A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Deepa, T.
- Multihomed Routing for Power Efficiency
Authors
Source
Fuzzy Systems, Vol 7, No 2 (2015), Pagination: 58-65Abstract
The use of the multi-interfaced devices has been increasing due to multi-homed streaming services. Running multiple interfaces simultaneously in a mobile interface may cause battery drain even in sleep modes and it can cause degradation of QOS. The abstract of our project is to provide dynamic load distribution which includes a multi-interfaced device with multi homing capability thereby preventing the mobile terminals going to sleep mode. The PELD (power efficiency load distribution) algorithm is used which can be easily adopted and deployed to find a optimal point with low complexity and convergence time. The tool used here is network simulator2 tool where the implementation is done and the coding is written in tcl.
- Network Security
Authors
1 Department of Computer Science, Sri Ramakrishna CAS, Coimbatore, IN
Source
Software Engineering, Vol 9, No 1 (2017), Pagination: 13-16Abstract
For the first few decades of their existence, computer networks were primarily used by university researchers for sending e-mail and by corporate employees for sharing printers. Under these conditions, security did not get a lot of attention. But now, as millions of ordinary citizens are using networks for banking, shopping, and filing their tax returns, network security is looming on the horizon as a potentially.
Keywords
Internet of Thing, Network Connectivity, Spoofing, Social Engineering, Eavesdropping.- Nano Computing
Authors
1 Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women, Coimbatore, IN
2 Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women, Coimbatore, IN
Source
Digital Signal Processing, Vol 9, No 1 (2017), Pagination: 7-9Abstract
Nanotechnology is now a days strengthening its ischolar_mains in the field of computer science by contributing in the creation of more efficient computing components .Nanotechnology has been developed in several fields of studies including, material science, engineering , chemistry, physics, biology and computer science as well .Novel nano materials and nano devices are fabricated and controlled by nanotechnology tools and techniques, This paper shows how these nanotechnology tools and devices can be used to benefit Information technology Industry and we look for the needs and chances of nanotechnology research in computer science. Nano technology in computer science is named as Nano computing .which is divided into categories- Electronic Nano computing, Mechanical
Nano computing, Chemical
Nano computing, Quantum Nano computing etc. The application and use of nano materials in electronic devices, in optical and magnetic components are the economically most important parts of the nanotechnology permeate the design of artefacts at nowadays and likely in the near future. Advances in technology repeatedly allow software to lower and lower levels. This occurred long ago with microprogramming, when computers were stand-alone; more recently it occurred with programmable networks (e.g. routers); it is now occurring with wireless networks. Nanotechnology will lead to a new dramatic step of this kind. Although it will include applications that require little computational input, it will also provide opportunities for exploitation that involve complex computational interactions among structured populations of nanoscopic agents. The more sophisticated these structures and interactions, the more Computer Science (CS) research will be involved. The modification of today’s top-down technology to produce nano scale structures is difficult and expensive, a new generation of computer components will be required. A new style of technology is introduced, which assembles individual atoms or molecules into a refined product. This technology is termed as molecular technology or bottom-up technology. This bottom-up technology could be the answer for the computer industry. Though topdown technology currently remains the choice for constructing mass produced devices, nanotechnologists are having increasing success in developing bottom-up technology. Such research is wide ranging and includes: software engineering, networking, internet security, image processing, virtual reality, human machine interface, artificial intelligence, and intelligent systems. Most work focuses on the development of fast computing components. The nanometer level new devices, methods of assembly, and architectures are being investigated. The crossbar, an old idea, is expected to play a prominent role in these new developments; its regular structure is well suited to nanotechnology-based assembly. . It is not an attempt to violate any laws; it is something, in principle, that can be done.
- Network Security
Authors
1 Department of Computer Science, Sri Ramakrishna CAS, Coimbatore, IN
2 Department of Computer Science, Sri Ramakrishna CAS, Coimbatore, IN
Source
Digital Signal Processing, Vol 9, No 1 (2017), Pagination: 13-16Abstract
For the first few decades of their existence, computer networks were primarily used by university researchers for sending e-mail and by corporate employees for sharing printers. Under these conditions, security did not get a lot of attention. But now, as millions of ordinary citizens are using networks for banking, shopping, and filing their tax returns, network security is looming on the horizon as a potentially.
Keywords
Internet of Thing, Network Connectivity, Spoofing, Social Engineering, Eavesdropping.- Comprehensive Feature Selection for Clinical Dataset
Authors
1 Sri Ramakrishna College of Arts and Science College for Women, Coimbatore - 641044, IN
Source
Fuzzy Systems, Vol 10, No 2 (2018), Pagination: 25-27Abstract
Feature selection plays a significant role in any data mining research problem. In this research work, comprehensive feature selection is applied for selecting the attributes in the chosen PIMA Indian diabetes dataset. The comprehensive feature selection mechanism makes use of maximum significance pattern for selecting the most edifying features, which effectively distinguish between different classes of samples.Keywords
Feature Selection, Data Mining, Gestational Diabetes, Accuracy, Time Taken, Feature Selection, Risk Prediction.References
- A. Konar, Computational Intelligence: Principles, Techniques and Applications, Springer-Verlag, 2005, pp. 24.
- O. Cordon, F. Herrera, F. Hoffmann, L. Magdalena, Genetic Fuzzy Systems, Evo- lutionary Tuning and Learning of Fuzzy Knowledge Bases, World Scientific, 2001.
- D. Wu, W.W. Tan, Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers, Eng. Appl. Artificial Intell. 19 (2006) 829–841.
- M. Last, S. Eyal, A fuzzy-based lifetime extension of genetic algorithms, Fuzzy Sets Syst. 149 (2005) 131–147.
- H. Wang, S. Kwong, Y. Jin, W. Wei, K.F. Man, Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction, Fuzzy Sets Syst. 149 (2005) 149–186.
- S.K. Oh, W. Pedrycz, H.S. Park, Rule-based multi-FNN identification with the aid of evolutionary fuzzy granulation, Knowledge-Based Syst. 17 (2004) 1–13.
- P.P. Angelov, R.A. Buswell, Automatic generation of fuzzy rule-based models from data by genetic algorithms, Inform. Sci. 150 (2003) 17–31.
- K.M. Chow, A.B. Rad, On-line fuzzy identification using genetic algorithms, Fuzzy Sets Syst. 132 (2002) 147–171.
- I. Jagielska, C. Matthews, T. Whitfort, An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems, Neuro Comput. 24 (1999) 37–54.
- M. Russo, FuGeNeSys—A fuzzy genetic neural system for fuzzy modeling, IEEE Trans. Fuzzy Syst. 6 (1998) 373–388.
- R. Alcala, J. Alcala-Fdez, F. Herrera, A proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection, IEEE Trans. Fuzzy Syst. 15 (4) (2007) 616–635.
- J. Casillas, B. Carse, L. Bull, Fuzzy-XCS: A Michigan Genetic Fuzzy System, IEEE Trans. Fuzzy Syst. 15 (4) (2007) 536–550.
- L. Sanchez, I. Couso, Advocating the use of imprecisely observed data in genetic fuzzy systems, IEEE Trans. Fuzzy Syst. 15 (4) (2007) 551–562.