Intelligent Browser-Based Systems to Assist Internet Users

Bergasa-Suso, J.; Sanders, David; Tewkesbury, Giles

doi:10.1109/te.2005.854570

Cited by 44 publications

(38 citation statements)

References 4 publications

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“…This is due to the fact that the fuzzy inference engine has been used in this work which is efficient in handling the uncertain information. The accuracy of dead band algorithm proposed by Sanders & Bergasa-Suso (2010) was higher when compared to the traditional Bayesian algorithm and the algorithm proposed by Bergasa-Suso et al (2005). However, Sanders & Bergasa-Suso (2010) model introduced a new category called 'unknown' in his model for the learners who cannot be predicted precisely.…”

Section: Results and Inferencementioning

confidence: 97%

“…According to this system, the learners were classified into three categories for the first dimension (active/reflective) as active, reflective, or unknown. The accuracy of classifying the learners in Sanders & Bergasa-Suso (2010) work has increased to 81% compared to the earlier work done by Bergasa-Suso et al (2005). However, this model can still be enhanced by resolving the unknown category of the learners.…”

Section: Work On Online Learning Style Prediction Using Online Envirmentioning

confidence: 91%

“…Learning Styles produces a greater impact on the learning performances, especially in e-learning environments (Bergasa-Suso et al 2005;Sanders & Bergasa-Suso 2010). The learning styles may differ for the same individual based on several factors like mood, time, course content and environmental factors.…”

Section: Related Workmentioning

confidence: 99%

“…The generic categorizations of learning styles fall under the factors elaborated in table 1. This paper considers the learners' online activity information and some of the well known systems for learning styles prediction through such online activities are intelligent browser-based system (Bergasa-Suso et al 2005), and computer interface system (Sanders & Bergasa-Suso 2010). The work proposed in this paper is an extension to the system developed by Sanders & Bergasa-Suso (2010) who developed a new intelligent system and the proposed model has been evaluated and tested for E-Learning environments.…”

Section: Related Workmentioning

confidence: 99%

“…In this work, the inference for Learning Styles was done using Matlab R2009a. The experiments were repeated, analyzed and compared with the other existing models provided by Bergasa-Suso et al (2005), and Sanders & Bergasa-Suso (2010). The proposed model is also compared with the traditional Bayesian Classification algorithm to test the significant difference in the accuracy of the proposed model.…”

Section: Experimental Set-upmentioning

confidence: 99%

See 4 more Smart Citations

Fuzzy-logic based learning style prediction in e-learning using web interface information

Deborah¹,

Sathiyaseelan

Audithan

et al. 2015

Sadhana

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The e-learners' excellence can be improved by recommending suitable e-contents available in e-learning servers that are based on investigating their learning styles. The learning styles had to be predicted carefully, because the psychological balance is variable in nature and the e-learners are diversified based on the learning patterns, environment, time and their mood. Moreover, the knowledge about the learners used for learning style prediction is uncertain in nature. This paper identifies Felder-Silverman learning style model as a suitable model for learning style prediction, especially in web environments and proposes to use Fuzzy rules to handle the uncertainty in the learning style predictions. The evaluations have used the Gaussian membership function based fuzzy logic for 120 students and tested for learning of C programming language and it has been observed that the proposed model improved the accuracy in prediction significantly.

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Section: Results and Inferencementioning

confidence: 97%

Section: Work On Online Learning Style Prediction Using Online Envirmentioning

confidence: 91%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Experimental Set-upmentioning

confidence: 99%

See 3 more Smart Citations

Fuzzy-logic based learning style prediction in e-learning using web interface information

Deborah¹,

Sathiyaseelan

Audithan

et al. 2015

Sadhana

View full text Add to dashboard Cite

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A Rule-Based Expert System to Decide on Direction and Speed of a Powered Wheelchair

Sanders

Gegov

Haddad

et al. 2018

Advances in Intelligent Systems and Computing

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Some rule based techniques are presented that can assist powered wheelchair drivers. The expert system decides on the direction and speed of their wheelchair. The system tends to avoid obstacles while having a tendency to turn and head in towards a desired destination. This is achieved by producing a new target angle as an extra input. Other inputs are from sensors and a joystick. Directions are recommended and mixed with user inputs from the joystick representing desired direction and desired speed. The rule-based system decides on an angle to turn the powered wheelchair and suggest it. Inputs from the joystick and sensors are mixed with the suggested angle from the Rule Based Expert System. A modified direction for the wheelchair is produced. The whole system helps disabled wheelchair users to drive their powered wheelchairs.

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Improving Human-Machine Interaction for a Powered Wheelchair Driver by Using Variable-Switches and Sensors that Reduce Wheelchair-Veer

Sanders

Langner²,

Bausch

et al. 2019

Advances in Intelligent Systems and Computing

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The integration of proportional switches for human-computer interaction and sensors with veer correction systems are presented. The transducers and sensors improve control, assist wheelchair drivers and reduced wheelchair veer, especially on slopes. The systems also reduce effort. The proportional switches are particularly useful for disabled people who do not have enough skill to use a joystick, or who lack sufficient hand-grasp and release ability, or who have movement disorders. The new systems were tested using laboratory test rigs. The test rigs were reused later to teach human users. A rolling road was then built to test the systems before user trials were undertaken. The angle of the wheelchair casters provided feedback and that feedback was used to reduce drift. A new electronic system matched the caster angles to the driver input. A case study is described. Results are presented, and they suggest there are advantages to using variable rather than digital or binary switches. The veer correction system can assist when a user is traversing a slope. The transducers and systems have been tested at Chailey heritage and proved to be useful in assisting poweredwheelchair users. The proportional switches isolate the gross motor functions and filter out uncontrolled movement. The sensor system helps users to steer on uneven or sloping ground. The transducers also provide more control during turning and can reduce the turn radius as well as lowering frustration and conserving energy.

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Intelligent Browser-Based Systems to Assist Internet Users

Cited by 44 publications

References 4 publications

Fuzzy-logic based learning style prediction in e-learning using web interface information

Fuzzy-logic based learning style prediction in e-learning using web interface information

A Rule-Based Expert System to Decide on Direction and Speed of a Powered Wheelchair

Improving Human-Machine Interaction for a Powered Wheelchair Driver by Using Variable-Switches and Sensors that Reduce Wheelchair-Veer

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