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Computer Science, Information Systems
Ceren Atik, Recep Alp Kut, Reyat Yilmaz, Derya Birant
Summary: This paper proposes a novel method called support vector machine chains (SVMC) that involves chaining together multiple SVM classifiers in a special structure, decrementing one feature at each stage. The paper also introduces a new voting mechanism called tournament voting, where classifiers' outputs compete in groups and the winning class label of the final round is assigned as the prediction. Experimental results show that SVMC outperforms SVM in terms of accuracy and achieves a 6.88% improvement over state-of-the-art methods.
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Biochemical Research Methods
Zhenxing Wu, Minfeng Zhu, Yu Kang, Elaine Lai-Han Leung, Tailong Lei, Chao Shen, Dejun Jiang, Zhe Wang, Dongsheng Cao, Tingjun Hou
Summary: A study on learning QSAR models using various ML algorithms for 14 public datasets showed that rbf-SVM, rbf-GPR, XGBoost, and DNN generally perform better than other algorithms. SVM and XGBoost are recommended for regression learning on small datasets, while XGBoost is an excellent choice for large datasets. Ensemble models integrating multiple algorithms can improve prediction accuracy.
BRIEFINGS IN BIOINFORMATICS
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Biology
Xueliang Zhu, Jie Ying, Haima Yang, Le Fu, Boyang Li, Bin Jiang
Summary: This study proposes a novel computerized method for accurately detecting deep myometrial invasion on MRI by utilizing the geometric feature LS and texture features in the ensemble model EPSVM. The results demonstrate that EPSVM outperforms commonly used classifiers in terms of accuracy, sensitivity, and specificity, and LS plays a significant role in DMI detection.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
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Quantum Science & Technology
Maxwell T. West, Martin Sevior, Muhammad Usman
Summary: The recent realization of physical quantum computers with hundreds of noisy qubits has sparked a search for useful applications of their unique capabilities. Quantum machine learning (QML) has received particular attention as the study of machine learning algorithms running natively on quantum computers. In this work, QML methods are developed and applied to B meson flavor tagging, a crucial component in experiments probing CP violation. Despite working with classically simulable QSVM architectures, the results demonstrate the potential for even higher performance when sufficiently powerful quantum hardware is developed.
ADVANCED QUANTUM TECHNOLOGIES
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Computer Science, Information Systems
Zichen Zhang, Shifei Ding, Yuting Sun
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INFORMATION SCIENCES
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Metallurgy & Metallurgical Engineering
Kasimcan Koruk, Julian M. Ortiz
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Computer Science, Information Systems
Mohammad Aslani, Stefan Seipel
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INFORMATION SCIENCES
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Environmental Sciences
Zhihao Wang, Alexander Brenning
Summary: Using active learning with uncertainty sampling can reduce the time and cost needed by experts under limited data conditions, improve model performance, and is particularly suitable for emergency response settings and landslide susceptibility modeling.
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Chemistry, Analytical
Michael R. Pinsky, Artur Dubrawski, Gilles Clermont
Summary: Early recognition and forecasting of cardiorespiratory decompensation in critically ill patients is challenging. Artificial Intelligence (AI) has the potential to support clinical decision-making and predict instability using patient data. However, building reliable and usable AI systems in healthcare settings remains a challenge.
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Computer Science, Artificial Intelligence
Liming Liu, Maoxiang Chu, Rongfen Gong, Li Zhang
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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
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Biotechnology & Applied Microbiology
Yifeng Dou, Wentao Meng
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Computer Science, Artificial Intelligence
Chen Ding, Tian-Yi Bao, He-Liang Huang
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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
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Computer Science, Artificial Intelligence
Pardis Birzhandi, Kyung Tae Kim, Hee Yong Youn
Summary: This paper presents a survey on the state-of-the-art methods for reducing the number of training data points and increasing the speed of SVM. The existing methods are categorized into five types based on the approach employed for data reduction process. The key characteristics of the schemes are reviewed and compared against six practical benchmarks.
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Computer Science, Artificial Intelligence
A. Sujan Reddy, S. Akashdeep, R. Harshvardhan, S. Sowmya Kamath
Summary: This paper proposes a stacking ensemble model for short-term electricity consumption prediction. The experimental results show that the ensemble model, which combines predictions from multiple base models, achieves higher accuracy while reducing training time and root mean square error compared to existing techniques.
ADVANCED ENGINEERING INFORMATICS
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Computer Science, Artificial Intelligence
Qingshuo Zhang, Eric C. C. Tsang, Qiang He, Yanting Guo
Summary: Multi-label learning is a type of machine learning that addresses the classification of data with multiple labels. Ensemble-based methods are commonly used in multi-label learning, but they typically use binary or multi-class classifiers as base learners. This study proposes an efficient multi-label classification method based on kernel extreme learning machine and ensemble learning, that addresses the time complexity and performance issues associated with existing methods. The experimental results demonstrate the superiority of the proposed method compared to baseline methods and other ensemble-based multi-label methods.
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Haifeng Wang, Husam Dauod, Nourma Khader, Sang Won Yoon, Krishnaswami Srihari
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(2018)
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Saeid Bahremand, Hoo Sang Ko, Ramin Balouchzadeh, H. Felix Lee, Sarah Park, Guim Kwon
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
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Qianqian Zhang, Haifeng Wang, Sang Won Yoon
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Husam Dauod, Duaa Serhan, Haifeng Wang, Nourma Khader, Sang Won Yoon, Krishnaswami Srihari
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
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Health Care Sciences & Services
Jestin N. Carlson, Sohyung Cho, Ikechukwu P. N. Ohu, Russell E. Griffin, Hoo Sang Ko, Chiho Lim, Henry E. Wang
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Engineering, Biomedical
Chiho Lim, Hoo Sang Ko, Sohyung Cho, Ikechukwu Ohu, Henry E. Wang, Russell Griffin, Benjamin Kerrey, Jestin N. Carlson
JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING
(2020)
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Engineering, Manufacturing
Shrouq Alelaumi, Haifeng Wang, Hongya Lu, Sang Won Yoon
IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY
(2020)
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Health Care Sciences & Services
Chiho Lim, Hoo Sang Ko, Sohyung Cho, Ikechukwu Ohu, Henry E. Wang, Russell Griffin, Benjamin Kerrey, Jestin N. Carlson
Summary: Endotracheal intubation is a critical skill in emergency or intensive care, and this study explores the feasibility of assessing ETI proficiency using hand motion features. Experimental results show that an artificial neural network classifier based on a small number of hand motion features achieves a high accuracy rate.
JOURNAL OF MEDICAL SYSTEMS
(2021)
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Multidisciplinary Sciences
Sakineh Esmaeili Mohsen Abadi, Ramin Balouchzadeh, Guney Uzun, Hoo Sang Ko, H. Felix Lee, Sarah Park, Guim Kwon
Proceedings Paper
Computer Science, Artificial Intelligence
Haifeng Wang, Tian He, Sang Won Yoon
28TH INTERNATIONAL CONFERENCE ON FLEXIBLE AUTOMATION AND INTELLIGENT MANUFACTURING (FAIM2018): GLOBAL INTEGRATION OF INTELLIGENT MANUFACTURING AND SMART INDUSTRY FOR GOOD OF HUMANITY
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Tian He, Haifeng Wang, Sang Won Yoon
28TH INTERNATIONAL CONFERENCE ON FLEXIBLE AUTOMATION AND INTELLIGENT MANUFACTURING (FAIM2018): GLOBAL INTEGRATION OF INTELLIGENT MANUFACTURING AND SMART INDUSTRY FOR GOOD OF HUMANITY
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Michael Brenner, Sakineh Esmaeili Mohsen Abadi, Ramin Balouchzadeh, H. Felix Lee, Hoo Sang Ko, Michael Johns, Nehal Malik, Joshua J. Lee, Guim Kwon
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Automation & Control Systems
Husam Dauod, Haifeng Wang, Nourma Khader, Sang Won Yoon, Krishnaswami Srihari
27TH INTERNATIONAL CONFERENCE ON FLEXIBLE AUTOMATION AND INTELLIGENT MANUFACTURING, FAIM2017
(2017)
Review
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Vinicius N. Motta, Miguel F. Anjos, Michel Gendreau
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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
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Philipp Schulze, Armin Scholl, Rico Walter
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Roshan Mahes, Michel Mandjes, Marko Boon, Peter Taylor
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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
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Oleg S. Pianykh, Sebastian Perez, Chengzhao Richard Zhang
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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Review
Management
Hamed Jahani, Babak Abbasi, Jiuh-Biing Sheu, Walid Klibi
Summary: Supply chain network design is a large and growing area of research. This study comprehensively surveys and analyzes articles published from 2008 to 2021 to detect and report financial perspectives in SCND models. The study also identifies research gaps and offers future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
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Management
Patrick Healy, Nicolas Jozefowiez, Pierre Laroche, Franc Marchetti, Sebastien Martin, Zsuzsanna Roka
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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
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Victor J. Espana, Juan Aparicio, Xavier Barber, Miriam Esteve
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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
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Stefano Nasini, Rabia Nessah
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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
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Zhiqiang Liao, Sheng Dai, Timo Kuosmanen
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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
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Kuo-Hao Chang, Ying-Zheng Wu, Wen-Ray Su, Lee-Yaw Lin
Summary: The damage and destruction caused by earthquakes necessitates the evacuation of affected populations. Simulation models, such as the Stochastic Pedestrian Cell Transmission Model (SPCTM), can be utilized to enhance disaster and evacuation management. The analysis of SPCTM provides insights for government officials to formulate effective evacuation strategies.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
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Qinghua Wu, Mu He, Jin-Kao Hao, Yongliang Lu
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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
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Management
Bjorn Bokelmann, Stefan Lessmann
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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
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Management
Congzheng Liu, Wenqi Zhu
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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
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Laszlo Csato
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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
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Guowei Dou, Tsan-Ming Choi
Summary: This study investigates the impact of channel relationships between manufacturers on government policies and explores the effectiveness of positive incentives versus taxes in increasing social welfare. The findings suggest that competition may be more effective in improving sustainability and social welfare. Additionally, government incentives for green technology may not necessarily enhance sustainability.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)