Case study in Data driven startegies for Brea

The sustainability of human existence is in dire danger and this threat applies to our environment, societies, and economies. Smartization of cities and societies has the potential to unite individuals and nations towards sustainability as it requires engaging with our environments, analyzing them, and making sustainable decisions regulated by triple bottom line (TBL). Poor healthcare systems affect individuals, societies, the planet, and economies. This paper proposes a data-driven artificial intelligence (AI) based approach called Musawah to automatically discover healthcare services that can be developed or co-created by various stakeholders using social media analysis. The case study focuses on cancer disease in Saudi Arabia using Twitter data in the Arabic language. Specifically, we discover 17 services using machine learning from Twitter data using the Latent Dirichlet Allocation algorithm (LDA) and group them into five macro-services, namely, Prevention, Treatment, Psychological Support, Socioeconomic Sustainability, and Information Availability. Subsequently, we show the possibility of finding additional services by employing a topical search over the dataset and have discovered 42 additional services. We developed a software tool from scratch for this work that implements a complete machine learning pipeline using a dataset containing over 1.35 million tweets we curated during September–November 2021. Open service and value healthcare systems based on freely available information can revolutionize healthcare in manners similar to the open-source revolution by using information made available by the public, the government, third and fourth sectors, or others, allowing new forms of preventions, cures, treatments, and support structures.

Case study based on Data Analytics Implemntat

Nowadays, automated processes in manufacturing industries are on the rise due to the need to increase productivity and product quality, but also to reduce operator cognitive-physical fatigue. This need is more felt in companies, such as footwear companies, where production is done entirely by hand and the success of the product relies totally on the skill of experienced artisans. The work presents the automation with a collaborative robot of the shoe polishing. This process is very delicate because of the high variability of leather types and the maximum quality to be achieved, as well as very tiring for the operator. An operational methodology for carrying out the polishing of a real leather shoe is proposed. Starting from the design of polishing trajectories, implementing them on UR5e, controlling the contact force of the tool, toe shoe polishing is performed, achieving a good quality standard. Experimental tests and their results are presented.KeywordsLeather shoe polishingCollaborative robotic applicationHuman robot collaboration

Clustering Algorithm

Clustering algorithms [51] are used to establish pattern similarities so that data that exhibit similar characteristics can be classified into their corresponding target groups. Popular examples of clustering algorithms include hierarchical, expectation maximization, k-medians and k-means clustering approaches.

Clustering Techniques

Types of clustering Centroid-based clustering. Density-based clustering. Distribution-based clustering. Hierarchical clustering.