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Multi Criteria Decision Making (MCDM) helps decision makers (DMs) solve highly
complex problems. Accordingly, MCDM has been widely used by DMs from various fields as an
effective and reliable tool for solving various problems, such as in site and supplier selection, ranking
and assessment. This work presents an in-depth survey of past and recent MCDM techniques
cited in the literature. These techniques are mainly categorised into pairwise comparison, outranking
and distance-based approaches. Some well-known MCDM methods include the Analytical Hierarchy
Process (AHP), Analytical Network Process (ANP), Elimination et Choix Traduisant la Realité
(ELECTRE), Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE),
Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and VIseKriterijumska
Optimizacija I Kompromisno Resenje (VIKOR). Each of these methods is unique and has
been used in a vast field of interest to support DMs in solving complex problems. For a complete
survey, discussions related to previous issues and challenges and the current implementation of
MCDM are also presented.
Resource optimisation is critical because 5G is intended to be a major enabler and a leading infrastructure provider in the information and communication technology sector by supporting a wide range of upcoming services with varying requirements. Therefore, system improvisation techniques, such as machine learning (ML) and deep learning, must be applied to make the model customisable. Moreover, improvisation allows the prediction system to generate the most accurate outcomes and valuable insights from data whilst enabling effective decisions. In this study, we first provide a literature study on the applications of ML and a summary of the hyperparameters influencing the prediction capabilities of the ML models for the communication system. We demonstrate the behaviour of four ML models: k nearest neighbour, classification and regression trees, random forest and support vector machine. Then, we observe and elaborate on the suitable hyperparameter values for each model based on the accuracy in prediction performance. Based on our observation, the optimal hyperparameter setting for ML models is essential because it directly impacts the model’s performance. Therefore, understanding how the ML models are expected to respond to the system utilised is critical.
In the future, as populations grow and more end-user applications become available, the current traditional electrical distribution substation will not be able to fully accommodate new applications that may arise. Consequently, there will be numerous difficulties, including network congestion, latency, jitter, and, in the worst-case scenario, network failure, among other things. Thus, the purpose of this study is to assist decision makers in selecting the most appropriate communication technologies for an electrical distribution substation through an examination of the criteria’s in-fluence on the selection process. In this study, nine technical criteria were selected and processed using machine learning (ML) software, RapidMiner, to find the most optimal technical criteria. Several ML techniques were studied, and Naïve Bayes was chosen, as it showed the highest performance among the rest. From this study, the criteria were ranked in order of importance from most important to least important based on the average value obtained from the output. Seven technical criteria were identified as being important and should be evaluated in order to determine the most appropriate communication technology solution for electrical distribution substation as a result of this study.
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