Memory-based collaborative recommender system (CRS) computes the similarity between users based on their declared ratings. However, not all ratings are of the same importance to the user. The set of ratings each user weights highly differs from user to user according to his mood and taste. This is usually reflected in the user's rating scale. Accordingly, many efforts have been done to introduce weights to the similarity measures of CRSs. This paper proposes fuzzy weightings for the most common similarity measures for memory-based CRSs. Fuzzy weighting can be considered as a learning mechanism for capturing the preferences of users for ratings. Comparing with genetic algorithm learning, fuzzy weighting is fast, effective and does not require any more space. Moreover, fuzzy weightings based on the rating deviations from the user's mean of ratings take into account the different rating scales of different users. The experimental results show that fuzzy weightings obviously improve the CRSs performance to a good extent.

Orthogonal frequency division multiple access (OFDMA) has been attracting much attention in the recent generation of wireless communications to meet the required demands arising from the explosive growth of Internet, multimedia and broadband services. Currently, images transmission is one of the main parts of wireless communication. However, an image file transmission requires sending a large amount of data which consumes a huge bandwidth so that the image must be compressed to reduce the amount of information to be transmitted. The aim of this paper is to evaluate the wireless transmission of the compressed image over discrete Fourier transform (DFT)-based OFDMA DFT-OFDMA system for different compression methods, different modulation schemes and different subcarriers mapping schemes over three wireless channels including SUI3, vehicular A and uniform. The required minimum signal-to-noise ratio (SNR) to recover the transmitted compressed image is determined to evaluate the system performance. For the sake of diversity, nine compression techniques are used in this work. The MATLAB simulator is used to determine the required minimum SNR to recover the transmitted compressed image for each compression method. The results show that the performance of the set partitioning in hierarchical trees three-dimensional for true colour images (SPIHT-3D) method is nearly better than that of other compression methods, especially when the QPSK and interleaved system are considered.

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Over the past decade, deep learning (DL) has been applied in a large number of optical sensors applications. DL algorithms can improve the accuracy and reduce the noise level in optical sensors. Optical sensors are considered as a promising technology for modern intelligent sensing platforms. These sensors are widely used in process monitoring, quality prediction, pollution, defence, security, and many other applications. However, they suffer major challenges such as the large generated datasets and low processing speeds for these data, including the high cost of these sensors. These challenges can be mitigated by integrating DL systems with optical sensor technologies. This paper presents recent studies integrating DL algorithms with optical sensor applications. This paper also highlights several directions for DL algorithms that promise a considerable impact on use for optical sensor applications. Moreover, this study provides new directions for the future development of related research.