Assessing perceptions of academic staff in using SmartUMS for teaching and learning

The use of educational technologies for teaching and learning has been implemented in many institutions of higher learning. Universiti Malaysia Sabah (UMS) uses Moodle on SmartUMS e-learning platform to deliver academic courses. Every lecturer needs to register their respective courses on SmartUMS to complement face-to-face teaching and learning routine. The purpose of the study was to investigate academic staff’s acceptance and perceptions towards SmartUMS usage in UMS. The findings will contribute to the development of the UMS’s e-learning strategy, providing information on active participations from the lecturers which will enable the most effective use of education technology. Data was collected from all participants of SmartUMS training sessions. A questionnaire was distributed to 86 respondents and statistically analyzed using SPSS statistical package. The findings revealed that lecturers developed positive acceptance and perception towards using SmartUMS. It also concludes that SmartUMS provide value tools for effective interaction between lecturers and students in their teaching and learning activities

Assessing Perceptions of Academic Staff in Using SmartUMS for Teaching and Learning

The use of educational technologies for teaching and learning has been implemented in many institutions of higher learning. Universiti Malaysia Sabah (UMS) uses Moodle on SmartUMS e-learning platform to deliver academic courses. Every lecturer needs to register their respective courses on SmartUMS to complement face-to-face teaching and learning routine. The purpose of the study was to investigate academic staff’s acceptance and perceptions towards SmartUMS usage in UMS. The findings will contribute to the development of the UMS’s e-learning strategy, providing information on active participations from the lecturers which will enable the most effective use of education technology. Data was collected from all participants of SmartUMS training sessions. A questionnaire was distributed to 86 respondents and statistically analyzed using SPSS statistical package. The findings revealed that lecturers developed positive acceptance and perception towards using SmartUMS. It also concludes that SmartUMS provide value tools for effective interaction between lecturers and students in their teaching and learning activities

Assessing Perceptions of Academic Staff in Using SmartUMS for Teaching and Learning

The use of educational technologies for teaching and learning has been implemented in many institutions of higher learning. Universiti Malaysia Sabah (UMS) uses Moodle on SmartUMS e-learning platform to deliver academic courses. Every lecturer needs to register their respective courses on SmartUMS to complement face-to-face teaching and learning routine. The purpose of the study was to investigate academic staff’s acceptance and perceptions towards SmartUMS usage in UMS. The findings will contribute to the development of the UMS’s e-learning strategy, providing information on active participations from the lecturers which will enable the most effective use of education technology. Data was collected from all participants of SmartUMS training sessions. A questionnaire was distributed to 86 respondents and statistically analyzed using SPSS statistical package. The findings revealed that lecturers developed positive acceptance and perception towards using SmartUMS. It also concludes that SmartUMS provide value tools for effective interaction between lecturers and students in their teaching and learning activities

Catalysing scientific research with AI: unlocking new frontiers through generative AI

In today's rapidly evolving scientific landscape, the integration of Artificial Intelligence (AI) offers exceptional opportunities. This paper delves into the role of AI in advancing scientific research, with a particular focus on the transformative potential of Generative AI. By leveraging Generative AI's capabilities, researchers can expand the boundaries of knowledge, redefine experimental approaches, improve data analysis, generate hypothesis generation, and innovatively address complex challenges. We provide a concise summary of key topics, including the applications of Generative AI in scientific literature review, knowledge synthesis, and the optimization of experimental parameters. This paper emphasises core concepts of Generative AI, real-world case studies, and the ethical considerations associated with this technological advancement. Furthermore, we present insights from a survey involving 35 participants, which shed light on their awareness, attitudes, and expectations regarding Generative AI. By responsibly embracing Generative AI and integrating it with human expertise, researchers can leverage its power to accelerate scientific advancements and unlock new horizons of discovery. This paper aims to inspire researchers to explore the untapped potential of AI, in their scientific pursuits, propelling scientific research into new frontiers

A Survey on Underwater Wireless Sensor Networks: Requirements, Taxonomy, Recent Advances, and Open Research Challenges

The domain of underwater wireless sensor networks (UWSNs) had received a lot of attention recently due to its significant advanced capabilities in the ocean surveillance, marine monitoring and application deployment for detecting underwater targets. However, the literature have not compiled the state-of-the-art along its direction to discover the recent advancements which were fuelled by the underwater sensor technologies. Hence, this paper offers the newest analysis on the available evidences by reviewing studies in the past five years on various aspects that support network activities and applications in UWSN environments. This work was motivated by the need for robust and flexible solutions that can satisfy the requirements for the rapid development of the underwater wireless sensor networks. This paper identifies the key requirements for achieving essential services as well as common platforms for UWSN. It also contributes a taxonomy of the critical elements in UWSNs by devising a classification on architectural elements, communications, routing protocol and standards, security, and applications of UWSNs. Finally, the major challenges that remain open are presented as a guide for future research directions

Crossover-first differential evolution for improved global optimization in non-uniform search landscapes

The differential evolution (DE) algorithm is currently one of the most widely used evolutionary-based optimizers for global optimization due to its simplicity, robustness and efficiency. The DE algorithm generates new candidate solutions by first conducting the mutation operation which is then followed by the crossover operation. This order of genetic operation contrasts with other evolutionary algorithms where crossover typically precedes mutation. In this study, we investigate the effects of conducting crossover first and then followed by mutation in DE which we named as crossover-first differential evolution (XDE). In order to test this simple and straightforward modification to the DE algorithm, we compared its performance against the original DE algorithm using the CEC2005 global optimization’s set of 25 continuous optimization test problems. The statistical results indicate that the average performance of XDE is better than the original DE and three other well-known global optimizers. This straightforward reversal in the order of the genetic operations in DE can indeed improve its performance, in particular when attempting to solve complex search spaces with highly non-uniform landscapes

A hybrid multi-objective optimisation for energy efficiency and better coverage in underwater wireless sensor networks

Underwater wireless sensor networks (UWSNs), which benefit ocean surveillance applica­tions, marine monitoring and underwater target detection, have advanced substantially in recent years. However, existing deployment solutions do not satisfy the deployment of mobile underwater sensor nodes as a stochastic system. Internal and external environmental problems concern maximum coverage in the deployment region while minimising energy consumption. To fill this gap, this research proposes and implements a multi-objective optimisation solution to balance conflicts concerning node deployment objectives. First, this research analyses the existing mobile underwater node deployment algorithms to identify the significant problems in existing solutions. Next, it establishes the research problems by implementing various existing algorithms using comparative analysis. Based on that analysis, this research suggests a hybrid algorithm: the Multi-Objective Optimisa­tion Genetic Algorithm based on Adaptive Multi-Parent Crossover and Fuzzy Dominance (MOGA-AMPazy). The method adapts the original Non-Dominated Sorting Genetic Algorithm II (NSGA-II) by introducing a hybridisation of adaptive multi-parent crossover genetic algorithm and fuzzy dominance-based decomposition techniques. The algorithm introduces the fuzzy Pareto dominance concept to compare two solutions and uses the scalar decomposition method when one solution cannot dominate the other in terms of the fuzzy dominance level. The solution also proposes adaptive multi-parent crossover (AMP) to balance exploration and exploitation with new offspring, changing the number of parents involved in the crossover based on the execution of the new generation. The solution is further improved by introducing prospect theory to guarantee convergence through risk evaluation. The results obtained are then analysed to assess the proposed solution's performance in obtaining each deployment objective's optimal value. Finally, the proposed algorithm's effectiveness regarding node coverage, energy consumption, Pareto-optimal value, and algorithm execution time is validated using three Pareto-optimal metrics: including inverted generation distance (IGD), hypervolume, and diversity. Furthermore, this research utilises five commonly used two-objective ZDT test instances as benchmark tests, namely ZDT-1, ZDT-2, ZDT-3, ZDT-4, and ZDT-6. These tests use specific problem characteristics to impose the underlying proposed solution as well as three other systems. Pareto-optimal values obtained indicate that the proposed solution has almost complete coverage involving the actual Pareto front. Furthermore, all analysis and evaluation attributes indicate that the MOGA-AMPazy deployment algorithm can handle the multi-objective underwater sensor deployment problem better than other solutions. Thus, MOGA-AMPazy provides an efficient and comprehensive deployment solution for mobile sensor nodes in UWSNs. This study makes several noteworthy contributions to the body of knowledge concerning UWSNs, and it provides an excellent multi-objective representation to decision-makers or mission planners to monitor the region of interest (Rol)