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Hybrid recommender system leveraging stacked convolutional networks

Journal of engineering science and Technology Review 2018年第3期11卷 89-96页

作者： Nelaturi, Naresh Lavanya Devi, G. Dpt of Computer Science and Systems Engineering College of Engineering Andhra University Visakhapatnam Andhra Pradesh530 003 India

Recommendation systems has emerged as an essential component in web-based systems, as their ability to analyze customers' behavior and generate recommendations seeking customers' satisfaction is successfully accomplished. However, the success of these systems depends on amount of customers' personal preference data and content (items') metadata available for harnessing. Therefore, data sparsity poses a major challenge here. To alleviate this problem, data and models from other domains can be leveraged to gain good insight about customers' preferences and content similarities. In specific, this paper proposes the idea of extracting knowledge for transfer learning leveraging pre-trained deep neural networks. Knowledge from pre-trained models is used to efficiently identify similarity and capture customers' preference among the contents. To attain the objective, this paper presented an approach, for generating efficient top-n recommendations using a hybrid recommender model. Performance analysis is performed on the proposed approach and results obtained are promising. Furthermore, extensions for this work are also discussed. © 2018 Eastern Macedonia and Thrace Institute of Technology.

关键词： Recommender systems

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IxaMed at CLEF eHealth 2018 task 1: ICD10 coding with a sequence-to-sequence approach 19

IxaMed at CLEF eHealth 2018 task 1: ICD10 coding with a sequ...

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19th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2018

作者： Atutxa, A. Casillas, A. Ezeiza, N. Fresno, V. Goenaga, I. Gojenola, K. Martínez, R. Oronoz, M. Perez-De-Viñaspre, O. Dpt. Languages and Computer Systems School of Engineering UPV EHU Bilbao Spain Dpt. Electricity and Electronics Fac. of Science and Technology UPV EHU Leioa Spain Dpt. Languages and Computer Systems Faculty of Informatics UPV EHU Donostia-San Sebastian United States Dpt. Languages and Computer Systems School of Computer Engineering UNED Madrid Spain

Hospital systems routinely assign disease codes (ICD10 codes) to medical records. The challenge stands on treating natural and nonstandard language in which doctors express their diagnoses and, additionally, to solve a large-scale classification problem, as there are thousands of possible codes. In this working notes paper, we present our system and the results of the CLEF 2018 eHealth Evaluation Task 1 on Multilingual Information Extraction-ICD10 coding. This benchmark addresses information extraction in written text with focus on several languages, specifically Hungarian, Italian and French. The goal is to automatically assign ICD10 codes to diagnostic terms of death certificates. The problem can be cast in different ways, for example as a multilabel classification task or as sequence-to-sequence prediction. Our proposal follows this last approach, with promising results, well above the average results for the task. It only relies on the material provided by the task organizers, allowing the application of the same system to all datasets.

关键词： Machine learning

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BACH: grand challenge on breast cancer histology images

arXiv

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arXiv 2018年

作者： Aresta, Guilherme Araújo, Teresa Kwok, Scotty Chennamsetty, Sai Saketh Safwan, Mohammed Alex, Varghese Marami, Bahram Prastawa, Marcel Chan, Monica Donovan, Michael Fernandez, Gerardo Zeineh, Jack Kohl, Matthias Walz, Christoph Ludwig, Florian Braunewell, Stefan Baust, Maximilian Vu, Quoc Dang Nhat To, Minh Nguyen Kim, Eal Kwak, Jin Tae Galal, Sameh Sanchez-Freire, Veronica Brancati, Nadia Frucci, Maria Riccio, Daniel Wang, Yaqi Sun, Lingling Ma, Kaiqiang Fang, Jiannan Kone, Ismael Boulmane, Lahsen Campilho, Aurélio Eloy, Catarina Polónia, António Aguiar, Paulo INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal Faculty of Engineering of University of Porto Porto4200-465 Portugal Seek AI Limited Hong Kong Bangalore India Gurgaon India Chennai India Center for Computational and Systems Pathology Dpt. of Pathology Icahn School of Medicine at Mount Sinai Mount Sinai Hospital New York United States Konica Minolta Laboratory Europe Munich Germany Institute of Pathology Faculty of Medicine LMU Munich Munich Germany Department of Computer Science and Engineering Sejong University Seoul05006 Korea Republic of Chicago IL United States Naples Italy University of Naples "Federico II" Naples Italy Key Laboratory of RF Circuits and Systems Ministry of Education Hangzhou Dianzi University Hangzhou310018 China Zhejiang Provincial Laboratory of Integrated Circuits Design Hangzhou Dianzi University Hangzhou310018 China p2MIA Research Group LEM2A Lab Faculté des Sciences Université Moulay Ismail Meknes Morocco qLaboratório de Anatomia Patológica Ipatimup Diagnósticos Rua Júilio Amaral de Carvalho 45 Porto4200-135 Portugal Faculdade de Medicina Universidade do Porto Alameda Prof Hernâni Monteiro Porto4200-319 Portugal Universidade do Porto Rua Alfredo Allen 208 Porto4200-135 Portugal Universidade do Porto Rua Alfredo Allen 208 Porto4200-135 Portugal

Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time- and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology. Copyright © 2018, The Authors. All rights reserved.

关键词： Neurons

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Reducing the size of the optimization problems in fuzzy ontology reasoning 11

Reducing the size of the optimization problems in fuzzy onto...

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11th International Workshop on Uncertainty Reasoning for the Semantic Web, URSW 2015 - collocated with the 14th International Semantic Web Conference, ISWC 2015

作者： Bobillo, Fernando Straccia, Umberto Dpt. of Computer Science and Systems Engineering University of Zaragoza Spain Pisa Italy

Fuzzy ontologies allow the representation of imprecise structured knowledge, typical in many real-world application domains. A key factor in the practical success of fuzzy ontologies is the availability of highly optimized reasoners. This short paper discusses a novel optimization technique: a reduction of the size of the optimization problems obtained during the inference by the fuzzy ontology reasoner fuzzyDL.

关键词： Ontology

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On partitioning-based optimisations in expressive fuzzy Description Logics

On partitioning-based optimisations in expressive fuzzy Desc...

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： Fernando Bobillo Umberto Straccia Dpt. of Computer Science and Systems Engineering University of Zaragoza Spain ISTI-CNR Istituto di Scienza e Tecnologie dell' Informazione Pisa Italy

Fuzzy Description Logics (DLs) are a formalism for the representation of structured knowledge affected by imprecision or vagueness. A key factor in the practical success of fuzzy DLs is the availability of highly implemented reasoners. This paper studies two optimisation techniques (ABox partitioning based on individual groups and optimisation problem partitioning) in the setting of the fuzzy ontology reasoner fuzzy DL. We study the applicability of these techniques in expressive fuzzy DL languages, proposing a new strategy, and perform an empirical evaluation proving that they are not helpful in practice so far.

关键词： Optimization Semantics Cognition Fuzzy logic Description logic Standards

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A MILP-based decision procedure for the (Fuzzy) description logic ALCB 27

A MILP-based decision procedure for the (Fuzzy) description ...

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27th International Workshop on Description Logics, DL 2014

作者： Bobillo, Fernando Straccia, Umberto Dpt. of Computer Science and Systems Engineering University of Zaragoza Spain Pisa Italy

To overcome the inability of Description Logics (DLs) to represent vague or imprecise information, several fuzzy extensions have been proposed in the literature. In this context, an important family of reasoning algorithms for fuzzy DLs is based on a combination of tableau algorithms and Operational Research (OR) problems, specifically using Mixed Integer Linear Programming (MILP). In this paper, we present a MILP-based tableau procedure that allows to reason within fuzzy ALCB, i.e., ALC with individual value restrictions. Interestingly, unlike classical tableau procedures, our tableau algorithm is deterministic, in the sense that it defers the inherent non-determinism in ALCB to a MILP solver.

关键词： Data description

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Pattern-based Transformation of Diagrammatic Conceptual Models for Semantic Enrichment in the Web of Data

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Procedia computer science 2015年 60卷 150-159页

作者： Robert Andrei Buchmann Dimitris Karagiannis Business Information Systems Dpt. Faculty of Economic Sciences and Business Administration University Babeş Bolyai str. T. Mihali 58-60 400591 Cluj Napoca Romania Knowledge Engineering Research Group Faculty of Computer Science University of Vienna Waehringerstr. 29 1090 Vienna Austria

The goal of this paper is to bridge the paradigms of Linked Data and Conceptual Modeling, which have been developed from quite distinct concerns, although certain opportunities may stimulate the evolution of the Web towards a new type of knowledge space driven by diagrammatic models. To this end, the work at hand investigates structural patterns in a multitude of modeling languages accumulated over time within the Open Model Initiative Laboratory and defines for each pattern transformation rules that produce graph-based model serializations, thus enabling the processing of diagrammatic models using the Web of Data tech-nological space and practices.

关键词： Web of Data Conceptual Modeling Linked Data Graph databases

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Finite fuzzy description logics and crisp representations

Finite fuzzy description logics and crisp representations

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6th International Workshop on Uncertainty Reasoning for the Semantic Web, URSW 2010, Held as Part of the 9th International Semantic Web Conferences, ISWC 2010 and 1st International Workshop on Uncertainty in Description Logics, UniDL2010

作者： Bobillo, Fernando Straccia, Umberto Dpt. of Computer Science and Systems Engineering University of Zaragoza Spain Pisa Italy

ISBN: (纸本)9783642359743

Fuzzy Description Logics (DLs) are a formalism for the representation of structured knowledge that is imprecise or vague by nature. In fuzzy DLs, restricting to a finite set of degrees of truth has proved to be useful, both for theoretical and practical reasons. In this paper, we propose finite fuzzy DLs as a generalization of existing approaches. We assume a finite totally ordered set of linguistic terms or labels, which is very useful in practice since expert knowledge is usually expressed using linguistic terms. Then, we consider fuzzy DLs based on any smooth t-norm defined over this set. Initially we focus on the finite fuzzy DL, studying some logical properties, and showing the decidability of the logic by presenting a reasoning preserving reduction to the classical case. Finally, we extend our logic in two directions: by considering non-smooth t-norms and by considering additional DL constructors. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Data description

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General concept inclusion absorptions for fuzzy description logics: A first step

General concept inclusion absorptions for fuzzy description ...

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26th International Workshop on Description Logics, DL 2013

作者： Bobillo, Fernando Straccia, Umberto Dpt. of Computer Science and Systems Engineering University of Zaragoza Spain Pisa Italy

General Concept Inclusion (GCIs) absorption algorithms have shown to play an important role in classical Description Logics (DLs) reasoners, as they allow to transform GCIs into simpler forms to which apply specialised inference rules, resulting in an important performance gain. In this work, we develop a first absorption algorithm for fuzzy DLs, and evaluate it over some ontologies.

关键词： Data description

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Learning System Based on Contextual Awareness for Clinical Practice in Nursing Courses

Learning System Based on Contextual Awareness for Clinical P...

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International Conference on Advanced Learning Technologies (ICALT)

作者： Jorge Gómez Gómez J.F. Huete Velssy Hernandez Riaño Department of Systems Engineering - Research Group GNOCIX Universidad del Sinu-Monteria Colombia Dpt. Computer Science and Artificial Intelligence University of Granada-CITIC_UGR Spain Research Group SOCRATES Universidad de Cordoba-Monteria Colombia

The ubiquitous learning is used as a means of supporting teaching processes by means of the use of mobile and wireless communication technologies, sensors and tracking mechanisms / tracking, working together to integrate students with their environment. The process of clinical learning for nursing students may be the main cause of increased stress on teachers and students, given the problematic nature of the patients. This paper presents an ubiquitous learning system based on active learning methodology that provides context awareness support for nursing courses. An experiment with control and experimental groups with nursing students demonstrates that for theoretical concepts to be successfully transferred into practice, the context of practice needs to be considered.

关键词： Medical services Context Education Mobile communication Learning systems Mobile handsets Personal digital assistants

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