Support Vector Machine optimization with fractional gradient descent for data classification

Data classification has several problems one of which is a large amount of data that will reduce computing time. SVM is a reliable linear classifier for linear or non-linear data, for large-scale data, there are computational time constraints. The Fractional gradient descent method is an unconstrained optimization algorithm to train classifiers with support vector machines that have convex problems. Compared to the classic integer-order model, a model built with fractional calculus has a significant advantage to accelerate computing time. In this research, it is to conduct investigate the current state of this new optimization method fractional derivatives that can be implemented in the classifier algorithm. The results of the SVM Classifier with fractional gradient descent optimization, it reaches a convergence point of approximately 50 iterations smaller than SVM-SGD. The process of updating or fixing the model is smaller in fractional because the multiplier value is less than 1 or in the form of fractions. The SVM-Fractional SGD algorithm is proven to be an effective method for rainfall forecast decisions

Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks

Due to the dynamic nature, chaotic time series are difficult predict. In conventional signal processing approaches signals are treated either in time or in space domain only. Spatio-temporal analysis of signal provides more advantages over conventional uni-dimensional approaches by harnessing the information from both the temporal and spatial domains. Herein, we propose an spatio-temporal extension of RBF neural networks for the prediction of chaotic time series. The proposed algorithm utilizes the concept of time-space orthogonality and separately deals with the temporal dynamics and spatial non-linearity(complexity) of the chaotic series. The proposed RBF architecture is explored for the prediction of Mackey-Glass time series and results are compared with the standard RBF. The spatio-temporal RBF is shown to out perform the standard RBFNN by achieving significantly reduced estimation error.Comment: Published in: 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST). arXiv admin note: substantial text overlap with arXiv:1908.0132

Developing entrepreneurial self-efficacy and individual entrepreneurial orientation: an action oriented approach.

Doctoral Degree. University of KwaZulu-Natal, Durban.This study was conducted to determine how entrepreneurship self-efficacy (ESE) can be developed to activate individual entrepreneurial orientation (IEO) in the South African students who participated in a systemic action learning action research programme. It has been widely acknowledged that the Department of Higher Education and Training has come a long way in incorporating various learning pedagogies to overcome entrepreneurship education and training challenges, yet the issue of youth unemployment remains a significant problem. Although studies have been conducted by scholars to proffer lasting solutions to the limited entrepreneurial activities and individual entrepreneurial orientation, the development of youth entrepreneurship action remains a challenge both in theory and practice. The challenges are associated with the systemic disconnect in the entrepreneurship ecosystem that affects the entrepreneurial development of the youth. The study adopted a quantitative design within the concept of nondualism philosophy in developing entrepreneurial self-efficacy to activate the individual entrepreneurial orientation of South African university students. The study was integrated into the longitudinal systemic action learning action research (SALAR) project SHAPE (Shifting Hope Activating Potential Entrepreneurship). where 230 registered students volunteered and recruited for participation in the training in the South African province of KwaZulu-Natal. Findings that emerged from the longitudinal study revealed that entrepreneurial self-efficacy development predicts individual entrepreneurial orientation behaviour, change and action, therefore, n=73 from the overall registered participants signified their intention to act immediately after the training as a result of the combined application of SALAR, SHAPE action-training model and Theory UThe study contributed to existing knowledge and practice through the developed SHAPE action-training model which can be applied for entrepreneurship development, and the refined instrument also, can be applied for entrepreneurship development in higher institutions of learning in South Africa and other developing nations who want to develop youth entrepreneurship. Based on the findings, the study recommends further research be conducted into ESE and IEO’s relationship with Entrepreneurial Intent (EI) and Entrepreneurial Action (EA). Expanding this research testing to other provinces in South Africa as well as other African countries will provide insight into the proposed models and instruments’ potential to boost youth entrepreneurship. This study also recommends that Higher Education Institutions that wish to enrich their youth entrepreneurship teaching and learning offerings should develop an institution-tailored model such as the SHAPE social technology and apply SALAR to monitor the process. Lastly, this study recommends fostering the youth entrepreneurship ecosystem and the continuous involvement of eco-systemic stakeholders in entrepreneurship teaching and learning offerings to ensure the sustainable long-term development of youth’s ESE and IEO – hopefully resulting in increased EI and possible