Smart Homes and Families to Enable Sustainable Societies: A Data-Driven Approach for Multi-Perspective Parameter Discovery using BERT Modelling

Abstract

Technological advancements and innovations have profoundly changed the lives of people giving rise to smart environments, cities, and societies. As homes are the building block of cities and societies, smart homes are critical to establishing smart living and are expected to play a key role in enabling smart cities and societies. The current academic literature and commercial advancements on smart homes have mainly focused on developing and providing smart functions for homes to provide security management and facilitate the residents in their various activities such as ambiance management. Homes are much more than physical structures, buildings, appliances, operational machines, and systems. Homes are composed of families and are inherently complex phenomena underlined by humans and their relationships with each other, subject to individual, intragroup, intergroup, and intercommunity goals. There is a clear need to understand, define, consolidate existing research, and actualize the overarching roles of smart homes, the roles of smart homes that would serve the needs of future smart cities and societies. This paper introduces our data-driven parameter discovery methodology and uses it to provide, for the first time, an extensive, rather fairly comprehensive, analysis of the families and homes landscape seen through the eyes of academics and the public using over a hundred thousand research papers and nearly a million tweets. We develop a methodology using deep learning, natural language processing (NLP), and big data analytics methods and apply it to automatically discover parameters that capture a comprehensive knowledge and design space of smart families and homes comprising social, political, economic, environmental, and other dimensions. The 66 discovered parameters and the knowledge space comprising 100s of dimensions are explained by reviewing and referencing over 300 articles from the academic literature and tweets. The knowledge and parameters discovered in this paper can be used to develop a holistic understanding of matters related to families and homes facilitating the development of better, community-specific, policies, technologies, solutions, and industries for families and homes, leading to strengthening families and homes, and in turn, empowering sustainable societies across the globe.

Multi-generational labour markets: Data-driven discovery of multi-perspective system parameters using machine learning

“…This paper is part of our broader work on the use of information and communication technology (ICT) to address challenges facing smart cities and societies. Our work on this topic has included the concept of Deep Journalism, 145 , 146 as well as research on topics such as transportation, 146 tourism, 147 smart families and homes, 148 healthcare services for cancer, 58 mental health, 149 education during the COVID-19 pandemic, 150 energy systems 151 and AI-based event detection. 152 Future work will be directed to improving the methodological approach presented in this paper using advanced deep learning methods and their applications to investigate and improve labour economics and other problems facing our societies.…”

AI explainability and governance in smart energy systems: A review

“…Duplicates, stop words, and irrelevant and noisy data were removed using pandas and NumPy. BERT, UMAP (uniform manifold approximation and projection), HDBSCAN (hierarchical density-based spatial clustering of applications with Noise), and class-based TF-IDF (term frequency-inverse document frequency) score were used to capture contextual relations, reduce the number of clusters, and cluster data (Grootendorst, 2021;Ahmad et al, 2022;Alqahtani et al, 2022). Finally, we used domain knowledge and a range of analysis and visualisation techniques (hierarchical clustering, topic word score, similarity matrix, term score decline) to discover parameters for AI governance in energy.…”

“…The methodology for discovering parameters or themes is based on "deep journalism," our data-driven deep learning (DL)-based big data analytics approach to automatically discover and analyse crosssectional multi-perspective information to enable better decisionmaking and develop better instruments for governance. We introduced the deep journalism approach (Ahmad et al, 2022) and applied it to different sectors (Alqahtani et al, 2022;Alswedani et al, 2022;Mehmood, 2022).…”

Smart Homes and Families to Enable Sustainable Societies: A Data-Driven Approach for Multi-Perspective Parameter Discovery Using BERT Modelling

“…We developed a methodology using deep learning, natural language processing (NLP), and big data analytics methods and applied it to automatically discover parameters that capture a comprehensive knowledge and design space of smart families and homes comprising social, political, economic, environmental, and other dimensions. The discovered parameters and the knowledge space are explained by reviewing and referencing over 300 articles from the academic literature and tweets (see [37] for a more detailed account of the discovered parameters). We discovered 44 parameters for families and homes from an academic perspective that provide comprehensive structural knowledge and design space of families and homes that could be used to extend smart homes research and practice.…”