Open Access
on Advances in artificial intelligence
John Hallam,Chris Mellish +1 more
- 01 Jul 1987
15
About: The article was published on 01 Jul 1987. and is currently open access.
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Citations
DEPTWEET: A Typology for Social Media Texts to Detect Depression Severities
Mohsinul Kabir,Tasnim Ahmed,Md. Bakhtiar Hasan,Md. Tahmid Rahman Laskar,Tarun Kumar Joarder,Hasan Mahmud,Kamrul Hasan +6 more
TL;DR: The clinical articulation of depression is leveraged to build a typology for social media texts for detecting the severity of depression that emulates the standard clinical assessment procedure DSSM-5 and PHQ-9 to encompass subtle indications of depressive disorders from tweets.
45
Genetic-based optimization in fog computing: Current trends and research opportunities
TL;DR: In this article , the authors present a comprehensive, exhaustive, and systematic review of the most recent research works in the field of genetic-based resource optimization in fog computing, with a special emphasis on genetic based solutions.
20
Simulation of liver function enzymes as determinants of thyroidism: a novel ensemble machine learning approach
A. G. Usman,Umar Muhammad Ghali,Mohamed Alhosen Ali Degm,Salisu M. Muhammad,Evren Hincal,Abdulaziz Umar Kurya,S. Isik,Qendresa Hoti,Sani Isah Abba +8 more
TL;DR: In this paper , the ability of single artificial intelligence (AI)-based models, namely multi-layer perceptron (MLP), support vector machine (SVM), and Hammerstein-Weiner (HW) models, were used in the simulation of thyroidism status.
10
Fintech Agents: Technologies and Theories
Anagh Pal,Shreya Gopi,Kwan Min Lee +2 more
TL;DR: This paper provides a comprehensive review of interactive fintech agents from technological and social science perspectives and examines issues and theories related to human-fintech agent interaction in the following areas: intelligence, understanding of users, and agents’ manifestation as social actors.
9
Image-based analysis of yield parameters in viticulture
TL;DR: In this article , a multi-camera system was used to estimate the yield of a modified grapevine harvester, which was mounted on a field phenotyping platform called Phenoliner.
7
References
Supervised Versus Unsupervised Deep Learning Based Methods for Skin Lesion Segmentation in Dermoscopy Images
Abder-Rahman Ali,Jingpeng Li,Thomas Trappenberg +2 more
- 28 May 2019
TL;DR: Results show that, by using the default parameter settings and network configurations proposed in the original approaches, although the unsupervised approach could detect fine structures of skin lesions in some occasions, the supervised approach shows much higher accuracy in terms of Dice coefficient and Jaccard index compared to the un supervised approach.
Name2Vec: Personal Names Embeddings
Jeremy Foxcroft,Adrian d’Alessandro,Luiza Antonie +2 more
- 28 May 2019
TL;DR: This paper proposes to create name-embeddings by employing a Doc2Vec methodology, where each name is viewed as a document and each letter in the name is considered a word, and shows that the new proposed method can predict with high accuracy when a pair of names matches.
17
In Vino Veritas: Estimating Vineyard Grape Yield from Images Using Deep Learning
Daniel Silver,Tanya Monga +1 more
- 28 May 2019
TL;DR: This research investigates five approaches to using convolution neural networks to develop models that can estimate the weight of grapes on the vine from an image taken by a smartphone and indicates that a combination of image processing and deep CNN machine learning can produce models that are sufficiently accurate within a variety of grape for data captured at harvest time.
15
Contextual Generation of Word Embeddings for Out of Vocabulary Words in Downstream Tasks.
Nicolas Garneau,Jean-Samuel Leboeuf,Luc Lamontagne +2 more
- 28 May 2019
TL;DR: This work introduces a model that predicts meaningful embeddings from the spelling of a word as well as from the context in which it appears for a downstream task without the need of pre-training on a given corpus.
10
Personality extraction through LinkedIn
Frédéric Piedboeuf,Philippe Langlais,Ludovic Bourg +2 more
- 28 May 2019
TL;DR: The possibility of collecting a corpus on LinkedIn labelled with a personality model, which has never been done before, and the possibility of extracting two different personalities from the user are looked at.
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