Search

Cloud Computing (CC) makes it possible for a common user to get an access to large pools of data and computational resources through a variety of interfaces. Among the so many important problems in CC, load balancing technique has been paid more and more attention for its important role. Good load balance algorithms can make whole system run more efficient. A new load balancing method combined with the advantage of online and offline load balancing algorithms are proposed in this paper. Two-choice algorithm and its improvement are used in the online step. Bacteria Foraging Optimization (BFO) and its improvement motivated by Lamarck Evolutionary Theory are introduced in our offline step. Online load balancing uses imperfect information, aiming at finishing tasks as fast as possible; while the offline makes full use of all information to make a supplement. Experiments on the heterogeneous tasks and serving points for computation intensive loads have been used here and the good performance shows the efficiency of our proposed method. [ABSTRACT FROM AUTHOR]

• We propose a novel multi-view DCNN-based approach which can take advantage of not only multi-view images but also the relationships between them. • The proposed networks learn the integrated features of multi-view fundus images for DR detection. • We introduce the attention mechanisms for mining the relationship between different views. • In order to boost the ability to capture the tiny lesion features in the retinal images, we combine the idea of attention mechanisms with the channel dimension. Diabetic retinopathy (DR) detection has attracted much attention recently, and the deep learning algorithms have gained traction in this area. At present, DR screening by deep learning algorithms is often based on single-view fundus images, which usually leads to an unsatisfactory accuracy of DR grading due to the incomplete lesion features. In this paper, we proposed a novel diabetic retinopathy detection convolutional network for automatic DR detection by integrating multi-view fundus images. Compared to existing single-view DCNN-based DR detection methods, the proposed method has the following advantages. First, our method fully utilizes the lesion features from the retina with a field-of-view around 120 ∘ − 150 ∘. Second, by introducing the attention mechanisms, more attention will be paid on the influential view and the performance can be improved. Besides, we also assign large weights to important channels in the network for effective feature extraction. Experiments are conducted on our collected multi-view DR dataset contained 15,468 images, in which each eye sample provides four-view images. The experimental results indicate that using multi-view images is suitable for automatic DR detection and our proposed method is superior to other benchmarking methods. [ABSTRACT FROM AUTHOR]