VADIVELAN N
@tkrec.ac.in
PROFESSOR
Teegala Krishna Reddy Engineering College
RESEARCH INTERESTS
Computer Networks
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
S. N. Manoharan, K. M. V. Madan Kumar, and N. Vadivelan
Springer Science and Business Media LLC
Shrikant Taware, R. Ravi Chakravarthi, C. Anna Palagan, Kumaresan Chandrasekaran, and N. Vadivelan
Springer Science and Business Media LLC
R. Ravi Chakravarthi, C. Anna Palagan, Ravindra Ratilal Dharamshi, N. Vadivelan, and Shrikant Taware
Springer Science and Business Media LLC
P. Ashwini, N. Suguna, and N. Vadivelan
Springer Science and Business Media LLC
N. Vadivelan, K. Bhargavi, Sarangam Kodati, and M. Nalini
AIP Publishing
K. Bhargavi, N. Vadivelan, Sarangam Kodati, and M. Nalini
AIP Publishing
N. Vadivelan, Shrikant Taware, R. Ravi Chakravarthi, C. Anna Palagan, and Sanjai Gupta
Elsevier BV
N. Vadivelan, Mr. Shrikant Taware, Mr. R. Ravi Chakravarthi, Dr. C. Anna Palagan, and Sanjai Gupta
Elsevier BV
Shrikant Taware, R. Ravi Chakravarthi, C. Anna Palagan, Kumaresan Chandrasekaran, and N. Vadivelan
Springer Science and Business Media LLC
Internet of Things (IoT) data security is one of the critical ideas in data security as the data to be transferred should be made secure. In the field of mobile commerce is the high effect that it will have on a few parts of the regular life and behavior of potential clients. In this present examination, we focused to build up the IoT security in the field of mobile commerce utilizing new cryptographic strategies. Fundamentally the sensitive data are classified from the entire dataset to enhance the accuracy utilizing Feed Forward Back Propagation Algorithm (FFBN). At that point the security of sensitive data is upgraded by utilizing Light Weight Cryptography (LWC) which encodes the input sensitive data called encryption. To enhance the data privacy and secrecy, an imaginative security model is proposed for example Lightweight SIMON block cipher. This will encrypt the data alongside optimal key selection; this LWC upgrades the m commerce data security level in cloud. For key optimization, a meta-heuristic algorithm called Crow Search Algorithm (CSA) is introduced. The proposed SIMON-CSA accomplishes least time to generate key value to decrypt the data.
D. Shiny Irene, T. Sethukarasi, and N. Vadivelan
Elsevier BV
Automated prediction can be offered for further treatment to make effective and relieve the difficulties in the diagnosis of heart condition of patient. In this paper, a hybrid method is proposed combining FKMAW and DBNKELM based ensemble method to enhance medical diagnosis process. Firstly, the input attributes are weighed using a fuzzy k-medoids clustering based attribute weighting (FKMAW) method. Subsequently, the medical data classification performance is improved by applying the weighing method and the linearly separable dataset is obtained with the transformation of non-linearly separable dataset. With the weighted attributes, a regression model based heart disease prediction scheme is proposed combining Deep belief Network and Extreme learning machine (DBNKELM), in which Extreme learning machine is the top layer of the deep belief network to work as a regression model. The results demonstrate that FKMAW + DBNKELM achieved good performance in rectifying the problems in medical data classification for all the six datasets.
In the context of networks where assurance of information delivery is a prime user requirement, it becomes essential to estimate the key performance indicators and carry out a proactive analysis to ascertain if the current network conditions would meet the Quality of Service requirement of particular service. In this project the key is to carry out a QoS aware transmission of Voice, Video and Data over an IP network for ensuring delivery assurance with requisite service specific QoS. An integrate GUI to be deployed at both the sender and the receiver will be developed and this will act as first front end for the transmission and the measurements. An ‘active and collaborative tool based or a passive tool based approach’ will be used for measurement of network KPI whereas ‘COTS (Commercial off the shelf)/FOSS (Free and Open source)/freely downloadable or a custom developed utility/tools’ would be used for generation of traffic.
N. Vadivelan, A. Ramamurthy, and P. Padmaja
American Scientific Publishers
Wireless sensor networks were organized with the collections of sensor nodes for the purpose of monitoring physical phenomenon such as temperature, humidity and seismic events, etc., in the real world environments where the manual human access is not possible. The major tasks of this type of networks are to route the information to sink systems in the sensor network from sensor nodes. Sensors are deployed in a large geographical area where human cannot enter such as volcanic eruption or under the deep sea. Hence sensors are not rechargeable and limited with battery backup; it is very complicated to provide the continuous service of sending information to sink systems from sensor nodes. To overcome the drawback of limited battery power, this paper proposes the concept of minimizing energy consumption with the help of neural networks. The modified form of HRP protocol called energy efficient HRP protocol has been implemented in this paper. Based on this concept, the workload of cluster head is shared by the cluster isolation node in order to increase the lifetime of the cluster head node. Also cluster monitoring node is introduced to reduce the re-clustering process. The implementation procedure, algorithm, results and conclusions were proved that the proposed concept is better than the existing protocols.