Alkire-Foster oriented ensemble fuzzy inference system for urban poverty classification

Malaysia is a developing country which relies on the monetary approach to measure poverty. The approach is simple to measure but it is insensitive towards changes of the poor in multiple dimensions such as education, health and living standards especially in urban areas. Several current issues in classifying the urban poor include rigid dichotomy of the poor and non-poor, unable to capture changes that happens in various sub-groups of urban poor population and misclassified poverty indicators. This study developed a multidimensional poverty measurement framework which integrated i) Alkire-Foster approaches in quantification of multidimensional urban poor, ii) Adaptive Neural Fuzzy Inference Systems (ANFIS) to predict classification of urban poor and resolve the misclassification of urban poor and iii) ensemble ANFIS. 300 questionnaires were distributed to targeted households in Bandar Tasik Selatan, Kuala Lumpur. This study started with a comparison of datadriven Fuzzy Rule-Based System (FRBS) with the domain expert comprising FRBS classification. Next, the Alkire-Foster method was introduced which included parameter selection, dual cut off identification and aggregation of the poor. Then, the ANFIS prediction was carried out using various ANFIS combination models such as Genfis 1, Genfis 2 and Genfis 3 to predict the classification of urban poor. This study proceeded to improve the classification by proposing the ensemble ANFIS that included ensemble weighting and ensemble integration method. The performance of this proposed framework was evaluated using Root Mean Square Error (RMSE), Mean Square Error (MSE), and R-Squared. For validation purposes, this study was reviewed by officers at the Zakat Collection Centre, Kuala Lumpur as the domain experts. The findings showed that the Genfis 3 using Fuzzy C-Means clustering algorithm in ANFIS outperformed all the ANFIS models, by obtaining the least MSE and RMSE values and highest R-Squared. These results included the Health dimension which was excluded in the current poverty measurement. Overall, this study has managed to address the urban poor classification by providing multiple dimensions of the poor and produce robust prediction results

Utilizing advanced modelling approaches for forecasting air travel demand: a case study of Australia’s domestic low cost carriers

One of the most pervasive trends in the global airline industry over the past few three decades has been the rapid development of low cost carriers (LCCs). Australia has not been immune to this trend. Following deregulation of Australia’s domestic air travel market in the 1990s, a number of LCCs have entered the market, and these carriers have now captured around 31 per cent of the market. Australia’s LCCs require reliable and accurate passenger demand forecasts as part of their fleet, network, and commercial planning and for scaling investments in fleet and their associated infrastructure. Historically, the multiple linear regression (MLR) approach has been the most popular and recommended method for forecasting airline passenger demand. In more recent times, however, new advanced artificial intelligence-based forecasting approaches – artificial neural networks (ANNs), genetic algorithm (GA), and adaptive neuro-fuzzy inference system (ANFIS) - have been applied in a broad range of disciplines. In light of the critical importance of passenger demand forecasts for airline management, as well as the recent developments in artificial intelligence-based forecasting methods, the key aim of this thesis was to specify and empirically examine three artificial intelligence-based approaches (ANNs, GA and ANFIS) as well as the MLR approach, in order to identify the optimum model for forecasting Australia’s domestic LCCs demand. This is the first time that such models – enplaned passengers (PAX) and revenue passenger kilometres performed (RPKs) – have been proposed and tested for forecasting Australia’s domestic LCCs demand. The results show that of the four modeling approaches used in this study that the new, and novel, ANFIS approach provides the most accurate, reliable, and highest predictive capability for forecasting Australia’s LCCs demand. A second aim of the thesis was to explore the principal determinants of Australia’s domestic LCCs demand in order to achieve a greater understanding of the factors which influence air travel demand. The results show that the primary determinants of Australia’s domestic LCCs demand are real best discount airfare, population, real GDP, real GDP per capita, unemployment, world jet fuel prices, real interest rates, and tourism attractiveness. Interestingly three determinants, unemployment, tourism attractiveness, and real interest rates, which have not been empirically examined in any previously reported study of Australia’s domestic LCCs demand, proved to be important predictor variables of Australia’s domestic LCCs demand. The thesis also found that Australia’s LCCs have increasingly embraced a hybrid business model over the past decade. This strategy is similar to LCCs based in other parts of the world. The core outcome of this research, the fact that modelling based on artificial intelligence approaches is far more effective than the traditional models prescribed by the International Civil Aviation Organization (ICAO), means that future work is essential to validate this. From an academic perspective, the modelling presented in this study offers considerable promise for future air travel demand forecasting. The results of this thesis provide new insights into LCCs passenger demand forecasting methods and can assist LCCs executives, airports, aviation consultants, and government agencies with a variety of future planning considerations

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018

Critical Thinking Skills Profile of High School Students In Learning Science-Physics

This study aims to describe Critical Thinking Skills high school students in the city of Makassar. To achieve this goal, the researchers conducted an analysis of student test results of 200 people scattered in six schools in the city of Makassar. The results of the quantitative descriptive analysis of the data found that the average value of students doing the interpretation, analysis, and inference in a row by 1.53, 1.15, and 1.52. This value is still very low when compared with the maximum value that may be obtained by students, that is equal to 10.00. This shows that the critical thinking skills of high school students are still very low. One fact Competency Standards science subjects-Physics is demonstrating the ability to think logically, critically, and creatively with the guidance of teachers and demonstrate the ability to solve simple problems in daily life. In fact, according to Michael Scriven stated that the main task of education is to train students and or students to think critically because of the demands of work in the global economy, the survival of a democratic and personal decisions and decisions in an increasingly complex society needs people who can think well and make judgments good. Therefore, the need for teachers in the learning device scenario such as: driving question or problem, authentic Investigation: Science Processes

Recent Development of Hybrid Renewable Energy Systems

Abstract: The use of renewable energies continues to increase. However, the energy obtained from renewable resources is variable over time. The amount of energy produced from the renewable energy sources (RES) over time depends on the meteorological conditions of the region chosen, the season, the relief, etc. So, variable power and nonguaranteed energy produced by renewable sources implies intermittence of the grid. The key lies in supply sources integrated to a hybrid system (HS)