Abstract-This paper attempts to determine the veiled information and identifying user behavior on the web by using the web data sources. With the help of this information, Prediction of user behavior can be done very easily. The user pattern is analyzed by applying the free tool similar web and page score on the website of the particular organization. This tool tries to conduct web mining in a domain independent manner. The experimental results provide an easier way to navigate the website and improve the website design architecture. This work deliberates the detailed results of a website in a specific education domain application. The main objective of conducting the study on websites of the different universities is to discern the pattern of behavioural expressions and user characteristics so that the organization can improve the structure of website and can upload the more information in a proper way.

No abstract

Current time represents the era of communication technology and in this revolution MANET is widely used and act as a key star for data communication in real life decisive scenario for e.g., disaster management, traffic control, military services etc. MANET is infrastructureless data communication network comprising of mobile nodes. For MANET it requires secure and energy efficient framework for the underlying routing protocol. To meet the need of efficient data communication in MANET, an Energy Efficient and Secure AODV (EES-AODV) protocol is proposed. In the projected routing protocol, first the order of network nodes happen dependent on energy and afterward encryption has been done. Simulation of projected protocol is performed for such as Average Delay, PDR and Throughput. Simulated results shows that modified AODV gives optimized performance and provides a more secure and energy aware protocol.

Academic success for students in any educational institute is the primary requirement for all stakeholders, i.e., students, teachers, parents, administrators and management, industry, and the environment. Regular feedback from all stakeholders helps higher education institutions (HEIs) rise professionally and academically, yet they must use emerging technologies that can help institutions to grow at a faster pace. Early prediction of students’ success using trending artificial intelligence technologies like machine learning, early finding of at-risk students, and predicting a suitable branch or course can help both management and students improve their academics. In our work, we have proposed a new student performance prediction model in which we have used ensemble machine learning with stacking of four multi-class classifiers, decision tree, k-nearest neighbor, Naïve Bayes, and One vs. Rest support vector machine classifiers. The proposed model predicts the final grade of a student at the earliest possible time and the suitable stream for a new student. A student dataset of over a thousand students from five different branches of an engineering institute has been taken to test the results. The proposed model compares the four-machine learning (ML) techniques being used and predicts the final grade with an accuracy of 93%.

With the unstable development of the data accessible on the Internet, WWW has turned into the most capable stage to store, recover, and communicate data. The same number of individuals have taken to the Internet for data gathering, examining client conduct from web get to the logs can be useful to make versatile framework, recommender framework, and clever online business applications. We get to log records are the documents that contain data about the connection amongst clients and the sites with the utilization of the Internet. It contains many points of interest as authentication of users, Address related to IP, time of visit, accessing, data transferred, status setting of result, URL and so on. To analyze web log data for prediction of consumer behavior, various free and paid tools is available online. By studying and comparing various analyzer, a website owner can select the best tool for prediction of consumer behavior. This paper gives a neat report between celebrated log analyzer instruments in light of their highlights and execution.