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Extracting visual features for image retrieval by mimicking human cognition remains a challenge. Opponent color and HSV color spaces can mimic human visual perception well. In this paper, we improve and extend the CDH method using a multi-stage model to extract and represent an image in a way that mimics human perception. Our main contributions are as follows (1) a visual feature descriptor is proposed to represent an image. It has the advantages of a histogram-based method and is consistent with visual perception factors such as spatial layout, intensity, edge orientation, and the opponent colors. (2) We improve the distance formula of CDHs; it can effectively adjust the similarity between images according to two parameters. The proposed method provides efficient performance in similar image retrieval rather than instance retrieval. Experiments with four benchmark datasets demonstrate that the proposed method can describe color, texture, and spatial features and performs significantly better than the color volume histogram, color difference histogram, local binary pattern histogram, and multi-texton histogram, and some SURF-based approaches.A heterogeneous wireless network (HWN) contains many kinds of wireless networks with overlapping areas of signal coverage. One of the research topics on HWNs is how to make users choose the most suitable network. This paper designs a user-oriented intelligent access selection algorithm in HWNs with five modules (input, user preference calculation, candidate network score calculation, output, and learning). Essentially, the input module uses a utility function to calculate the utility value of the judgment parameter; the user preference calculation module calculates the weight of the judgment parameter using the fuzzy analysis hierarchy process (FAHP) approach; the candidate network score calculation module calculates the network score through a fuzzy neural network; the output module calculates the error between the actual output value and the expected output value; and the learning module corrects the parameter of the membership function in the fuzzy neural network structure according to the error. Simulation results show that the algorithm proposed in this paper can enable users to select the most suitable network according to service characteristics and can enable users to obtain higher gains.Text classification has many applications in text processing and information retrieval. Instance-based learning (IBL) is among the top-performing text classification methods. However, its effectiveness depends on the distance function it uses to determine similar documents. In this study, we evaluate some popular distance measures' performance and propose new ones that exploit word frequencies and the ordinal relationship between them. In particular, we propose new distance measures that are based on the value distance metric (VDM) and the inverted specific-class distance measure (ISCDM). The proposed measures are suitable for documents represented as vectors of word frequencies. this website We compare these measures' performance with their original counterparts and with powerful Naïve Bayesian-based text classification algorithms. We evaluate the proposed distance measures using the kNN algorithm on 18 benchmark text classification datasets. Our empirical results reveal that the distance metrics for nominal values render better classification results for text classification than the Euclidean distance measure for numeric values. Furthermore, our results indicate that ISCDM substantially outperforms VDM, but it is also more susceptible to make use of the ordinal nature of term-frequencies than VDM. Thus, we were able to propose more ISCDM-based distance measures for text classification than VDM-based measures. We also compare the proposed distance measures with Naïve Bayesian-based text classification, namely, multinomial Naïve Bayes (MNB), complement Naïve Bayes (CNB), and the one-versus-all-but-one (OVA) model. It turned out that when kNN uses some of the proposed measures, it outperforms NB-based text classifiers for most datasets.

Males have a higher prevalence of waterpipe tobacco smoking (WTS) than females in most Eastern Mediterranean Region (EMR) countries, with a smaller gender gap than that of cigarette smoking. The objective of this study was to determine gender differences among university students with respect to WTS initiation, smoking behavior, tobacco flavors, and expenditure on WTS, in four EMR countries.

A cross-sectional online survey was conducted based on convenient samples of ever waterpipe smokers among university students in four EMR countries (Egypt, Jordan, Occupied Palestinian Territories, and the United Arab Emirates) in 2016. The total samples included 2470 participants. Study participants were invited through flyers, university portals, emails and Facebook, followed by emails with links to the internet survey.

Females (80.4%) were more likely than males (66.4%, p<0.001) to be in the younger age group (18-22 years) and they were less likely to be current waterpipe smokers (females, 60.0%; males 69.5%, p<0.001). Two-thirds of students across both genders smoked their first waterpipe at the age of 15-19 years, with more females starting with family members. Over one-third of males and 14.9% of the females usually smoked ≥10 heads (p<0.001). About half (46.6%) of females smoked for less than half an hour compared to 30.5% of males (p<0.001). Only 1% of females smoked non-flavored tobacco compared to 11% of males (p<0.001). There was a significant (p=0.05) positive correlation (r=0.808) with respect to tobacco flavor usually smoked between males and females with apple/double apple being the most popular.

There were gender differences in WTS in several aspects. The study has implications for educational establishments, tobacco control and women civil society groups, as well as policymakers.

There were gender differences in WTS in several aspects. The study has implications for educational establishments, tobacco control and women civil society groups, as well as policymakers.