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In other words, it detects users' shape of expertise by learning patterns from documents of users and queries simultaneously. ... In this study, we have proposed a new deep model for T-shaped experts finding based on Convolutional Neural Networks. ... level of expertise. ...arXiv:2004.02184v1 fatcat:5nwvbji5pbdnrhe6rkrb4ktozq
Neural Network Learning Paradigm A fundamental problem in mapping a classification task to a neural network model is to derive network connection weights. ... the actual classification and the resulting network classification. ...doi:10.1108/02686909410054745 fatcat:xqvjkdgh2bcftmz4bdyx6e4vuq
The model neural network is formed and modified as a result of news service articles read by a user. ... To perform search on a neural tree, training begins from the root and proceeds level by level. ...
The final sample includes a list of 83 relevant articles authored in academia as well as industry that have been published from January 1, 2008 to March 1, 2019. ... convenient for those who have only just begun their research into Community Question Answering (CQA) expert recommendation, and for those who are already concerned with this issue, to ease the extension of ... machines (FM) Artificial neural network Ensemble learning Table IV . ...doi:10.1108/ijcs-03-2019-0011 fatcat:5waemn4e3zfu5b4n55f6qxafbu
We propose an approach based on convolutional neural networks (CNN) to resolve this issue. ... Community Question Answering (CQA) is becoming an increasingly important web service for people to search for expertise and to share their own. ... Conflict of interest The authors declare that they have no conflict of interest. ...doi:10.1007/s11432-016-9197-0 fatcat:jl5wzb3grvhiljp7kzue4i2mii
2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES)
Kinematic data have been proven to directly correlate with the expertise of surgeons performing RAMIS procedures, but for traditional MIS it is not readily available. ... Minimally Invasive Surgery (MIS) benefits the patients by using smaller incisions than open surgery, resulting in less pain and quicker recovery, but increasing the difficulty of the surgical task manyfold ... Given the ROI data and the generated output, the final output needs to be created by grouping the data according to the surgical tasks and the expertise level of users. ...doi:10.1109/ines52918.2021.9512917 fatcat:jqt6qcjtj5gihbdohknuvhisnu
IFIP Advances in Information and Communication Technology
This paper presents a systematic framework for the design of intelligent decision support systems based upon soft computing paradigms Iike neural networks, genetie algorithms, simulated annealing and juzzy ... The long-term goal of this research is to automate the design of soft-computing systems from a domain expert's description ofthe problem situation and a set of input data. ... We acknowledge the support of Army SBIR program and Mr. Kachesh Pathak in particular in our endeavors. ...doi:10.1007/978-0-387-35602-0_12 fatcat:rdpxguwoqjgnxjddwfdllej43e
Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences HICSS-94
After briefly reviewing conventional information retrieval techniques and the prevailing database query paradigms, we introduce the ID5R algorithm, previously developed by Utgoff, for "intelligent" and ... describe in detail how we adapted the ID5R algorithm for IR/DBMS applications and we present two examples, one for IR applications and the other for DBMS applications, to demonstrate the feasibility of ... Acknowledgment This project was supported in part by an NSF grant, IRI-9211418, 1992IRI-9211418, -1994 Bibliography ...doi:10.1109/hicss.1994.323329 fatcat:rzi3rlrijrfefiv4gnznrlaj6y
Experiments show that NEAT improves drug side effect discovery from online health discussion by 3.04% from user-credibility agnostic baselines, and by 9.94% from non-neural baselines in term of F1. ... such as number of posts by 0.113 in term of Spearman's rank correlation coefficient. ... Figure 1 shows the detailed network architecture of our model. User Expertise Representation (UE). ...doi:10.1186/s13326-020-00221-1 pmid:32641159 fatcat:bksfxpdtkrepre4ze4y5onzciy
In this context, machine learning techniques automate the process of constructing user models. ... Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. ... The review also demonstrates that one of the main problems that personalized DL faces is the lack of any kind of standardization for the design of DL user models. ...doi:10.1016/j.ijinfomgt.2006.02.006 fatcat:p7cija2p2rfu5kbmlkud4ks55u
We present some preliminary results on the brain-gene ontology (BGO) project that is concerned with the collection, presentation and use of knowledge in the form of ontology. ... The first version of the brain-gene ontology has been completed as a hierarchical structure and as an initial implementation in the Protégé ontology building environment. ... Different parts of it can be used by different users, from a school level to postgraduate and PhD student level. ...doi:10.1109/ijcnn.2007.4370943 dblp:conf/ijcnn/KasabovJGBWJ07 fatcat:3swevuwiknakzd2edwfrbiduom
of unsupervised deep feature learning and classification by state-of-the-art models. ... ., gamer's fNIRS data in combination with emotional state estimation from gamer's facial expressions, the expertise level of the gamers has been decoded per trial in a multi-modal framework comprising ... By comparing the F1-scores for the classifiers used in this work with those of baseline classifer, we can test the H0 1 that it is not possible to recognise gamer's expertise level from fNIRS neural responses ...doi:10.3390/brainsci11010106 pmid:33466787 fatcat:uqygjxyzvvbydlirjhpyt5uyu4
The network is thus very much of a “black-box” solution, whose structure and reasoning are relatively inaccessible to higher level reasoning or control processes, such as the human user. ... We show that by referencing both rule-based systems and neural networks to the common normative frame of probability, a novel and practical architecture emerges. ...
The model's parameters can be trained using conventional gradient descent techniques, and the model's trained convolution neural network can learn the image's features and finish the extraction and classification ... By examining users' historical listening patterns for personalised recommendations, the music recommendation algorithm can lessen message fatigue for users and enhance user experience. ... Every level adds the weight determined by a set of weights to the input, which is taken from a local area of the previous level at each level. e output of the previous level is said to have been convolved ...doi:10.1155/2022/5749359 fatcat:4gwu572sg5fqtoyycxvyw4odaa
Our method introduces a user-session network architecture, as well as session dropout as a novel way of data augmentation. ... Our method shows significant improvement over existing methods when the learned representation is transferred to downstream tasks such as experience and expertise classification. ... We use two recurrent neural networks with GRU layers. ...arXiv:2207.14760v1 fatcat:yoy2o4gkyjdjhgldtpp2okxf7i
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