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Wenjiang Jiao, Xingwei Hao and Chao Qin
CNN is particularly effective in extracting spatial features. However, the single-layer classifier constructed by activation function in CNN is easily interfered by image noise, resulting in reduced classification accuracy. To solve the problem, the adva...
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Yaru Li, Yulai Zhang and Yongping Cai
The selection of the hyper-parameters plays a critical role in the task of prediction based on the recurrent neural networks (RNN). Traditionally, the hyper-parameters of the machine learning models are selected by simulations as well as human experience...
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David Opeoluwa Oyewola, Emmanuel Gbenga Dada, Temidayo Oluwatosin Omotehinwa, Onyeka Emebo and Olugbenga Oluseun Oluwagbemi
From the development and sale of a product through its delivery to the end customer, the supply chain encompasses a network of suppliers, transporters, warehouses, distribution centers, shipping lines, and logistics service providers all working together...
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Jesús Alejandro Navarro-Acosta, Irma D. García-Calvillo, Vanesa Avalos-Gaytán and Edgar O. Reséndiz-Flores
In this study, a system for faults detection using a combination of Support Vector Data Description (SVDD) with metaheuristic algorithms is presented. The presented approach is applied to a real industrial process where the set of measured faults is scar...
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Ying Sun, Lin Lv, Peng Lee and Yunkai Cai
In-cylinder pressure is one of the most important references in the process of diesel engine performance optimization. In order to acquire effective in-cylinder pressure value, many physical tests are required. The cost of physical testing is high; vario...
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Alexandru-Razvan Manescu and Bogdan Dumitrescu
In this paper, a novel global optimization approach in the form of an adaptive hyper-heuristic, namely HyperDE, is proposed. As the naming suggests, the method is based on the Differential Evolution (DE) heuristic, which is a well-established optimizatio...
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Wenqi Cheng and Baigang Mi
A new high-efficiency method based on a particle swarm optimization and long short-term memory network is proposed in this study to predict the aerodynamic forces in an unsteady state. Based on the predicted aerodynamic forces, the dynamic derivative is ...
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Georgiana Magu, Radu Lucaciu and Alexandru Isar
A way to reduce the uncertainty at the output of a Kalman filter embedded into a tracker connected to an automotive RADAR sensor consists of the adaptive selection of parameters during the tracking process. Different informed strategies for automatically...
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Matija Hribersek, Lucijano Berus, Franci Pusavec and Simon Klancnik
Implementation of a machine learning procedure to model the temperature drop in the cryogenic cooling of Inconel 718. Machine learning is performed by an adaptive neuro-fuzzy inference system (ANFIS). Based on a representative set of cryogenic cooling ex...
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Rahmeh Ibrahim, Rawan Ghnemat and Qasem Abu Al-Haija
Convolutional Neural Networks (CNNs) have exhibited remarkable potential in effectively tackling the intricate task of classifying MRI images, specifically in Alzheimer?s disease detection and brain tumor identification. While CNNs optimize their paramet...
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Abdul Rahaman Wahab Sait
Globally, lung cancer (LC) is the primary factor for the highest cancer-related mortality rate. Deep learning (DL)-based medical image analysis plays a crucial role in LC detection and diagnosis. It can identify early signs of LC using positron emission ...
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Wenyu Cao, Benbo Sun and Pengxiao Wang
Rapidly developed deep learning methods, widely used in various fields of civil engineering, have provided an efficient option to reduce the computational costs and improve the predictive capabilities. However, it should be acknowledged that the applicat...
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Jian Liu, Jin Yuan, Jiyuan Cui, Yunru Liu and Xuemei Liu
Achieving fast and accurate recognition of garlic clove bud orientation is necessary for high-speed garlic seed righting operation and precision sowing. However, disturbances from actual field sowing conditions, such as garlic skin, vibration, and rapid ...
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Sema Atasever, Zafer Aydin, Hasan Erbay and Mostafa Sabzekar
Predicting the secondary structure from protein sequence plays a crucial role in estimating the 3D structure, which has applications in drug design and in understanding the function of proteins. As new genes and proteins are discovered, the large size of...
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