Timing data visualization: tactical intent recognition and portable framework by SONG Yafei, LI Lemin, QUAN Wen, NI Peng, WANG Ke

“…Curve filtering technology effectively reduced redundancy in numerous time-domain features, model parameters, and training time, an enhanced Gramian angular field (GAF) method was proposed to encode time series into images, enhancing the feature extraction capabilities of convolutional neural networks. The EfficientNetV2 network was adept at processing intent images and could serve as a pre-trained model, facilitating transfer learning across different systems. …”
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Using a Solution Construction Algorithm for Cyclic Shift Network Coding under Multicast Network to the Transformation of Musical Performance Styles by Xiuqin Wang, Jun Geng, Zhiyuan Li

“…A two-way recurrent neural network based on the gated recurrent unit is used to extract a sequence of note feature vectors of different styles, and a one-dimensional convolutional neural network is used to predict the intensity of the extracted note feature vector sequence for a specific style, which better learns the intensity variation of different styles of MIDI music.…”
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A Generalized Zero-Shot Deep Learning Classifier for Emotion Recognition Using Facial Expression Images by Vishal Singh Bhati, Namita Tiwari, Meenu Chawla

“…Zero-shot classification is performed on different facial expression datasets to justify the generalizability of the proposed model. …”
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AI-based orchard monitoring at night: Enhancing sustainable fruit production through real-time apple detection by Kutyrev Alexey, Khort Dmitry, Smirnov Igor, Zubina Valeria

“…Accurate recognition, classification and segmentation of apple fruits on tree crowns are of key importance for improving the efficiency of remote monitoring and forecasting of fruit orchard yields at different stages of the production process. The study evaluates the performance of the state-of-the-art convolutional neural network model YOLO11 (You Only Look Once version 11) under artificial lighting conditions at night. …”
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Dynamic Scene Segmentation and Sentiment Analysis for Danmaku by Limin Li, Jie Jing, Peng Shi

“…With this as a base, a new Danmaku-E model is made to find and group seven different emotional categories within Danmaku comments. …”
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Foreign object detection on coal conveyor belt enhanced by attention mechanism by ZHANG Yang, CHENG Zhiyu, CHEN Yunjiang, ZHANG Jiannan, YUAN Wensheng, ZHANG Hui

“…There are many complex factors in the special environment of coal transportation in power plants, such as uneven light, dust interference, and the different shapes, sizes, and materials of foreign objects on the coal conveyor belt. …”
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Prediction of COVID-19 cases by multifactor driven long short-term memory (LSTM) model by Yanwen Shao, Tsz Kin Wan, Kei Hang Katie Chan

“…Through the selection of several important features, we identified the factors that have stronger impact on the increase of new cases in different groups. Then, we use a long-time span data to predict the future COVID-19 new cases by training a long short-term memory (LSTM) model, a support vector regressor (SVR) and a temporal convolutional network (TCN), among which LSTM possessed the best performance and offered a good generalization ability. …”
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Image retrieval method based on data mining and deep residual network by Hongzhi Yuan, Wei Hu

“…The experimental results showed that the improved model achieved an average retrieval accuracy of 81.1 % on three different image sets, which was 27 percentage points higher than that of the traditional model. …”
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Automated analysis of high‐content microscopy data with deep learning by Oren Z Kraus, Ben T Grys, Jimmy Ba, Yolanda Chong, Brendan J Frey, Charles Boone, Brenda J Andrews

“…We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone‐arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. …”
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Pilot Maneuvering Performance Analysis and Evaluation with Deep Learning by Shiwen Zhang, Zhimei Huo, Yanjin Sun, Fujuan Li, Bo Jia

“…Finally, the indicators were grouped into 5 common factors by factor analysis and fed into 1-D CNN in different combinations. Each common factor plays a different role in pilot performance evaluation, which can provide advice for the future.…”
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On the improvement of schizophrenia detection with optical coherence tomography data using deep neural networks and aggregation functions by Paweł Karczmarek, Małgorzata Plechawska-Wójcik, Adam Kiersztyn, Adam Domagała, Agnieszka Wolinska, Steven M. Silverstein, Kamil Jonak, Paweł Krukow

“…Among over 300, 000 different variants of tested aggregation operators, a few versions provided satisfactory results.…”
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Real-time Jordanian license plate recognition using deep learning by Salah Alghyaline

“…Countries have different specifications for License Plates (LPs), therefore developing one Automatic license plate recognition (ALPR) system that works well for all LPs types is a difficult task. …”
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Investigating Brain Responses to Transcutaneous Electroacupuncture Stimulation: A Deep Learning Approach by Tahereh Vasei, Harshil Gediya, Maryam Ravan, Anand Santhanakrishnan, David Mayor, Tony Steffert

“…Additionally, the responsiveness of different EEG frequency bands to TEAS was investigated. …”
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A Novel Parallel Multi-Scale Attention Residual Network for the Fault Diagnosis of a Train Transmission System by Yong Chang, Tengfei Gao, Juanhua Yang, Zongyao Liu, Biao Wang

“…Firstly, multi-scale learning modules (MLMods) with different structures and convolutional kernel sizes are designed by combining a residual neural network (ResNet) and an Inception network, which can automatically learn multi-scale fault information from vibration signals. …”
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Identification of Subtypes of Post-Stroke and Neurotypical Gait Behaviors Using Neural Network Analysis of Gait Cycle Kinematics by Andrian Kuch, Nicolas Schweighofer, James M. Finley, Alison McKenzie, Yuxin Wen, Natalia Sanchez

“…We first trained a Convolutional Neural Network and a Temporal Convolutional Network to extract features that distinguish impaired from neurotypical gait. …”
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Exploration and application of deep learning based wellbore deformation forecasting model by Hui LIU, Guoqiang LI, Xiaojun ZHU, Pengfei ZHANG, Hua CHENG, Jinzheng WANG, Peishuai LI

“…The study shows that: ① The wellbore tilt mainly occurs in the loose layer, the tilt value decreases linearly from shallow to deep, and is biased towards the side of the extraction zone, with a maximum of 352 mm, and the deformation of the bedrock layer is smaller, with a maximum of 88 mm; the increase in the range of deformation propagation in the thick loose layer caused by the mining, and the change of seepage hydrophobicity of the aquifer at the bottom along the wall of the well and the seepage field of the groundwater are the main causes of the tilted deformation of the wellbore. ② The Spearman correlation coefficient between the model and the measured value is 0.978 at the maximum and 0.867 at the minimum;the maximum difference between the four models and the field measured offsets is 0.043 m, the mean absolute error EMA is within 0.003–0.009 m, and the root mean square error ERMS is within 0.004–0.011 m. …”
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Identification of Alzheimer’s disease brain networks based on EEG phase synchronization by Jiayi Cao, Bin Li, Xiaoou Li

“…Abstract Objective Using the phase synchronization of EEG signals, two different phases, PLI and PLV, were used to construct brain network analysis and graph convolutional neural network, respectively, to achieve automatic identification of Alzheimer’s disease (AD) and to assist in the early diagnosis of Alzheimer’s disease. …”
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Assessment of a Hyperspectral Remote Sensing Model Performance for Particulate Phosphorus in Optically Shallow Lake Water by Banglong Pan, Wuyiming Liu, Zhuo Diao, Qianfeng Gao, Lanlan Huang, Shaoru Feng, Juan Du, Qi Wang, Jiayi Li, Jiamei Cheng

“…This indicates that based on the differences in phosphorescence scattering signals of different morphologies in water bodies, the use of hyperspectral remote sensing and the CNN-RF model can effectively extract PP spatiotemporal information, strengthen the learning capability of multiscale characteristics, and contribute to the improvement of the precision of estimating PP concentration, which could provide an innovative approach for determining the degree of eutrophication of lake water bodies.…”
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Defect Diagnosis of Photovoltaic Module Visible Light Images Under Imbalanced Sample Conditions by Huiqing Rao, Qiong Li, Long Chen, Sha Jin, Yong Lu, Zhiguang Li

“…Firstly, in the DCGAN model, fully connected layers are incorporated into both the generator and the discriminator, and the transpose convolutional layers and convolutional layers are replaced with residual blocks. …”
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Unraveling Cyberbullying Dynamis: A Computational Framework Empowered by Artificial Intelligence by Liliana Ibeth Barbosa-Santillán, Bertha Patricia Guzman-Velazquez, Ma. Teresa Orozco-Aguilera, Leticia Flores-Pulido

“…Weapon detection is a complex task due to the variability in object exposures and differences in weapon shapes, sizes, orientations, colors, and image capture methods. …”
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