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Introduction:Digital three-dimensional models are widely used for orthodontic diagnosis. The purpose of this study was to appraise the accuracy of digital models obtained from computer-aided design/computer-aided manufacturing (CAD/CAM) and cone-beam computed tomography (CBCT) for tooth-width measurements and the Bolton analysis.Materials and Methods:Digital models (CAD/CAM, CBCT) and plaster model were made for each of 50 subjects. Tooth-width measurements on the digital models (CAD/CAM, CBCT) were compared with those on the corresponding plaster models. The anterior and overall Bolton ratios were calculated for each participant and for each method. The paired t-test was applied to determine the validity.Results:Tooth-width measurements, anterior, and overall Bolton ratio of digital models of CAD/CAM and CBCT did not differ significantly from those on the plaster models.Conclusion:Hence, both CBCT and CAD/CAM are trustable and promising technique that can replace plaster models due to its overwhelming advantages.

Few things exist in life from the beginning but you don't realize until they become a habit. Education is also one of those things which cannot be exempted from this. The lockdown forced most of the academicians to take some determining decisions and new interests to revamp the teaching–learning process. The objective of this study is to analyze the impact of technology in teaching–learning process before and after a pandemic. This analysis is done by statistical tests using paired t-test and z-test by collecting data from students and teachers. Results show that this pandemic is an eye-opener for academicians to use the technology in teaching–learning process.

PurposeThe purpose of this paper is to provide adaptive access to learning resources in the digital library.Design/methodology/approachA novel method using ontology-based multi-attribute collaborative filtering is proposed. Digital libraries are those which are fully automated and all resources are in digital form and access to the information available is provided to a remote user as well as a conventional user electronically. To satisfy users' information needs, a humongous amount of newly created information is published electronically in digital libraries. While search applications are improving, it is still difficult for the majority of users to find relevant information. For better service, the framework should also be able to adapt queries to search domains and target learners.FindingsThis paper improves the accuracy and efficiency of predicting and recommending personalized learning resources in digital libraries. To facilitate a personalized digital learning environment, the authors propose a novel method using ontology-supported collaborative filtering (CF) recommendation system. The objective is to provide adaptive access to learning resources in the digital library. The proposed model is based on user-based CF which suggests learning resources for students based on their course registration, preferences for topics and digital libraries. Using ontological framework knowledge for semantic similarity and considering multiple attributes apart from learners' preferences for the learning resources improve the accuracy of the proposed model.Research limitations/implicationsThe results of this work majorly rely on the developed ontology. More experiments are to be conducted with other domain ontologies.Practical implicationsThe proposed approach is integrated into Nucleus, a Learning Management System (https://nucleus.amcspsgtech.in). The results are of interest to learners, academicians, researchers and developers of digital libraries. This work also provides insights into the ontology for e-learning to improve personalized learning environments.Originality/valueThis paper computes learner similarity and learning resources similarity based on ontological knowledge, feedback and ratings on the learning resources. The predictions for the target learner are calculated and top N learning resources are generated by the recommendation engine using CF.

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In this work, we present a simple but a different approach to correct colors of digital photographs. Pictures taken on digital cameras do not portray the actual colors of the photo that a naked eye can see. This is because of the surroundings and the lighting conditions a photo is captured in. The problem of colors is solved here using an external component called a color checker. The algorithm takes 2 inputs and gives a color corrected output which helps photographers and a few other areas of work where pictures play an important role. The method we propose has been tuned and tested on various image data.The paper deals with image processing and machine learning for color calibration and to detect the colorchecker on the target image and uses Python programming language to get the output.

In the e-learning system an abundant amount of information is created and delivered to the learners over electronic media. Learners are often getting confusion by the flow of information and have difficulty in selecting the topic to learn that satisfies their needs and interests. There are several researches have been performed to provide personalized learning paths for individual learners. But many of them collect the learners' interest, habits and behavior from their profile and based on that they recommend learning path. It is the fact that the learners' interest, learning attitude and need will vary from time to time and course to course. In this paper a recommendation system is proposed using semantic net that helps the learners by offering a more intelligent approach to navigating and searching course content. In this the learner will get more personalized and contextual recommendation. The results show that semantic net based methods enable interoperability of heterogeneous course content representation and result in accurate recommendations. The validity of the proposed model is shown using sample learners and performance measures for the recommendation effects are given for evaluating the proposed system.