To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer’s disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed.

Learning and extracting high-level features from point cloud is the key to improving the segmentation performances on point clouds for many networks. At present, many networks present very deep structures to extract high-level features for 3D perception. However, we argue that even better results can be achieved by (i) building feature vectors that integrates multi-scale geometric features, and (ii) exerting discriminative constraints on the learning of mid-levels features. In this paper, we propose a Multi-scale Neighborhood Feature Extraction and Aggregation Model (MNFEAM) to enhance feature extraction for point cloud learning. We try to first extract multi-scale neighborhood information for each input point and then aggregate local information of a mid-level locality feature space, and finally integrate the aggregated local and the global feature vectors. A new discriminative loss function is designed to strengthen the coarse semantics on mid-levels features so that the semantic abstraction process can be improved and accelerated. We improve the performances of three popular networks for point cloud segmentation using the proposed MNFEAM on standard 3D datasets.

Recommender systems are currently utilized widely in e-commerce for product recommendations and within content delivery platforms. Previous studies usually use independent features to represent item content. As a result, the relationship hidden among the content features is overlooked. In fact, the reason that an item attracts a user may be attributed to only a few set of features. In addition, these features are often semantically coupled. In this paper, we present an optimization model for extracting the relationship hidden in content features by considering user preferences. The learned feature relationship matrix is then applied to address the cold-start recommendations and content-based recommendations. It could also easily be employed for the visualization of feature relation graphs. Our proposed method was examined on three public datasets: 1) hetrec-movielens-2k-v2; 2) book-crossing; and 3) Netflix. The experimental results demonstrated the effectiveness of our method in comparison to the state-of-the-art recommendation methods.

The emergence of energy storage system (ESS) enables the service provider to profit from the price difference between purchasing electric energy from utility companies and selling it to customers through battery operations while more frequent charging/discharging behaviors cause battery degradation. However, the accurate estimation of the ESS degradation cost is one of the main obstacles for ESS participating in energy management. This paper considers a smart grid scenario, consisting of an accurate lithium-ion battery degradation (ALBD) model, renewable energy generators and bilateral energy flow from/to the utility grid. We develop an online energy-scheduling approach aiming to maximize the operating profit of the system from energy trading and guarantee the long-term battery usage. We formulate the trade-off between energy trading profit and battery degradation cost as a joint optimization problem, and further transform it into a Markov Decision Process (MDP) problem. To solve the uncertainty of electricity price and demand, we apply a gated recurrent unit to forecast the next day’s price and demand. Then, we develop a deep Q-network to learn an optimized energy-scheduling strategy. The numerical simulations show that our proposed approach can improve the operating profit from 6.04% to 20.54% compared with the three baselines.