Stepping ahead based hybridization of meta - heuristic model for solving global optimization problems

Intelligent optimization algorithms based on swarm principles have been widely researched in recent times. The Firefly Algorithm (FA) is an intelligent swarm algorithm for global optimization problems. In literature, FA has been seen as one of the efficient and robust optimization algorithm. However, the solution search space used in FA is insufficient, and the strategy for generating candidate solutions results in good exploration ability but poor exploitation performance. Although, there are a lot of modifications and hybridizations of FA with other optimizing algorithms, there is still a room for improvement. Therefore, in this paper, we first propose modification of FA by introducing a stepping ahead parameter. Second, we design a hybrid of modified FA with Covariance Matrix Adaptation Evolution Strategy (CMAES) to improve the exploitation while containing good exploration. Traditionally, hybridization meant to combine two algorithms together in terms of structure only, and preference was not taken into account. To solve this issue, preference in terms of user and problem (time complexity) is taken where CMAES is used within FA's loop to avoid extra computation time. This way, the structure of algorithm together with the strength of the individual solution are used. In this paper, FA is modified first and later combined with CMAES to solve selected global optimization benchmark problems. The effectiveness of the new hybridization is shown with the performance analysis

Predicting media literacy level of secondary school students in Fiji

The digital revolution has set a platform for all the information and means of communication to be digitised, thus creating a digital media society. This explosion of digital media requires individuals to have a set of skills and knowledge to survive in this lifelong digital media society. In such a context, many countries around the world are now leveraging on Media Literacy to enhance the necessary skills of individuals and improve upon responsible media engagement. Therefore, predicting media literacy of students is essential so that suitable interventions can be put in place. This paper presents an analysis of Media Literacy status of Year 12 and Year 13 students at randomly selected secondary schools in Fiji, and it presents a set of predictive models using classification techniques. A quantitative study using a reliable survey was conducted to determine the Media Literacy of students using a Likert scale of 1-5. The analysis for this study was using the R software whereby classification algorithms such as Random Forest Classifiers, Decision Trees and Support Vector Machine Algorithm (SVM) were used to build the predictive models. These models will be used to derive appropriate interventions to improve Media Literacy of students. The baseline data from the study provide information on media literacy of Fijian students. The paper concludes with the important attributes that contribute towards an individual's competency on media literacy

Using ensemble decision tree model to predict student dropout in computing science

Science, Technology, Engineering and Mathematics (STEM) professionals play a key role in the development of an economy. STEM workers are critical thinkers as they contribute immensely by driving innovations. There is a high demand for professionals in the STEM fields but there is also a shortage of human resource in these areas. One way to reduce this problem is by identifying students who are at-risk of dropping out and then intervening with focused strategies that will ensure that these students remain in same the programme till graduation. Therefore, this research aims to use a data mining classification technique to identify students who are at-risk of dropping out from their Computing Science (CS) degree programmes. The Random Forest (RF) decision tree algorithm is used to learn patterns from historical data about first-year undergraduate CS students who are enrolled in a tertiary institute in the South Pacific. A number of factors are used which comprise of students demographic information, previous education background, financial information as well as data about students' academic interaction. Feature selection is performed to determine which factors have greater influence in students' decision in dropping out. Cross-validation techniques are used to ensure that the models are not over-fitted. Two models were built using a 5fold and 10-fold cross-validation and the results were compared using several measures of model performance. The results show that the factors corresponding to students' academic performance in a first-year programming course had the greatest impact student attrition in CS

Digital literacy: a catalyst for the 21st century education

The seamless integration of new digital technologies into higher education teaching and learning has transformed education pedagogies and changed how students learn. The students are now required to have digital competencies to survive in the era of learning with technology; therefore, measuring the students’ digital competencies is of utmost importance. This study evaluates the first-year university students’ digital competencies at a higher education institute using a newly designed digital literacy measuring tool named digitlitfj. The digitlitfj is an online tool consisting of a 5 point Likert scale questionnaire ranging from ‘No understanding’ to ‘Advanced level of understanding’ that was piloted to the first-year university students. The results show that 86.15% of the students were average to very highly digitally literate. Also, Deep learning, Support Vector Machine, Random Forest and Decision Tree algorithms in RapidMiner were used to evaluate the most important and influential variables in predicting an individual’s digital literacy competency. The results show that all the variables utilized in the research were important, with computer literacy being the most influential variable in predicting an individual’s digital literacy

A digital literacy model to narrow the digital literacy skills gap

The concept of digital literacy is increasingly prevalent in the 21st century. Growing demands from the work sector for individuals to be digitally literate has prompted targeted interventions and innovations from the education sector to instil digital skills into the future workforce. However, despite efforts the digital skills gap remains visible globally. This paper explores the prominent educational frameworks and models, their advantages and limitations in 21st-century learning and teaching. Furthermore, a new innovative digital literacy model has been proposed to be integrated into the existing and future education frameworks and models to assist educationists in narrowing the digital skills gaps and preparing graduates for the work sector. The digital literacy model consists of two components: (1) the digital literacy framework- South Pacific Digital Literacy Framework (SPDLF) and (2) the digital literacy tool. The SPDLF reflects six major literacies identified for the 21st-century while the digital literacy tool--digilitFJ consists of a digital literacy measuring scale and an online intervention program. The exploratory factor analysis showed that the SPDLF was valid. On the other hand, heuristics, student attitude, and satisfaction and effectiveness of the digital literacy tool were also evaluated from the student's perspective to reflect its usefulness. The survey results also showed a positive attitude and perception of the use of the tool. Additionally, Cohen's d value showed that the digital literacy tool was effective. Therefore, if the tool is implemented and adopted, it can narrow the existing digital skills gap in the South Pacific

A face - off - classical and heuristic - based path planning approaches

Robot path planning is a computational problem to find a valid sequence of configurations to move a robot from an initial to a final destination. Several classical and heuristic-based methods exist that can be used to solve the problem. This paper compares the performance of a classical method based on potential field, Lyapunov-based Control Scheme, with those of the standard and stepping ahead Firefly Algorithms. The performance comparison is based on the optimal path distance and time. The results show that the stepping ahead Firefly algorithm finds a shorter path in lesser duration when compared with the Lyapunov-based method. The LbCS also inherently faces the local minima problem when the start, target, and obstacle’s center coordinates are collinear. This problem is solved using the firefly algorithm where the diversification of the fireflies helps escape local minima

Modeling and verification for the micropayment protocol netpay

There are many virtual payment systems available to conduct micropayments. It is essential that the protocols satisfy the highest standards of correctness. This paper examines the Netpay Protocol [3], provide its formalization as automata model, and prove two important correctness properties, namely absence of deadlock and validity of an ecoin during the execution of the protocol. This paper assumes a cooperative customer and will prove that the protocol is executing according to its description