1,721,042 research outputs found
Face Expression Recognition via transformer-based classification models
No full textFacial Expression Recognition (FER) tasks have widely studied in the literature since it has many applications. Fast development of technology in deep learning computer vision algorithms, especially, transformer-based classification models, makes it hard to select most appropriate models. Using complex model may increase accuracy performance but decreasing infer- ence time which is a crucial in near real-time applications. On the other hand, small models may not give desired results. In this study, it is aimed to examine accuracy and data process time per- formance of 5 different relatively small transformer-based image classification algorithms for FER tasks. Used models are vanilla Vision Transformer (ViT), Pooling-based Vision Transformer (PiT), Shifted Windows Transformer (Swin), Data-efficient image Transformers (DeiT), and Cross-attention Vision Transformer (CrossViT) with considering their trainable parameter size and architectures. Each model has 20-30M trainable parameters which means relatively small. Moreover, each model has different architectures. As an illustration, CrossViT focuses on image using multi-scale patches and PiT model introduces convolution layers and pooling techniques to vanilla ViT model. Model performances are evaluated on CK+48 and KDEF datasets that are well- known and most used in the literature. It was observed that all models exhibit similar performance with literature results. PiT model that includes both Convolutional Neural Network (CNN) and Transformer layers achieved the best accuracy scores 0.9513 and 0.9090 for CK+48 and KDEF datasets, respectively. It shows CNN layers boost performance of Transformer based models and help to learn data more efficiently for CK+48 and KDEF datasets. Swin Transformer performs 0.9080 worst accuracy score for CK+48 dataset and 0.8434 nearly worst score for KDEF dataset. Swin Transformer and PiT exhibit worst and best image processing performance in terms of spent time, respectively. This makes PiT model suitable for real-time applications too. Moreover, PiT model require 25 and 83 second least training epoch to reach these performance for CK+48 and KDEF, respectively
Vision Transformer Based Photo Capturing System
No full textPortrait photo is one of the most crucial documents that many people need for official transactions in many public and private organizations. Despite the developing technologies and high resolution imaging devices, people need such photographer offices to fulfil their needs to take photos. In this study, a Photo Capturing System has been developed to provide infrastructure for web and mobile applications. After the system detects the person's face, facial orientation and facial expression, it automatically takes a photo and sends it to a graphical user interface developed for this purpose. Then, with the help of the user interface of the photo taken by the system, it is automatically printed out. The proposed study is a unique study that uses imaging technologies, deep learning and vision transformer algorithms, which are very popular image processing techniques in several years. Within the scope of the study, face detection and facial expression recognition are performed with a success rate of close to 100\% and 95.52\%, respectively. In the study, the performances of Vision Transformer algorithm is also compared with the state of art algorithms in facial expression recognition
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Multiple Physics Design of Induction Motor
No full textToday, induction motors which have a widespread usage network is constantly developed by manufacturers. It is frequently used in heavy industry, the mines household appliances and consumer electronics due to its cheap maintenance, efficiency, effective torque and easy control features. Designers go to increase productivity with various analysis methods. Moreover, designers want to check the manufacturability of their desired machines. In this study, the multi physics design of an induction motor is discussed. Mechanical, electromagnetic, thermal, harmonic and stress analyzes are carried out in the design. Various analysis methods are discussed for electromagnetic and thermal analysis. Among these analysis techniques, Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD) methods are generally preferred. SEA and CFD methods, which have various advantages over other analysis methods are used. In addition, the components required in multi physics design are emphasized. ANSYS Maxwell, Steady-State Thermal, Modal and Static Structural software are used in the analysis. In this study, the necessary analyzes for the design of an induction motor are carried out
Forensic Dental Age Estimation Using Modified Deep Learning Neural Network
No full textDental age is one of the most reliable methods to identify an individual’s age. By using dental panoramic radiography (DPR) images, physicians and pathologists in forensic sciences try to establish the chronological age of individuals with no valid legal records or registered patients. The current methods in practice demand intensive labor, time, and qualified experts. The development of deep learning algorithms in the field of medical image processing has improved the sensitivity of predicting truth values while reducing the processing speed of imaging time. This study proposed an automated approach to estimate the forensic ages of individuals ranging in age from 8 to 68 using 1332 DPR images. Initially, experimental analyses were performed with the transfer learning-based models, including InceptionV3, DenseNet201, EfficientNetB4, MobileNetV2, VGG16, and ResNet50V2; and accordingly, the best-performing model, InceptionV3, was modified, and a new neural network model was developed. Reducing the number of the parameters already available in the developed model architecture resulted in a faster and more accurate dental age estimation. The performance metrics of the results attained were as follows: mean absolute error (MAE) was 3.13, root mean square error (RMSE) was 4.77, and correlation coefficient R2 was 87%. It is conceivable to propose the new model as potentially dependable and practical ancillary equipment in forensic sciences and dental medicine
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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