As one of the hot issues in cloud computing, task scheduling is an important way to meet user needs and achieve multiple goals. With the increasing number of cloud users and growing demand for cloud computing, how to reduce the task completion time and improve the system load balancing ability have attracted increasing interest from academia and industry in recent years. To meet the two aforementioned goals, this paper develops an EDA-GA hybrid scheduling algorithm based on EDA (estimation of distribution algorithm) and GA (genetic algorithm). First, the probability model and sampling method of EDA are used to generate a certain scale of feasible solutions. Second, the crossover and mutation operations of GA are used to expand the search range of solutions. Finally, the optimal scheduling strategy for assigning tasks to virtual machines is realized. This algorithm has advantages of fast convergence speed and strong search ability. The algorithm proposed in this paper is compared with EDA and GA via the CloudSim simulation experiment platform. The experimental results show that the EDA-GA hybrid algorithm can effectively reduce the task completion time and improve the load balancing ability.

As an emerging computing model close to the end-user, fog computing can move tasks from the cloud to the fog device to process, and make up for the lack of cloud computing in terms of mobile support, delay and location-aware. Due to the less computing resources and weak processing ability of fog devices, the lightweight computing has been the primary choice, higher requirements are imposed for time and load. Therefore, in the Cloud-fog computing architecture, a new task scheduling method(I-FASC) is proposed for the characteristics of tasks and resources, including an improved firework algorithm(I-FA) is proposed by introducing the explosion radius detection mechanism of fireworks. By two sets of experiments show this method can better reduce the processing time of the task and ensure better overall load balancing of fog devices in a cloud-fog computing system.

Timely diagnosis and determination to the type of lung cancer has important clinical significance. Generally, it requires multiple imaging methods to complement each other to obtain a comprehensive diagnosis. In this work, we propose a deep learning model to identify lung cancer type from CT images for patients in Shandong Provincial Hospital. It has a two-fold challenge: artificial intelligent models trained by public datasets cannot meet such practical requires, and the amount of collected patients’ data is quite few. To solve the two-fold problem, we use image rotation, translation and transformation methods to expand and balance our training data, and then densely connected convolutional networks (DenseNet) is used to classify malignant tumor from images collected from, and finally adaptive boosting (adaboost) algorithm is used to aggregate multiple classification results to improve classification performance. Experimental results show that our method can achieve identifying accuracy 89.85%, which performs better than DenseNet without adaboost, ResNet, VGG16 and AlexNet. This provides an efficient, non-invasive detection tool for pathological diagnosis to lung cancer type.

The intelligent fault diagnosis model has made a significant development, whose high-precision results rely on a large amount of labeled data. However, in the actual industrial environment, it is very difficult to obtain a large amount of labeled data. It will make it difficult for the fault diagnosis model to converge with limited labeled industrial data. To address this paradox, we propose a novel unsupervised domain adaptation framework (M-Net) for fault diagnosis of rotating machinery, which only requires unlabeled industrial data. The M-Net will be pretrained using the labeled data, which can be accessed through the labs. In this stage, we propose a multi-scale feature extractor that can extract and fuse multi-scale features. This operation will generalize the features further. Then, we will align the distribution of the labeled data and unlabeled industrial data using the generator model based on multi-kernel maximum mean discrepancy. This will reduce the distribution distance between the labeled data and the unlabeled industrial data. For now, the unsupervised domain adaptation problem has shifted to a semi-supervised domain adaptation problem. The results, obtained through experimental comparison, demonstrate that the M-Net can achieve an accuracy of over 99.99% with labeled data and a maximum transfer accuracy of over 99% with unlabeled industrial data.