, IN
2 Department of Computer Technology, Kongu Engineering College, Perundurai, IN
3 Department of Computer Science, Nehru Arts and Science College, Coimbatore, IN
Source
Wireless Communication, Vol 10, No 1 (2018), Pagination: 14-15Abstract
Unsupervised learning algorithms are major Data mining techniques that can be used for clinical data analysis. Asthma is a constant inflammatory disease of the respiratory channels in which the reason for its prevalence is not clear. Its dominance is rising all over the world. Clustering techniques can be used to identify the hidden disease characteristics that may assist in the treatment and to create awareness about the disease. This paper implements the k-means clustering algorithm to identify the asthma endotypes and related ischolar_main causes from the epidemiological data that was collected through questionnaire from asthma patients.Keywords
Data Mining, Clustering, Partition Clustering, Clinical Data Analysis, Asthma, Endotypes, K-Means.References
- http://www.who.int/respiratory/asthma/en/:referenced on7th December 2017.
- Somayeh Akhavan Darabi et al. Case-Based-Reasoning System for Feature Selection and Diagnosing Disease; Case Study: Asthma. Innovative Systems Design and Engineering www.iiste.org, Vol.5, No.5, 2014:pp.43-59.
- Wendy C. Moore et al. Identification of Asthma Phenotypes Using Cluster Analysis in the Severe Asthma Research Program. American Journal of Respiratory and Critical Care Medicine. 181(4). Feb 15 2010: pp.315–323.
- Matea Deliu et al. Identification of Asthma Subtypes Using Clustering Methodologies. Pulmonary Therapy Vol.2. Issue.1. 2016:pp.19–41.
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- Wei Wu et al. Unsupervised Phenotyping of Severe Asthma Research Program participants using expanded lung data. Journal of Allergy and Clinical Immunology. 133(5). May 2014:pp.1280-1288.
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- Pranab Haldar et al. Cluster Analysis and Clinical Asthma Phenotypes. American Journal of Respiratory and Critical Care Medicine. 178(3). August 2008:pp.218-224.
- Loza MJ et al. Validated and Longitudinally Stable Asthma Phenotypes based on cluster Analysis of the ADEPT study. Respiratory Research.2016: pp.
- Arnaud Bourdin et al. Prognostic value of cluster analysis of severe asthma phenotypes. Journal of Allergy and Clinical Immunology. Vol 134. Issue 5.November 2014: pp. 1043-1050.
- Social Network and Sentiment Analysis on Twitter
Abstract Views :150 |
PDF Views:2
Authors
R. Amutha
1,
D. Vimal Kumar
2
Affiliations
1 Department of Computer Science, Nehru Arts & Science College, Thirumalayampalayam, Coimbatore- 05, IN
2 Department of Computer Science, Nehru Arts & Science College, Thirumalayampalayam, Coimbatore- 05, IN
1 Department of Computer Science, Nehru Arts & Science College, Thirumalayampalayam, Coimbatore- 05, IN
2 Department of Computer Science, Nehru Arts & Science College, Thirumalayampalayam, Coimbatore- 05, IN
Source
Software Engineering, Vol 10, No 1 (2018), Pagination: 3-5Abstract
The growth of web technology produces huge volumes of data in the web. Internet provides a platform for sharing opinions & sharing ideas. Social networking sites are rapidly gaining popularity as they allow people to discuss with different communities and post messages across the world. Twitter is the most widely used site where people can share their reviews in the form of tweets. It provides richer content of sentiment and opinions of popular topics. Opinions are categorized into positive, negative or neutral. It is very useful where the company wants the feedback about their product. This paper focuses different information analysis techniques, ranking & classifying tweeter user, fuzzy logic based sentiment classification and analysis of sentiment.
Keywords
Social Media, Twitter, Classification, Fuzzy Logic.References
- Wu, S., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011). Who says what to whom on twitter. Proceedings of the 20th International Conference on World Wide Web. ACM New York, NY, USA.
- Bastos, M. T., Travitzki, R., & Puschmann, C. (2012). What sticks with whom? Twitter follower- followee networks and news classification. Proceedings of 6th International AAAI Conference on Weblogs and Social Media—Workshop on the Potential of Social Media Tools and Data for Journalists in the News Media Industry.
- Natural language processing. From https://en.wikipedia.org/wiki/Natural_language_processing
- Akshi Kumar and Teeja Mary Sebastian, “Sentiment Analysis on Twitter”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 4, No 3, July 2012, pp. 372-378.
- Minimizing Delay in Mobile Ad-Hoc Network Using Ingenious Grey Wolf Optimization Based Routing Protocol
Abstract Views :200 |
PDF Views:2
Authors
K. Sumathi
1,
D. Vimal Kumar
1
Affiliations
1 Department of Computer Science, Nehru Arts and Science College, Coimbatore, Tamil Nadu, IN
1 Department of Computer Science, Nehru Arts and Science College, Coimbatore, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 9, No 2 (2022), Pagination: 251-261Abstract
One of the most groundbreaking concepts in wireless networking is the mobile ad hoc network (MANET). It is an ever-shifting network of wireless nodes that may be adaptively and indiscriminately positioned, with the interconnections between nodes constantly changing. Defense networks, in particular, are becoming more prominent, and it is the goal and passion of technology to update and improve its components. There is a significant rise in transmission costs due to the high energy usage. Routing protocols have a critical role in reducing energy utilization. Weak routing protocol leads to exhaustive energy consumption, packet delay and packet loss. Ingenious Grey Wolf Optimization-based Routing Protocol (IGWORP) is proposed in this paper to discover the most efficient path to a destination and reduce the amount of delay and energy spent. IGWORP mirrors the natural tendencies of the grey wolf towards foraging for its prey. IGWORP looks for a global route rather than assembling many local routes. Encircling and hunting characteristics of wolves are used in IGWORP to discover and utilize the route for data transmission. Standard network metrics are used in NS3 to evaluate IGWORP's performance. The findings of IGWORP demonstrate that it reduces delays and energy consumption better than the current routing methods.Keywords
Delay, Routing, Optimization, Wolf, Delay, Energy.Full Text
References
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- Ambient Intelligence-Based Fish Swarm Optimization Routing Protocol for Congestion Avoidance in Mobile Ad-Hoc Network
Abstract Views :194 |
PDF Views:1
Authors
K. Sumathi
1,
D. Vimal Kumar
2
Affiliations
1 Department of Computer Science, Nehru Arts and Science College, Coimbatore, Tamil Nadu, IN
2 Department of Computer Science, Nehru Arts and Science College, Coimbatore, IN
1 Department of Computer Science, Nehru Arts and Science College, Coimbatore, Tamil Nadu, IN
2 Department of Computer Science, Nehru Arts and Science College, Coimbatore, IN
Source
International Journal of Computer Networks and Applications, Vol 9, No 3 (2022), Pagination: 340-349Abstract
In mobile ad hoc networks, path stability estimation is a major difficulty because of connection failures that affect network nodes' mobility. In MANETs, path stability estimates must be based on a unified model that accounts for network node mobility and topology-triggered reactive path distribution statistics between surrounding nodes. It is possible to increase the collaboration between nodes in MANET by implementing an effective, trustworthy cum optimization-based routing protocol. This paper proposes the Ambient Intelligence-based Fish Swarm Optimization Routing Protocol (AIFSORP) to find the most efficient route to a destination and decrease the time and energy required. AIFSORP is designed to mimic the ant's innate instincts to forage its food. In AIFSORP, nodes quickly notify their neighbors when they discover a possible route to their target. Only when the route meets the threshold criterion is it picked for data transmission and shared with neighboring nodes. Optimization plays a significant part in AIFSORP towards determining the best route to the destination. AIFSORP's performance is evaluated using NS3s with standard network metrics. Compared to current routing systems, AIFSORP decreases delays and energy usage more effectively.Keywords
Routing, Congestion, Delay, MANET, Optimization, Fish-Swarm.Full Text
References
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