Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets by Kumar Abhishek, Aditi Jain, Ghassan Hamarneh

“…However, while large datasets play a crucial role in the development of reliable deep neural network models, the quality of data therein and their correct usage are of paramount importance. …”
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Statistical mechanics and machine learning of the α-Rényi ensemble by Andrew Jreissaty, Juan Carrasquilla

“…We conclude by performing a variational minimization of the α-Rényi free energy using a recurrent neural network (RNN) Ansatz where we find that the RNN performs well in two dimensions when compared to the Monte Carlo simulations. …”
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Optimization of Photonic Nanocrystals for Invisibility Using Artificial Intelligence by Z. Dorrani

“…Therefore, this paper employs the deep neural network architecture ResNet to optimize photonic crystals. …”
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THE ALGORITHM OF DETERMINATION OF EYE FUNDUS VESSELS BLOOD FLOW CHARACTERISTICS ON VIDEOSEQUENCE by O. V. Nedzvedz, S. V. Ablameyko, A. M. Nedzved, A. V. Glinsky, G. M. Karapetyan, A. A. Anisimov, I. B. Gurevich, V. V. Yashina

“…Developed algorithm includes four stages: the video sequence stabilization, the vessels segmentation with the help of a neural network, the determination of the instantaneous velocity in the vessels based on the optical flow and the analysis of the results.…”
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Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNN by Mengmeng Huang, Fang Liu, Xianfa Meng

“…Some researchers point out that existing convolutional neural network (CNN) paid more attention to texture information, which is often not as good as shape information. …”
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Prediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentials by Soham Mandal, Prabal K. Maiti

“…Here, we report the thermal transport study of CALF-20 using artificial neural network-based machine learning potentials. We use the Green-Kubo approach based on equilibrium molecular dynamics, with a heat-flux renormalization technique, to determine the thermal conductivity (κ) of CALF-20. …”
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Advanced Techniques and Antenna Design for Pulse Shaping in UWB Cognitive Radio by Lise Safatly, Mohammed Al-Husseini, Ali El-Hajj, Karim Y. Kabalan

“…The Parks-McClellan algorithm is employed, a neural network is trained, and a reconfigurable band stop filter is designed to generate an adaptive waveform with nulls at specific frequencies. …”
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Analyzing the speed of sound in neutron star with machine learning by Sagnik Chatterjee, Harsha Sudhakaran, Ritam Mallick

“…In this work, we use a neural network approach to study matter at intermediate densities to analyze the variation of the speed of sound and the measure of trace anomaly considering astrophysical constraints of mass–radius measurement of 18 neutron stars. …”
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Designing of Neuro-Fuzzy Controllers for Brushless DC Motor Drives Operating with Multiswitch Three-Phase Topology by Ch. Vinay Kumar, G. Madhusudhana Rao, A. Raghu Ram, Y. Prasanna Kumar

“…The fuzzy logic controller is implanted to adjust the speed of the neural network and is also designed for the analysis of the stability of the system. …”
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A Hybrid Temporal-Spatio Forecasting Approach for Passenger Flow Status in Chinese High-Speed Railway Transport Hub by Zhengyu Xie, Limin Jia, Yong Qin, Li Wang

“…The approach combined temporal forecasting based on radial basis function neural network (RBF NN) and spatio forecasting based on spatial correlation degree. …”
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Maximum Power Point Tracking of PV Grids Using Deep Learning by K. Rafeeq Ahmed, Farrukh Sayeed, K. Logavani, T. J. Catherine, Shimpy Ralhan, Mahesh Singh, R. Thandaiah Prabu, B. Bala Subramanian, Adane Kassa

“…In this paper, we develop a deep learning model using back propagation neural network (BPNN) that helps to obtain maximum power point. …”
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Neural processing of naturalistic audiovisual events in space and time by Yu Hu, Yalda Mohsenzadeh

“…Comparing neural representations to a two-branch deep neural network model highlighted the necessity of early cross-modal connections to build a biologically plausible model of audiovisual perception. …”
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Data Preprocessing Method and Fault Diagnosis Based on Evaluation Function of Information Contribution Degree by Siyu Ji, Chenglin Wen

“…Neural network is a data-driven algorithm; the process established by the network model requires a large amount of training data, resulting in a significant amount of time spent in parameter training of the model. …”
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MAGInet based on deep learning for magnetic multi-parameter inversion by Wudi Wen, Yi Li, Zhongle Liu

“…Furthermore, the efficacy of the MAGInet inversion approach is contrasted with the traditional, deep, fully connected neural network methodologies. Comparative analyses reveal that MAGInet significantly outperforms traditional deep neural networks in terms of accuracy for predicting the magnetic multi-parameters of complex structures, showcasing superior performance.…”
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Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system. by Yuan Wang, Shaolin Hu

“…The results show that this method effectively overcomes the problems of false alarms and missed alarms based on fixed threshold alarm methods, and achieves 100% classification of two types of faults: non starting of the drive machine and low oil pressure by constructing a PCA (Principal Component Analysis)-SPE (Square Prediction Error)-CNN (Convolutional Neural Network) classifier. Combined with dynamic knowledge graph and NLP (Natural Language Processing) inference, it achieves good diagnostic results.…”
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Research and Application of 3D Clothing Design Based on Deep Learning by Chunnan Cao

“…Simulation results show that for the training of convolutional neural networks in the research of feature recognition algorithms the final feature recognition accuracy is made to reach 92.56%. …”
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A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods by Meng Meng, Kun Zhu, Keqin Chen, Hang Qu

“…With the development of artificial intelligence especially the combination of deep learning and computer vision, greater advantages have been brought to the concrete crack detection based on convolutional neural network (CNN) over the traditional methods. However, these machine learning (ML) methods still have some defects, such as it being inaccurate or not strong, having poor generalization ability, or the accuracy still needs to be improved, and the running speed is slow. …”
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Machine learning-based analyzing earthquake-induced slope displacement. by Jiyu Wang, Niaz Muhammad Shahani, Xigui Zheng, Jiang Hongwei, Xin Wei

“…This study evaluates the capabilities of various machine learning models, including artificial neural network (ANN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) in analyzing earthquake-induced slope displacement. …”
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Personalized Travel Route Recommendation Model of Intelligent Service Robot Using Deep Learning in Big Data Environment by Xiang Huang

“…Firstly, by crawling the relevant website data, obtain the basic information data and comment the text data of tourism service items, as well as the basic information data, and comment the text data of users and preprocess them, such as data cleaning. Then, a neural network model based on the self-attention mechanism is proposed, in which the data features are obtained by the Gaussian kernel function and node2vec model, and the self-attention mechanism is used to capture the long-term and short-term preferences of users. …”
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Learning Spatial-Temporal Features of Ride-Hailing Services with Fusion Convolutional Networks by Dapeng Zhang, Feng Xiao, Gang Kou, Jian Luo, Fan Yang

“…In this paper, we propose a new deep learning framework, called the locally connected spatial-temporal fully convolutional neural network ( LC-ST-FCN), to learn the spatial-temporal correlations and local statistical differences among regions simultaneously. …”
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