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Marko Ðurasevic, Domagoj Jakobovic, Stjepan Picek and Luca Mariot
The automated design of dispatching rules (DRs) with genetic programming (GP) has become an important research direction in recent years. One of the most important decisions in applying GP to generate DRs is determining the features of the scheduling pro...
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Abdul Rehman Khalid, Nsikak Owoh, Omair Uthmani, Moses Ashawa, Jude Osamor and John Adejoh
In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble met...
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Niwan Wattanakitrungroj, Pimchanok Wijitkajee, Saichon Jaiyen, Sunisa Sathapornvajana and Sasiporn Tongman
For the financial health of lenders and institutions, one important risk assessment called credit risk is about correctly deciding whether or not a borrower will fail to repay a loan. It not only helps in the approval or denial of loan applications but a...
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Broderick Crawford, Felipe Cisternas-Caneo, Katherine Sepúlveda, Ricardo Soto, Álex Paz, Alvaro Peña, Claudio León de la Barra, Eduardo Rodriguez-Tello, Gino Astorga, Carlos Castro, Franklin Johnson and Giovanni Giachetti
The digitization of information and technological advancements have enabled us to gather vast amounts of data from various domains, including but not limited to medicine, commerce, and mining. Machine learning techniques use this information to improve d...
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Wen Cao, Jiaqi Xu, Yong Zhang, Siqi Zhao, Chu Xu and Xiaofeng Wu
The artificial bee colony algorithm (ABC) is a promising metaheuristic algorithm for continuous optimization problems, but it performs poorly in solving discrete problems. To address this issue, this paper proposes a hybrid discrete artificial bee colony...
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Rubén E. Nogales and Marco E. Benalcázar
Gesture recognition is widely used to express emotions or to communicate with other people or machines. Hand gesture recognition is a problem of great interest to researchers because it is a high-dimensional pattern recognition problem. The high dimensio...
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Zhan Tan, Jiu Ren, Shaoda Li, Wei Li, Rui Zhang and Tiegang Sun
Remote sensing is widely used for lake-water-quality monitoring, but the inversion of the total nitrogen (TN) and total phosphorus (TP) of rivers and non-optical parameters is still a difficult problem. The use of high spatial and temporal resolution mul...
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Annwesha Banerjee Majumder, Somsubhra Gupta, Dharmpal Singh, Biswaranjan Acharya, Vassilis C. Gerogiannis, Andreas Kanavos and Panagiotis Pintelas
Heart disease is a leading global cause of mortality, demanding early detection for effective and timely medical intervention. In this study, we propose a machine learning-based model for early heart disease prediction. This model is trained on a dataset...
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Deepesh Chugh, Himanshu Mittal, Amit Saxena, Ritu Chauhan, Eiad Yafi and Mukesh Prasad
Determining the optimal feature set is a challenging problem, especially in an unsupervised domain. To mitigate the same, this paper presents a new unsupervised feature selection method, termed as densest feature graph augmentation with disjoint feature ...
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Hairui Wang, Junming Li and Guifu Zhu
Extracting effective features from high-dimensional datasets is crucial for determining the accuracy of regression and classification models. Model predictions based on causality are known for their robustness. Thus, this paper introduces causality into ...
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