In the current era of online learning, the recommendation system for the eLearning process is quite important. Since the COVID-19 pandemic, eLearning has undergone a complete transformation. Existing eLearning Recommendation Systems worked on collaborative filtering or content-based filtering based on historical data, students’ previous grade, results, or User Profiles. The eLearning system selected courses based on these parameters in a generalized manner rather than on a personalized basis. Personalized recommendations, information relevancy, choosing the proper course, and recommendation accuracy are some of the issues in eLearning recommendation systems. In this paper, existing conventional eLearning and course recommendation systems are studied in detail and compared with the proposed approach. We have used, the dataset of User Profile and User Rating for a recommendation of the course. K Nearest Neighbor, Support Vector Machine, Decision Tree, Random Forest, Nave Bayes, Linear Regression, Linear Discriminant Analysis, and Neural Network were among the Machine Learning techniques explored and deployed. The accuracy achieved for all these algorithms ranges from 0. 81 to 0. 97. The proposed algorithm uses a hybrid approach by combining collaborative filtering and deep learning. We have improved accuracy to 0. 98 which indicate that the proposed model can provide personalized and accurate eLearning recommendation for the individual User.

The personalized local mobile search aims at finding the right on the spot information that is most relevant to the User's requests. It is implemented as a mobile application where the User can access nearby places based on his/ her current location. In Today's technology driven world User Profiles are the virtual representation of each User and they include a variety of User information such as personal, interest and preference data. These Profiles are the outcome of the User profiling process and they are essential to service personalization. The User Profile based personalization approach can be applied to enhance the power of mobile local search for local spots and contributes to a significant convenience in location-based mobile searching. The system takes the User information such as personal, health, entertainment and choice of preference and these parameters are passed to Google Maps API key for personalized query processing. As a result, the User will get prominent services rather than closing one.

Efficient distribution of service requests between fog and cloud nodes considering User mobility and fog nodes’ overload is an important issue of fog computing. This paper proposes a heuristic method for task placement considering the mobility of Users, aiming to serve a higher number of requested services and minimize their response time. This method introduces a formula to overload prediction based on the entry-exit ratio of Users and the estimated time required to perform current requests that are waiting in the queue of a fog node. Then, it provides a solution to avoid the predicted overloading of fog nodes by sending all delay-tolerant requests in the overloaded fog node’s queue to the cloud to reduce the time required for servicing delay-sensitive requests and to increase their acceptance rate. In addition, to prevent requests from being rejected when the mobile User leaves the coverage area of the current fog node, the requests in the current fog node’s queue will be transferred to the destination fog node. Simulation results indicate that the proposed method is effective in avoiding the overloading of the fog nodes and outperforms the existing methods in terms of response time and acceptance rate.

Objective: User Profile is a key element in order to implement and develop personalization services. The User Profile describes the User's preferences and lead to an understanding of the User's needs. The aim of this study was to identify indicators related to the creation and maintenance of User Profiles and models in academic digital libraries.Methodology: The present study is an applied one done with the systematic review and Delphi method. Systematic review method was used to determine indicators related to creating and maintaining User Profiles in the context of digital libraries. The primary keywords were searched in the different databases such as Google Scholar, Emerald, Ebsco, Scopus, Proquest, Magiran, Irandoc and Civilica. Search keywords include "Personali *", "Customiz *", "Recommendation System", "Personalized Recommendation" and combine them with the keywords "Librar *", "Digital Librar *", "Academic Librar *", "Services Librar *". In addition, the "My Library" keyword was searched. The scope of this systematic review included studies that were published from 1990 to 2019. Finally, 47 studies were selected on User Profiles in personalized services of libraries. After reviewing the studies, the indicators on Profile User were identified and then the Delphi method was used to determine important and basic indicators. Delphi's group is consisted of 15 experts in the field of digital library and library software who were selected using Purposeful sampling. Delphi's group reached a consensus on indicators after three rounds. Experts were asked to indicate the importance of the indicators using a 10-point scale ranging. The criterion score for the consensus of the experts was a mean of 7 or higher. The collected data were analyzed using SPSS version 16 software. Descriptive and inferential statistics including mean, standard deviation, binomial distribution and Kendall coefficient were used to analyze the data.Findings: 72 indicators were extracted to create and maintain User Profiles. From the point of view of Delphi panel members, 49 indicators were recognized as important indicators. Important factors in creating and maintaining a User Profile include the type of information, data collection approach, display and presentation of User modeling, User model interoperability, quality of data, privacy and security, and User Profile management.Conclusion: Identifying indicators of User Profiles can be used to develop and enhance the use of service personalization in digital libraries. It is suggested that more research be done in this area, especially to determine the quality of data and privacy in the User Profile.

The huge amount of information has forced researchers to find a solution to face this fundamental problem called data overload. Recommender systems try to suggest the required information to the User by examining the User's preferences directly or based on the behavior of other similar Users in a way that best matches the User's needs. Meanwhile, the use of textual information hidden in the User's biography or comments can be very useful. Declarative systems try to find similarities by examining each word in Users' comments with the comments of other Users, this is if different meanings for a word are ignored. In this way, the use of auto-encoder networks in order to check the semantic relationship of words in a sentence with respect to the opinions of other Users can overcome this challenge. In this article, a personalization approach is presented based on the recommendation system in social networks using the combination of collaborative filter and deep auto-encoder networks. In proposed recommendation system, the information in the User Profile and User comments to each website is used as the input of the presented combined deep auto-encoder network and the collaborative filter method in order to find similar Users accurately and predict the website's rating by Users. Finally, after finding similar Users, it provides recommendations to visit and personalize the web page of serious Users based on the favorite websites of similar Users. Due to the convolutional layers of proposed deep auto-encoder network, the training process in the middle layer has performed on semantic relationship of words in a sentence to find similar comments and Users. This method implemented on two standard datasets titled TripAdvisor and Yelp. The proposed method has improved in terms of statistical accuracy of about 39%, the ratio of successful recommendations to useful recommendations of about 6%, and the accuracy of recognizing similar Users is about 18% from other classification methods.

Introduction: Tailoring the content of health information to the needs, preferences and abilities of individuals, leads to more informed and empowered health consumers. Computerized tailoring of Health Information requires patient’ s characteristics. A User Profile consists of personal data which are basic components in designing computer-tailoring systems. The present study aimed to identify and categorize aspects related to designing, implementing, and interpreting User Profile in health computer-tailoring. Methods: In this scoping review, leading databases such as PubMed and Scopus were reviewed using Arksey and O’ Malley’ s scoping review methodology as a guide. Furthermore, reference lists of relevant literature and key journals were searched. The search was limited to English language articles published from 1990 onwards. Search strings consisted of several keywords related to four main concepts: Individualization, Information, ICT platform, and Health Domain. Results: The analysis of data, collected from a total of 32 eligible studies, highlighted three aspects in designing a User Profile. 1-Identifying common factors used in Profiles and classifying them thematically, 2-Data collection tools and methods, and 3-Data interpretation. Conclusion: Different aspects of designing a User Profile in health information tailoring systems were investigated. The proposed model could be considered as a valuable guide for new researchers in the field.

Introduction: Choosing the right type of device is important to support Users of Health Information Systems. In addition to cost, durability and simplicity, staffing levels and hospital workflows affect in the selected. The purpose of this study was to identify the preferences of Users in device selection.Methods: This applied research with qualitative method, Focus group, was conducted in 2014. Population research are Users of HIS. They have been divided into three distinct groups based on their computer skills and medical knowledge. The sixteen Users, were invited to three focus group meetings and have been asked to explain their criteria for selection of the device. Questionnaires were also used in order to compare the results of discussions quantitatively. The reliability and validity of the questionnaire were confirmed by Cronbach’s alpha (0.86). The analysis was performed using descriptive statistics.Results: The mean response of participants shows that end-Users with high level of medical knowledge and computer skills prefer to use tablets (3.75) and then PCs (3.67). Medical staff without computer skills selected tablets (4), workstation on wheels (WOW) (2.5), wall mounted workstation (2.5) and PCs (2.25). Ultimately members with skills in using the computer and lack of medical knowledge, selected PCs (3.37), tablets (3.29), Personal Digital Assistant (PDA) (3.17), wall mounted workstation (4.2) and WOW (2.67).Conclusion: This study showed that the best device for the medical staff is Tablet and PC is the best device for administrative staff.

Statement of Problem: Root canal cleaning and elimination of the source of infection are the most important basis and the main requirements for successful root treatment since the main cause of failure in root treatment is the permeation of bacteria or their products into the periapical tissues. Nowadays, the newly designed and prcsented instruments for canal instrumentation can improve root treatment. Purpose: The aim of this study was to compare the decrease in the number of intracanal Enterococcus-faecalis (Ef) among three mechanical instrumentation methods: manual (K-type) and rotary (Race, Profile). Materials and Methods: In this experimental study, 30 single rooted teeth were selected. Three cases were considered as negative and three cases as posetive controls and 24 remainder cases were divided into three experimental groups. All root canals were inoculated by Ef and samples were taken from all canals to prepare microbial cultures. The three instrumentation procedures were: - Crown- down technique with K-type manual system file - Crown- down technique with Profile rotary system - Crown- down technique wiht Race rotary system After instrumentation, microbial cultures were taken from root canals and the reduction rate of bacteria were evaluated and compared using one way ANOVA test with P<0.05 as the limit of significance. Results: There was no significant difference among the three instrumentation procedures regarding bacterial elimination. Conclusion: According to the finding of this study, K-type manual file, Profile and Race rotary systems, all can be used in canal instrumentaion. However, since manual files are more accessible and require less equipment compared with rotary systems, and since the ability of all of these methods is identical regarding bacterial elimination, manual files can be used in straight canal instead of rotary systems.