Recommendation Systems (RSs) have significant applications in many industrial systems. The duty of a recommender algorithm is to operate available data (users/items contextual data and rating (or purchase) the consumption history for items), as well as to provide a recommendation list for any target user. The recommended items should be selected so that the target user is compelled to give them positive reviews. In this manuscript, we propose a novel of RS algorithm that makes advantage of user-user trust relationships, rating histories, and their frequency of occurrence. We also provide a brand new overlapping community detection algorithm. The information about the users' community structure is used to handle the cold-start and sparsity problems. We compare the performance of the proposed RS algorithm with a number of state-of-the-art algorithms on the extended Epinions dataset, which has both information on trust relations and the timing of the ratings. Numerical simulations reveal the superiority of the proposed algorithm over others. We also investigate how the algorithms perform when only cold-start users and items are considered. As a cold-start user (item) we consider those that have made (received) less than five ratings. The experiments show significant outperformance of the proposed algorithm over others, which is mainly due to the use of information on overlapping community structures between users.
Wireless sensor networks (WSNs) have found great appeal and popularity among researchers, especially in the field of monitoring and surveillance tasks. However, it has become a challenging issue due to the need to balance different optimization criteria such as power consumption, packet loss rate, and network lifetime, and coverage. The novelty of this research discusses the applications, structures, challenges, and issues we face in designing WSNs. And proposed new Topology control mechanisms it will focus more on building a reliable and energy efficient network topology step by step through defining available amount of energy for each node within its cluster, sorting all within header, and selecting an active one (more power header) for signal routing. While sensor cover topology demonstrates network monitoring capability, connection topology should remain as a requirement for the successful delivery of information including queries, data collected, and control messages. How to build an optimized coating topology while remaining efficient and low-cost connection is not well understood and needs further research. Power control and power management are two different types of topology controllers. Also in our study, we examine network lifetime, compared to other schemas time of death of the first node and the last node, and found that network lifetime was increased. Finally, a topology control method for extending network lifetime is presented.
In recent years, due to the non-linear nature, complexity, and irregularity of time series, especially in energy consumption and climate, studying this field has become very important. Therefore, this study aims to provide a high accuracy and efficiency hybrid approach to time series forecasting. The proposed model is called EDFPSO-SVR (Empirical Mode Decomposition- Discrete Wavelet Transform- Feature selection with Particle Swarm Optimization-Support Vector Regression). In the proposed hybrid approach, the first step is to decompose the signal into the Intrinsic Mode Functions (IMF) component using the Empirical Mode Decomposition (EMD) algorithm. In the second step, each component is transformed into subsequences of approximation properties and details by converting the Wavelets. In the third step, the best feature is extracted by the PSO algorithm. The purpose of using the PSO algorithm is feature extraction and error minimization of the proposed approach. The fourth step, using time vector regression, has dealt with time series forecasting. Four data sets in two different fields have been used to evaluate the proposed method. The two datasets are electric load of England and Poland, and the other two datasets are related to the temperature of Australia and Belgium. Evaluation criteria include MSE, RMSE, MAPE, and MAE. The evaluation results of the proposed method with other Principal component analysis (PCA) feature extraction algorithms, and comparisons with methods and studies in this field, indicate the proper performance of the proposed approach.
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