Software channels : a distributed object-communication mechanism

In this paper, we describe a software mechanism, a software channel, that allows a group of distributed objects to communicate with each other automatically once they are connected to it. Software channels and predesigned distributed objects that are connected to them encapsulate the communication protocol and the network topology to allow distributed applications to be constructed by distributed structural object composition. Software channels thus can reduce the implementation cost of distributed applications that use such components as shared text areas, shared whiteboards, audio and video delivery subsystems, and remote monitoring subsystems.Key Words and Phrases: software channel, distributed structural object composition, peer-to-peer communication, client-server communication, session manager, session port, Java.**1996 best estimate for issue date and commencement year based on available information.*

Ensemble Classifier for Stock Trading Recommendation

This paper presents a heterogeneous ensemble classifier for price trend prediction of a stock, in which the prediction results are subsequently used in trading recommendation. The proposed ensemble model is based on Support vector machine, Artificial neural networks, Random forest, Extreme gradient boosting, and Light gradient boosting machine. A feature selection is performed to choose an optimal set of 45 technical indicators as input attributes of the model. Each base classifier is executed with an extensive hyperparameter tuning to improve performance. The prediction results from five base classifiers are aggregated through a modified majority voting among three classifiers with the highest accuracies, to obtain final prediction result. The performance of proposed ensemble classifier is evaluated using daily historical prices of 20 stocks from Stock Exchange of Thailand, with 3 overlapping datasets of 5-year intervals during 2014–2020 for different market conditions. The experimental results show that the proposed ensemble classifier clearly outperforms buy-and-hold strategy, individual base classifiers, and the ensemble with straightforward majority voting in terms of both trading return and Sharpe ratio

Particle swarm optimization with crossover: a review and empirical analysis

Since its inception in 1995, many improvements to the original particle swarm optimization (PSO) algorithm have been developed. This paper reviews one class of such PSO variations, i.e. PSO algorithms that make use of crossover operators. The review is supplemented with a more extensive sensitivity analysis of the crossover PSO algorithms than provided in the original publications. Two adaptations of a parent-centric crossover PSO algorithm are provided, resulting in improvements with respect to solution accuracy compared to the original parent-centric PSO algorithms. The paper then provides an extensive empirical analysis on a large benchmark of minimization problems, with the objective to identify those crossover PSO algorithms that perform best with respect to accuracy, success rate, and efficiency.http://link.springer.com/journal/104622017-02-20hb201