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Danilo Pau, Andrea Pisani and Antonio Candelieri
In the context of TinyML, many research efforts have been devoted to designing forward topologies to support On-Device Learning. Reaching this target would bring numerous advantages, including reductions in latency and computational complexity, stronger ...
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Han Zhang, Yadong Wu, Weihan Zhang and Yuling Zhang
The precise ascertainment of stellar ages is pivotal for astrophysical research into stellar characteristics and galactic dynamics. To address the prevalent challenges of suboptimal accuracy in stellar age determination and limited proficiency in apprehe...
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Vinh Pham, Maxim Tyan, Tuan Anh Nguyen and Jae-Woo Lee
Multi-fidelity surrogate modeling (MFSM) methods are gaining recognition for their effectiveness in addressing simulation-based design challenges. Prior approaches have typically relied on recursive techniques, combining a limited number of high-fidelity...
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Konstantinos Filippou, George Aifantis, George A. Papakostas and George E. Tsekouras
In this paper, we built an automated machine learning (AutoML) pipeline for structure-based learning and hyperparameter optimization purposes. The pipeline consists of three main automated stages. The first carries out the collection and preprocessing of...
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Xiaoqin Lian, Xue Huang, Chao Gao, Guochun Ma, Yelan Wu, Yonggang Gong, Wenyang Guan and Jin Li
In recent years, the advancement of deep learning technology has led to excellent performance in synthetic aperture radar (SAR) automatic target recognition (ATR) technology. However, due to the interference of speckle noise, the task of classifying SAR ...
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Yu-Ting Tsai and Ching-Piao Tsai
Deep learning techniques have revolutionized the field of artificial intelligence by enabling accurate predictions of complex natural scenarios. This paper proposes a novel convolutional neural network (CNN) model that involves deep learning technologies...
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Duo Sun, Lei Zhang, Kai Jin, Jiasheng Ling and Xiaoyuan Zheng
Aiming at the imbalance of industrial control system data and the poor detection effect of industrial control intrusion detection systems on network attack traffic problems, we propose an ETM-TBD model based on hybrid machine learning and neural network ...
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Mohammad H. Alshayeji and Jassim Al-Buloushi
Improved disease prediction accuracy and reliability are the main concerns in the development of models for the medical field. This study examined methods for increasing classification accuracy and proposed a precise and reliable framework for categorizi...
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Jackson Horlick Teng, Thian Song Ong, Tee Connie, Kalaiarasi Sonai Muthu Anbananthen and Pa Pa Min
The finger vein recognition system uses blood vessels inside the finger of an individual for identity verification. The public is in favor of a finger vein recognition system over conventional passwords or ID cards as the biometric technology is harder t...
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Enas Elgeldawi, Awny Sayed, Ahmed R. Galal and Alaa M. Zaki
Machine learning models are used today to solve problems within a broad span of disciplines. If the proper hyperparameter tuning of a machine learning classifier is performed, significantly higher accuracy can be obtained. In this paper, a comprehensive ...
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