The research objective of the present study is the development of a model for increased accuracy of steel-price forecasts, which is of paramount importance for firms who use steel as an input and thus need to make informed decisions with regard to an optimal amount and type of hedge against...

This study proposes a modified Particle Swarm Optimization (PSO) algorithm based on Hummingbird Flight (HBF) patterns to enhance the search quality and population diversity. The HBF has five concepts: (1) Smaller steps toward position updating are more likely than larger ones, (2) Position...

This study proposes an integrated Decision Support System (DSS) with Multi-Criteria Decision-Making (MCDM) to evaluate trainers in organizations and choose the most suitable one(s) for a training program. The clustering stage determines the most appropriate number of trainees to be assigned to a...

Online Portfolio Selection (OLPS) has attracted extensive interest in recent years. Accurate prediction of future prices and determining the optimal portfolio selection strategy based on the estimated return is a challenging topic in machine learning. We propose a novel adjusted learning...

Fraudsters are now more active in their attacks on credit card transactions than ever before. With the advancement in data science and machine learning, various algorithms have been developed to determine whether a transaction is fraudulent. We study the performance of three different machine...

The generation of huge data with high velocity creates alteration in the distribution of the stream data, which is defined as the concept of drifts. The concept drifts negatively influence the classification accuracy and the stability of the data streams. Numerous machine learning-based models...

The transmission lines are used for power distribution across large distances. Different parameters affect the power transmission efficiency, and the quality of service. Furthermore, the transmission system parameter estimation is crucial for power flow analysis, electric power system expansion...

An Autoencoder (AE) is an independent feature extractor from data samples and a deep network can be obtained by stacking several AEs. This paper presents a novel hybrid stacked Autoencoder-based Deep Kernel-based Random Vector Functional Link Network (DKRVFLN-AE) for forecasting and trend...

In this paper, we describe the Factored Value MCTS Hybrid Cost-Max-Plus algorithm, a collection of decision-making algorithms (centralized, decentralized, and hybrid) for a multi-agent system in a collaborative setting that considers action costs. Our proposed algorithm is made up of two steps....