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This paper aims to utilize machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications.

The objective of this work is to propose a model for deep rough set theory that uses more than decision table and approximating these tables to a classification system, i.e. the paper propose a novel framework of deep learning based on multi-decision tables.

The paper tries to coordinate the local properties of individual decision table to provide an appropriate global decision from the system.

The rough set learning assumes the existence of a single decision table, whereas real-world decision problem implies several decisions with several different decision tables. The new proposed model can handle multi-decision tables.

The proposed classification model is implemented on social networks with preferred features which are freely distribute as social entities with accuracy around 91 per cent.

The deep learning using rough sets theory simulate the way of brain thinking and can solve the problem of existence of different information about same problem in different decision systems

This paper utilizes machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications.

The rough set concept is a new mathematical approach to imprecision, vagueness and uncertainty. This paper introduces the emergent computational paradigm and discusses its applicability and potential in rough set theory.

A conceptual discussion and approach are taken.

For accepting a system is displaying an emergent behavior, the system should be constructed by describing local elementary interactions between components in different ways of describing global behavior and properties of the running system over a period of time. The proposals of an emergent computation structure for implementing basic rough sets theory operators are also given in this paper.

The results will have an important impact on the development of new methods for knowledge discovery in databases, in particular for development of algorithmic methods for pattern extraction from data.

The aim is identifying and analyzing some well‐defined types of emergence where the paper uses ideas from machine learning and artificial intelligence to provide the model of cellular automata based on rough set theory and response in simulated cars.

This paper proposes, as practical part, a road traffic system based on two‐dimensional cellular automata combined with rough set theory to model the flow and jamming that is suitable to an urban environment.

The automaton mimics realistic traffic rules that apply in everyday experience.

The modeled development process in this paper involves simulated processes of evolution, learning and self‐organization.

Recently, the examination and modeling of vehicular traffic has become an important subject of research.

The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of the state machine behavior, which can give an emergent to the model.

A methodology for using rough set for preference modeling in decision problem is presented in this paper; where we will introduce a new approach for deriving knowledge rules from database based on rough set combined with genetic programming. Genetic programming belongs to the most new techniques in applications of artificial intelligence. Rough set theory, which emerged about 20 years back, is nowadays a rapidly developing branch of artificial intelligence and soft computing. At the first glance, the two methodologies that we discuss are not in common. Rough set construct is the representation of knowledge in terms of attributes, semantic decision rules, etc. On the contrary, genetic programming attempts to automatically create computer programs from a high‐level statement of the problem requirements. But, in spite of these differences, it is interesting to try to incorporate both the approaches into a combined system. The challenge is to obtain as much as possible from this association.

Nature is the single and most complex system that has been always studied, and no one can compete Mother Nature, but we can learn from her, by many new methodologies through biology. The paper aims to discuss this issue.

In this paper, being inspired by the mechanism through which our Mother Nature handling human taste, a proposed model for clustering and classification hand gesture is introduced based on human taste controlling strategy.

The model can extract information from measurement data and handling it as the structure of tongue and the nervous systems of human taste recognition.

The efficiency of proposed model is demonstrated experimentally on classifying the sign language data set; in the high recognition accuracy obtained for numbers of ASL was 95.52 percent.

It has recently been shown that an approach termed emergence system has gained popularity in a variety of fields, however, emergent computation in decentralized spatially extended systems, such as in cellular automata, is still not well understood. To accept that a system is displaying emergent behavior, the system should be constructed by describing local elementary interactions between components. This is achieved in a different way by describing global behavior and properties of the running system over a period of time. This paper introduces the emergent computational paradigm, and discusses its theoretical formulation using a new general model of cellular automata. We have also developed a technique to study the structure of the state transition of cellular automata in the limit of large system size.

Media has always been used as a key manipulator of public agendas, political beliefs and individuals’ attitudes. The purpose of this study is to investigate the impact of three adaptive media strategies on the pattern and dynamics of potential crowds.

An agent-based approach is used to simulate the three adaptive media strategies on the pattern and dynamics of potential crowds. During the experiments, the media broadcast is intensified to gather momentum for crowd movements or is lessened to maintain the budget.

The results show that a slight change in the media management strategy could lead to a radical different impact on the crowd dynamics. The results also show that a quite smart media strategy could outperform a strategy with an unlimited budget. Finally, the structure of the society shows a significant influence on the crowd dynamics than it could be inferred.

The model presents an explanatory toolkit for the crowd complexity. The results provide deep insights into the crowd formation and a basis for understanding the influence of media and the impact of its strategies on the crowd dynamics.

Blockchain (BC) has recently gained attention from educational organizations as a reliable and trustworthy technology for storing crucial data. This study aims to explore the factors that influence the students’ intention to use BC to host an automated reward system that is based on objective criteria of students’ performance and activities. Predefined smart contracts would guarantee unbiased judgements and fair rewards.

A mixed method approach is used. Based on an iterative rounds of Delphi approach along with a comprehensive literature review, this research proposed an extended Unified Theory of Acceptance and Use of Technology model. The model hypotheses relate performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), perceived innovativeness (PI), trust (TR), perceived security and privacy (S&P), complexity (CX) and enjoyment (EN) to students behavioural intention to use the reward system. The study tests the hypotheses using survey data from 138 students who are familiar with BC technology and its applications. The data analysis is performed using partial least squares-structural equation modelling method.

The analysis revealed that PE, EE, PI, TR and EN positively affect students’ intention to use the BC rewarding system, while CX negatively impacts it. Conversely, FC and, interestingly, S&P, which showed a significant influence on trust, were found to be insignificant in influencing students’ intention to use the system.

This study significantly contributes to the educational technology field by addressing research gaps regarding the adoption and acceptance of BC technology in academia. Firstly, it proposes a basic form of a student reward system. Secondly, it introduces an empirically validated model of students’ adoption, pinpointing factors influencing their readiness to embrace a BC reward system and providing practical implementation insights.

The growing popularity of mobile technology has led governments across the world to develop mobile business models and encouraged the transition from the electronic government (e-government) to the mobile government (m-government). However, only a small amount of data is available regarding m-government implementation in developing countries and users’ behavioural intentions (BIs) with respect to the utilization of such services. To address this issue, this study attempts to identify factors that affect m-government acceptance among citizens of the United Arab Emirates (UAE). This study aims to examine the relationships between m-government service characteristics and m-government technology characteristics by analysing their perceived ease-of-use, perceived usefulness, user attitudes towards m-government usage and corresponding BIs with respect to utilization of m-government services.

This study is based on a quantitative survey conducted among 326 users of m-government services in the UAE. The responses were tabulated on a five-point Likert scale. Structural equation modelling was used to evaluate the performance of the proposed model and verify the hypothesized relationships between its constructs.

Overall, the research findings indicate that: users’ intention to use m-government applications has three determinants – attitude towards m-government use, m-government ease of use and m-government usefulness; m-government usefulness and ease of use have a significant influence on user attitudes towards m-government; and perceived ease of use of the service, currency of the services, the accuracy of the provided information, the security concerns associated with the utilization of the m-government services, trust in the m-government services and the risks perceived in using the m-Government service are antecedents of users’ perceptions of the m-government services.

This study has improved our understanding of the critical mobile technology factors that are needed to improve user acceptance of m-government services. The study presents important implications for both theory and practice.