Mobile learning applications utilise the advantages of mobile technologies to increase learning opportunities, mainly on an anytime, anywhere basis. The advancement of mobile technology has facilitated the development of numerous applications to improve students’ learning experience and performance. Successful implementation of m-learning is highly dependent on learning context and environment awareness. This work presents a multiphase exploration of early phases responsible for defining and validating the m-learning context, and later phases based on context validation results achieved from the previous phases, involving the development and evaluation of a new m-learning context prototype. This new prototype proved to provide context-aware and ubiquitous learning services fulfilling several diverse user interaction levels and requirements.

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Journal: International Journal of Computer applications in Technology. 58 (4), 296- 307.

This paper delves into the capacity of enhanced Big Bang-Big Crunch (EBB-BC) metaheuristic to handle data clustering problems. BB-BC is a product of an evolution theory of the universe in physics and astronomy. Two main phases of BB-BC are big bang and big crunch. The big bang phase involves a creation of a population of random initial solutions, while in the big crunch phase these solutions are shrunk into one elite solution exhibited by a mass center. This study looks into enhancing the BB-BC’s effectiveness in clustering data. Where, the inclusion of an elite pool alongside implicit solution recombination and local search method, contribute to such enhancement. Such strategies resulted in a balanced search of good quality population that is also diverse. The proposed elite pool-based BB-BC was compared with the original BB-BC and other identical metaheuristics. Fourteen different clustering datasets were used to test BB-BC and the elite pool-based BB-BC showed better performance compared to the original BB-BC. BB-BC was impacted more by the incorporated strategies. The experiments outcomes demonstrate the high quality solutions generated by elite pool-based BB-BC. Its performance in fact supersedes that of identical metaheuristics such as swarm intelligence and evolutionary algorithms.

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Journal: Journal of Computer Science. 14 (12), 1611-1626.

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Journal: 20th International Congress on Modelling and Simulation, Adelaide, Australia

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Journal: 19th International Congress on Modelling and Simulation, Perth, Australia

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Journal: International Journal of Data Mining, Modelling and Management

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Journal: International Journal of Advanced Computer Science and Applications,

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Journal: International Journal of Advanced Computer Science and Applications, Vol. 11, No. 5, 2020

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Journal: Methods Mol Biol • • . 2021;2190:195-208. doi: 10.1007/978-1-0716-0826-5_9.

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Journal: Springer International Publishing Switzerland 2016 S. Shanmuganathan and S. Samarasinghe (eds.), Artificial Neural Network Modelling, Studies in Computational Intelligence 628, DOI 10.1007/978-3-

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Journal: International Journal of Electrical and Computer Engineering