Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications

Business optimization is becoming increasingly important because all business activities aim to maximize the profit and performance of products and services, under limited resources and appropriate constraints. Recent developments in support vector machine and metaheuristics show many advantages of these techniques. In particular, particle swarm optimization is now widely used in solving tough optimization problems. In this paper, we use a combination of a recently developed Accelerated PSO and a nonlinear support vector machine to form a framework for solving business optimization problems. We first apply the proposed APSO-SVM to production optimization, and then use it for income prediction and project scheduling. We also carry out some parametric studies and discuss the advantages of the proposed metaheuristic SVM.Comment: 12 page

Mathematics summer schools for acoustics research training

Mathematical methods are important for research in many aspects of acoustics. Most researchers in acoustics in the United Kingdom do not have access to master level courses to broaden their postgraduate study, so they advance their fundamental mathematical methodologies taught at under-graduate level through independent learning. They develop their mathematical skills as appropriate rather than being made aware of the potential of advanced mathematical tools at the onset of their research career. Attempts to improve this situation were made through summer schools held in 2003 and 2005 at Southampton University and in 2007 at Salford University. The background to these Summer Schools, their content and structure, recruitment figures and student feedback are reported together with conclusions about their performance and role particularly in respect of PhD completion

Program on application of communications satellites to educational development. Computer-based instruction: A background paper on its status, cost/effectiveness and telecommunications requirements

Status, cost/effectiveness, and telecommunication/satellite requirements of computer-based instructio

e-Learning for expanding distance education in tertiary level in Bangladesh: Problems and progress

E-learning has broadly become an important enabler to promote distance education (DE) and lifelong learning in most of the developed countries, but in Bangladesh it is still a new successful progressive system for the learning communities. Distance education is thought to be introduced as an effective way of educating people of all sections in Bangladesh. Bangladesh Open University (BOU), the only distance education provider in Bangladesh, has been trying to adopt the use of various e-learning materials for its distance delivery. This paper has tried to describe the current progress of quality e-learning for expanding distance education, identifying the major problems of e-learning in distance education at tertiary level in Bangladesh, with special reference to BOU, and finally to put forward some valuable recommendations for solving the problems. The study is based on both primary and secondary sources. It is observed from the research that e-learning is going to ensure its bright prospect as an alternative mode of education at the tertiary level in Bangladesh. There are several problems that are identified and can be mitigated and solved through Information and Communication Technology (ICT) development, greater acceptance by learners, and much research in this sector in Bangladesh to face globalization. DOI: 10.18870/hlrc.v3i4.17

Support Vector Regression for Non-Stationary Time Series

The difficulty associated with building forecasting models for non-stationary and volatile data has necessitated the development and application of new sophisticated techniques that can handle such data. Interestingly, there are a lot of real-world phenomena where data that are “difficult to analyze” are generated. One of these is the stock market where data series generated are often hard to forecast because of their peculiar characteristics. In particular, the stock market has been referred to as a complex environment and financial time series forecasting is often tagged as the most challenging application of time series forecasting. In this study, a novel approach known as Support Vector Regression (SVR) for forecasting non-stationary time series was adopted and the feasibility of applying this method to five financial time series was examined. Prior to implementing the SVR algorithm, three different methods of transformation namely Relative Difference in Percentages (RDP), Z-score and Natural Logarithm transformations were applied to the data series and the best prediction results obtained along with the associated transformation technique was presented. Our study indicated that the Z-score transformation is the best scaling method for financial time series, exhibiting superior performance than the other two transformations on the basis of five different performance measures. To determine the optimum values of the SVR parameters, a cross-validation method was implemented. For this purpose, the value of C and ε was varied from 5 to 100, and 0.001 and 0.1 respectively. The cross-validation method, though computationally expensive, is better than other proposed techniques for determining the values of these parameters. Another highlight of this study is the comparison of the SVR results to that obtained using 5-day Simple Moving Averages (SMA). The SMA was selected as a comparative method because it has been identified as the most popular quantitative forecasting method used by US corporations. Discussions with financial analysts also suggest that the SMA is one of the widely used in the financial industry. The popularity of the SMA can be explained by the fact that it is easy and cheap to use and it produces forecasts that can be easily interpreted by econometricians and other interested practitioners