A Type-2 Fuzzy Based Explainable AI System for Predictive Maintenance within the Water Pumping Industry

Industrial maintenance has undergone a paradigm shift due to the emergence of artificial intelligence (AI), the Internet of Things (IoT), and cloud computing. Rather than accepting the drawbacks of reactive maintenance, leading firms worldwide are embracing "predict-and-prevent" maintenance. However, opaque box AI models are sophisticated and complex for the average user to comprehend and explain. This limits the AI employment in predictive maintenance, where it is vital to understand and evaluate the model before deployment. In addition, it's also important to comprehend the maintenance system's decisions. This paper presents a type-2 fuzzy-based Explainable AI (XAI) system for predictive maintenance within the water pumping industry. The proposed system is optimised via Big-Bang Big-Crunch (BB-BC), which maximises the model accuracy for predicting faults while maximising model interpretability. We evaluated the proposed system on water pumps using real-time data obtained by our hardware placed at real-world locations around the United Kingdom and compared our model with Type-1 Fuzzy Logic System (T1FLS), a Multi-Layer Perceptron (MLP) Neural Network, an effective deep learning method known as stacked autoencoders (SAEs) and an interpretable model like decision trees (DT). The proposed system predicted water pumping equipment failures with good accuracy (outperforming the T1FLS accuracy by 8.9% and DT by 529.2% while providing comparable results to SAEs and MLPs) and interpretability. The system predictions comprehend why a specific problem may occur, which leads to better and more informed customer visits to reduce equipment failure disturbances. It will be shown that 80.3% of water industry specialists strongly agree with the model's explanation, determining its acceptance

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover

Distance-Based and Low Energy Adaptive Clustering Protocol for Wireless Sensor Networks

A wireless sensor network (WSN) comprises small sensor nodes with limited energy capabilities. The power constraints of WSNs necessitate efficient energy utilization to extend the overall network lifetime of these networks. We propose a distance-based and low-energy adaptive clustering (DISCPLN) protocol to streamline the green issue of efficient energy utilization in WSNs. We also enhance our proposed protocol into the multi-hop-DISCPLN protocol to increase the lifetime of the network in terms of high throughput with minimum delay time and packet loss. We also propose the mobile-DISCPLN protocol to maintain the stability of the network. The modelling and comparison of these protocols with their corresponding benchmarks exhibit promising results

Intelligent Technique for Seamless Vertical Handover in Vehicular Networks

Seamless mobility is a challenging issue in the area of research of vehicular networks that are supportive of various applications dealing with the intelligent transportation system (ITS). The conventional mobility management plans for the Internet and the mobile ad hoc network (MANET) is unable to address the needs of the vehicular network and there is severe performance degradation because of the vehicular networks’ unique characters such as high mobility. Thus, vehicular networks require seamless mobility designs that especially developed for them. This research provides an intelligent algorithm in providing seamless mobility using the media independent handover, MIH (IEEE 802.21), over heterogeneous networks with different access technologies such as Worldwide Interoperability for Microwave Access (WiMAX), Wireless Fidelity (Wi-Fi), as well as the Universal Mobile Telecommunications System (UMTS) for improving the quality of service (QoS) of the mobile services in the vehicular networks. The proposed algorithm is a hybrid model which merges the biogeography-based optimization or BBO with the Markov chain. The findings of this research show that our method within the given scenario can meet the requirements of the application as well as the preferences of the users

Effect of Military Training on Soldiers' Emotional Reactions

ABSTRACT A major part of armed forces consists of soldiers and draftees, who are considered the most valuable treasures of the world's armies. This study aimed to investigate the psychological impacts of military training course on soldiers.This preexperimental study was done on 373 draftees in military training INTRODUCTION Military atmosphere is considered as genuine generator of complex behaviors. The military training aims both adaptation to high uncertainty situations in conflict areas and the specific situations in peacetim

Changes in climate and vegetation with altitude on Mount Batilamu, Viti Levu, Fiji

To investigate changes in vegetation and climate with altitude, we established forest plots and recorded climatic data at 100-m intervals between 550–1100 m asl on the western slopes of Mount Batilamu, Mount Koroyanitu range, Viti Levu, Fiji. Trees with a dbh ≥10 cm were identified and measured in 21 10×10-m plots, starting at 750 m altitude. Temperature and relative humidity sensors were deployed in two habitats, leaf litter and 50 cm above the ground, and two vegetation types, grasslands and forest, at six altitudes over a 48-h period. Two significantly distinct forest types, lowland and montane, were present. Montane forest was found at higher elevations (>950 m asl) and had significantly higher stem density. Mean temperature decreased significantly with altitude and was strongly moderated by vegetation type (lower average and less variation in forest). While average relative humidity significantly increased with altitude, it was strongly moderated by both habitat and vegetation type (higher average and less variation in leaf litter and forest). The lapse rate varied with time of day (higher during the day) and vegetation type (higher in grasslands). Therefore, vegetation and microhabitats create uniquemicroclimates, and this should be considered when investigating current or future climatic patterns along altitudinal gradients on forested mountains