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Framing the interpreter : towards a visual perspective
\"Situations of conflict offer special insights into the history of the interpreter figure, and specifically the part played in that history by photographic representations of interpreters. This book analyses photo postcards, snapshots and press photos from several historical periods of conflict, associated with different photographic technologies and habits of image consumption: the colonial period, the First and Second World War, and the Cold War. The book's methodological approach to the \"framing\" of the interpreter uses tools taken primarily from visual anthropology, sociology and visual syntax to analyse the imagery of the modern era of interpreting. By means of these interpretative frames, the contributions suggest that each culture, subculture or social group constructed its own representation of the interpreter figure through photography. The volume breaks new ground for image-based research in translation studies by examining photographic representations that reveal the interpreter as a socially constructed category. It locates the interpreter's mediating efforts at the core of the human sciences. This book will be of interest to researchers and advanced students in translation and interpreting studies, as well as to those working in visual studies, photography, anthropology and military/conflict studies\"-- Provided by publisher.
Book
Game localization : translating for the global digital entertainment industry
Video games are part of the growing digital entertainment industry for which game localization has become pivotal in serving international markets. As well as addressing the practical needs of the industry to facilitate translator and localizer training, this book seeks to conceptualize game localization in an attempt to locate it in Translation Studies in the context of the technologization of contemporary translation practices. Designed to provide a comprehensive introduction to the topic of game localization the book draws on the literature in Game Studies as well as Translation Studies. The book's readership is intended to be translation scholars, game localization practitioners and those in Game Studies developing research interest in the international dimensions of the digital entertainment industry. The book aims to provide a road map for the dynamic professional practices of game localization and to help readers visualize the expanding role of translation in one of the 21st century's key global industries.
eBook
Frontiers of Multimedia Research
The field of multimedia is unique in offering a rich and dynamic forum for researchers from \"traditional\" fields to collaborate and develop new solutions and knowledge that transcend the boundaries of individual disciplines. Despite the prolific research activities and outcomes, however, few efforts have been made to develop books that serve as an introduction to the rich spectrum of topics covered by this broad field. A few books are available that either focus on specific subfields or basic background in multimedia. Tutorial-style materials covering the active topics being pursued by the leading researchers at frontiers of the field are currently lacking. In 2015, ACM SIGMM, the special interest group on multimedia, launched a new initiative to address this void by selecting and inviting 12 rising-star speakers from different subfields of multimedia research to deliver plenary tutorial-style talks at the ACM Multimedia conference for 2015. Each speaker discussed the challenges and state-of-the-art developments of their prospective research areas in a general manner to the broad community. The covered topics were comprehensive, including multimedia content understanding, multimodal human-human and human-computer interaction, multimedia social media, and multimedia system architecture and deployment. Following the very positive responses to these talks, the speakers were invited to expand the content covered in their talks into chapters that can be used as reference material for researchers, students, and practitioners. Each chapter discusses the problems, technical challenges, state-of-the-art approaches and performances, open issues, and promising direction for future work. Collectively, the chapters provide an excellent sampling of major topics addressed by the community as a whole. This book, capturing some of the outcomes of such efforts, is well positioned to fill the aforementioned needs in providing tutorial-style reference materials for frontier topics in multimedia.
eBook
Audiovisual translation : theoretical and methodological challenges
This volume discusses the advantages and drawbacks of ten approaches to AVT and highlights the potential avenues opened up by new methods. Originally published as a special issue of Target 28:2 (2016).
eBook
Medical image-based detection of COVID-19 using Deep Convolution Neural Networks
The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures.
Journal Article
Machine learning and deep learning approach for medical image analysis: diagnosis to detection
Computer-aided detection using Deep Learning (DL) and Machine Learning (ML) shows tremendous growth in the medical field. Medical images are considered as the actual origin of appropriate information required for diagnosis of disease. Detection of disease at the initial stage, using various modalities, is one of the most important factors to decrease mortality rate occurring due to cancer and tumors. Modalities help radiologists and doctors to study the internal structure of the detected disease for retrieving the required features. ML has limitations with the present modalities due to large amounts of data, whereas DL works efficiently with any amount of data. Hence, DL is considered as the enhanced technique of ML where ML uses the learning techniques and DL acquires details on how machines should react around people. DL uses a multilayered neural network to get more information about the used datasets. This study aims to present a systematic literature review related to applications of ML and DL for the detection along with classification of multiple diseases. A detailed analysis of 40 primary studies acquired from the well-known journals and conferences between Jan 2014–2022 was done. It provides an overview of different approaches based on ML and DL for the detection along with the classification of multiple diseases, modalities for medical imaging, tools and techniques used for the evaluation, description of datasets. Further, experiments are performed using MRI dataset to provide a comparative analysis of ML classifiers and DL models. This study will assist the healthcare community by enabling medical practitioners and researchers to choose an appropriate diagnosis technique for a given disease with reduced time and high accuracy.
Journal Article
Watermarking techniques for medical data authentication: a survey
With the widespread growth of medical images and improved communication and computer technologies in recent years, authenticity of the images has been a serious issue for E-health applications. In order to this, various notable watermarking techniques are developed by potential researchers. However, those techniques are unable to solve many issues that are necessary to be measured in future investigations. This paper surveys various watermarking techniques in medical domain. Along with the survey, general concepts of watermarking, major characteristics, recent applications, concepts of embedding and recovery process of watermark, and the summary of various techniques (in tabular form) are highlighted in brief. Further, major issues associated with medical image watermarking are also discussed to find out research directions for fledgling researchers and developers.
Journal Article
Content-based processing and analysis of endoscopic images and videos: A survey
In recent years, digital endoscopy has established as key technology for medical screenings and minimally invasive surgery. Since then, various research communities with manifold backgrounds have picked up on the idea of processing and automatically analyzing the inherently available video signal that is produced by the endoscopic camera. Proposed works mainly include image processing techniques, pattern recognition, machine learning methods and Computer Vision algorithms. While most contributions deal with real-time assistance at procedure time, the post-procedural processing of recorded videos is still in its infancy. Many post-processing problems are based on typical Multimedia methods like indexing, retrieval, summarization and video interaction, but have only been sparsely addressed so far for this domain. The goals of this survey are (1) to introduce this research field to a broader audience in the Multimedia community to stimulate further research, (2) to describe domain-specific characteristics of endoscopic videos that need to be addressed in a pre-processing step, and (3) to systematically bring together the very diverse research results for the first time to provide a broader overview of related research that is currently not perceived as belonging together.
Journal Article
An enhanced deep learning method for multi-class brain tumor classification using deep transfer learning
Multi-class brain tumor classification is an important area of research in the field of medical imaging because of the different tumor characteristics. One such challenging problem is the multiclass classification of brain tumors using MR images. Since accuracy is critical in classification, computer vision researchers are introducing a number of techniques; However, achieving high accuracy remains challenging when classifying brain images. Early diagnosis of brain tumor types can activate timely treatment, thereby improving the patient’s chances of survival. In recent years, deep learning models have achieved promising results, especially in classifying brain tumors to help neurologists. This work proposes a deep transfer learning model that accelerates brain tumor detection using MR imaging. In this paper, five popular deep learning architectures are utilized to develop a system for diagnosing brain tumors. The architectures used is this paper are Xception, DenseNet201, DenseNet121, ResNet152V2, and InceptionResNetV2. The final layer of these architectures has been modified with our deep dense block and softmax layer as the output layer to improve the classification accuracy. This article presents two main experiments to assess the effectiveness of the proposed model. First, three-class results using images from patients with glioma, meningioma, and pituitary are discussed. Second, the results of four classes are discussed using images of glioma, meningioma, pituitary and healthy patients. The results show that the proposed model based on Xception architecture is the most suitable deep learning model for detecting brain tumors. It achieves a classification accuracy of 99.67% on the 3-class dataset and 95.87% on the 4-class dataset, which is better than the state-of-the-art methods. In conclusion, the proposed model can provide radiologists with an automated medical diagnostic system to make fast and accurate decisions.
Journal Article
Blockchain-based privacy and security preserving in electronic health: a systematic review
In today’s world, health and medicine play an undeniable role in human life. Traditional and current Electronic Health Records (EHR) systems that are used to exchange information between medical stakeholders (patients, physicians, insurance companies, pharmaceuticals, medical researchers, etc.) suffer weaknesses in terms of security and privacy due to having centralized architecture. Blockchain technology ensures the privacy and security of EHR systems thanks to the use of encryption. Moreover, due to its decentralized nature, this technology prevents central failure and central attack points. In this paper, a systematic literature review (SLR) is proposed to analyze the existing Blockchain-based approaches for improving privacy and security in electronic health systems. The research methodology, paper selection process, and the search query are explained. 51 papers returned from our search criteria published between 2018 and Dec 2022 are reviewed. The main ideas, type of Blockchain, evaluation metrics, and used tools of each selected paper are discussed in detail. Finally, future research directions, open challenges, and some issues are discussed.
Journal Article