International Journal of Computational Intelligence and Applications

Recently, document recommendation has become a very hot research area in online services. Since rating information is usually sparse with exploding growth of the numbers of users and items, conventional collaborative filtering-based methods degrade significantly in recommendation performance. To address this sparseness problem, auxiliary information such as item content information may be utilized. Convolution matrix factorization (ConvMF) is an appealing method, which tightly combines the rating and item content information. Although ConvMF captures contextual information of item content by utilizing convolutional neural network (CNN), the latent representation may not be effective when the rating information is very sparse. To address this problem, we generalize recent advances in supervised CNN and propose a novel recommendation model called supervised convolution matrix factorization (Super-ConvMF), which effectively combines the rating information, item content information and tag information into a unified recommendation framework. Experiments on three real-world datasets, two datasets come from MovieLens and the other one is from Amazon, show our model outperforms the state-of-the-art competitors in terms of the whole range of sparseness.

In forecasting the fuzzy time series data, several authors took grades of membership 1, 0.5 and 0 for linguistic interval corresponding to fuzzy set. In this paper, we have proposed high-order approach for forecasting the fuzzy time series data by using the grade of membership value defined for each datum corresponding to triangular fuzzy sets and fuzzify the historical data by triangular fuzzy sets which have their maximum membership values. Also, we establish high-order fuzzy logical relationship groups and give a new technique for defuzzification process, by which we can compute the forecasted value in a more efficient way with lower value of MSE. For verifying the suitability of proposed method, we illustrate time series data of student enrollments at the University of Alabama, USA, and crop (Lahi) production of Pantnagar farm, G. B. Pant University of Agriculture and Technology, Pantnagar, India. The forecasting accuracy rate of proposed high-order forecasting method is better than those of existing methods and the forecasted production is much closer to the actual production.

A novel slow-wave structure optimization method on Chaos-improved Normal mutation cat swarm optimization (CI-NMCSO) algorithm is proposed. Under the variable helix section length in STWT, the CI-NMCSO combined with 1D CHRISTINE code is used to calculate the best set of pitch distribution and section length with the objective function of electron beam efficiency improvement. Quantum particle swarm optimization (QPSO) and Cauchy mutated cat swarm optimization (CMCSO) algorithms are applied to make performance comparison. Experimental results show that the beam efficiency has been increased by CI-NMCSO from rated value 30% to 45.3%, and the values using CMCSO and QPSO are 41.8% and 36.5%, respectively, the convergence speed of CI-NMCSO is the fastest, only 16 iterations, while CMCSO and QPSO take 19 and 23 iterations, so the performance of CI-NMCSO is better than CMCSO and QPSO on both optimization precision and calculation speed in terms of slow-wave structure optimization, and is also superior to that with equal section length when the helix section length is variable.

Monitoring wild animals became easy due to camera trap network, a technique to explore wildlife using automatically triggered camera on the presence of wild animal and yields a large volume of multimedia data. Wild animal detection is a dynamic research field since the last several decades. In this paper, we propose a wild animal detection system to monitor wildlife and detect wild animals from highly cluttered natural images. The data acquired from the camera-trap network comprises of scenes that are highly cluttered that poses a challenge for detection of wild animals bringing about low recognition rates and high false discovery rates. To deal with the issue, we have utilized a camera trap database that provides candidate regions utilizing multilevel graph cut in the spatiotemporal area. The regions are utilized to make a validation stage that recognizes whether animals are present or not in a scene. These features from cluttered images are extracted using Deep Convolutional Neural Network (CNN). We have implemented the system using two prominent CNN models namely VGGNet and ResNet, on standard camera trap database. Finally, the CNN features fed to some of the best in class machine learning techniques for classification. Our outcomes demonstrate that our proposed system is superior compared to existing systems reported in the literature.

In this paper, we propose a new hybrid population-based meta-heuristics algorithm inspired by grey wolves in order to solve integer programming and minimax problems. The proposed algorithm is called Multidirectional Grey Wolf Optimizer (MDGWO) algorithm. In the proposed algorithm, we try to accelerate the standard grey wolf optimizer algorithm (GWO) by invoking the multidirectional search method with it in order to accelerate the search instead of letting the standard GWO run for more iterations without significant improvement in the results. MDGWO starts the search by applying the standard GWO search for a number of iterations, and then the best-obtained solution is passed to the multidirectional search method as an intensification process in order to accelerate the search and overcome the slow convergence of the standard GWO algorithm. We test MDGWO algorithm on seven integer programming problems and 10 minimax problems. Moreover, we compare against 11 algorithms for solving integer programming problems and 10 algorithms for solving minimax problems. Furthermore, we show the efficiency of the proposed algorithm and its ability to solve integer and minimax optimization problems in reasonable time by giving several results of the experiments.