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Artificial intelligence may have the potential to deliver more efficient, reliable and sustainable energy systems, particularly by helping to manage the complexity of distributed generation and demand-side management. However, stakeholders expect interpretability and explainability, and AI tools must also be transparent, secure and trustworthy. Explainable AI (XAI) models are intended to make AI algorithms more transparent and understandable without reducing their performance and efficiency.

In healthcare, a digital twin is a digital representation of a patient or healthcare system using integrated simulations and service data. The digital twin tracks a patient's records, crosschecks them against registered patterns and analyses any diseases or contra indications. The digital twin uses adaptive analytics and algorithms to produce accurate prognoses and suggest appropriate interventions. A digital twin can run various medical scenarios before treatment is initiated on the patient, thus increasing patient safety as well as providing the most appropriate treatments to meet the patient's requirements.

Fog computing is a decentralized computing infrastructure in which computing resources are located between the data source and the cloud or any other data centers. The word "fog" refers to its cloud-like properties, which are closer to the "ground", using edge devices that carry out locally computation, storage and communication tasks. An additional benefit is that the processed data is likely to be needed by the same devices that generated the data. By processing locally rather than remotely, the latency between input and response are minimized. This technology has countless application domains such as industrial process control, smart cities, transportation, healthcare and agriculture.

Smart environments such as smart homes and industrial automation have been transformed by the rapid developments in internet of things (IoT) devices and systems. However, the widespread use of these devices poses significant difficulties, particularly in settings with limited energy resources. Due to the significant energy consumption and communication overhead associated with delivering huge amounts of data, traditional machine learning algorithms which rely on centralized cloud servers for training are not always suitable.

Multimedia technologies have opened up a wide range of applications by combining a variety of information sources such as voice, graphics, animation, images, audio, and full-motion video which can be successfully implemented in banking, financial services and insurance (BFSI) industries to support their activities and strategic goals.