In order to effectively extract features and improve classification accuracy for hyperspectral remote sensing images (HRSIs), the advantages of enhanced particle swarm optimization (PSO) algorithm, convolutional neural network (CNN), and extreme learning machine (ELM) are fully utilized to propose an innovative classification method of HRSIs (IPCEHRIC) in this paper. In the IPCEHRIC, an enhanced PSO algorithm (CWLPSO) is developed by improving learning factor and inertia weight to improve the global optimization performance, which is employed to optimize the parameters of the CNN in order to construct an optimized CNN model for effectively extracting the deep features of HRSIs. Then, a feature matrix is constructed and the ELM with strong generalization ability and fast learning ability is employed to realize the accurate classification of HRSIs. Pavia University data and actual HRSIs after Jiuzhaigou M7.0 earthquake are applied to test and prove the effectiveness of the IPCEHRIC. The experiment results show that the optimized CNN can effectively extract the deep features from HRSIs, and the IPCEHRIC can accurately classify the HRSIs after Jiuzhaigou M7.0 earthquake to obtain the villages, bareland, grassland, trees, water, and rocks. Therefore, the IPCEHRIC takes on stronger generalization, faster learning ability, and higher classification accuracy.

An outer-independent Italian dominating function (OIIDF) on a graph G with vertex set V(G) is defined as a function f : V(G) → {0, 1, 2}, such that every vertex v ∈ V(G) with f(v) = 0 has at least two neighbors assigned 1 under f or one neighbor w with f(w) = 2, and the set {u ∈ V | f (u) = 0} is independent. The weight of an OIIDF f is the value w(f) = Σ _u∈V(G) f(u). The minimum weight of an OIIDF on a graph G is called the outer-independent Italian domination number γ _oiI (G) of G. In this paper, we initiate the study of the outer-independent Italian domination number and present the bounds on the outer-independent Italian domination number in terms of the order, diameter, and vertex cover number. In addition, we establish the lower and upper bounds on γoiI (T) when T is a tree and characterize all extremal trees constructively. We also give the Nordhaus-Gaddum-type inequalities.

Inspired by many deadlock detection applications, the feedback vertex set is defined as a set of vertices in an undirected graph, whose removal would result in a graph without cycle. The Feedback Vertex Set Problem, known to be NP-complete, is to search for a feedback vertex set with the minimal cardinality to benefit the deadlock recovery. To address the issue, this paper presents NewkLS FVS(LS, local search; FVS, feedback vertex set), a variable depth-based local search algorithm with a randomized scheme to optimize the efficiency and performance. Experimental simulations are conducted to compare the algorithm with recent metaheuristics, and the computational results show that the proposed algorithm can outperform the other state-of-art algorithms and generate satisfactory solutions for most DIMACSbenchmarks.

Let $\gamma (D)$ denote the domination number of a digraph D and let $C_m\square C_n$ denote the Cartesian product of $C_m$ and $C_n$ , the directed cycles of length $n\ge m\ge 3$ . Liu et al. obtained the exact values of $\gamma (C_m\square C_n)$ for m up to 6 [Domination number of Cartesian products of directed cycles, Inform. Process. Lett. 111 (2010) 36–39]. Shao et al. determined the exact values of $\gamma (C_m\square C_n)$ for $m=6,7$ [On the domination number of Cartesian product of two directed cycles, Journal of Applied Mathematics, Volume 2013, Article ID 619695]. Mollard obtained the exact values of $\gamma (C_m\square C_n)$ for $m=3k+2$ [M. Mollard, On domination of Cartesian product of directed cycles: Results for certain equivalence classes of lengths, Discuss. Math. Graph Theory 33(2) (2013) 387–394.]. In this paper, we extend the current known results on $C_m\square C_n$ with m up to 21. Moreover, the exact values of $\gamma (C_n\square C_n)$ with n up to 31 are determined.