2.5D Facial Personality Prediction Based on Deep Learning by Jia Xu, Weijian Tian, Guoyun Lv, Shiya Liu, Yangyu Fan

“…Our experimental results show that the deep neural network trained by large labeled datasets can reliably predict people’s multidimensional personality characteristics through 2.5D static facial contour images, and the prediction accuracy is better than the previous method using 2D images.…”
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Reconstruction of Three-Dimensional Porous Media Using Deep Transfer Learning by Yi Du, Jie Chen, Ting Zhang

“…Hence, a method for reconstructing porous media is presented by applying DTL to extract features from a training image (TI) of porous media to replace the process of scanning a TI for different patterns as in multiple-point statistical methods. The deep neural network is practically used to extract the complex features from the TI of porous media, and then, a reconstructed result can be obtained by transfer learning through copying these features. …”
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Validation of the New Algorithm for Rain Rate Retrieval from AMSR2 Data Using TMI Rain Rate Product by Elizaveta Zabolotskikh, Bertrand Chapron

“…AMSR2 brightness temperature differences at C- and X-band channels are then used as inputs to train a neural network (NN) function for RR retrieval. Validation is performed against Tropical Rain Measurement Mission (TRMM) Microwave Instrument (TMI) RR products. …”
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Analysis of Psychological and Emotional Tendency Based on Brain Functional Imaging and Deep Learning by Lin Zhou

“…Then, combining data enhancement (Mixup) with three-dimensional convolutional neural network (3D-CNN), an emotion-related EEG topographic map classification method based on M-3DCNN is proposed. …”
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Image Recognition and Simulation Based on Distributed Artificial Intelligence by Tao Fan

“…By analyzing the existing digital image recognition methods, an improved BP neural network algorithm is proposed. Under the premise of ensuring accuracy, the recognition speed of digital images is accelerated, the time required for recognition is reduced, real-time performance is guaranteed, and the effectiveness of the algorithm is verified.…”
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Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix by P. Dhanalakshmi, V. Venkatesh, P. S. Ranjit, N. Hemalatha, S. Divyapriya, R. Sandhiya, Sumit Kushwaha, Asmita Marathe, Mekete Asmare Huluka

“…In this paper, we introduce a deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here. …”
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Automatic Evaluation of Internal Combustion Engine Noise Based on an Auditory Model by Kai Liang, Haijun Zhao

“…To improve the accuracy and efficiency of the objective evaluation of noise quality from internal combustion engines, an automatic noise quality classification model was constructed by introducing an auditory model-based acoustic spectrum analysis method and a convolutional neural network (CNN) model. A band-pass filter was also designed in the model to automatically extract the features of the noise samples, which were later used as input data. …”
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A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device by Debajyoti Pal, Vajirasak Vanijja

“…We also use an Artificial Neural Network approach for building the model and compare its performance with the regressive approach.…”
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Haptic Perception with Self-Organizing ANNs and an Anthropomorphic Robot Hand by Magnus Johnsson, Christian Balkenius

“…The four systems differ in the kind of self-organizing neural network used for clustering. For the mapping of the explored objects, one system uses a Self-Organizing Map (SOM), one uses a Growing Cell Structure (GCS), one uses a Growing Cell Structure with Deletion of Neurons (GCS-DN), and one uses a Growing Grid (GG). …”
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Emotion Recognition from EEG Signals Using Advanced Transformations and Deep Learning by Jonathan Axel Cruz-Vazquez, Jesús Yaljá Montiel-Pérez, Rodolfo Romero-Herrera, Elsa Rubio-Espino

“…To improve the separability of emotions, we explored various data transformation techniques, including Fourier Neural Networks and quantum rotations. The convolutional neural network model, combined with quantum rotations, achieved a 95% accuracy in emotion classification, particularly in distinguishing sad emotions. …”
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Classification of Animal Behaviour Using Deep Learning Models by M. Sowmya, M. Balasubramanian, K. Vaidehi

“…The proposed system detects animal behaviours in real time using deep learning-based models, namely, convolution neural network and transfer learning. Specifically, 2D-CNN, VGG16 and ResNet50 architectures have been used for classification. 2D-CNN, «VGG-16» and «ResNet50» have been trained on the video frames displaying a range of animal behaviours. …”
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Event-Trigger Reinforcement Learning-Based Coordinate Control of Modular Unmanned System via Nonzero-Sum Game by Yebao Liu, Tianjiao An, Jianguo Chen, Luyang Zhong, Yuhan Qian

“…With the help of the ET mechanism, the periodic communication mechanism of the system is avoided. The ET-critic neural network (NN) is used to approximate the performance index function, thus obtaining the ETRL coordinate control policy. …”
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A Reduced Order Model Based on ANN-POD Algorithm for Steady-State Neutronics and Thermal-Hydraulics Coupling Problem by Hanxing Liu, Han Zhang

“…Then, the backpropagation neural network (BPNN) is utilized to map the relationship between the boundary conditions and POD coefficients. …”
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Bifurcation Analysis and Synchronous Patterns between Field Coupled Neurons with Time Delay by Li Zhang, Xinlei An, Jiangang Zhang, Qianqian Shi

“…Exploring the biophysical properties of coupling channels is of great significance for further understanding the rhythm transitions of neural network electrical activity patterns and preventing neurological diseases. …”
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Integration and Fusion of Geologic Hazard Data under Deep Learning and Big Data Analysis Technology by Feng He, Chunxue Liu, Hongjiang Liu

“…On the premise of optimizing the processing of landslide images, first, the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) based on the natural statistical characteristics of the spatial domain is introduced, which is initially combined with Super-Resolution Convolutional Neural Network (SRCNN). Then, the AlexNet is fine-tuned and applied to highway landslide monitoring and surveying. …”
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Road Performance and Ice-Melting Characteristics of Steel Wool Asphalt Mixture by Yifeng Xia, Jie Jia, Qian Chen

“…A prediction model of the ice-melting rate of steel wool asphalt mixture based on a double-hidden layer backpropagation (BP) neural network was established. The results show that the road performance of the asphalt mixture mixed with steel wool mostly meets the requirements of the specification. …”
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Accelerating materials property prediction via a hybrid Transformer Graph framework that leverages four body interactions by Mohammad Madani, Valentina Lacivita, Yongwoo Shin, Anna Tarakanova

“…We propose a framework utilizing a Graph Neural Network with composition-based and crystal structure-based architectures, combined with a transfer learning scheme. …”
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Simulation-Based Optimization on the System-of-Systems Model via Model Transformation and Genetic Algorithm: A Case Study of Network-Centric Warfare by Bong Gu Kang, Seon Han Choi, Se Jung Kwon, Jun Hee Lee, Tag Gon Kim

“…The method consists of two processes: (1) the transformation of the SoS-based model into an integrated model using the neural network to reduce the execution time and (2) the optimization of the integrated model using the genetic algorithm with ranking and selection to decrease the number of simulation runs. …”
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Recycled integrated circuit detection using reliability analysis and machine learning algorithms by Udaya Shankar Santhana Krishnan, Kalpana Palanisamy

“…In this work, three machine learning methods, namely K‐means clustering, back propagation neural network (BPNN) and support vector machines (SVMs), are used to detect the recycled IC aged for a shorter period (1 day) with minimum data size. …”
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Semantic Recognition and Location of Cracks by Fusing Cracks Segmentation and Deep Learning by Qing An, Xijiang Chen, Xiaoyan Du, Jiewen Yang, Shusen Wu, Ya Ban

“…Therefore, a recognition method based on the combination of convolutional neural network and cluster segmentation is proposed. The proposed method realizes the accurate identification of concrete surface crack image under complex background and improves the efficiency of concrete surface crack identification. …”
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