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PurposeThe human resource (HR) allocation problem is one of the critical dimensions of the project management process. Due to this nature of the problem, researchers are continually optimizing one or more critical scheduling and allocation challenges in different ways. This study aims to optimize two goals, increasing customer satisfaction and reducing costs using the imperialist competitive algorithm.Design/methodology/approachCloud-based e-commerce applications are preferred to conventional systems because they can save money in many areas, including resource use, running expenses, capital costs, maintenance and operation costs. In web applications, its core functionality of performance enhancement and automated device recovery is important. HR knowledge, expertise and competencies are becoming increasingly valuable carriers for organizational competitive advantage. As a result, HR management is becoming more relevant, as it seeks to channel all of the workers’ energy into meeting the organizational strategic objectives. The allocation of resources to maximize benefit or minimize cost is known as the resource allocation problem. Since discovering solutions in polynomial time is complicated, HR allocation in cloud-based e-commerce is an Nondeterministic Polynomial time (NP)-hard problem. In this paper, to promote the respective strengths and minimize the weaknesses, the imperialist competitive algorithm is suggested to solve these issues. The imperialist competitive algorithm is tested by comparing it to the literature’s novel algorithms using a simulation.FindingsEmpirical outcomes have illustrated that the suggested hybrid method achieves higher performance in discovering the appropriate HR allocation than some modern techniques.Practical implicationsThe paper presents a useful method for improving HR allocation methods. The MATLAB-based simulation results have indicated that costs and waiting time have been improved compared to other algorithms, which cause the high application of this method in practical projects.Originality/valueThe main novelty of this paper is using an imperialist competitive algorithm for finding the best solution to the HR allocation problem in cloud-based e-commerce.

Measuring brain activity through Electroencephalogram (EEG) analysis for eye state prediction has attracted attention from machine learning researchers. There have been many methods for EEG analysis using supervised and unsupervised machine learning techniques. The tradeoff between the accuracy and computation time of these methods in performing the analysis is an important issue that is rarely investigated in the previous research. This paper accordingly proposes a new method for EEG signal analysis through Self-Organizing Map (SOM) clustering and Deep Belief Network (DBN) approaches to efficiently improve the computation and accuracy of the previous methods. The method is developed using SOM clustering and DBN, which is a deep layer neural network with multiple layers of Restricted Boltzmann Machines (RBMs). The results on a dataset with 14980 instances and 15 attributes representing the values of the electrodes demonstrated that the method is efficient for EEG analysis. In addition, compared with the other supervised methods, the proposed method was able to significantly improve the accuracy of the EEG prediction.