Summary Accurate path loss prediction models are indispensable in modern wireless communication systems. In recent times, several path loss prediction models have been proposed to improve network performance. However, most of these models have not addressed the fundamental issues. The problem of deploying a single path loss prediction model that fits well in all wireless propagation environments remains. To address this problem, we present machine learning‐based ensemble methods to path loss predictions. Specifically, ensemble methods have been introduced to improve signal prediction accuracy and performance. Additionally, the radial basis function (RBF) and multilayer perceptron (MLP) neural network models were deployed and improved by adding more network parameters to their respective input layers. Results revealed that the RBF model with an increase in the number of centroids reduces the mean square error (MSE). Also, the Gaussian kernel function gives lower MSE compared to the multiquadric and inverse multiquadric functions. The bagging and blending ensemble path loss prediction models were introduced for optimal performance. An RBF neural network of different clusters was incorporated into the bagging algorithm as base learners. The RBF, MLP, blending, and bagging were examined using standard metrics for the training, testing, and validation dataset for model validation. The developed bagging ensemble path loss prediction model gave the lowest errors (MSE = 0.0011 dB, SSE = 0.6069 dB, MAE = 0.0245 dB, & R = 0.7484 dB) for the datasets. The bagging ensemble method acts as a variance and error reduction mechanism because it predicted path loss closest to measured data and is suitable for near precise path loss predictions.

Cyber-physical systems, also known as CPS, have come to stay. There is no doubt, CPS would one day outnumber humans in industries. How do we evaluate the adaptation progress of these systems considering changing environmental conditions? A failed implementation of a CPS can result to a loss. Since CPSs are designed to automate industrial activities, which are centred on the use of several technologies, collaboration with humans may sometimes be inevitable. CPSs are needed to automate several processes and thus help firms compete favourably within an industry. This chapter focuses on the adaptation of CPS in diverse work environment. Considering the ecosystem of the CPS, the authors present a Bayesian model evaluating the progress of adaptation of a CPS given some known conditions.

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Improved technology has led to significant changes in society over time. This has been accompanied by significant changes in the economy. The improvement in technology has also been accompanied by significant changes in the modeling of network-based systems. This is comprised of significant updates of computer and mobile operating systems. The development of mobile phones and operating systems have endangered essential individual and corporate data over time by making it vulnerable and prone to viruses, worms, and malware. This chapter focuses on reviewing literature that serves as guides for modeling a network flow-based detection system for malware categorization. The Author begins with an in-depth definition of mobile devices and how they have eased the spread of malicious software. Identifying Android OS as the most used operating system, Android OS operating system layer was explained, and the reason for user preferability unveiled. The chapter continued with a review of known malware and their behaviors as has been observed over time.