15,610 research outputs found

    A scoping review of natural language processing of radiology reports in breast cancer

    Get PDF
    Various natural language processing (NLP) algorithms have been applied in the literature to analyze radiology reports pertaining to the diagnosis and subsequent care of cancer patients. Applications of this technology include cohort selection for clinical trials, population of large-scale data registries, and quality improvement in radiology workflows including mammography screening. This scoping review is the first to examine such applications in the specific context of breast cancer. Out of 210 identified articles initially, 44 met our inclusion criteria for this review. Extracted data elements included both clinical and technical details of studies that developed or evaluated NLP algorithms applied to free-text radiology reports of breast cancer. Our review illustrates an emphasis on applications in diagnostic and screening processes over treatment or therapeutic applications and describes growth in deep learning and transfer learning approaches in recent years, although rule-based approaches continue to be useful. Furthermore, we observe increased efforts in code and software sharing but not with data sharing

    Big Tech and research funding: A bibliometric approach

    Get PDF
    Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsTechnology companies have radically transformed our daily life in the recent years with help of the wide usage of internet. While transforming our lives, these companies also have grown up even bigger in the recent times and have become more powerful not only financially, but also in terms of computing power and data. Although there have been lots of research done on the influence of large digital economy players (Big Tech) in different fields, the academic influence of these companies is little understood. By drawing on 130,000 academic papers for which there is evidence of support by the Big Tech, the present work applies bibliometric approaches (on the metadata) and text mining techniques (on the contents) to shed a light on the outcomes of this relationship. In particular, we take into consideration research funding (direct strategies) and conference sponsorships (indirect strategies) to empirically explore this relatively unexplored side of Big Tech’s influence in contemporary society. While developing the analysis a key limitation was the scarcity of prior work exploring the connections between digital platforms and the scientific enterprise. There are several results that come to light from such a perspective, one of these findings is that among the research supported by Big Tech companies, there is big gap between the number of outcomes with the content about the technical perspectives (like machine learning or artificial intelligence) than the content about reflexive (say ethical or environmental) dimensions of innovation, ladder being very small. These findings may stimulate further inquiries into identifying the possible risks, if any, are generated from the direct and indirect financial support by corporate informational giants to academia. The causes and consequences of this non-market activity by companies with big market power may require further attention and research in this field

    Deep Learning for Scene Flow Estimation on Point Clouds: A Survey and Prospective Trends

    Get PDF
    Aiming at obtaining structural information and 3D motion of dynamic scenes, scene flow estimation has been an interest of research in computer vision and computer graphics for a long time. It is also a fundamental task for various applications such as autonomous driving. Compared to previous methods that utilize image representations, many recent researches build upon the power of deep analysis and focus on point clouds representation to conduct 3D flow estimation. This paper comprehensively reviews the pioneering literature in scene flow estimation based on point clouds. Meanwhile, it delves into detail in learning paradigms and presents insightful comparisons between the state-of-the-art methods using deep learning for scene flow estimation. Furthermore, this paper investigates various higher-level scene understanding tasks, including object tracking, motion segmentation, etc. and concludes with an overview of foreseeable research trends for scene flow estimation

    Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

    Full text link
    In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 482 citations published in 903 journals during the past 30 years. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent

    Bridging technology and educational psychology: an exploration of individual differences in technology-assisted language learning within an Algerian EFL setting

    Get PDF
    The implementation of technology in language learning and teaching has a great influence onthe teaching and learning process as a whole and its impact on the learners’ psychological state seems of paramount significance, since it could be either an aid or a barrier to students’ academic performance. This thesis therefore explores individual learner differences in technology-assisted language learning (TALL) and when using educational technologies in higher education within an Algerian English as a Foreign Language (EFL) setting. Although I initially intended to investigate the relationship between TALL and certain affective variables mainly motivation, anxiety, self-confidence, and learning styles inside the classroom, the collection and analysis of data shifted my focus to a holistic view of individual learner differences in TALL environments and when using educational technologies within and beyond the classroom. In an attempt to bridge technology and educational psychology, this ethnographic case study considers the nature of the impact of technology integration in language teaching and learning on the psychology of individual language learners inside and outside the classroom. The study considers the reality constructed by participants and reveals multiple and distinctive views about the relationship between the use of educational technologies in higher education and individual learner differences. It took place in a university in the north-west of Algeria and involved 27 main and secondary student and teacher participants. It consisted of focus-group discussions, follow-up discussions, teachers’ interviews, learners’ diaries, observation, and field notes. It was initially conducted within the classroom but gradually expanded to other settings outside the classroom depending on the availability of participants, their actions, and activities. The study indicates that the impact of technology integration in EFL learning on individual learner differences is both complex and dynamic. It is complex in the sense that it is shown in multiple aspects and reflected on the students and their differences. In addition to various positive and different negative influences of different technology uses and the different psychological reactions among students to the same technology scenario, the study reveals the unrecognised different manifestations of similar psychological traits in the same ELT technology scenario. It is also dynamic since it is characterised by constant change according to contextual approaches to and practical realities of technology integration in language teaching and learning in the setting, including discrepancies between students’ attitudes and teacher’ actions, mismatches between technological experiences inside and outside the classroom, local concerns and generalised beliefs about TALL in the context, and the rapid and unplanned shift to online educational delivery during the Covid-19 pandemic situation. The study may therefore be of interest, not only to Algerian teachers and students, but also to academics and institutions in other contexts through considering the complex and dynamic impact of TALL and technology integration at higher education on individual differences, and to academics in similar low-resource contexts by undertaking a context approach to technology integration

    Decoding spatial location of attended audio-visual stimulus with EEG and fNIRS

    Get PDF
    When analyzing complex scenes, humans often focus their attention on an object at a particular spatial location in the presence of background noises and irrelevant visual objects. The ability to decode the attended spatial location would facilitate brain computer interfaces (BCI) for complex scene analysis. Here, we tested two different neuroimaging technologies and investigated their capability to decode audio-visual spatial attention in the presence of competing stimuli from multiple locations. For functional near-infrared spectroscopy (fNIRS), we targeted dorsal frontoparietal network including frontal eye field (FEF) and intra-parietal sulcus (IPS) as well as superior temporal gyrus/planum temporal (STG/PT). They all were shown in previous functional magnetic resonance imaging (fMRI) studies to be activated by auditory, visual, or audio-visual spatial tasks. We found that fNIRS provides robust decoding of attended spatial locations for most participants and correlates with behavioral performance. Moreover, we found that FEF makes a large contribution to decoding performance. Surprisingly, the performance was significantly above chance level 1s after cue onset, which is well before the peak of the fNIRS response. For electroencephalography (EEG), while there are several successful EEG-based algorithms, to date, all of them focused exclusively on auditory modality where eye-related artifacts are minimized or controlled. Successful integration into a more ecological typical usage requires careful consideration for eye-related artifacts which are inevitable. We showed that fast and reliable decoding can be done with or without ocular-removal algorithm. Our results show that EEG and fNIRS are promising platforms for compact, wearable technologies that could be applied to decode attended spatial location and reveal contributions of specific brain regions during complex scene analysis

    Animating potential for intensities and becoming in writing: challenging discursively constructed structures and writing conventions in academia through the use of storying and other post qualitative inquiries

    Get PDF
    Written for everyone ever denied the opportunity of fulfilling their academic potential, this is ‘Chloe’s story’. Using composite selves, a phrase chosen to indicate multiplicities and movement, to story both the initial event leading to ‘Chloe’s’ immediate withdrawal from a Further Education college and an imaginary second chance to support her whilst at university, this Deleuzo-Guattarian (2015a) ‘assemblage’ of post qualitative inquiries offers challenge to discursively constructed structures and writing conventions in academia. Adopting a posthuman approach to theorising to shift attention towards affects and intensities always relationally in action in multiple ‘assemblages’, these inquiries aim to decentre individual ‘lecturer’ and ‘student’ identities. Illuminating movements and moments quivering with potential for change, then, hoping thereby to generate second chances for all, different approaches to writing are exemplified which trouble those academic constraints by fostering inquiry and speculation: moving away from ‘what is’ towards ‘what if’. With the formatting of this thesis itself also always troubling the rigid Deleuzo-Guattarian (2015a) ‘segmentary lines’ structuring orthodox academic practice, imbricated in these inquiries are attempts to exemplify Manning’s (2015; 2016) ‘artfulness’ through shifts in thinking within and around an emerging PhD thesis. As writing resists organising, the verb thesisising comes into play to describe the processes involved in creating this always-moving thesis. Using ‘landing sites’ (Arakawa and Gins, 2009) as a landscaping device, freely creating emerging ‘lines of flight’ (Deleuze and Guattari, 2015a) so often denied to students forced to adhere to strict academic conventions, this ‘movement-moving’ (Manning, 2014) opens up opportunities for change as in Manning’s (2016) ‘research-creation’. Arguing for a moving away from writing-representing towards writing-inquiring, towards a writing ‘that does’ (Wyatt and Gale, 2018: 127), and toward writing as immanent doing, it is hoped to animate potential for intensities and becoming in writing, offering opportunities and glimmerings of the not-yet-known

    Diagnosis of Pneumonia Using Deep Learning

    Get PDF
    Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and react like humans. Some of the activities computers with artificial intelligence are designed for include, Speech, recognition, Learning, Planning and Problem solving. Deep learning is a collection of algorithms used in machine learning, It is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is a technique used to produce Pneumonia detection and classification models using x-ray imaging for rapid and easy detection and identification of pneumonia. In this thesis, we review ways and mechanisms to use deep learning techniques to produce a model for Pneumonia detection. The goal is find a good and effective way to detect pneumonia based on X-rays to help the chest doctor in decision-making easily and accuracy and speed. The model will be designed and implemented, including both Dataset of image and Pneumonia detection through the use of Deep learning algorithms based on neural networks. The test and evaluation will be applied to a range of chest x-ray images and the results will be presented in detail and discussed. This thesis uses deep learning to detect pneumonia and its classification
    • …
    corecore