A joint green-edge computing idea is now realized in practice with the help of intelligent infrastructure for modern sport venues, based on Internet of Things (IoT) platforms and cyber-physical systems (CPS). To monitor their sports actions, athletes need smart environments. Using edge-enabled low-cost and low-power sensors, such as infrared monitoring systems that analyze thermal information, this environment should alert to possible physical damages. Early recognition of sports injuries and joint injuries can usually prevent athletes from pain and missing exercise. One of the most efficient methods for identifying pain and movement problems is to monitor the energy emitted by lower limb injuries. By analyzing thermal images of the lower body parts, this research attempts to automatically identify sports injuries. The thermal image is first isolated from the region of interest. Convolutional structures are applied to identify lesions using a newly developed and optimized method. The performance of the classifier is performed with the possibility of deep learning by pruning the features and to reduce the computational complexity and improve the accuracy, and a model has been developed based on which, the classification of sports injuries in binary mode (i.e., whether the lesions are present or not) and multiclass mode (i.e., the severity of sports injuries) resulted in optimal results. Thermal images show the different states of joints, including lesions caused by various sports in the lower limbs. This model could provide the ability of solving uncertainty of answers, repeatability, and convergence towards minimum error, to name a few. As compared to conventional feature extraction and classification approaches, the outputs were more acceptable. Based on the injury detection mode, the severity of the damage, and the K-fold cross-validation method, the average error is less than 0.45% and 1.67%, respectively.

Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user devices. However, the distributed and rich multi-source big data resources raise challenges to the centralized cloud-based data storage and value mining approaches in terms of economic cost and effective service recommendation methods. In view of these challenges, we propose a deep neural collaborative filtering based service recommendation method with multi-source data (i.e., NCF-MS) in this paper, which adopts the cloud-edge collaboration computing paradigm to build recommendation model. More specifically, the Stacked Denoising Auto Encoder (SDAE) module is adopted to extract user/service features from auxiliary user profiles and service attributes. The Multiple Layer Perceptron (MLP) module is adopted to integrate the auxiliary user/service features to train the recommendation model. Finally, we evaluate the effectiveness of the NCF-MS method on three public datasets. The experimental results show that our proposed method achieves better performance than existing methods.