BackgroundLung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. The possibility to obtain a manually accurate interpretation from CT scans demands a big effort by the radiologist and might be a fatiguing process. Therefore, the design of a computer-aided diagnosis (CADx) system would be helpful as a second opinion tool.MethodsThe stages of the proposed CADx are: a supervised extraction of the region of interest to eliminate the shape differences among CT images. The Daubechies db1, db2, and db4 wavelet transforms are computed with one and two levels of decomposition. After that, 19 features are computed from each wavelet sub-band. Then, the sub-band and attribute selection is performed. As a result, 11 features are selected and combined in pairs as inputs to the support vector machine (SVM), which is used to distinguish CT images containing cancerous nodules from those not containing nodules.ResultsThe clinical data set used for experiments consists of 45 CT scans from ELCAP and LIDC. For the training stage 61 CT images were used (36 with cancerous lung nodules and 25 without lung nodules). The system performance was tested with 45 CT scans (23 CT scans with lung nodules and 22 without nodules), different from that used for training. The results obtained show that the methodology successfully classifies cancerous nodules with a diameter from 2 mm to 30 mm. The total preciseness obtained was 82%; the sensitivity was 90.90%, whereas the specificity was 73.91%.ConclusionsThe CADx system presented is competitive with other literature systems in terms of sensitivity. The system reduces the complexity of classification by not performing the typical segmentation stage of most CADx systems. Additionally, the novelty of the algorithm is the use of a wavelet feature descriptor.
Recent studies have reported that the inclusion of new technological elements such as augmented reality (AR), for educational purposes, increases the learning interest and motivation of students. However, developing AR applications, especially with mobile content, is still a rather technical subject; thus the dissemination of the technology in the classroom has been rather limited. This paper presents a new software architecture for AR application development based on freely available components; it provides a detailed view of the subsystems and tasks that encompass the creation of a mobile AR application. The typical task of plotting a quadratic equation was selected as a case study to obtain feasibility insights on how AR could support the teaching-learning process and to observe the student’s reaction to the technology and the particular application. The pilot study was conducted with 59 students at a Mexican undergraduate school. A questionnaire was created in order to obtain information about the students’ experience using the AR application and the analysis of the results obtained is presented. The comments expressed by the users after the AR experience are positive, supporting the premise that AR can be, in the future, a valuable complimentary teaching tool for topics that benefit from contextual learning experience and multipoint visualization, such as the quadratic equation.
This paper proposes a mobile augmented reality (MAR) system aimed to support students in the use of a milling and lathe machines at a university manufacturing laboratory. The system incorporates 3D models of machinery and tools, text instructions, animations and videos with real processes to enrich the information obtained from the real world. The elements are shown when the user points the camera of a mobile device to specific parts of the machinery, where augmented reality (AR) markers are placed. The main goals of the project were (1) create an AR system that guides inexperienced users in machinery handling and (2) measure the acceptance rate and performance of the system in the school manufacturing laboratory. The guidance is provided by means of virtual information about how to operate the machinery when the trainer is not present. The system was implemented as a mobile app for Android devices and it was tested by 16 students and teachers at the university manufacturing laboratory through a survey. The results of this study revealed that students, laboratory technicians, and teachers had positive opinions and good acceptance about the use of the MAR system in the manufacturing laboratory. ß 2016 Wiley Periodicals, Inc. Comput Appl Eng Educ 24:967-981, 2016; View this article online at wileyonlinelibrary.com/journal/cae;
<p class="ADYNAAbstrac"><span lang="EN-US">The supplier selection is a critical activity within the administration of the supply chain. It is considered a complex problem given that it involves different aspects such as the alternatives to evaluate, the multiple criteria involved as well as the group of decision makers with different opinions. In this sense, the literature reports several methods to help in this difficult activity of selecting the best supplier. However, there are still some gaps in these methods; therefore, it is imperative to further develop research. Thus, the purpose of this paper is to report a hybrid method between MOORA and intuitionistic fuzzy sets for the selection of suppliers with a focus on multi-criteria and multi-group environment. The importance of decision makers, criteria and alternatives are evaluated in terms of intuitionistic fuzzy sets. Then, MOORA is used in order to determine the best supplier. An experimental case is developed in order to explain the proposed method in detail and to demonstrate its practicality and effectiveness.</span></p>
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