The Role of Eye Gaze in Security and Privacy Applications: Survey and Future HCI Research Directions

For the past 20 years, researchers have investigated the use of eye tracking in security applications. We present a holistic view on gaze-based security applications. In particular, we canvassed the literature and classify the utility of gaze in security applications into a) authentication, b) privacy protection, and c) gaze monitoring during security critical tasks. This allows us to chart several research directions, most importantly 1) conducting field studies of implicit and explicit gaze-based authentication due to recent advances in eye tracking, 2) research on gaze-based privacy protection and gaze monitoring in security critical tasks which are under-investigated yet very promising areas, and 3) understanding the privacy implications of pervasive eye tracking. We discuss the most promising opportunities and most pressing challenges of eye tracking for security that will shape research in gaze-based security applications for the next decade

Automatic Detection of Melanoma and Non Melanoma Skin Cancer: Using Classification Framework of Neural Network

In Automatic Detection of Melanoma and Non Melanoma skin Cancer, the relationship of skin cancer image across different type of neural network are studied with different types of preprocessing and compare the result with two method Discrete wavelet transformation and lifting wavelet transformation. The collected images are feed into the system, and across different image processing procedure to enhance the image properties. Then the normal skin is removed from the skin affected area and the cancer cell is left in the image. Useful information can be extracted from these images and pass to the classification system for training and testing. Recognition accuracy of the 3-layers back-propagation neural network classifier is 90.2% in LWT and 89.1% in DWT method. Auto-associative neural network is 81.2% in LWT method and 80.2% in DWT method .The image in database include dermoscopy photo and digital photo

Skin Cancer Detection using Classification Framework of Neural Network

Skin cancers are the most standard form of cancers found in humans. The unending bring of this cancer up in the around the world, the high restorative cost and demise rate have organized the early analysis of cancer. The different parts in finding of skin cancer include: a naturally skin cancer classification framework is created and the relationship of skin cancer picture utilizing diverse kind of neural network are contemplated with different sorts of preprocessing. The dataset images are feed into the system, and across different image processing procedure to enhance the image properties. Statistical region merging (SRM) algorithm is based on region growing and merging. At that point the typical skin is expelled from the skin influenced district lastly cancer cell is left in the picture. Helpful data can be removed from these pictures and go to the classification framework for preparing and testing. Two neural networks are used as classifier, Back-propagation neural network (BNN) and Auto-associative neural network (AANN). Recognition accuracy of the 3- layers back-propagation neural network classifier is 91% and auto-associative neural network is 82.6% in the image database that include dermoscopy photo and digital photo. The analysis of work is totally based on MATLAB software

Magnify Lifeless Nodes in WSN Using Shortest Path ALGO for Reducing Energy Diversion

— To magnify network of lifeless node in Wireless Sensor Networks (WSNs).The selection of path for data deportation are eclectic in such a form that the over all energy absorbed along the path is lessen. Wireless sensor network is a set of a large number of small devices which gain information from physical environment using sensor nodes. These nodes measure, store and send the information to other nodes in the network. To transmit data sensor nodes require battery power. So power boost is a major issue in wireless sensor network. To support high extensible and better data gathering, sensor nodes are often grouped into dislocate, non-flapping subsets called clusters. An efficient routing algorithm is required to utilize power of nodes. In this paper we aim to improve network lifetime using LEACH based protocol. We are using DBEA-LEACH (distance-based energy aware) additionally selects a cluster head not only based on distance, but also by examining residual energy of the node greater than the average residual energy level of nodes in the network. We propose the enhanced LEACH convention for first node die time upgrade. In this paper i am doing one more enhancement to use Dijkstra's algorithm as routing algorithm to reduce power consumption. We are using Dijkstra‟s algorithm to reduce the power consumption and finding the shortest power consumed path between Source to Destination using minimum number no nodes

Compression of medical images using feed-forward neural network with LWT

Volume 8 Issue 5 (May 201

Medical images Compression using convolutional neural network with LWT

In compression of medical image using convolutional neural network trained with the back-propagation algorithm and lefted wavelet transformation is proposed to compress high quality medical images. It gives much better result as compared to feed-forward neural network . Medical image processing process is one of the most important section of research in medical applications in digital medical information. In this new approach , a three hidden layer convolutional network (CNN) is applied directly as the main compression algorithm to compress an MRI, X-ray, computer tomography images. After training with sufficient sample images, the compression process will be carried out on the target image. The coupling weights and activation values of each neuron in the hidden layer will be stored after training. Compression is achieved by using smaller number of hidden neurons as compare to the number of image pixels due to lesser information being stored. experimental results proves that the anticipated algorithm is superior to another algorithm in both lossy and lossless compression for all medical images tested Experimental results show that the CNN is able to achieve comparable compression performance to popular existing medical image compression schemes such as JPEG2000 and JPEG-LS. The Wavelet-SPIHT algorithm provides PSNR very important values for MRI and CT scan images

Separation Based Advanced Energy Efficient Cluster Head Selection Techniques for WSN

To maximize network lifetime in Wireless Sensor Networks (WSNs) the paths for data transfer are selected in such a way that the total energy consumed along the path is minimized. To support high scalability and better data aggregation, sensor nodes are often grouped into disjoint, non-overlapping subsets called clusters.In this paper we aim to improve network lifetime byusing LEACH based protocol by considering residualenergy and distance of nodes in WSN. We adoptdynamic clustering with dynamic selection of clusterheads in first round and static clustering with dynamicselection of cluster heads from second round. One morereason for network to die early is unbalance clustersize, to handle that number of nodes in the cluster isfixed to a predefined value. We propose two new distance-based clustering routing protocols, which we call DBLEACH and DBEA-LEACH. The first approach (distance-based) selects a cluster head node by considering geometric distance between the candidate nodes to the base station. To further improve DB-LEACH, DBEA-LEACH (distance-based energy aware) additionally selects a cluster head not only based on distance, but also by examining residual energy of the node greater than the average residual energy level of nodes in the networ