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Yupu Huang, Li Zhuo and Jingjing Cao
Accurately measuring industrial spatial agglomeration patterns is crucial for promoting regional economic development. However, few studies have considered both agglomeration degrees and cluster locations of industries. Moreover, the traditional multi-sc...
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Iman I. M. Abu Sulayman, Peter Voege and Abdelkader Ouda
The increasing significance of data analytics in modern information analysis is underpinned by vast amounts of user data. However, it is only feasible to amass sufficient data for various tasks in specific data-gathering contexts that either have limited...
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Sufyan Danish, Asfandyar Khan, L. Minh Dang, Mohammed Alonazi, Sultan Alanazi, Hyoung-Kyu Song and Hyeonjoon Moon
Bioinformatics and genomics are driving a healthcare revolution, particularly in the domain of drug discovery for anticancer peptides (ACPs). The integration of artificial intelligence (AI) has transformed healthcare, enabling personalized and immersive ...
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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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Mojtaba Nayyeri, Modjtaba Rouhani, Hadi Sadoghi Yazdi, Marko M. Mäkelä, Alaleh Maskooki and Yury Nikulin
One of the main disadvantages of the traditional mean square error (MSE)-based constructive networks is their poor performance in the presence of non-Gaussian noises. In this paper, we propose a new incremental constructive network based on the correntro...
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Zvi Mendel, Hillary Voet, Ilan Nazarian, Svetlana Dobrinin and Dana Ment
The red palm weevil (Rhynchophorus ferrugineus) inflicts widespread damage in date palm plantations and urban settings, leading to stand loss and safety concerns, intensified by the economic and ecological burdens of synthetic preventive treatments. A no...
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Fengyun Xie, Gang Li, Hui Liu, Enguang Sun and Yang Wang
In the context of addressing the challenge posed by limited fault samples in agricultural machinery rolling bearings, especially when early fault characteristics are subtle, this study introduces a novel approach. The proposed multi-domain fault diagnosi...
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Alexander Isaev, Tatiana Dobroserdova, Alexander Danilov and Sergey Simakov
This study introduces an innovative approach leveraging physics-informed neural networks (PINNs) for the efficient computation of blood flows at the boundaries of a four-vessel junction formed by a Fontan procedure. The methodology incorporates a 3D mesh...
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Qizhe Lu, Yicheng Jing and Xuefeng Zhao
Machine vision based on deep learning is gaining more and more applications in structural health monitoring (SHM) due to the rich information that can be achieved in the images. Bolts are widely used in the connection of steel structures, and their loose...
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Guilherme Yukio Sakurai, Jessica Fernandes Lopes, Bruno Bogaz Zarpelão and Sylvio Barbon Junior
The stream mining paradigm has become increasingly popular due to the vast number of algorithms and methodologies it provides to address the current challenges of Internet of Things (IoT) and modern machine learning systems. Change detection algorithms, ...
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