Nowadays, various online resources are growing and disseminating rapidly. In order to organize these resources, attempts have been made to use automatic Classification, which often uses statistical algorithms and machine learning. Recently, attention has been drawn to the use of library Classifications. The main challenge here is that Classification is an abstract, thought-provoking process, and machine techniques and artificial intelligence have not yet been able to completely replace the human mind. In this paper, we provide an overview of the importance of automatic Classification, machine learning, and practical algorithms and techniques of clustering and Classification like K-nearest neighbor, Bayesian models, artificial neural networks, deep learning, and hybrid Classifications. Also, the steps of automatic Classification of Web Pages and the techniques used in each step were mentioned. Achieving a clearer understanding of automatic Classification will enable LIS experts to communicate with experts in the field of artificial intelligence and computers. This could pave the way for interdisciplinary research.

Personalized recommenders have proved to be of use as a solution to reduce the information overload problem. Especially in Adaptive Hypermedia System, a recommender is the main module that delivers suitable learning objects to learners. Recommenders suffer from the cold-start and the sparsity problems. Furthermore, obtaining learner‘ s preferences is cumbersome. Most studies have only focused on similarity between the interest profile of a user and those of others. However, it can lead to the gray-sheep problem, in which users with consistently different opinions from the group do not benefit from this approach. On this basis, matching the learner‘ s learning style with the Web Page features and mining specific attributes is more desirable. The primary contribution of this research work is to introduce a feature-based recommender system that delivers educational Web Pages according to the user's individual learning style. We propose an Educational Resource recommender system that interacts with the users based on their learning style. The learning style determination is based on the Felder-Silverman theory. Furthermore, we incorporate all the explicit/implicit data features of a Web Page and the elements contained in them that have an influence on the quality of recommendation, and help the system make more effective recommendations.

Web Pages are crawled and indexed by search engines for fast accessing data on the Web. One of the challenges in the search engines is Web spam Pages. There are many approaches to Web spam Pages detection such as measurement of HTML code style similarity, Pages linguistic pattern analysis and machine learning algorithm on Page content features. One of the famous algorithms has been used in machine learning approach is Support Vector Machine (SVM) classifier. Unfortunately SVM could not achieve a reasonable accuracy in this scope. In order to classify non-linear data in a linear manner, the SVM needs to use the idea of the kernel, which leads to enhanced Classification capabilities. A kernel, implicitly maps the data to a higher-dimensional space. Recently basic structure of SVM has been changed by new extensions called Twin SVM (TSVM) to increase robustness and Classification accuracy using two separate hyperplanes. Because of using two separate hyperplanes in TSVM, it is better to use multiple kernels in it. Kernel functions are designed based on specific data sample. Therefore they cannot use for general purpose. In this paper we improved accuracy of Web spam detection by using two nonlinear kernels into TSVM as an improved extension of SVM. These two kernels have been created based on genetic algorithm. The classifier ability to data separation has been increased by using two separated kernels for each class of data. Effectiveness of new proposed method has been experimented with two publicly used spam datasets called UK-2007 and UK-2006.

Background: Snack bars, a globally popular food category, contain a diverse variety of  ingredients and appeal to different dietary preferences. Even though snack alternatives are gaining prominence as viable options for promoting healthy dietary patterns, the composition of certain snacks may pose sustainability-related concerns and effects on environment. This study investigates the energy and nutrient composition of fruit-based snack bars, with a focus on sustainable practices, and the significant inclusion of dates and nuts in production. Methods: Between 10 and 25 July 2023, 49 healthy bars from 12 companies, accessible in five supermarkets, underwent a comprehensive analysis. The researchers visited selected supermarkets, identified the products on the shelves, and collected information on the contents from the labels. The label information was then cross-referenced with the brands' Websites. The average values of energy, protein, total fat, saturated fat, carbohydrate, fiber, sugar, and salt contents of 100 g samples of the products were assessed. Group proportions were investigated through the Chi-square test. As comparisons involving numerical variables failed to meet the assumption of normal distribution, independent comparisons were performed using the Kruskal-Wallis test for more than two groups and Mann-Whitney U test for two groups. The significance level was set at p<0.05. Results: Dried fruits, particularly date and their derivatives, are the most prominently featured ingredients (87.8%) of the nutritious snacks, followed by other dried fruits containing apple, cherry, and orange. Oily seeds are the most preferred additional ingredient (75.5%), followed by cocoa (57.1%), spices (57.1%), sweeteners, and coconut. Almonds are the most preferred variety among nuts (36.7%). Other ingredients frequently utilized in bars, such as chickpea flour and chicory root fiber. It is noteworthy that, several snack bars were including  multiple ingredients simultaneously whereas others lacked certain ingredients  entirely. Furthermore, combination of those ingredients were discovered to be prevalent in various snack bars. Conclusion: In conclusion, this research provides valuable insights for consumers and industry stakeholders, guiding them towards choices aligned with nutritional preferences and supporting environmental and economic sustainability. DOI: 10.18502/jfqhc.11.2.15648

The conversion of document image to its electronic version is a very important problem in the saving, searching and retrieval application in the official automation system. For this purpose, analysis of the document image is necessary. In this paper, a hierarchical Classification structure based on a two-stage segmentation algorithm is proposed. In this structure, image is segmented using the proposed two-stage segmentation algorithm. Then, the type of the image regions such as document and non-document image is determined using multiple classifiers in the hierarchical Classification structure. The proposed segmentation algorithm uses two algorithms based on wavelet transform and thresholding. Texture features such as correlation, homogeneity and entropy that extracted from co-occurrenc matrix and also two new features based on wavelet transform are used to classifiy and lable the regions of the image. The hierarchical classifier is consisted of two Multilayer Perceptron (MLP) classifiers and a Support Vector Machine (SVM) classifier. The proposed algorithm is evaluated on a database consisting of document and non-document images that provides from Internet. The experimental results show the efficiency of the proposed approach in the region segmentation and Classification. The proposed algorithm provides accuracy rate of 97.5% on Classification of the regions.

This research aimed to analyze the content of computer science textbooks work- knowledge branch, design and development of Web Pages in terms of the concept of soft skills and it was done by content analysis method. Statistical population included computer science textbooks Kardansh branch,  and the sample size includes seven s books which was selected in a purposeful manner. The unit of analysis in this study was sentence. A researcher-made checklist was used to collect the data. Categorical method was used to analyze the collected data and frequency and percentage were used to analyze the data. The results showed the following six components: Commitment and Responsibility Skills, Communication and Teamwork Skills, Creativity and Problem Solving, Ability to plan and organize activities, the skills of using modern computing and technology and practical skills and specialized knowledge in its textbooks have not been considered as appropriate.