G. R. Sakthidharan

In large-scale Wireless Rechargeable Sensor Networks (WRSNs), limited battery capacity of nodes may reduce the network longevity. For enhancing the network lifetime, the nodes in the network can recharged periodically based on their operational executions. The rechargeable sensor nodes in the network are replenished using external sources. Using single charging device can be feasible only for small scale WSNs, whereas in managing large scale wireless sensor networks, multiple charging devices are to be modelled for efficiently recharging the sensor nodes, since single devices are having energy constraints to recharge more number of nodes. On focussing those issues, this paper contributes on developing a new model called Load Balanced Constant Scheduling (LBCS) for the replenishment of the sensor nodes. Moreover, multiple Mobile Charging Devices (MCDs) are used here for recharging the sensor nodes effectively, without facing resource limitations. In this model, constant and time based charge scheduling approach and charging route for MCD has been frame optimally. The scheduling mode focuses on a concrete classification procedure for avoiding needless visits of nodes having adequate energy. Providing further improvement in schedule based node replenishment, algorithm for Charging Route Definition (CRD) is also developed in this work. For evidencing the efficiency of the proposed model, the work is simulated and evaluated. The simulation results are compared with some existing models based on the network lifetime, time taken for recharge and efficiency.

Gender Classification from facial images is an open research area with wide range of computer vision applications like security, biometrics and human computer interaction applications. In the proposed method the LL band image of facial image is obtained by using wavelet then on this image Fibonacci Weighted Neighborhood Central pixel Flood binary Matrix is computed and then shape features are evaluated. SVM method uses these shape features for gender classification. The proposed approach has been experimented on FG NET database. The experimental results has shown the more accuracy compared to with other existing methods.

In this paper, we introduced new Age Classification method with Contrast, Correlation, Energy, and Local homogeneity features on Central Local Binary Pattern based Structure. Local Binary Pattern is computed on the image and then Central Local Binary Pattern based Structure is formed, and on this matrix Cooccurrence features Contrast, correlation, energy and homogeneity are evaluated in four directions 0°, 45°, 90° and 135. The proposed Age Classification algorithm is trained and tested with FG-Net aging database and scanned facial images has shown considerable improvement in the grouping of adult and child groups.

The rapidly growing sector of big-data analysis [1] shows primal function in devolution of recitations and an inquiry in the health sector. It has provided tools to roll up, succeed, examine and absorb large mass of integrated and ambiguous data which is, produced by incumbent health care systems. Big Data Analysis has recently been applied to help in the process of attention and disease exploration. However, the degree of acceptance and development of research in this space has been hampered by some underlying problems inherent the big data paradigm. Here, we have optimized machine erudition-formula for the impressive prevision of epidemics of prolonged diseases in communities of predominant diseases. Here the newfound multimodal disease prediction algorithm for convolutional neuronal networks (CNN-MDRP) that uses structured and unstructured hospital data.

Wireless sensor network are very essential in real time exploration of data with less latency comparing to wireless network. The function of wireless networks can't be compared with sensor network, though the usage makes the difference. In this paper, the wireless sensor network is viewed in aspects of features, environment and application. The motivation of this paper is to give an overall coverage of applications that can be bridge for a new beginning.

Routing is the process of moving packets through an internetwork, such as the Internet. Routing consists of two separate but related tasks: i) Defining and selecting path in the network ii) Forwarding packets based upon the defined paths from a designated source node to a designated destination node. With the advance of wireless communication technology, small size and high performance computing and communication devices like commercial laptops and personal computers are increasingly used in convention centers, conferences and electronic classrooms. In wireless ad-hoc networks, a collection of nodes with wireless communications and networking capability communicate with each other without the aid of any centralized administrator. The nodes are powered by batteries with limited energy reservoir. It becomes difficult to recharge or replace the batteries of the nodes hence energy conservation is essential. An energy efficient routing protocol (EERP) balances node energy utilization to reduce energy consumption and increase the life of nodes thus increasing the network lifetime, reducing the routing delay and increasing the reliability of the packets reaching the destination. Wireless networks do not have any fixed communication infrastructure. For an active connection the end host as well as the intermediate nodes can be mobile. Therefore routes are subject to frequent disconnection. In such an environment it is important to minimize disruptions caused by changing topology for applications using voice and video. Power Aware Routing enables the nodes to detect misbehavior like deviation from regular routing and forwarding by observing the status of the node. By exploiting non-random behaviors for the mobility patterns that mobile user exhibit, state of network topology can be predicted and perform route reconstruction proactively in a timely manner. In this paper we propose an Energy Efficient-Power Aware routing algorithm where we have integrated energy efficiency with power awareness parameters for routing of packets.