Hyperspectral Image-Based Identification of Maritime Objects Using Convolutional Neural Networks and Classifier Models by Dongmin Seo, Daekyeom Lee, Sekil Park, Sangwoo Oh

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Artificial neural network forecast application for fine particulate matter concentration using meteorological data by M. Memarianfard, A.M. Hatami, M. Memarianfard

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Enhancing Convolutional Neural Network Robustness Against Image Noise via an Artificial Visual System by Bin Li, Yuki Todo, Sichen Tao, Cheng Tang, Yu Wang

“…The convolutional neural network (CNN) was initially inspired by the physiological visual system, and its structure has become increasingly complex after decades of development. …”
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Vehicle Information Influence Degree Screening Method Based on GEP Optimized RBF Neural Network by Jingfeng Yang, Nanfeng Zhang, Ming Li, Yanwei Zheng, Li Wang, Yong Li, Ji Yang, Yifei Xiang, Lufeng Luo

“…To solve the problem for a large number of data transmissions in an actual operation, wireless transmission is proposed for text information (including position information) on the basis of the principles of the maximum entropy probability and the neural network prediction model combined with the optimization of the Huffman encoding algorithm, from the exchange of data to the entire data extraction process. …”
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Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural Networks by Meng-Hui Wang, Jing-Xuan Hong, Shiue-Der Lu

Subjects: “…convolutional neural network…”
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Applicability of Artificial Neural Networks to Predict Mechanical and Permeability Properties of Volcanic Scoria-Based Concrete by Aref M. al-Swaidani, Waed T. Khwies

“…The investigated concrete properties were the compressive strength, the water permeability, and the concrete porosity. Artificial neural networks (ANNs) were used for prediction of the investigated properties. …”
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Construction and Application of Recognition Model for Black-Odorous Water Bodies Based on Artificial Neural Network by Zhonghua Xu, Changguo Dai, Jing Wang, Lejun Liu, Lei Jiang

“…It can thus be suggested that the sensory description can be accurately recognized by BP neural network. The application results indicate that all seven rivers had black-odorous phenomenon within a year. …”
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Characterizing out-of-distribution generalization of neural networks: application to the disordered Su–Schrieffer–Heeger model by Kacper Cybiński, Marcin Płodzień, Michał Tomza, Maciej Lewenstein, Alexandre Dauphin, Anna Dawid

“…Here, we show how the informed use of an interpretability method called class activation mapping, and the analysis of the latent representation of the data with the principal component analysis can increase trust in predictions of a neural network (NN) trained to classify quantum phases. …”
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Prediction of Deformation Response of Target Plate in Underwater Explosion Based on Deep Learning Neural Network by Zhiguo LI, Feng MA, Wei ZHU, Xiyu JIA, Yifan LI, Lei CHEN

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Stability analysis for μ-p.a.a. solutions of MAM neural network with neuron gains by Feng-Xia Zheng, Ni Zeng, Chuan-Yun Gu

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A Bearing Performance Degradation Modeling Method Based on EMD-SVD and Fuzzy Neural Network by Jingbo Gai, Yifan Hu, Junxian Shen

“…A novel degradation modeling method based on EMD-SVD and fuzzy neural network (FNN) was proposed to identify and evaluate the degradation process of bearings in the whole life cycle accurately. …”
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Global Exponential Stability of Antiperiodic Solutions for Discrete-Time Neural Networks with Mixed Delays and Impulses by Xiaofeng Chen, Qiankun Song

“…The problem on global exponential stability of antiperiodic solution is investigated for a class of impulsive discrete-time neural networks with time-varying discrete delays and distributed delays. …”
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Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networks by P. Noorunnisa Khanam, MA AlMaadeed, Sumaaya AlMaadeed, Suchithra Kunhoth, M. Ouederni, D. Sun, A. Hamilton, Eileen Harkin Jones, Beatriz Mayoral

“…These applied conditions are used to optimize the following properties: thermal conductivity, crystallization temperature, degradation temperature, and tensile strength while prediction of these properties was done through artificial neural network (ANN). The three first properties increased with increase in both screw speed and C-GNP content. …”
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Classification of palm oil fruit ripeness based on AlexNet deep Convolutional Neural Network by Rudi Kurniawan, Samsuryadi Samsuryadi, Fatma Susilawati Mohamad, Harma Oktafia Lingga Wijaya, Budi Santoso

“…The experimental setup involved training AlexNet and comparing its performance with a conventional Convolutional Neural Network (CNN). The results demonstrated that AlexNet significantly outperforms the traditional CNN, achieving a validation loss of 0.0261 and an accuracy of 0.9962, compared to the CNN's validation loss of 0.0377 and accuracy of 0.9925. …”
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RESEARCH ON BEARING FAULT DIAGNOSIS BASED ON CEEMDAN FUZZY ENTROPY AND CONVOLUTIONAL NEURAL NETWORK (MT) by XIAO JunQing, JIN JiangTao, LI Chun, XU ZiFei, SUN Kang

Subjects: “…Convolutional neural network…”
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The Estimation of German Football League (Bundesliga) Team Ranking via Artificial Neural Network Model by Faruk Kılıç, Hasan Aka, Serkan İbiş, Zait Burak Aktuğ

Subjects: “…artificial neural network…”
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DDAC: a feature extraction method for model of image steganalysis based on convolutional neural network by Xiaodan WANG, Jingtai LI, Yafei SONG

“…To solve the problem that for image steganalysis based on convolution neural network, manual designed filter kernels were used to extract residual characteristics, but in practice, these kernels filter were not suitable for each steganography algorithm and have worse performance in application, a directional difference adaptive combination (DDAC) method was proposed.Firstly, the difference was calculated between center pixel and each directional pixel around, and 1 × 1 convolution was adopted to achieve linear combinations of directional difference.Since the combination parameters self-adaptively update according to loss function, filter kernels could be more effective in extracting diverse residual characteristics of embedding information.Secondly, truncated linear unit (TLU) was applied to raise the ratio of embedding information residual to image information residual.The model’s coveragence was accelerated and the ability of feature extraction was promoted.Experimental results indicate that substituting the proposed method could improve the accuracy of Ye-net and Yedroudj-net by 1.30%~8.21% in WOW and S-UNIWARD datasets.Compared with fix and adjustable SRM filter kernels methods, the accuracy of test model using DDAC increases 0.60%~20.72% in various datasets, and the training progress was more stable.DDAC-net was proved to be more effective in comparsion with other steganalysis model.…”
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Klasifikasi Sinyal Phonocardiogram Menggunakan Short Time Fourier Transform dan Convolutional Neural Network by Muhammad Alwi Adnan Amal, Dodi Zulherman, Rahmat Widadi

“…Penelitian ini bertujuan merancang suatu sistem klasifikasi sinyal PCG berdasarkan metode ekstraksi fitur menggunakan Short Time Fourier Transform (STFT) dan metode klasifikasi menggunakan Convolutional Neural Network (CNN). Pengujian rancangan sistem menggunakan dataset sekunder dengan 2.575 rekaman PCG normal dan 665 rekaman PCG abnormal dalam format wav. …”
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Improving person re-identification based on two-stage training of convolutional neural networks and augmentation by S. A. Ihnatsyeva, R. P. Bohush

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Empirical modeling potential transfer of land cover change pa city with neural network algorithms by fatemeh mohammadyary, hamidreza pourkhabbaz, hossin aghdar, morteza Tavakoly

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