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The Journal of Machine Learning Research 2020年第1期21卷 9253-9290页

作者： Peipei Tang Chengjing Wang Defeng Sun Kim-Chuan Toh School of Computer and Computing Science Zhejiang University City College Hangzhou China School of Mathematics and National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom Hong Kong Department of Mathematics and Institute of Operations Research and Analytics National University of Singapore Singapore

In this paper, we consider high-dimensional nonconvex square-root-loss regression problems and introduce a proximal majorization-minimization (PMM) algorithm for solving these problems. Our key idea for making the proposed PMM to be efficient is to develop a sparse semismooth Newton method to solve the corresponding subproblems. By using the Kurdyka-Łojasiewicz property exhibited in the underlining problems, we prove that the PMM algorithm converges to a d-stationary point. We also analyze the oracle property of the initial subproblem used in our algorithm. Extensive numerical experiments are presented to demonstrate the high efficiency of the proposed PMM algorithm.

关键词： nonconvex square-root regression problems proximal majorization-minimization semismooth Newton method

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A Deep learning study on osteosarcoma detection from histological images

arXiv

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arXiv 2020年

作者： Anisuzzaman, D.M. Barzekar, Hosein Tong, Ling Luo, Jake Yu, Zeyun Big Data Analytics and Visualization Laboratory Department of Computer Science University of Wisconsin-Milwaukee MilwaukeeWI53211 United States Department of Health Informatics and Administration University of Wisconsin-Milwaukee MilwaukeeWI53211 United States Department of Biomedical Engineering University of Wisconsin-Milwaukee MilwaukeeWI53211 United States

In the U.S, 5-10% of new pediatric cases of cancer are primary bone tumors. The most common type of primary malignant bone tumor is osteosarcoma. The intention of the present work is to improve the detection and diagnosis of osteosarcoma using computer-aided detection (CAD) and diagnosis (CADx). Such tools as convolutional neural networks (CNNs) can significantly decrease the surgeon’s workload and make a better prognosis of patient conditions. CNNs need to be trained on a large amount of data in order to achieve a more trustworthy performance. In this study, transfer learning techniques, pre-trained CNNs, are adapted to a public dataset on osteosarcoma histological images to detect necrotic images from non-necrotic and healthy tissues. First, the dataset was preprocessed, and different classifications are applied. Then, Transfer learning models including VGG19 and Inception V3 are used and trained on Whole Slide Images (WSI) with no patches, to improve the accuracy of the outputs. Finally, the models are applied to different classification problems, including binary and multi-class classifiers. Experimental results show that the accuracy of the VGG19 has the highest, 96%, performance amongst all binary classes and multiclass classification. Our fine-tuned model demonstrates state-of-the-art performance on detecting malignancy of Osteosarcoma based on histologic images. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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When crowds give you lemons: Filtering innovative ideas using a diverse-bag-of-lemons strategy

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Proceedings of the ACM on Human-computer Interaction 2018年第CSCW期2卷 1-23页

作者： Lykourentzou, Ioanna Ahmed, Faez Papastathis, Costas Sadien, Irwyn Papangelis, Konstantinos Utrecht University Netherlands University of Maryland United States University of Peloponnese Greece Research Institute of Big Data Analytics Department of Computer Science and Software Engineering Xi’an Joaoton-Liverpool University China

Following successful crowd ideation contests, organizations in search of the "next big thing" are left with hundreds of ideas. Expert-based idea filtering is lengthy and costly;therefore, crowd-based strategies are often employed. Unfortunately, these strategies typically (1) do not separate the mediocre from the excellent, and (2) direct all the attention to certain idea concepts, while others starve. We introduce DBLemons – a crowd-based idea filtering strategy that addresses these issues by (1) asking voters to identify the worst rather than the best ideas using a "bag of lemons" voting approach, and (2) by exposing voters to a wider idea spectrum, thanks to a dynamic diversity-based ranking system balancing idea quality and coverage. We compare DBLemons against two state-of-the-art idea filtering strategies in a real-world setting. Results show that DBLemons is more accurate, less time-consuming, and reduces the idea space in half while still retaining 94% of the top ideas. © 2018 Association for Computing Machinery.

关键词： Citrus fruits

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Retraction notice to “Neural network-based optimization of hydrogen fuel production energy system with proton exchange electrolyzer supported nanomaterial” [Fuel 332 (2023) 125827]

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Fuel 2025年 391卷

作者： Tao Hai Kosar Hikmat Hama Aziz Jincheng Zhou Hayder A. Dhahad Kamal Sharma Sattam Fahad Almojil Abdulaziz Ibrahim Almohana Abdulrhman Fahmi Alali Teeba Ismail Kh Sadok Mehrez Anas Abdelrahman School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou 558000 China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor 40450 Malaysia Department of Chemistry College of Science University of Sulaimani Qlyasan Street Kurdistan Region 46001 Iraq Department of Medical Laboratory of Science College of Health Sciences University of Human Development Sulaimaniyah Iraq Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan Duyun 558000 China Mechanical Engineering Department University of Technology Baghdad Iraq Institute of Engineering and Technology GLA University Mathura Uttar Pradesh 281406 India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Computer Engineering College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Mechanical Engineering College of Engineering at Al Kharj Prince Sattam bin Abdulaziz University 16273 Saudi Arabia Department of Mechanical Engineering University of Tunis El Manar ENIT BP 37 Le Belv’ed‘ere Tunis 1002 Tunisia Department of Chemical Engineering Faculty of Engineering & Technology Future University in Egypt New Cairo 11500 Tunisia

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Emotion correlation mining through deep learning models on natural language text

arXiv

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arXiv 2020年

作者： Wang, Xinzhi Kou, Luyao Sugumaran, Vijayan Luo, Xiangfeng Zhang, Hui School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China Institute of Public Safety Research Department of Engineering Physics Tsinghua University Beijing Beijing100084 China Key Laboratory of City Integrated EmergencyResponse Science Tsinghua University Beijing100084 China Department of Decision and Information Sciences Oakland University RochesterMI48309 United States Center for Data Science and Big Data Analytics Oakland University RochesterMI48309 United States

Emotion analysis has been attracting researchers’ attention. Most previous works in the artificial-intelligence field focus on recognizing emotion rather than mining the reason why emotions are not or wrongly recognized. The correlation among emotions contributes to the failure of emotion recognition. In this article, we try to fill the gap between emotion recognition and emotion correlation mining through natural language text from Web news. The correlation among emotions, expressed as the confusion and evolution of emotion, is primarily caused by human emotion cognitive bias. To mine emotion correlation from emotion recognition through text, three kinds of features and two deep neural-network models are presented. The emotion confusion law is extracted through an orthogonal basis. The emotion evolution law is evaluated from three perspectives: one-step shift, limited-step shifts, and shortest path transfer. The method is validated using three datasets: 1) the titles;2) the bodies;and 3) the comments of news articles, covering both objective and subjective texts in varying lengths (long and short). The experimental results show that in subjective comments, emotions are easily mistaken as anger. Comments tend to arouse emotion circulations of love–anger and sadness–anger. In objective news, it is easy to recognize text emotion as love and cause fear–joy circulation. These findings could provide insights for applications regarding affective interaction, such as network public sentiment, social media communication, and human–computer interaction. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Integrating the pressure-state-response model with the extension catastrophe progression for flood risk and resilience assessment

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Environmental and Sustainability Indicators 2025年 27卷

作者： Shahab S. Band Sultan Noman Qasem Sajad Eslami Zulkefli Mansor Brij B. Gupta Hao-Ting Pai Meghdad Biyari Amir Mosavi Farzaneh Sajedi Hosseini Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia School of Business Stevens Institute of Technology Hoboken 07030 NJ USA Research Center for Software Technology and Management Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Bandar Baru Bangi Selangor Darul Ehsan Malaysia Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan School of Cybersecurity Korea University Seoul South Korea Symbiosis Centre for Information Technology Symbiosis International University Pune India Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan Department of Information Management National Yunlin University of Science and Technology Yunlin Taiwan John von Neumann Faculty of Informatics Obuda University 1034 Budapest Hungary Ludovika University of Public Service Budapest Hungary Abylkas Saginov Karaganda Technical University Karaganda Kazakhstan Reclamation of Arid and Mountainous Regions Department Faculty of Natural Resources University of Tehran Karaj Iran

Despite the devastating effects of floods, the concept of resilience is still not fully considered in the assessment and management of flood risk. To study how resilience can lower the risk of floods and further enhance disaster response, this research aims to close this knowledge gap. With a focus on the Kashkan watershed in Iran, the study combines the extended catastrophe progression method with the pressure-state-response model. Three catastrophe models, namely the cusp, swallowtail, and butterfly, are applied. According to the findings, southern regions, i.e., Pol-Dokhtar city, have the highest risk of floods and the lowest resilience. Resilience and flood risk have a complementary relationship, according to the analysis, and resilience is a helpful metric for risk assessment. The results emphasize the necessity to incorporate resilience-focused pre-disruption planning and post-disaster recovery into flood risk management strategy. This work offers a foundation to incorporate resilience into future flood policies and strategies.

关键词： Flood Risk Resilience Pressure-state-response model Extension catastrophe progression method

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Benchmarking in Optimization: Best Practice and Open Issues

arXiv

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arXiv 2020年

作者： Bartz-Beielstein, Thomas Doerr, Carola Bossek, Jakob Chandrasekaran, Sowmya Eftimov, Tome Fischbach, Andreas Kerschke, Pascal López-Ibáñez, Manuel Malan, Katherine M. Moore, Jason H. Naujoks, Boris Orzechowski, Patryk Volz, Vanessa Wagner, Markus Weise, Thomas Institute for Data Science Engineering and Analytics TH Köln Germany Sorbonne Université CNRS LIP6 Paris France Optimisation and Logistics School of Computer Science University of Adelaide Adelaide Australia Computer Systems Department Jožef Stefan Institute Ljubljana Slovenia Statistics and Optimization Group University of Münster Münster Germany School of Computer Science and Engineering University of Málaga Málaga Spain Department of Decision Sciences University of South Africa Pretoria South Africa Institute for Biomedical Informatics University of Pennsylvania PhiladelphiaPA United States Department of Automatics AGH University of Science and Technology Krakow Poland modl.ai Copenhagen Denmark Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Hefei China

This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice in benchmarking is its main goal. The article discusses eight essential topics in benchmarking: clearly stated goals, well-specified problems, suitable algorithms, adequate performance measures, thoughtful analysis, effective and efficient designs, comprehensible presentations, and guaranteed reproducibility. The final goal is to provide well-accepted guidelines (rules) that might be useful for authors and reviewers. As benchmarking in optimization is an active and evolving field of research this manuscript is meant to co-evolve over time by means of periodic *** Codes 68W50. Copyright © 2020, The Authors. All rights reserved.

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Forecasting Thailand’s Precipitation with Cascading Model of CNN and GRU

Forecasting Thailand’s Precipitation with Cascading Model o...

引用

International Conference on Information Technology and Electrical engineering (ICITEE)

作者： Fuenglada Manokij Kanoksri Sarinnapakorn Peerapon Vateekul Department of Computer Engineering Faculty of Engineering Chulalongkorn University Big Data Analytics and IoT Center (CUBIC) Chulalongkorn University Bangkok Thailand Hydro-Informatics Institute (Public Organization) Ministry of Higher Education Science Research and Innovation Bangkok Thailand

Precipitation prediction is necessary to use in water management, especially in Thailand, it can be applied for various ways, such as flood warning, agriculture planning, etc. There are many prior attempts to forecast rainfall from the rain-gauge station. Some deployed traditional machine learning approaches: ARIMA, k-NN, etc. Recently, deep learning approach has shown promising results in this task. However, the accuracy is still limited since the raining period throughout the year in Thailand is very scarce, so most rainfall amount is zero. In this paper, we propose to cascade two deep learning networks to tackle this problem: one is a classification model to classify whether it rains or not, and the other is a regression model to predict rainfall amount. Our network is a combination of CNN and GRU, where CNN aims to capture relationship between various sensors and GRU aims to capture time-series information. Furthermore, we also perform multi-step forecasting by applying a rolling mechanism that uses the predicted rainfall and involved features to predict the next 6 steps. The experiment was conducted on hourly rainfall dataset for 6 years (2013-2018) provided from the public government sector in Thailand. We use RMSE as performance metric to evaluate three periods of rainfall: Overall, Rain, and non-rain periods and the results show that our cascading model is the winner with only 4.53% in term of RMSE which is the average percentage of difference from over all regions.

关键词： Deep learning Rain Predictive models data models Sensors Forecasting Task analysis

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2022年 13469 LNAI卷 v-vi页

作者： Bringas, Pablo García García, Hilde Pérez de Pisón, Francisco Javier Martínez Flecha, José Ramón Villar Lora, Alicia Troncoso de la Cal, Enrique A. Herrero, Álvaro Álvarez, Francisco Martínez Psaila, Giuseppe Quintián, Héctor Corchado, Emilio University of Deusto Bilbao Spain University of León León Spain University of La Rioja La Rioja Logroño Spain University of Oviedo Oviedo Spain Data Science and Big Data Analytics Lab Pablo de Olavide University Sevilla Spain Department of Computer Science University of Oviedo Oviedo Spain Applied Computational Intelligence University of Burgos Burgos Burgos Spain Universidad Pablo de Olavide Seville Spain DIGIP University of Bergamo Dalmine Bergamo Italy Department of Industrial Engineering University of A Coruña Ferrol Spain University of Salamanca Salamanca Spain

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Towards a Personalized Item Recommendation Approach in Social Tagging Systems Using Intuitionistic Fuzzy DBSCAN 10

Towards a Personalized Item Recommendation Approach in Socia...

引用

10th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2018

作者： Guan, Chun Yuen, Kevin Kam Fung Yue, Yong Department of Computer Science University of Liverpool Liverpool United Kingdom Research Institute of Big Data Analytics Department of Computer Science and Software Engineering Xi'an Jiaotong-Liverpool University Suzhou China School of Business University of Social Sciences Singapore Singapore Department of Computer Science and Software Engineering Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9781538658369

In folksonomies, users annotate items with abundant personalized tags. The tags can be used in recommendation systems to produce meaningful information. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) can be applied in recommendation systems. This paper proposes the Intuitionistic Fuzzy DBSCAN (IF-DBSCAN) for the personalized item recommendation in folksonomy. The IF-DBSCAN is used to cluster items with respect to the user-defined tags. The Intuitionistic Fuzzy Set (IFS) is used to represent tag values which are vague and uncertain. DBSCAN can cluster items with the tags represented by using IFSs into different groups. An example of movie recommendation is demonstrated for the applicability of the proposed method. © 2018 IEEE.

关键词： Recommender systems

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