An Overview of Personalized Recommendation System to Improve Web Navigation

We present a new personalized recommendation system, which means the searches of each user is done according to their interest which is based on ranking or preference method. It also maintains the logs which records the sessions of each user and brings out the exact data required by the user. This is done by fetching the data that is already stored in the database. Web server logs maintains history of page results and consists of a log file which automatically creates and maintains the list of activities performed by the users. For extracting the data according to the user’s previous searches, we are using Stemming Algorithm. The Stemming Algorithm is a process where the exact, meaningful words are extracted from the URL. Because of this process the user’s search time will be reduced. It also improves the quality of web navigation and overcomes the limitation of existing system. In the proposed system we extract user’s behaviour from web server logs in the actual process whereas, in the anticipated system, the user’s behaviour is done with the help of cognitive user model and we perform the comparison between the two usage processes. The data produced from this comparison can help the users to discover usability issues and take actions to improve usability. In the anticipated usage the cognitive user model is done that can be used to simulate or predict human behaviour or by performance and task. Finally, the system is executed by using the top-k ranking algorithm. The advantage of this system are accuracy and better processing speed. The user’s convenience deals with the ease of navigation which helps the users to interact with their interface

Detection of Android Malware using Feature Selection with a Hybrid Genetic Algorithm and Simulated Annealing (SVM and DBN)

Because of the widespread use of the Android operating system and the simplicity with which applications can be created on the Android platform, anyone can easily create malware using pre-made tools. Due to the spread of malware among many helpful applications, Android users are experiencing issues. In this study, we showed how to use permissions gleaned from static analysis to identify Android malware. Utilising support vector machines and deep belief networks, we choose the pertinent features from the set of permissions based on this methodology. The suggested technique increases the effectiveness of Android malware detection

Binary Particle Swarm Optimization based Biclustering of Web usage Data

Web mining is the nontrivial process to discover valid, novel, potentially useful knowledge from web data using the data mining techniques or methods. It may give information that is useful for improving the services offered by web portals and information access and retrieval tools. With the rapid development of biclustering, more researchers have applied the biclustering technique to different fields in recent years. When biclustering approach is applied to the web usage data it automatically captures the hidden browsing patterns from it in the form of biclusters. In this work, swarm intelligent technique is combined with biclustering approach to propose an algorithm called Binary Particle Swarm Optimization (BPSO) based Biclustering for Web Usage Data. The main objective of this algorithm is to retrieve the global optimal bicluster from the web usage data. These biclusters contain relationships between web users and web pages which are useful for the E-Commerce applications like web advertising and marketing. Experiments are conducted on real dataset to prove the efficiency of the proposed algorithms

Relevance of Negative Links in Graph Partitioning: A Case Study Using Votes From the European Parliament

In this paper, we want to study the informative value of negative links in signed complex networks. For this purpose, we extract and analyze a collection of signed networks representing voting sessions of the European Parliament (EP). We first process some data collected by the VoteWatch Europe Website for the whole 7 th term (2009-2014), by considering voting similarities between Members of the EP to define weighted signed links. We then apply a selection of community detection algorithms, designed to process only positive links, to these data. We also apply Parallel Iterative Local Search (Parallel ILS), an algorithm recently proposed to identify balanced partitions in signed networks. Our results show that, contrary to the conclusions of a previous study focusing on other data, the partitions detected by ignoring or considering the negative links are indeed remarkably different for these networks. The relevance of negative links for graph partitioning therefore is an open question which should be further explored.Comment: in 2nd European Network Intelligence Conference (ENIC), Sep 2015, Karlskrona, Swede

A Methodology for Planning Road Best Management Practices Combining WEPP: Road Erosion Modeling and Simulated Annealing Optimization

Erosion from forest roads is a known problem in mountainous terrain. To abate these negative consequences, physical Best Management Practices (BMPs) are implemented, sometimes with no knowledge of erosion hot spots. With the need to minimize water quality impacts while at the same time accounting for multiple considerations and constraints, road BMP planning at the watershed scale is a difficult task. To assist in this planning process, a methodology is presented here that combines WEPP: Road erosion predictions with simulated annealing optimization. Under this methodology, erosion predictions associated with BMP options for a segment comprise the objective function of an optimization problem. This methodology was tested on a watershed in the Lake Tahoe Basin. WEPP: Road input data was gathered through road surveys. Modeling results predicted relatively little sediment leaving the forest buffer, as a result of numerous well-maintained BMPs and the dry climate found in the watershed. A sensitivity analysis for all WEPP: Road input parameters is presented, which provides insight into the general applicability of these erosion estimates as well as the relative importance of each input parameter. After evaluating erosion risk across the entire watershed, applicable BMPs were assigned to problem road segments and WEPP: Road was used to predict change in sediment leaving the buffer with BMP implementation at a given site. These predictions, combined with budget constraints as well as equipment scheduling considerations, were incorporated into an algorithm using simulated annealing as its optimization engine. Three modeled scenarios demonstrate the viability of this methodology in reducing total sediment leaving the road buffer over a planning horizon. Of the 173 segments surveyed, 38 segments could be treated using generic BMPs. For all three scenarios, BMP-SA reduced sediment leaving the buffer by as much as 70% over the course of a 20-year planning horizon. For the 38 segments treated with BMPs, sediment was reduced by greater than 90% over the planning horizon. This methodology is a viable approach for streamlining watershed-scale road network BMP planning, despite its heavy reliance on road erosion estimates