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10th International Joint Conference on Computational Intelligence, IJCCI 2018

作者： Kerdprasop, Kittisak Chuaybamroong, Paradee Kerdprasop, Nittaya Data and Knowledge Engineering Research Unit School of Computer Engineering Suranaree University of Technology Nakhon Ratchasima Thailand Department of Environmental Science Thammasat University Thailand

ISBN: (纸本)9789897583278

This study applies intelligent analytical methods to analyze temperature anomaly events during the past seven centuries of countries in the Southeast Asia including Thailand, Malaysia, Myanmar, and Cambodia. The temperature reconstruction during the years 1300 to 1999 were used as data source for anomaly analysis. In the analytical process, correlation analysis was applied to initially investigate the temperature variability concordance among the Southeast Asian countries. The results are that temperature variability patterns in Thailand, Myanmar, and Cambodia are moderately correlated to each other. On the contrary, the temperature variation patterns of Malaysia do not correlate to other countries in the same region. The further in-depth analysis focuses on the temperature anomaly of Thailand that shows high variability from the 14th to 16th centuries. Several machine learning algorithms had been applied to estimate the temperature anomaly of Thailand based on the anomaly events among the neighbors. The learned models reveal that Myanmar temperature anomaly most associate to the Thailand’s temperature variation. The performance of each model had been assessed and the results reveal that the chi-squared automatic interaction detection, or CHAID, is the best one with 0.624 correlation coefficient and relative error around 0.611. Copyright © 2018 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved.

关键词： Machine learning

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Forest fire area estimation using support vector machine as an approximator 10th

Forest fire area estimation using support vector machine as ...

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10th International Joint Conference on Computational Intelligence, IJCCI 2018

作者： Kerdprasop, Nittaya Poomka, Pumrapee Chuaybamroong, Paradee Kerdprasop, Kittisak Data and Knowledge Engineering Research Unit School of Computer Engineering Suranaree University of Technology Nakhon Ratchasima Thailand Department of Environmental Science Thammasat University Thailand

ISBN: (纸本)9789897583278

Forest fire is critical environmental issue that can cause severe damage. Fast detection and accurate estimation of forest fire burned area can help firefighters to effectively control damage. Thus, the purpose of this paper is to apply state of the art data modeling method to estimate the area of forest fire burning using support vector machine (SVM) algorithm as a tool for area approximation. The dataset is real forest fires data from the Montesinho natural park in the northeast region of Portugal. The original dataset comprises of 517 records with 13 attributes. We randomly sample the data 10 times to obtain 10 data-subsets for building estimation models using two kinds of SVM kernel: radial basis function and polynomial function. The obtained models are compared against other proposed techniques to assess performances based on the two measurement metrics: mean absolute error (MAE) and root mean square error (RMSE). The experimental results show that our SVM predictor using polynomial kernel function can precisely estimate forest fire damage area with the MAE and RMSE as low as 6.48 and 7.65, respectively. These errors are less than other techniques reported in the literature. Copyright © 2018 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved.

关键词： Fires

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A Coarse-to-Fine Multi-stream Hybrid Deraining Network for Single Image Deraining

A Coarse-to-Fine Multi-stream Hybrid Deraining Network for S...

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IEEE International Conference on Data Mining (ICDM)

作者： Yanyan Wei Zhao Zhang Haijun Zhang Richang Hong Meng Wang Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science Harbin Institute of Technology (Shenzhen) Xili University Town Shenzhen China

Single image deraining task is still a very challenging task due to its ill-posed nature in reality. Recently, researchers have tried to fix this issue by training the CNN-based end-to-end models, but they still cannot extract the negative rain streaks from rainy images precisely, which usually leads to an over de-rained or under de-rained result. To handle this issue, this paper proposes a new coarse-to-fine single image deraining framework termed Multi-stream Hybrid Deraining Network (shortly, MH-DerainNet). To obtain the negative rain streaks during training process more accurately, we present a new module named dual path residual dense block, i.e., Residual path and Dense path. The Residual path is used to reuse com-mon features from the previous layers while the Dense path can explore new features. In addition, to concatenate different scaled features, we also apply the idea of multi-stream with shortcuts between cascaded dual path residual dense block based streams. To obtain more distinct derained images, we combine the SSIM loss and perceptual loss to preserve the per-pixel similarity as well as preserving the global structures so that the deraining result is more accurate. Extensive experi-ments on both synthetic and real rainy images demonstrate that our MH-DerainNet can deliver significant improvements over several recent state-of-the-art methods.

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Inference of latent shape expressions associated to DBpedia ontology

Inference of latent shape expressions associated to DBpedia ...

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2018 ISWC Posters and Demonstrations, Industry and Blue Sky Ideas Tracks, ISWC-P and D-Industry-BlueSky 2018

作者： Fernández-Álvarez, Daniel García-González, Herminio Frey, Johannes Hellmann, Sebastian Gayo, José Emilio Labra Department of Computer Science University of Oviedo Oviedo33007 Spain Agile Knowledge Engineering Semantic Web University of Leipzig Leipzig04109 Germany

In order to perform any operation in an RDF graph, it is recommendable to know the expected topology of the targeted information. Some technologies have been developed in the last years to describe the expected shapes in an RDF graph, such as ShEx or SHACL. In general, a domain expert can define the expected shapes in a graph, but there are some scenarios in which the schema cannot be predicted a priori, but it emerges at the same time that the graph is filled with new information (the shapes are latent). We have developed a prototype which is able to infer shapes of classes in a knowledge graph and used it with classes of DBpedia ontology. We serialize our results using ShEx. © 2018 CEUR-WS. All rights reserved.

关键词： Ontology

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Bias in data-driven AI systems - An introductory survey

arXiv

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arXiv 2020年

作者： Ntoutsi, Eirini Fafalios, Pavlos Gadiraju, Ujwal Iosifidis, Vasileios Nejdl, Wolfgang Vidal, Maria-Esther Ruggieri, Salvatore Turini, Franco Papadopoulos, Symeon Krasanakis, Emmanouil Kompatsiaris, Ioannis Kinder-Kurlanda, Katharina Wagner, Claudia Karimi, Fariba Fernandez, Miriam Alani, Harith Berendt, Bettina Kruegel, Tina Heinze, Christian Broelemann, Klaus Kasneci, Gjergji Tiropanis, Thanassis Staab, Steffen L3S Research Center Leibniz University Hannover Germany Institute of Computer Science FORTH-ICS Greece TIB Leibniz Information Centre For Science and Tecnhnology Hannover Germany KDDLAB Dipartimento di Informatica Università di Pisa Italy Information Technologies Institute CERTH Thessaloniki Greece GESIS Leibniz Institute for the Social Sciences Cologne Germany Knowledge Media Institute Open University Milton Keynes United Kingdom Faculty of Electrical Engineering and Computer Science TU Berlin Department of Computer Science KU Leuven Germany Institute for Legal Informatics Leibniz University of Hanover Germany Innovation Lab SCHUFA Holding AG Wiesbaden Germany Electronics and Computer Science University of Southampton United Kingdom IPVS Universität Stuttgart Germany

AI-based systems are widely employed nowadays to make decisions that have far-reaching impacts on individuals and society. Their decisions might affect everyone, everywhere and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed. Copyright © 2020, The Authors. All rights reserved.

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Coastal water quality prediction based on machine learning with feature interpretation and Spatio-temporal analysis

arXiv

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arXiv 2021年

作者： Grbčić, Luka Družeta, Siniša Mauša, Goran Lipić, Tomislav Lušić, Darija Vukić Alvir, Marta Lučin, Ivana Sikirica, Ante Davidović, Davor Travaš, Vanja Kalafatović, Daniela Pikelj, Kristina Fajković, Hana Holjević, Toni Kranjčević, Lado Department of Fluid Mechanics and Computational Engineering Faculty of Engineering University of Rijeka Vukovarska 58 Rijeka51000 Croatia Department of Computer Engineering Faculty of Engineering University of Rijeka Vukovarska 58 Rijeka51000 Croatia Center for Advanced Computing and Modelling University of Rijeka Radmile Matejčić 2 Rijeka51000 Croatia Department of Hydrotechnics and Geotechnics Faculty of Civil Engineering University of Rijeka Radmile Matejčić 3 Rijeka51000 Croatia Department of Environmental Health Faculty of Medicine University of Rijeka Braće Branchetta 20/1 Rijeka51000 Croatia Department of Environmental Health Teaching Institute of Public Health of Primorje-Gorski Kotar County Krešimirova 52a Rijeka51000 Croatia Department of Biotechnology University of Rijeka Radmile Matejčić 2 Rijeka51000 Croatia Laboratory for Machine Learning and Knowledge Representations Rudjer Boskovic Institute Biljenička cesta 54 Zagreb10000 Croatia Center for Informatics and Computing Ruđer Bošković Institute Biljenička cesta 54 Zagreb10000 Croatia Department of Geology Faculty of Science University of Zagreb Horvatovac 102a Zagreb10000 Croatia

Coastal water quality management is a public health concern, as poor coastal water quality can potentially harbor pathogens that are dangerous to human health. Tourism-oriented countries need to actively monitor the condition of coastal water at tourist popular sites during the summer season. In this study, routine monitoring data of Escherichia Coli and enterococci across 15 public beaches in the city of Rijeka, Croatia, were used to build machine learning models for predicting their levels based on environmental parameters as well as to investigate their dynamics and relationships with environmental stressors. Gradient Boosting algorithms (Catboost, Xgboost), Random Forests, Support Vector Regression and Artificial Neural Networks were trained with routine monitoring measurements from all sampling sites and used to predict E. Coli and enterococci values based on environmental features. The evaluation of stability and generalizability with 10-fold cross validation analysis of the machine learning models, showed that the Catboost algorithm performed best with R2 values of 0.71 and 0.68 for predicting E. Coli and enterococci, respectively, compared to other evaluated ML algorithms including Xgboost, Random Forests, Support Vector Regression and Artificial Neural Networks. We also use the SHapley Additive exPlanations technique to identify and interpret which features have the most predictive power. The results show that site salinity measured is the most important feature for forecasting both E. Coli and enterococci levels. Finally, the spatial and temporal predictive accuracy of both ML models were examined at sites with the historically lowest coastal water quality. The spatial *** and enterococci models achieved strong R2 values of 0.85 and 0.83, while the temporal models achieved R2 values of 0.74 and 0.67. The temporal model also achieved moderate R2 values of 0.44 and 0.46 at a site with historically high coastal water quality. © 2021, CC BY-NC-ND.

关键词： Machine learning

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Block-Structured Optimization for Anomalous Pattern Detection in Interdependent Networks

Block-Structured Optimization for Anomalous Pattern Detectio...

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IEEE International Conference on Data Mining (ICDM)

作者： Fei Jie Chunpai Wang Feng Chen Lei Li Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science University at Albany – SUNY Albany NY USA Erik Jonsson School of Engineering & Computer Science The University of Texas at Dallas Dallas TX USA Mininglamp Academy of Sciences Mininglamp Technology Beijing China Institute of Big Knowledge Science Hefei University of Technology Hefei China

We propose a generalized optimization framework for detecting anomalous patterns (subgraphs that are interesting or unexpected) in interdependent networks, such as multi-layer networks, temporal networks, networks of networks, and many others. We frame the problem as a non-convex optimization that has a general nonlinear score function and a set of block-structured and non-convex constraints. We develop an effective, efficient, and parallelizable projection-based algorithm, namely Graph Block-structured Gradient Projection (GBGP), to solve the problem. It is proved that our algorithm 1) runs in nearly-linear time on the network size, and 2) enjoys a theoretical approximation guarantee. Moreover, we demonstrate how our framework can be applied to two very practical applications, and we conduct comprehensive experiments to show the effectiveness and efficiency of our proposed algorithm.

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2018 IEEE International Conference on Advances in Computing, Communication Control and Networking, ICACCCN 2018

作者： Gupta, Aditi Raina, Neetika Jha, Bharti Jain, Vinay Kumar Department of Computer Science Engineering Manav Rachna University FaridabadHaryana India Accendere Knowledge Management Pvt Ltd. New Delhi India

ISBN: (纸本)9781538641194

With an increasing usage of social media to exchange, share and store information, cybercriminals also get attracted to it, to take advantage of the network for their illegal and unethical benefits. Fake online accounts pop up every day. Spammers are the users behind the screen who share unsolicited and irrelevant texts to a huge number of users with an intent of advertising some product or to make people to click on unsecured links and infecting user's system usually to make money (click bait). They often use Trending topics on social media as a medium to spam. Sometimes, spam and fake trending is created by Spammers and many a times spammers use Trending topics to lure victims into clicking them. Much research has been done and is going on to detect spammers in OSNs. This paper reviews the existing techniques to detect spammers in social media. Our Current study and future work provides an overview of the traditional classifiers, Naïve Bayes, Support Vector and how they are used to detect spam and classify a dataset taken from social media into trending and non-trending topics based spam. © 2018 IEEE.

关键词： Support vector machines

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Deep learning - A first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact

arXiv

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arXiv 2020年

作者： Egger, Jan Pepe, Antonio Gsaxner, Christina Jin, Yuan Li, Jianning Kern, Roman Institute of Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria University Medicine Essen Essen Germany Research Center for Connected Healthcare Big Data Zhejiang Lab Zhejiang Hangzhou China Research Unit Experimental Neurotraumatology Department of Neurosurgery Medical University of Graz Graz Austria Knowledge Discovery Know-Center Graz Austria Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image,

关键词： Surveys

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FLAIR MRI biomarkers of the Normal-Appearing Brain Matter (NABM) are related to APOE-4 status and CSF markers Aβ42 and Tau

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Alzheimer's & Dementia 2023年第S3期19卷

作者： Karissa Chan Corinne E. Fischer Houman Khosravani Sandra E. Black Pascal Tyrrell Pejman Jabehdar Maralani Alan R. Moody April Khademi Electrical Computer and Biomedical Engineering Department Ryerson University Toronto ON Canada St. Michael’s Hospital Toronto ON Canada University of Toronto Toronto ON Canada Keenan Research Centre for Biomedical Science St. Michael’s Hospital Toronto ON Canada Faculty of Medicine Department of Psychiatry University of Toronto Toronto ON Canada Institute of Medical Sciences University of Toronto Toronto ON Canada Keenan Research Centre for Biomedical Science the Li Ka Shing Knowledge Institute St. Michael’s Hospital Toronto ON Canada Division of Neurology Department of Medicine University of Toronto Toronto ON Canada Heart and Stroke Foundation Canadian Partnership for Stroke Recovery Toronto ON Canada Sunnybrook Research Institute Toronto ON Canada Hurvitz Brain Sciences Program Sunnybrook Research Institute Toronto ON Canada Toronto Dementia Research Alliance Toronto ON Canada LC Campbell Cognitive Neurology Research Unit Sunnybrook Research Institute Toronto ON Canada Department of Statistical Science University of Toronto Toronto ON Canada Department of Medical Imaging University of Toronto Toronto ON Canada Keenan Research Centre for Biomedical Research the Li Ka Shing Knowledge Institute St. Michael’s Hospital Toronto ON Canada Ryerson University IAMLAB Department of Electrical Computer and Biomedical Engineering Toronto ON Canada

Background Reduced amyloid-beta-42 (Aβ42) and elevated total tau (t-tau) protein concentrations in the CSF are characteristic of Alzheimer’s disease (AD) pathology [1]. Aβ42 contributes to extracellular plaque formation between neurons [2]. The total level of tau, a microtubule-stabilizer in neuron axons [3], is associated with neuronal death [1]. Additionally, presence of ApoE4 (ε4) alleles is an established identifier for increased risk of AD [4]. Non-invasive imaging-based correlates of the CSF markers are lacking. This study examines correlations of novel FLAIR texture, intensity, and volume biomarkers, along with white matter lesion (WML) burden, with Aβ42, t-tau, and ε4 status. Method FLAIR volumes (n = 4044) from the ADNI database were used for analysis with CSF biomarkers. A subset (n = 921) was used for ε4 analysis based on available data. Normal-appearing brain matter (NABM) was obtained by removing CSF and WML. Macrostructural damage (MAD), microstructural damage (MID), and integrity (MII) texture biomarkers [5] were extracted from each NABM volume (Fig. 1), along with median intensity, NABM/ICV ratio, and normalized WML volume [5]. Relationships between FLAIR biomarkers and AD markers were described using parametric statistical analyses. Statistical significance was set at p<0.05. Result All FLAIR biomarkers were statistically significantly correlated with CSF Aβ42 levels. All features except MID and WML burden were significantly correlated with CSF t-tau levels (Table 1). MAD, MID, and WML burden showed negative correlations with Aβ42, while MAD showed a positive correlation with t-tau. Opposite trends were seen with MII, intensity, and NABM/ICV (Fig. 2). Significant differences between groups without and with one ε4 allele were found in all FLAIR biomarkers except WML burden. Significant differences in MAD, MII and NABM/ICV were found between groups with one and two ε4 alleles (Table 2). Conclusion FLAIR biomarkers were inversely associated with CSF Aβ

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