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The use of digital gadgets and services is pervasive. Digitalization in data, information, and technologies is driving the Fourth Industrial Revolution (also known as 4IR or Industry 4.0), resulting in a global digital divide. The chapter proposes a strategy in collaboration with a variety of service sector partners that would allow governments to capitalize on the possibility of closing the technology gap between nations in a manner that benefits inclusive and sustainable growth in middle and low countries. Emerging revolution and internet access can strengthen socio-economic development and improve people's way of living, but they also have the potential to widen political divides, undermine democracy, and increase inequality. In response to COVID-19, the chapter also proposes digital technologies as tools for achieving sustainable development goals and digitalization response measures in marginalized societies.

The amalgamation of modern innovation, technological skills, and processes used to establish business models and satisfy customers in different societies is referred to as digital transformation. Digital transformation facilitates the development of economies, ways of doing business, and social constructs among marginalized communities. Digital inclusiveness has significantly been applied in a number of areas in communities and resource-constrained environments such as health, education, and agriculture. A systematic literature review was carried out. The authors find out that inclusiveness has great opportunities and challenges in digitally transforming marginalized communities. The implementation of interconnected digital innovation technologies provides new challenges and opportunities spanning the whole of Africa. This chapter explores the key drivers of digital technologies, the contribution of technology, gender imbalances, and challenges and opportunities for digital inclusiveness in remote areas.

Most societies have been in abject poverty owing to a lack of proper education, gender alienation, and socio-economic and political factors. Localized external economies, particularly economies of scale and scope, as small firms specialize and engage in a division of labor, are among the benefits of clustering. Clusters are important because geographical agglomeration has the potential to help small businesses overcome size constraints, advance technologically, and improve competence in local and global markets. The adoption of technologies has the effect of replacing the old way of doing things with manual and mechanical methods. The study examined the potential of clustering to help marginalized communities to become integrated and improve their quality of life in a digital society and concluded that ICTs can, if adopted and used properly, shift and destroy social boundaries between the elite and the segregated. Future research can look at the adoption of digital technologies in marginalized areas.

Real-world nonstationary data are usually characterized by high nonlinearity and complex patterns due to the effects of different exogenous factors that make prediction a very challenging task. An ensemble strategically combines multiple techniques and tends to be robust and more precise compared to a single intelligent algorithmic model. In this work, a dynamic particle swarm optimization-based empirical mode decomposition ensemble is proposed for nonstationary data prediction. The proposed ensemble implements an environmental change detection technique to capture concept drift occurring and the intrinsic nonlinearity in time series, hence improving prediction accuracy. The proposed ensemble technique was experimentally evaluated on electric time series datasets. The obtained results show that the proposed technique improves prediction accuracy and it outperformed several stateof-the-art techniques in several cases. For future work direction, a detailed empirical analysis of the proposed technique can be considered such as the effect of the cost of prediction errors, and the technique's search capability.