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Ubiquitous mobile edge computing (MEC) using the internet of things (IoT) is a promising technology for providing low-latency and high-throughput services to end-users. Resource allocation and quality of service (QoS) optimization are critical challenges in MEC systems due to the large number of devices and applications involved. This results in poor latency with minimum throughput and energy consumption as well as a high delay rate. Therefore, this paper proposes a novel approach for resource allocation and QoS optimization in MEC using IoT by combining the hybrid kernel random Forest (HKRF) and ensemble support vector machine (ESVM) algorithms with crossover-based hunter–prey optimization (CHPO). The HKRF algorithm uses decision trees and kernel functions to capture the complex relationships between input features and output labels. The ESVM algorithm combines multiple SVM classifiers to improve the classification accuracy and robustness. The CHPO algorithm is a metaheuristic optimization algorithm that mimics the hunting behavior of predators and prey in nature. The proposed approach aims to optimize the parameters of the HKRF and ESVM algorithms and allocate resources to different applications running on the MEC network to improve the QoS metrics such as latency, throughput, and energy efficiency. The experimental results show that the proposed approach outperforms other algorithms in terms of QoS metrics and resource allocation efficiency. The throughput and the energy consumption attained by our proposed approach are 595 mbit/s and 9.4 mJ, respectively. [ABSTRACT FROM AUTHOR]

Logistics in the healthcare industry involves coordinating the distribution of medical supplies and equipment across various departments and organizations. Supply Chain Management can help healthcare facilities identify weaknesses and devise strategies to address them. Using the Unified Theory of Acceptance and Use of Technology (UTAUT), the study investigates the motivations behind the individuals' desire to use Internet of Things (IoT) solutions in healthcare. In order to better understand the factors that influence the use of IoT for e-HMS, a survey was administered to 210 healthcare IoT users. The study focuses on the potential medicinal applications of IoT technologies and incorporates the concepts of performance expectations, healthcare hazard, and trust (PHT) and perceived enabling circumstances (PFC) to complement past findings in the field. Overall, the study appears to be focused on contributing to the existing knowledge about the factors that influence the adoption of IoT technologies in healthcare, and it emphasizes the importance of considering theoretical constructs such as PHT and PFC in this context. The findings of the study can be used by IoT creators, medical experts, and vendors to optimize e-HMS and provide insight into the potential and limitations of UTAUT simulation to improve the logistic of Supply Chain Management in healthcare 4.0. The results have been analyzed by applying machine learning classifiers and have been visualized using different metrics. [ABSTRACT FROM AUTHOR]

The last decade has seen a rapid increase in big data, which has led to a need for more tools that can help organizations in their data management and decision making. Business intelligence tools have removed many of the obstacles to data visibility, and numerous data mining technologies are playing an essential role in this visibility. However, the increase in big data has also led to an increase in 'dark data', data that does not have any predefined structure and is not generated intentionally. In this paper, we show how dark data can be mined for practical purposes and utilized to gain business insight. The most common type of dark data is a log file generated on a web server. Using the example of log files generated by e-commerce transactions, this paper shows how residual data and data trails can prove to be valuable when an actual dataset is inaccessible, and explains the usage of residual data for modeling purposes. The work uses a system identification approach, based on natural language processing for log file tokenization and feature extraction. The features are then embedded into the next step, which uses a deep neural network to identify customers for targeted advertising. The results achieve a significant accuracy and show how dark data has the potential to deliver value for business. Locating, organizing, and understanding dark data can unlock its relevance, usefulness, and potential monetization, but it is important to act when the benefits of use outweigh the costs of access and analysis. [ABSTRACT FROM AUTHOR]