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Purpose: With this study, the authors aim to highlight the application of machine learning in smart appliances used in our day-to-day activities. This chapter focuses on analysing intelligent devices used in our daily lives to examine various machine learning models that can be applied to make an appliance ‘intelligent’ and discuss the different pros and cons of the implementation.

Methodology: Most smart appliances need machine learning models to decrypt the meaning and functioning behind the sensor’s data to execute accurate predictions and come to appropriate conclusions.

Findings: The future holds endless possibilities for devices to be connected in different ways, and these devices will be in our homes, offices, industries and even vehicles that can connect each other. The massive number of connected devices could congest the network; hence there is necessary to incorporate intelligence on end devices using machine learning algorithms. The connected devices that allow automatic control appliance driven by the user’s preference would avail itself to use the Network to communicate with devices close to its proximity or use other channels to liaise with external utility systems. Data processing is facilitated through edge devices, and machine learning algorithms can be applied.

Significance: This chapter overviews smart appliances that use machine learning at the edge. It highlights the effects of using these appliances and how they raise the overall living standards when smarter cities are introduced by integrating such devices.

Purpose: This chapter aims to analyse and highlight the current landscape of performance management (PM) systems, and the benefits of integrating modern technology such as smart analytics (SA) and artificial intelligence (AI) into PM systems. The chapter discusses the application of AI in PM tasks which successively simplify many offline PM tasks.

Methodology: To carry out this analysis, a systematic literature review was performed. The review covers literature detailing PM components as well as research concerned with the integration of SA and AI into PM systems.

Findings: This study uncovers the merits of using SA and AI in PM. SA technology provides organisations with a clear direction for improvement, rather than simply state failure in performance. AI can be used to automate redundant tasks while retaining the human element of decision-making. AI also helps reduce the time required to take action on feedback.

Significance: The findings of this research provide insights into the use of SA and AI to make PM tasks fast, scalable, and error-free.

The conflicting results on the corrosion characteristics of aluminium matrix composites reinforced with agrarian waste have stimulated an investigation on the hardness and corrosion rate of sugar palm fibre ash (SPFA) reinforced LM26 Al/alloy composite by varying the SPFA from 0 to 10 wt% in an interval of 2 wt%. This paper aims to discuss the aforementioned issue.

The composites were produced via stir-casting and the hardness was determined using a Vickers hardness testing machine, corrosion rate was examined through the weight loss method by immersion in 0.5, 1.0 and 1.5 M hydrochloric acid (HCl) at temperatures of 303, 318, and 333 K for the maximum duration of 120 h. The morphological study was conducted using a scanning electron microscope (SEM) on the samples before and after immersion in HCl.

The incorporation of SPFA improved the hardness of the alloy from 58.22 to 93.62 VH after 10 wt% addition. The corrosion rate increases with increased content of SPFA, the concentration of HCl and temperature. The least corrosion rate of 0.0272 mpy was observed for the LM26 Al alloy in 0.5 M after 24 h while the highest corrosion rate of 0.8511 mpy was recorded for LM26 Al/10 wt% SPFA in 1.5 M HCl acid after 120 h. The SEM image of corroded samples revealed an increased number of pits with increased SPFA content.

The work is limited to SPFA up to 10 wt% as reinforcement in LM26 Al alloy, the use of HCl as corrosion medium, temperatures in the range of 303–333 K, and a weight loss method were used to evaluate the corrosion rate.

The corrosion rate was determined for LM26 Al/SPFA composites with various amounts of SPFA in 0.5, 1.0 and 1.5 M HCl at 303, 318 and 333 K and compared with the matrix alloy.