Recommender systems have been widely used in e-commerce applications. They are a sub-class of information filtering system used to either predict whether a user will prefer an item (prediction problem) or identify a set of k items that will be of user-interest (Top-k recommendation problem). Demanding sufficient ratings to make robust predictions and suggesting qualified recommendations are two significant challenges in recommender systems. However, the latter is far from satisfactory because human decisions are affected by environmental conditions, and they might change over time. In this paper, we introduce an innovative method to impute ratings to missed components of the rating matrix. We also design an ensemble-based method to obtain Top-k recommendations. In order to evaluate the performance of the proposed method, several experiments have been conducted based on 10-fold cross-validation over real-world Datasets. The experimental results show that the proposed method is superior to the state-of-the-art competing methods regarding the applied evaluation metrics.

Feature selection is a critical step in machine learning, especially when dealing with high-dimensional and Incomplete Data. Traditional methods often struggle with missing values, which are common in real-world applications. This paper introduces Neural Network Feature Selection (NNFS), a novel deep learning-based approach that effectively identifies important features even in the presence of missing Data. We provide a variety of comparisons to evaluate the suggested algorithm over existing methods. We demonstrate the accuracy, speed and sensitivity to missed Data. According to numerical results, the proposed algorithm outperforms existing methods especially for medium size Datasets. Both random and real tests are presented to make the results more realistic.

It is well known that damaged structural members may considerably alter the behaviour of the structures. Careful observation of these changes has often been viewed as a means to identify and assess the location and severity of damages in structures. Among the responses of a structure, natural frequencies and mode shapes are both relatively easy to obtain and independent from external excitation, and therefore, can be used as a measure of the structural behaviour before and after an extreme event which might have led to damage in the structure. This paper applies Charged System Search algorithm to the problem of damage detection using vibration Data. The objective is to identify the location and extent of multi-damage in a structure. Both natural frequencies and mode shapes are used to form the required objective function. To moderate the effect of noise on measured Data, a penalty approach is applied. Varity of numerical examples including beams, frames and trusses are examined, and the results show that the present methodology can reliably identify damage scenarios using noisy measurements and Incomplete Data.