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Navigation usability improvement by using actual and anticipated data
Abstract
Web Navigation is the procedure of exploring a system of data assets in the World Wide Web, Which is sorted out as hypertext or hypermedia. Here a system is predicated for comparing so as to explore the case of use issues the genuine information and foreseen utilization designs. By utilizing web server logs the genuine use examples are recorded for operational sites by log information to recognize clients, Users errand Oriented exchanges and client’s sessions. We center issues on the useful still, small voice part of usability, In Particular, we concentrate on distinguishing route related issues as described by a powerlessness to finish certain assignments, no data applying so as to contain in server logs. These are lightened psychological client models and we utilize Data mining calculations to find designs among real use paths. This study gives a starting approval of the pertinence and adequacy of our system. © 2018, Institute of Advanced Scientific Research, Inc. All rights reserved.
... SVM uses the hyper plane for classifying the data points into separate classes [15]. The data points which are near the hyper plane and boundary points of the differentiating classes are called as support vectors, as these are used to maximize the distance between the hyper plane and the support vectors to bring the accuracy in the classification of the data [16]. It is one of the successful algorithms of machine learning and implemented in different classification problems by different researchers. ...
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This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings.
The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
We present a new method to identify navigation related Web usability problems based on comparing actual and anticipated usage patterns. The actual usage patterns can be extracted from Web server logs routinely recorded for operational websites by first processing the log data to identify users, user sessions, and user task-oriented transactions, and then applying an usage mining algorithm to discover patterns among actual usage paths. The anticipated usage, including information about both the path and time required for user-oriented tasks, is captured by our Ideal User Interactive Path (IUIP) models constructed by cognitive experts based on their cognition of user behavior. The comparison is performed via the mechanism of test oracle for checking results and identifying user navigation difficulties. The deviation data produced from this comparison can help us discover usability issues and suggest corrective actions to improve usability. A software tool was developed to automate a significant part of the activities involved. With an experiment on a small service-oriented website, we identified usability problems which were cross-validated by domain experts, and quantified usability improvement by the higher task success rate and lower time and effort for given tasks after suggested corrections were implemented. This case study provides an initial validation of the applicability and effectiveness of our method.
This paper describes the implementation and evaluation of a framework for modeling emotions in complex, decision-making agents. Sponsored by U.S. Army Research Institute (ARI), the objective of this research is to make the decision-making process of complex agents less predictable and more realistic, by incorporating emotional factors that affect humans. In tune with modern theories of emotions, we regard emotions essentially as subconscious signals and evaluations that inform, modify, and receive feedback from a variety of sources including higher cognitive processes and the sensorimotor system. Thus, our work explicitly distinguishes the subconscious processes (in a connectionist implementation) and the decision making that is subject to emotional influences (in a symbolic cognitive architecture).It is our position that "emotional states" are emergent patterns of interaction between decision-making knowledge and these emotional signal systems. To this end, we have adopted an approach that promotes the emergence of behavior as a result of complex interactions between factors affecting emotions, integrated in the connectionist-style model, and factors affecting decision making, represented in the symbolic model.This paper presents the implementation of emotions architecture and explains how we evaluated the system. This includes a description of the behaviors we used in our prototype, the design of our experiments, a representative set of behavior patterns that emerged as a result of exercising our model over the design space, and our project's lessons learned.
In this paper, we describe two separate studies that improved our ability to understand our users’ experience of our products
at salesforce.com. The first study explored a methodology of combining expert and novice performance data to yield a measure
of intuitiveness. The second study created a methodology that combines both verbal and nonverbal emotion scales to better
understand the emotional effect our products have on our users. We present both these methods as expansions on the standard
usability study and examples of ways to better understand your users within an industry environment.
Frequent Pattern Growth (FP-Growth) Algorithm
- Florian Verhein
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