1,720,962 research outputs found
Deep multimodal fusion for video game age rating classification
No full textVideo games appeal to a wide range of ages, from children to adults. As a result, reliable age rating systems like the Entertainment Software Rating Board (ESRB) and Pan European Game Information (PEGI) are essential for guarding younger gamers from improper content. These organizations rate games based on content submitted by video game developers. This paper proposes a multimodal deep learning framework that predicts age ratings by analyzing both video game cover images and textual descriptions. A dataset of 39,212 games was constructed using publicly available information, including ESRB and PEGI labels. Both individual models based on visual or textual features and fusion models that combine these modalities using simple concatenation and Deep Canonical Correlation Analysis (DCCA) were employed to perform the classification task. Experimental results indicate that the simple concatenation model achieves the highest accuracy compared to the individual modalities and the DCCA-based approach, reaching 0.678 for ESRB prediction and 0.584 for PEGI prediction. The findings highlight that using only visual information has limitations, and that textual descriptions play an important role in determining the appropriate age rating for a game. This study shows that future research can benefit from using additional content like gameplay videos and audio
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
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
Creating an AI fashioner through deep learning and computer vision
No full textFashion is a multibillion-dollar industry that concerns many people both socially and culturally. Thanks to social networks, there is a lot of data about the fashion industry on the internet. This has led researchers to shift their attention to this area, especially recently. This paper proposes an end-to-end framework to build an AI fashioner that can diagnose clothing compatibility and generate recommendations to improve compatibility. First, fashion compatibility reviews are analyzed, and incompatible clothing items are identified for each outfit combination. Next, the items of clothing that make up the outfit combination are separated using Mask R-CNN. Then, the incompatible clothing items were removed from the outfit combination, and the most similar outfit combinations were identified among the compatible clothing items. In addition, an attribute detection network was developed to extract the attributes of compatible outfits with the same category in the detected compatible outfits. Finally, recommendation sentences are generated using the detected attributes, and encoder-decoder models are used to train a deep network that generates recommendations from clothing images. Extensive experiments based on existing datasets demonstrate the effectiveness of the proposed method
Diagnosing fashion outfit compatibility with deep learning techniques
Get PDFFashion image understanding is a popular research field with many different machine learning applications. There have been many studies regarding outfit prediction and outfit composition in the field of fashion. However, there are few works that explain the prediction. This paper addresses a method of diagnosing outfit compatibility through clothing images. The proposed system not only predicts compatibility, but also diagnoses incompatible clothing items in outfits. First, a new dataset named ModAI, which has clothing images and compatibility comments from different users was created. After this, a common compatibility comment was created according to user comments for each clothing image. Lastly, image captioning techniques were used to generate compat-ibility suggestion texts from clothing images. Different segmentation techniques were also used to improve captioning capabilities. The model achieves a 0.62 BLEU-4 score. Experiments show that image captioning techniques can also be used to diagnose outfit compatibility.Scientific and Technological Research Council of Turkey (TUBITAK); Eski-sehir Osmangazi University Scientific Research Project Commissions; [116E284]This work is part of a research project No. 116E284 and is supported by The Scientific and Technological Research Council of Turkey (TUBITAK) . At the same time, this work is also supported by the Eski-sehir Osmangazi University Scientific Research Project Commissions (Grant No. 2018-2020)
Derin Öğrenme ile Biber Yaprağı Görüntülerinden Antraknoz Tespiti
No full text2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- 2024-10-16 through 2024-10-18 -- Ankara -- 204562The main objective in plant breeding is to obtain quality crops by growing healthy plants from planting to harvest. Anthracnose is a common fungal disease that causes yield losses in plants. Anthracnose disease causes lesions on various organs and fruits of the plant and impairs the overall health of the plant. In this study, we propose a system that detects anthracnose on images of chili leaves. In the study, six different convolutional neural networks with different characteristics were analyzed using transfer learning techniques. As a result of the experiments, an average accuracy of approximately 97% was obtained. The results obtained using different models and transfer learning techniques were compared with state-of-the-art convolutional neural network architectures. © 2024 IEEE.IEEE SMC; IEEE Turkiye Sectio
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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