The purpose of this study is to investigate relationship of fifth, sixth, seventh and eighth grade students’ scientific epistemological beliefs with their frequency of participating in laboratory activities. For this purpose, correlational study method was used. Sample of the study involved 301 students purposively selected from convenient schools. The data of the study was collected by a Likert type scale called “Scientific Epistemological Beliefs Scale” and information form about frequency of participating in laboratory activities and description of sample laboratory activities. The findings showed that majority of the students never participated in lab activities or participated in one time during one year. Also the fifth, sixth, seventh and eighth grade students’ scientific epistemological beliefs were significantly related to their frequency of participating in laboratory activities. They are positively correlated to each other. However there were no significant relationships between the students’ scientific epistemological beliefs and their frequency of participating in laboratory activities. Hence it can be claimed that participating in lab activities is associated with development of sophisticated scientific epistemological beliefs.

Vehicle identification or classification is one of the application areas that come under real time image processing. Vehicle recognition is having the significance in various applications including the traffic monitoring, load monitoring, number plate recognition, vehicle theft prevention, traffic violation detection, management of traffic etc. As the images are captured as primary data source, it can have number of associated impurities which include the background inclusion, object overlapping etc. Because of this, object detection and recognition is always a challenge in real time scenario. In present work, a robust feature based model is presented for feature extraction and classification of vehicle images. The presented model is applied on real time captured image to categorize the vehicle in light, medium and heavy vehicle. Firstly, the vehicle area segmentation is performed and later on the Gaussian filter is applied to extract the image features. This featured dataset is processed under Support Vector Machine (SVM) based distance analysis model for vehicle recognition and vehicle class identification. The experimentation results of present investigation shows the recognition rate of devised system over 90%.

Age is a human attribute which grows alongside an individual. Estimating human age is quite difficult for machine as well as humans, however there has been and are still ongoing efforts towards machine estimation of human age to a high level of accuracy. In a bid to improve the accuracy of age estimation from facial image, several approaches have been proposed many of which used Machine Learning algorithms. The several Machine Learning algorithms employed in these works have made significant impact on the results and of performances of the proposed age estimation approaches. In this paper, we examined and compared the performance of a number of Machine Learning algorithms used for age estimation in several previous works. Considering two publicly available facial ageing datasets (FG-NET and MORPH) which have been mostly used in previous works, we observed that Support Vector Machine (SVM) has been most popularly used and a combination/hybridization of SVM for classification (SVC) and regression (SVR) have shown the best performance so far. We also observed that the face modelling or feature extraction techniques employed significantly impacted the performance of age estimation algorithms.

The usage of remote servers network on the Internet to process data, store and manage, instead of using a local server or any computer” is called cloud computing. Cloud computing is that which totally based on resource sharing rather than any other device to handle applications. Today cloud computing is facing numerous challenges and one of those is Attack on the cloud environment. There are many types of hazardous attack on cloud, as the attack is always in wait for some important data or resource. The most common and most affective attack is Packet Flooding attack and there are many faces of packet flooding. EDoS Attack one of the most commonly and strong packet flood attack on the cloud to make the resources almost inaccessible to the user by flooding the unnecessary packet to the network or site more that its capacity. This paper deals with the analysis of EDoS and a mechanism is proposed to mitigate the EDoS by using filtration mechanism. The filtration is done on the basis of secure key Exchange which differentiate legitimate user from attacker. The simulation is done by cloud sim as well as Net-Beans and the performance is analyzed over time and data. Using filter the packet loss and time delay occurs in EDoS attack is much reduced.

As Watermarking have been considered as an emerging technique for copyright protection, data security and protection of ownership, several techniques and algorithms have been developed and created either in spatial domain or frequency domain mainly to achieve the properties of digital watermarking like robustness, imperceptibility, fidelity, etc. It is also found that the frequency domain techniques provides a better solution in obtaining these properties than the spatial domain. So, most researchers have used this domain in developing a good watermarking scheme for achieving robustness and other important properties of digital watermarking against all kinds of intentional and unintentional attacks. The attacks may include image processing attacks like compression, frame dropping, piracy, geometric distortions, loss of quality, etc. So, utmost importance must be given on how to create a scheme which can achieve robustness against all these attacks at the same time. The medium for watermarking is chosen as video because of its vast future scope So, in this paper, a thorough research on the various techniques available in watermarking based on frequency domain has been done to find out a secure technique that can withstand any kinds of attacks in a digital video.

Computer programming (CP) course offered in universities is difficult coupled with insufficient infrastructures and teaching staff. In spite of these, several undergraduate Computer Science (CS) students are increasingly acquiring programming skills and developing commercial applications even without attending formal programming classes. However, software intended for use other than by the developer requires teamwork, the use of software engineering methodologies and quality. What is not known about these undergraduate students is how their programming is learnt or applications developed. This is important in the light of software dependability and cost of failures today. Therefore, this paper investigates how undergraduate CS students learn programming and their software engineering knowledge. The purpose is to gain insights into how knowledge is gained and applied. To accomplish this, the paper conducted a survey utilizing questionnaire and interview on undergraduate students of CS in the University of Venda (UNIVEN). The data collected were analyzed and results quantitatively and qualitatively presented. The results showed that many CS students learned programming via the Internet reusable code, applied development methodology and are aware of software quality during development.

MapReduce is programming model to process the large set of data. Apache Hadoop an implementation of MapReduce has been developed to process the Big Data. Hadoop Cluster sharing introduces few challenges such as scheduling the jobs, processing data locality, efficient resource usage, fair usage of resources, fault tolerance. Accordingly, we focused on a job scheduling system in Hadoop in order to achieve efficiency. Schedulers are responsible for doing task assignment. When a user submits a job, it will move to a job queue. From the job queue, the job will be divided into tasks and distributed to different nodes. By the proper assignment of tasks, job completion time will reduce. This can ensure better performance of the jobs. By default, Hadoop uses the FIFO scheduler. In our experiment, we are discussing and comparing FIFO scheduler with Fair scheduler and Capacity scheduler job execution time.

To scrutinize the uniqueness of software quality model it is crucial to compare it with existing ones. Quality is generally apprehended in a model that illustrates the features and their interactions. Numerous models for measuring quality of software processes have been recommended to assess particular type of software products. Numerous methodologies and practices have been suggested to perform the specific or general scope based comparisons among eminent models. These comparisons are leak. The Suggested comparison lacks the clear differentiation and in depth analysis. Consequently, a prescribed method of comparison among software quality models has been defined. The suggested technique is applied on an inclusive comparison among renowned software quality models. The consequence of suggested technique demonstrates the power and faintness of quality models.