Reinforcement Learning for Systematic FX Trading

We explore online inductive transfer learning, with a feature representation transfer from a radial basis function network formed of Gaussian mixture model hidden processing units to a direct, recurrent reinforcement learning agent. This agent is put to work in an experiment, trading the major spot market currency pairs, where we accurately account for transaction and funding costs. These sources of profit and loss, including the price trends that occur in the currency markets, are made available to the agent via a quadratic utility, who learns to target a position directly. We improve upon earlier work by learning to target a risk position in an online transfer learning context. Our agent achieves an annualised portfolio information ratio of 0.52 with a compound return of 9.3%, net of execution and funding cost, over a 7-year test set; this is despite forcing the model to trade at the close of the trading day at 5 pm EST when trading costs are statistically the most expensive.<br>

Towards Grading Chest X-rays of COVID-19 Patients Using a Dynamic Radial Basis Function Network Classifier

The high volume of COVID-19 Chest X-rays and less number of radiologists to interpret those is a challenge for the highly populous developing nations. Moreover, correct grading of the COVID-19 stage by interpreting the Chest X-rays manually is time-taking and could be biased. It often delays the treatment. Given the scenario, the purpose of this study is to develop a deep learning classifier for multiple classifications (e.g., mild, moderate, and severe grade of involvement) of COVID-19 Chest X-rays for faster and accurate diagnosis. To accomplish the goal, the raw images are denoised with a Gaussian filter during pre-processing followed by the Regions of Interest, and Edge Features are identified using Canny’s edge detector algorithm. Standardized Edge Features become the training inputs to a Dynamic Radial Basis Function Network classifier, developed from scratch. Results show that the developed classifier is 88% precise and 86% accurate in classifying the grade of illness with a much faster processing speed. The contribution lies in the dynamic allocation of the (i) number of Input and Hidden nodes as per the shape and size of the image, (ii) Learning rate, (iii) Centroid, (iv) Spread, and (v) Weight values during squared error minimization; (vi) image size reduction (37% on average) by standardization, instead of dimensionality reduction to prevent data loss; and (vii) reducing the time complexity of the classifier by 26% on average. Such a classifier could be a reliable assistive tool to human doctors in screening and grading COVID-19 patients and in turn, would help faster management of the patients as per the stages of COVID-19.

On the Classification of MR Images Using “ELM-SSA” Coated Hybrid Model

Computer-aided diagnosis permits biopsy specimen analysis by creating quantitative images of brain diseases which enable the pathologists to examine the data properly. It has been observed from other image classification algorithms that the Extreme Learning Machine (ELM) demonstrates superior performance in terms of computational efforts. In this study, to classify the brain Magnetic Resonance Images as either normal or diseased, a hybridized Salp Swarm Algorithm-based ELM (ELM-SSA) is proposed. The SSA is employed to optimize the parameters associated with ELM model, whereas the Discrete Wavelet Transformation and Principal Component Analysis have been used for the feature extraction and reduction, respectively. The performance of the proposed “ELM-SSA” is evaluated through simulation study and compared with the standard classifiers such as Back-Propagation Neural Network, Functional Link Artificial Neural Network, and Radial Basis Function Network. All experimental validations have been carried out using two different brain disease datasets: Alzheimer’s and Hemorrhage. The simulation results demonstrate that the “ELM-SSA” is potentially superior to other hybrid methods in terms of ROC, AUC, and accuracy. To achieve better performance, reduce randomness, and overfitting, each algorithm has been run multiple times and a k-fold stratified cross-validation strategy has been used.

A federated data-driven evolutionary algorithm for expensive multi-/many-objective optimization

Data-driven optimization has found many successful applications in the real world and received increased attention in the field of evolutionary optimization. Most existing algorithms assume that the data used for optimization are always available on a central server for construction of surrogates. This assumption, however, may fail to hold when the data must be collected in a distributed way and are subject to privacy restrictions. This paper aims to propose a federated data-driven evolutionary multi-/many-objective optimization algorithm. To this end, we leverage federated learning for surrogate construction so that multiple clients collaboratively train a radial-basis-function-network as the global surrogate. Then a new federated acquisition function is proposed for the central server to approximate the objective values using the global surrogate and estimate the uncertainty level of the approximated objective values based on the local models. The performance of the proposed algorithm is verified on a series of multi-/many-objective benchmark problems by comparing it with two state-of-the-art surrogate-assisted multi-objective evolutionary algorithms.

Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning Techniques

Fault diagnosis occurrence and its precise prediction in nuclear power plants are extremely important in avoiding disastrous consequences. The inherent limitations of the current fault diagnosis methods make machine learning techniques and their hybrid methodologies possible solutions to remedy this challenge. This study sought to develop, examine, compare, and contrast three robust machine learning methodologies of adaptive neurofuzzy inference system, long short-term memory, and radial basis function network by modeling the loss of feed water event using RELAP5. The performance indices of residual plots, mean absolute percentage error, root mean squared error, and coefficient of determination were used to determine the most suitable algorithms for accurately diagnosing the loss of feed water transient signatures. The study found out that the adaptive neurofuzzy inference system model outperformed the other schemes when predicting the temperature of the steam generator tubes, the radial basis function network scheme was best suited in forecasting the mass flow rate at the core inlet, while the long short-term memory algorithm was best suited for the estimation of the severities of the loss of the feed water fault.

Predicting the discharge coefficient of oblique cylindrical weir using neural network techniques

Cylindrical weir shapes offer a steady-state overflow pattern, where the type of weirs can offer a simple design and provide the ease-to-pass floating debris. This study considers a coefficient of discharge (Cd) prediction for oblique cylindrical weir using three diameters, the first is of D1 = 0.11 m, the second is of D2 = 0.09 m, and the third is of D3 = 0.06.5 m, and three inclination angles with respect to channel axis, the first is of θ1 = 90 ͦ, the second is of θ2 = 45 ͦ, and the third is of θ3 = 30 ͦ. The Cd values for total of 56 experiments are estimated by using the radial basis function network (RBFN), in addition of comparing that with the back-propagation neural network (BPNN) and cascade-forward neural network (CFNN). Root mean square error (RMSE), mean square error (MSE), and correlation coefficient (CC) statics are used as metrics measurements. The RBFN attained superior performance comparing to the other neural networks of BPNN and CFNN. It is found that, for the training stage, the RBFN network benchmarked very small RMSE and MSE values of 1.35E-12 and 1.83E-24, respectively and for the testing stage, it also could benchmark very small RMSE and MSE values of 0.0082 and 6.80E-05, respectively.