Hearing vocals to recognize schizophrenia: speech discriminant analysis with fusion of emotions and features based on deep learning by Jie Huang, Yanli Zhao, Zhanxiao Tian, Wei Qu, Xia Du, Jie Zhang, Meng Zhang, Yunlong Tan, Zhiren Wang, Shuping Tan

“…Current diagnostic criteria rely primarily on clinical symptoms, which may not fully capture individual differences and the heterogeneity of the disorder. In this study, a discriminative model of schizophrenic speech based on deep learning is developed, which combines different emotional stimuli and features. …”
Get full text

Deep-Learning-Based Computer-Aided Grading of Cervical Spinal Stenosis from MR Images: Accuracy and Clinical Alignment by Zhiling Wang, Xinquan Chen, Bin Liu, Jinjin Hai, Kai Qiao, Zhen Yuan, Lianjun Yang, Bin Yan, Zhihai Su, Hai Lu

“…<b>Objective:</b> This study aims to apply different deep learning convolutional neural network algorithms to assess the grading of cervical spinal stenosis and to evaluate their consistency with clinician grading results as well as clinical manifestations of patients. …”
Get full text

CNN Performance Improvement for Classifying Stunted Facial Images Using Early Stopping Approach by Yunidar Yunidar, Y Yusni, N Nasaruddin, Fitri Arnia

“…Children affected by stunting usually show different physical and cognitive characteristics compared to their peers. …”
Get full text

A Sparse Feature-Based Mixed Signal Frequencies Detecting for Unmanned Aerial Vehicle Communications by Yang Wang, Yongxin Feng, Fan Zhou, Xi Chen, Jian Wang, Peiying Zhang

“…On this basis, complex dilated convolution and deconvolution are used successively to perform feature extraction on the separated signals, which enhances the receptive field and frequency resolution ability of the network for signals, reduces the interference between noise and different component signals, and realizes the accurate estimation of the number of components and carrier frequencies. …”
Get full text

A Multi-Scale Adaptive Fusion Network: End-to-End Interpretable Small-Sample Classifier for Motor Imagery EEG by Qiulei Han, Yan Sun, Ze Song, Hongbiao Ye, Tingwei Chen, Jian Zhao

“…Additionally, long-term temporal dependence is modeled by a temporal convolution network (TCN) to enhance the extraction capability of temporal features of EEG signals. …”
Get full text

SR-YOLO: Spatial-to-Depth Enhanced Multi-Scale Attention Network for Small Target Detection in UAV Aerial Imagery by Shasha Zhao, He Chen, Di Zhang, Yiyao Tao, Xiangnan Feng, Dengyin Zhang

“…First, the Space-to-Depth layer and Receptive Field Attention Convolution are combined, and the SR-Conv module is designed to replace the Conv module within the original backbone network. …”
Get full text

Tomato Yield Estimation Using an Improved Lightweight YOLO11n Network and an Optimized Region Tracking-Counting Method by Aichen Wang, Yuanzhi Xu, Dong Hu, Liyuan Zhang, Ao Li, Qingzhen Zhu, Jizhan Liu

“…To address these issues, this study proposed an improved lightweight YOLO11n network and an optimized region tracking-counting method, which estimates the quantity of tomatoes at different maturity stages. An improved lightweight YOLO11n network was employed for tomato detection and semantic segmentation, which was combined with the C3k2-F, Generalized Intersection over Union (GIoU), and Depthwise Separable Convolution (DSConv) modules. …”
Get full text

Food security: state Financial support Measures for sustainable Development of Agriculture in Russian Regions by A. I. Borodin, I. Yu. Vygodchikova, E. I. Dzyuba, G. I. Panaedova

“…The hierarchical procedure is based on a system of mathematical filtering of data, which is fundamentally different from existing methods for analyzing hierarchies. …”
Get full text

Small object detection in complex open-pit mine backgrounds based on improved YOLOv11 by ZHU Yongjun, CAI Guangqi, HAN Jin, MIAO Yanzi, MA Xiaoping, JIAO Wenhua

“…The improved YOLOv11 model introduced a Robust Feature Downsampling (RFD) module to replace the stride convolution downsampling module, effectively preserving the feature information of small objects. …”
Get full text

SMILES all around: structure to SMILES conversion for transition metal complexes by Maria H. Rasmussen, Magnus Strandgaard, Julius Seumer, Laura K. Hemmingsen, Angelo Frei, David Balcells, Jan H. Jensen

“…We compare these three different ways of obtaining SMILES for a subset of the CSD (tmQMg) and find >70% agreement for all three pairs. …”
Get full text

Efficient Identification and Classification of Pear Varieties Based on Leaf Appearance with YOLOv10 Model by Niman Li, Yongqing Wu, Zhengyu Jiang, Yulu Mou, Xiaohao Ji, Hongliang Huo, Xingguang Dong

“…Images were collected at different times of the day to cover changes in natural lighting and ensure model robustness. …”
Get full text

Enhancing Satellite Image Coregistration Using Mirror Array as Artificial Point Source for Multisource Image Harmonization by Muhammad Daniel Iman bin Hussain, Vaibhav Katiyar, Masahiko Nagai, Dorj Ichikawa

“…A comparison of commonly used resampling techniques&#x2014;nearest neighbor, bilinear interpolation, and cubic convolution&#x2014;was also performed to assess the tradeoffs between image quality and positional accuracy. …”
Get full text

Forecasting Surface Velocity Fields Associated With Laboratory Seismic Cycles Using Deep Learning by G. Mastella, F. Corbi, J. Bedford, F. Funiciello, M. Rosenau

“…The onset, magnitude, and propagation of analog earthquakes can thus be predicted at different prediction horizons. From all architectures tested in this study, convolutional recurrent neural networks (CNN‐LSTM and CONVLSTM) provide the best predictions although their performances depend on experiment characteristics and hyperparameters tuning. …”
Get full text

Intelligent Sports Weights by Olga dos Santos Duarte, Gustavo Jacinto, Mário Véstias, Rui Policarpo Duarte

“…To counter this, this work proposes a different approach to automatic weightlifting supervision off-the-person. …”
Get full text

LayerFold: A Python library to reduce the depth of neural networks by Giommaria Pilo, Nour Hezbri, André Pereira e Ferreira, Victor Quétu, Enzo Tartaglione

“…Large-scale models are the backbone of Computer Vision and Natural Language Processing, and their generalizability allows for transfer learning and deployment in different scenarios. However, their large size means that reducing their computational and memory demands remains a challenge. …”
Get full text

Application of Traffic Cone Target Detection Algorithm Based on Improved YOLOv5 by Mingwu Wang, Dan Qu, Zedong Wu, Ao Li, Nan Wang, Xinming Zhang

“…The system used the lightweight shuffle Net network as a backbone for feature extraction, replaced convolutional layers with focus modules to reduce computational complexity, and reduced the use of the C3 layer to increase network speed, thereby meeting the speed and accuracy requirements of traffic cone placement and retraction operations while maintaining acceptable model inference accuracy. …”
Get full text

Deep learning-developed multi-light source discrimination capability of stretchable capacitive photodetector by Su Bin Choi, Jun Sang Choi, Hyun Sik Shin, Jeong-Won Yoon, Youngmin Kim, Jong-Woong Kim

“…It shows high sensitivity at both 448 and 505 nm wavelengths, detecting light sources under mechanical deformations, different wavelengths and frequencies. By integrating a one-dimensional convolutional neural network (1D-CNN) model, we classified the light source power level with 96.52% accuracy even the light of two wavelengths is mixed. …”
Get full text

THE EMPIRICAL COMPARISON OF DEEP NEURAL NETWORK OPTIMIZERS FOR BINARY CLASSIFICATION OF OCT IMAGES by R. Loganathan, S. Latha

“…The Adam optimizer could train all binary convolutional neural networks based on these results.…”
Get full text

RoBERTa-Based Multi-Feature Integrated BiLSTM and CNN Model for Ceramic Review Analysis by LiHua Yang, Jun Wang, WangRen Qiu

“…To address the limitation that the Robustly Optimized BERT Pretraining Approach (RoBERTa) may not effectively capture local dependencies and salient features within the text, we propose a feature fusion framework based on RoBERTa&#x2019;s multi-output architecture. By feeding different outputs of RoBERTa into Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks, the model effectively captures both local static patterns and global contextual dependencies, thereby enhancing its capability to handle complex textual inputs. …”
Get full text

Inter-turn Short-circuit Fault Diagnosis and Severity Estimation for Five-phase PMSM by Yijia Huang, Wentao Huang, Tinglong Pan, Dezhi Xu

“…In this article, an inter-turn short-circuit (ITSC) fault diagnosis and severity estimation method based on extended state observer (ESO) and convolutional neural network (CNN) is proposed for five-phase permanent magnet synchronous motor (PMSM) drives. …”
Get full text