Daniyal M. Alghazzawi

Towards a linear general type-2 fuzzy logic based approach for computing with words

Within the last two decades, the paradigm of Computing With Words (CWW) has been gaining more attention. Mainly, CWW has an exciting vision which tries to tackle the problem of human intelligence by taking the human mind as a role model. The human intelligence has been investigated by various disciplines including psychology, philosophy, neuroscience, linguistics, computer science, and cognitive sciences. Notably, it is not a straightforward task to map the human’s brain reasoning into computer processes. In this paper, we propose to facilitate such mapping by investigating a key element, which is to identify the step-by-step formation of perceptual judgments. Herein, we first introduce an approach that employs general type-2 fuzzy logic to dynamically model the human perceptions based on the human experience. This approach can be regarded as a step to enable the CWW vision. We have deployed the proposed approach in real-world settings and we will present two sets of real-world experiments which were conducted in the intelligent apartment (iSpace) in the University of Essex. The first set of experiments demonstrates the results of the proposed approach for the adaptive modeling of ambient luminance perception. In the second set of experiments, we show that our approach performs better in the rule base evaluation processing time and in output accuracy with comparison to an interval type-2 fuzzy logic system.

Online Social Networks Impact in Secondary Education

This paper presents and analyzes the potential uses and motivations of online social networks in education, with special emphasis on secondary education. First, we show several previous researches supporting the use of social networking as an educational tool and discuss Edmodo, an educative online social network. The work carried out during two academic years with senior students of primary and secondary schools is also analyzed. This research has allowed us to see the reality of social network use among young people and identify the challenges of its application to education environment.

A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms

Abstract The adaptive educational systems within e-learning platforms are built in response to the fact that the learning process is different for each and every learner. In order to provide adaptive e-learning services and study materials that are tailor-made for adaptive learning, this type of educational approach seeks to combine the ability to comprehend and detect a person’s specific needs in the context of learning with the expertise required to use appropriate learning pedagogy and enhance the learning process. Thus, it is critical to create accurate student profiles and models based upon analysis of their affective states, knowledge level, and their individual personality traits and skills. The acquired data can then be efficiently used and exploited to develop an adaptive learning environment. Once acquired, these learner models can be used in two ways. The first is to inform the pedagogy proposed by the experts and designers of the adaptive educational system. The second is to give the system dynamic self-learning capabilities from the behaviors exhibited by the teachers and students to create the appropriate pedagogy and automatically adjust the e-learning environments to suit the pedagogies. In this respect, artificial intelligence techniques may be useful for several reasons, including their ability to develop and imitate human reasoning and decision-making processes (learning-teaching model) and minimize the sources of uncertainty to achieve an effective learning-teaching context. These learning capabilities ensure both learner and system improvement over the lifelong learning mechanism. In this paper, we present a survey of raised and related topics to the field of artificial intelligence techniques employed for adaptive educational systems within e-learning, their advantages and disadvantages, and a discussion of the importance of using those techniques to achieve more intelligent and adaptive e-learning environments.

A Type-2 Fuzzy Logic based system for linguistic summarization of video monitoring in indoor intelligent environments

Video monitoring can provide vital context awareness information from indoor intelligent environments where privacy is not a limitation. However, there is a need to develop linguistic summarization tools which are capable of summarizing in a layman language the information of interest within long video sequences. The key module which can enable the linguistic summarization of video monitoring is human activity/behaviour recognition. However, human behavior recognition is an important yet challenging task due to the behavior uncertainty, activity ambiguity, and uncertain factors such as position, orientation and speed, etc. In order to handle such high levels of uncertainties in activity analysis, we introduce a system based on Interval Type-2 Fuzzy Logic Systems (IT2FLSs) whose parameters are optimized by the Big Bang-Big Crunch (BB-BC) algorithm which allows for robust behaviour recognition using 3D machine vision techniques in intelligent environments. We present several experiments which were performed in real-world intelligent environments to fairly make comparisons with the state-of-the-art algorithms. The experimental results demonstrate that the proposed BB-BC paradigm is effective in tuning the parameters of the membership functions and the rule base of the IT2FLSs to improve the recognition accuracy. It will be shown through real-world experiments that the proposed IT2FLSs outperformed the Type-1 FLSs (TIFLSs) counterpart as well as other traditional non-fuzzy based systems. Based on the recognition results, higher-level applications will presented including video linguistic summarizations event searching and activity retrieval/playback.

An experience based linear general type-2 fuzzy logic approach for Computing With Words

In this paper, we present an approach to interpret the Computing With Words (CWWs) paradigm merging the advancements from neuroscience, psychology and artificial intelligence. The presented approach will incorporate fuzzy composite concepts (FCCs), a special case of linguistic weighted average (LWA) and case-based reasoning (CBR). The focus of the paper is on the inception of the CWWs paradigm to bridge the gap between the human and machine intelligence. The investigation of FCCs processing is performed using linear general type-2 (LGT2) and interval type-2 (IT2) fuzzy sets. The results show that LGT2 fuzzy sets outperform IT2 fuzzy sets in the processing time of complete rule base evaluation, in providing better modeling of the human perceptual judgment, and in producing richer range of output intervals.

A general type-2 fuzzy logic approach for adaptive modeling of perceptions for Computing With Words

In a broad sense, the Computing With Words (CWW) vision tries to tackle the problem of human intelligence by taking the human mind as a role model. A key element to facilitate the mapping of the humans brain reasoning into computer processes is to identify the step-by-step formation of perceptual judgments. In this paper, we present an approach that employs general type-2 fuzzy logic which enables the human perceptions to be dynamically modeled and adapted depending on the human experience. This approach can be regarded as a step to enable the CWW vision. We will present real-world experiments which were conducted with real users in the intelligent apartment (iSpace) in the University of Essex. The experiments demonstrate the results of the proposed approach for the adaptive modeling of ambient luminance perception.

A type-2 fuzzy logic recommendation system for adaptive teaching

E-learning platforms facilitate the interaction between students and instructors while mitigating temporal or spatial constraints. Nevertheless, such platforms require measuring the degree of students’ engagement with the delivered course content and teaching style. Such information is highly valuable for evaluating the quality of the teaching and altering the teaching delivery style in massively crowded online learning platforms. When the number of learners is high, it is essential to attain overall engagement and feedback, yet doing so is highly challenging due to the high levels of uncertainties related to students and the learning context. To handle these uncertainties more robustly, we present a method based on type-2 fuzzy logic utilizing visual RGB-D features, including head pose direction and facial expressions captured from Kinect v2, a low-cost but robust 3D camera, to measure the engagement degree of students in both remote and on-site education. This system augments another self-learning type-2 fuzzy logic system that helps teachers with recommendations of how to adaptively vary their teaching methods to suit the level of students and enhance their instruction delivery. This proposed dynamic e-learning environment integrates both on-site and distance students as well as teachers who instruct both groups of students. The rules are learned from the students’ and teachers’ learning/teaching behaviors, and the system is continuously updated to give the teacher the ability to adapt the delivery approach to varied learners’ engagement levels. The efficiency of the proposed system has been tested through various real-world experiments in the University of Essex iClassroom among a group of thirty students and six teachers. These experiments demonstrate the capabilities—compared to type-1 fuzzy systems and non-adaptive systems—of the proposed interval type-2 fuzzy logic-based system to handle the uncertainties and improve average learners’ motivations to engage during learning.

Usability Heuristics: Reinventing the Wheel?

Heuristic evaluation is a well-known and widely accepted usability evaluation method. When performing a heuristic evaluation, generic or specific heuristics may be used. But forming heuristic evaluators may be a challenging task. The paper presents a study that evaluates the perception of (novice) evaluators on Nielsen’s usability heuristics. A standard survey was applied in five experiments.

MoNGEL: monotonic nested generalized exemplar learning

In supervised prediction problems, the response attribute depends on certain explanatory attributes. Some real problems require the response attribute to represent ordinal values that should increase with some of the explaining attributes. They are called classification problems with monotonicity constraints. In this paper, we aim at formalizing the approach to nested generalized exemplar learning with monotonicity constraints, proposing the monotonic nested generalized exemplar learning (MoNGEL) method. It accomplishes learning by storing objects in