Systematic literature review of the performance characteristics of Chebyshev polynomials in machine learning applications for economic forecasting in low-income communities in sub-Saharan Africa.

Chebyshev polynomials have unique properties that place them in a class of functions that are highly efficient in the approximation of non-linear functions. Machine learning techniques are being applied to solve complex non-linear problems in the financial markets where there is a proliferation of financial products. The techniques for valuing diverse portfolios of these products can be time consuming and expensive. Formal research has been conducted to determine how machine learning can considerably reduce the computational effort without losing accuracy. The objective of this systematic literature review is to discover evidence of research on the optimal use of Chebyshev polynomials in machine learning and neural networks that may be used for the estimation of generalized financial outcomes of large clusters of small economic units in low-income communities in sub-Saharan Africa. Scopus, ProQuest, and Web of Science databases were queried with search criteria designed to recover peer-reviewed research articles that addressed this objective. Many articles discussing broader applications in engineering, computer science, and applied mathematics were found. Several articles provided insights into the challenges of forecasting stock price outcomes from unpredictable market activities, and in investment portfolio valuations. One article addressed specific environmental issues relating to energy, biology, and ecological situations, and presented encouraging results. While the literature search did not find any similar articles that address economic forecasting for low-income communities, the applications and techniques used in stock market forecasting and portfolio valuations can contribute to formative theory on sustainable development. There is currently no theoretical underpinning of sustainable development initiatives in developing countries. A framework for small business structures, data collection, and near real-time processing is proposed as a potential data-driven approach to guide policy decisions and private sector involvement. Supplementary Information: The online version contains supplementary material available at 10.1007/s43546-022-00328-w.

Machine Learning and Radiomic Features to Predict Overall Survival Time for Glioblastoma Patients.

Glioblastoma is an aggressive brain tumor with a low survival rate. Understanding tumor behavior by predicting prognosis outcomes is a crucial factor in deciding a proper treatment plan. In this paper, an automatic overall survival time prediction system (OST) for glioblastoma patients is developed on the basis of radiomic features and machine learning (ML). This system is designed to predict prognosis outcomes by classifying a glioblastoma patient into one of three survival groups: short-term, mid-term, and long-term. To develop the prediction system, a medical dataset based on imaging information from magnetic resonance imaging (MRI) and non-imaging information is used. A novel radiomic feature extraction method is proposed and developed on the basis of volumetric and location information of brain tumor subregions extracted from MRI scans. This method is based on calculating the volumetric features from two brain sub-volumes obtained from the whole brain volume in MRI images using brain sectional planes (sagittal, coronal, and horizontal). Many experiments are conducted on the basis of various ML methods and combinations of feature extraction methods to develop the best OST system. In addition, the feature fusions of both radiomic and non-imaging features are examined to improve the accuracy of the prediction system. The best performance was achieved by the neural network and feature fusions.