Earticle

Breast cancer is a common disease around the world. Much work has been done to predict the survivability of breast cancer patients. While the prediction is an information that is important in many applications, the reliability of an individual classification result, is of great interest in many application areas including bioinformatics. In this paper, we apply the Conformal Prediction algorithm to the classification results of four data mining algorithms in order to eliminate the non-reliable predictions and enhance the overall classification results. The proposed technique shows an enhancement in the effectiveness of the classification results.

Yttrium-Doped Barium Zirconate (BZY20) powders were synthesized by the solution combustion method, using two different fuels (urea and citric acid). The ignition of the reagent mixture with urea takes a longer time and the maximum temperature is higher than using citric acid. The theoretical calculations of exothermic heat and adiabatic flame temperatures indicate favorable conditions for combustion to occur. X-ray diffraction patterns showed formation of only perovskite phase for the samples synthesized with both of fuels. The crystallite sizes are in the range of 24-28 nm, with smaller values when citric acid was used as fuel. Scanning electron microscopy showed the presence of small agglomerates, formed by fine particles of different shapes and the size of BZY20 nanoparticles obtained using citric acid (30– 35 nm) was so smaller and more uniform than those obtained using urea (40-50 nm), and their BET surface areas are 30.9 and 20.57 m2/g, respectively. calcination is an essential step for a good crystallization of perovskite pure phase, which result showed 1150 oC for 5 h.

Video generation is the strategy of making videos by discovering moving pictures (videography) and making blends in live creation. During this aspect of videos creation, sequence of images gets contorted in the image acquisition and transmission phase. This paper presents a non-linear patch model for enhancing the degraded quality of video sequences. The proposed method navigates the frames of videos by selecting a patch of considerable width and with the assistance of this patch, a regression model is applied in order to have a robust effect while performing de-noising, de-blurring or super-resolution. The proposed work implements on three models i.e. search model, regression model and non-linear patch model. Various filters have been propounded e.g. kernel filters, total variation filters, adaptive median filter etc. Regression model is applied to every frame with predefined number of iterations with estimated number of frames. The proposed method inculpates two kinds of noise i.e. Gaussian noise and impulse noise. Various performance comparison metrics have been evaluated to check the coherence and productivity of imaging system like Peak signal to noise ratio (PSNR), Mean squared error (MSE), Root mean squared error (RMSE), Standard deviation (SD), Linear correlation and structural entropy.

Due to the frequently changing topology and high mobility of nodes in multihop wireless network, there may be chances of congestion which results in loss of TCP sequence and degrades the network performance. In existing congestion control works, hop by hop detection incurs huge overhead. Moreover, the waiting time of the source node in obtaining the acknowledgement from the receiver, will be high when the number of hops in creases. To overcome these issues, this paper proposes a Proxy based intelligent acknowledgment technique for multihop wireless network. In this technique, proxy nodes are selected along the transmission path, which determine any missing TCP sequence. In this way, packet loss is monitored and retransmission is requested from the source node, thus, ensuring proficient data transmission in the network. Simulation results show that the proposed technique reduces the delay and improves the throughput.

VANET stands for vehicular ad-hoc network. Vehicles attached to one another viva a temporary data type an instant system named “VANET”.VANET is a technology which is used to getting the information and transmitting the information between the moving vehicles on the road. And the devices are installed on the vehicles like GPS and road transmitter which gives the signal. It incorporates a vehicle to vehicle transmission as well as vehicle to Road side transmission and is important component of ITS (Information transportation system). Node Selection Algorithm purpose to choose next-hop vehicle to help keep in touch with, through utilize “bridging approach” with information forwarding i.e., to select the vehicle from the east (west) to west(east). Quality of services assures is much more challengeable and difficulties MANET than traditional wired network. It is mainly used for hop to hop communication, channel access contention. VANET is to provide safety for passenger and drivers on the road.

India is going to have the largest number of youth in the world by 2020 and it is a great demographic advantage over other developing countries. The current demographic dividend of India can only be fully realized if this large number of youth can be utilized as productive work force with high value skilled output, rather than unskilled / low value output [23][24]. To materialise this dream each state of India is to be uplifted enough so that the GER of every state benchmarks the global GER. In our study the female students of West Bengal are targeted as the GER of female students in this state is exceptionally poor as compared with the national GER[9]. Identification of the reasons of large scale drop outs of female students in West Bengal is done along with the possible way outs to bring these dropouts back to the classrooms or alternative streams of education were pulled up. In this paper the key reasons of poor GER of female students are identified through hierarchical clustering technique. And an e-Learning based model is established with the objective to offer a plausible way out over the identified reasons. The model will be effectual to the young female students of West Bengal for continuing their higher studies and that will gradually increase the GER as well.

Cloud computing is a new technology provides computing resources as services, and allows users to access these resources via the Internet without the need to own knowledge and experience, or even control of infrastructure that support these services. Job scheduling is considered one of the main issues in cloud computing. The main task of job scheduling is how to find an optimal mapping of set of jobs to a set of available resources. Unsuitable mapping of jobs to resources usually leads to inefficient cloud performance. The current methods for cloud job scheduling process produce acceptable solution but not optimal solution. This paper proposes a new job scheduling mechanism using Glowworm Swarm Optimization (GSO). The proposed mechanism aims to find the best mapping in order to minimize the execution time of jobs. The proposed mechanism based on information of jobs (cloudlets) and resources (virtual machines) such as length of jobs, speed of resources and identifier for both. The scheduling function in the proposed job scheduling mechanism firstly creates a set of jobs and resources to generate the population by assigning the jobs to resources randomly and evaluates the population using fitness values which represent the execution times of jobs. Secondly the function used iterations to regenerate populations based on glowworms behavior to produce the best job schedule that gives the minimum execution time of jobs. The methodology of this research is based on simulation of the proposed mechanism using the CloudSim simulator. The evaluation process of the proposed mechanism started with a set of different experiments. These experiments revealed that, the proposed mechanism minimized the execution time of jobs. The proposed mechanism is compared with the First Come First Servers (FCFS) algorithm and experimental results revealed that the proposed mechanism has a better performance than FCFS for minimizing the execution time of jobs.