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The twenty-first century has seen a vast technological revolution characterized by the development of cyber-physical systems, integration of things, and new and computationally improved machines and systems. However, there have been seemingly little strides in the development of user interfaces, specifically for industrial machines and equipment. The aim of this study was to assess the efficiency of the human-machine interfaces in the Kenyan context in providing a consistent and reliable working environment for industrial machine operators. The researcher employed a convenient purposive sampling to select 15 participants who had at least two years of hands-on experience in machines operation, control, or instrumentation. The results of the study are herein presented, including the recommendations to enhance workforce productivity and efficiency.

The modern technologies, which are characterized by cyber-physical systems and internet of things expose organizations to big data, which in turn can be processed to derive actionable knowledge. Machine learning techniques have vastly been employed in both supervised and unsupervised environments in an effort to develop systems that are capable of making feasible decisions in light of past data. In order to enhance the accuracy of supervised learning algorithms, various classification-based ensemble methods have been developed. Herein, we review the superiority exhibited by ensemble learning algorithms based on the past that has been carried out over the years. Moreover, we proceed to compare and discuss the common classification-based ensemble methods, with an emphasis on the boosting and bagging ensemble-learning models. We conclude by out setting the superiority of the ensemble learning models over individual base learners.

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