Machine learning techniques are now very common in many spheres, and there is a growing popularity of these approaches in macroeconomic forecasting as well. Are these techniques really useful in the prediction of macroeconomic outcomes? Are they superior in performance compared to their traditional counterparts? We carry out a meta-analysis of the existing literature in order
Abstract-This paper presents an improved machine learning approach for the accurate and robust state of charge (SOC) in electric vehicle (EV) batteries using differential search optimized random forest regression (RFR) algorithm. The precise SOC estimation confirms the safety and reliability of EV. Nevertheless, SOC is influenced by numerous factors which cannot be measured directly. RFR is suitable for real-time SOC estimation due to its robustness to noise, overfitting issues and capacity to work with huge datasets. However, proper selection of RFR architecture and hyper-parameters combination remains a key issue to be explored. Hence, a differential search algorithm (DSA) is employed to search for the optimal values of trees and leaves in the RFR algorithm. DSA optimized RFR eliminates the utilization of the filter in data pre-processing steps and does not require a detailed understanding and knowledge about battery chemistry, rather only needs sensors to monitor battery voltage and
The source of sedimentary organic matter in lakes can help to elucidate climate and catchment variation and processes that reflect lake development. Common techniques for tracing sediment organic matter sources, such as the stable isotopes and elemental concentrations of C and N, can be too imprecise to identify the specific provenance of organic matter. By contrast, organic geochemical techniques such as gas or liquid chromatography and nuclear magnetic resonance provide detailed organic molecular characterisation but are both expensive and time consuming. Fourier Transform Infrared (FTIR) spectroscopy is a rapid, non-destructive, and well-established method for determining the constituents of lake sediments. However, the potential for identifying the sources of organic matter in lake sediments has not been fully explored. In this study, we assess the extent to which FTIR can be used to identify varying organic matter sources through analysis of modern autotrophs from Blue Lake, North
Software testing is often hindered where it is impossible or impractical to determine the correctness of the behaviour or output of the software under test (SUT), a situation known as the oracle problem. An example of an area facing the oracle problem is automatic image classification, using machine learning to classify an input image as one of a set of predefined classes. An approach to software testing that alleviates the oracle problem is metamorphic testing (MT). While traditional software testing examines the correctness of individual test cases, MT instead examines the relations amongst multiple executions of test cases and their outputs. These relations are called metamorphic relations (MRs): if an MR is found to be violated, then a fault must exist in the SUT. This paper examines the problem of classifying images containing visually hidden markers called Artcodes, and applies MT to verify and enhance the trained classifiers. This paper further examines two MRs, Separation and O