The world is facing major societal challenges because of an aging population that is putting increasing pressure on the sustainability of care. While demand for care and social services is steadily increasing, the supply is constrained by the decreasing workforce. The development of smart, physical, social and age-friendly environments is identified by World Health Organization (WHO) as a key intervention point for enabling older adults, enabling them to remain as much possible in their residences, delay institutionalization, and ultimately, improve quality of life. In this study, we survey smart environments, machine learning and robot assistive technologies that can offer support for the independent living of older adults and provide age-friendly care services. We describe two examples of integrated care services that are using assistive technologies in innovative ways to assess and deliver of timely interventions for polypharmacy management and for social and cognitive activity support in older adults. We describe the architectural views of these services, focusing on details about technology usage, end-user interaction flows and data models that are developed or enhanced to achieve the envisioned objective of healthier, safer, more independent and socially connected older people.

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human’s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions.

The increasing availability of relevant information, events and constraints in the environment of the modern factories due to deployment of IoT sensor technologies on the production line has led to an “explosion” in contextual big data. At the same time the advancements in the machine learning field from the last years opened new approaches for the analysis of the manufacturing processes datasets that are characterized by noisy data, a large number of features and an imbalanced classification of the samples. In this paper we investigate the applicability and the impact of machine learning techniques for managing production processes considering the data from a semiconductor manufacturing process (SECOM dataset). We have applied algorithms such as Boruta and MARS for the selection of the most relevant features and the Random Forest and the Gradient Boosted Trees for the samples classification. The results show better values for precision when the features are selected using Boruta and MARS rather than PCA and better values for accuracy when the data is unsampled and classified using Random Forest and Logistic Regression rather than Gradient Boosted Trees.

The bio-inspired research field has evolved greatly in the last few years due to the large number of novel proposed algorithms and their applications. The sources of inspiration for these novel bio-inspired algorithms are various, ranging from the behavior of groups of animals to the properties of various plants. One problem is the lack of one bio-inspired algorithm which can produce the best global solution for all types of optimization problems. The presented solution considers the proposal of a novel approach for feature selection in classification problems, which is based on a binary version of a novel bio-inspired algorithm. The principal contributions of this article are: (1) the presentation of the main steps of the original Horse Optimization Algorithm (HOA), (2) the adaptation of the HOA to a binary version called the Binary Horse Optimization Algorithm (BHOA), (3) the application of the BHOA in feature selection using nine state-of-the-art datasets from the UCI machine learning repository and the classifiers Random Forest (RF), Support Vector Machines (SVM), Gradient Boosted Trees (GBT), Logistic Regression (LR), K-Nearest Neighbors (K-NN), and Naïve Bayes (NB), and (4) the comparison of the results with the ones obtained using the Binary Grey Wolf Optimizer (BGWO), Binary Particle Swarm Optimization (BPSO), and Binary Crow Search Algorithm (BCSA). The experiments show that the BHOA is effective and robust, as it returned the best mean accuracy value and the best accuracy value for four and seven datasets, respectively, compared to BGWO, BPSO, and BCSA, which returned the best mean accuracy value for four, two, and two datasets, respectively, and the best accuracy value for eight, seven, and five datasets, respectively.

In this paper we investigate the use of machine learning techniques for optimizing manufacturing processes operation. More precisely we propose, compare and contrast two approaches for predicting errors in manufacturing processes. The first approach is based on machine learning algorithms while the second one uses deep learning techniques. Both approaches are validated using a dataset from literature, the SECOM dataset, which is representative for manufacturing processes. For the machine learning approach features are selected using the Multivariate Adaptive Regression Splines (MARS) algorithm and data is classified using the Gradient Boosted Trees (GBT) algorithm, while for the deep learning approach features are selected using a Support Vector Machine (SVM) algorithm and data is predicted using a Neural Network (NN). The evaluation results show that the best results are obtained using the deep learning approach.