Emotion Recognition from Speech in a Subject-Independent Approach by Andrzej Majkowski, Marcin Kołodziej

“…Machine learning techniques such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and support vector machines with a cubic kernel (cubic SVMs) were employed in the emotion classification task. …”
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The effectiveness of a novel artificial intelligence (AI) model in detecting oral and dental diseases by Ravi Rathod, Saffa Dean, Christopher Sproat

“…Ninety different unseen images were selected and presented to the AI model to test the accuracy of disease detection. …”
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Time series prediction based on the variable weight combination of the T-GCN-Luong attention and GRU models by Yushu Guo, Jiacheng Huang, Xuchu Jiang

“…Considering the spatiotemporal features of temperature changes, this paper proposes a variable weight combination model based on a temporal graph convolutional network (T-GCN), Luong attention network (LUA) and gated recurrent unit (GRU) network, which fully utilizes spatiotemporal information to predict future temperature changes more accurately. …”
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Benchmarking Deep Learning for Wetland Mapping in Denmark Using Remote Sensing Data by Muhammad Rizwan Asif

“…While remote sensing combined with deep learning (DL) offers a promising solution, inconsistencies in wetland classification systems—where different regions define wetland types based on their policy frameworks and conservation priorities—limit the applicability of these models. …”
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Landslide Segmentation in High-Resolution Remote Sensing Images: The Van–UPerAttnSeg Framework with Multi-Scale Feature Enhancement by Chang Li, Quan Zou, Guoqing Li, Wenyang Yu

“…Then, it adopts an encoder–decoder structure, where the encoder is a visual attention network (Van) that focuses on extracting discriminative features of different scales from landslide images. The decoder consists of a pyramid pooling module (PPM) and feature pyramid network (FPN), combined with a convolutional block attention module (CBAM) module. …”
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Obstacle inversion based on the self-healing property of structured light by Shuailing Wang, Zhe Zhao, Mingjian Cheng, Jingping Xu, Yaping Yang

“…Firstly, we investigated the impact of obstacles of varying sizes and shapes on PVB at different stages of propagation, leading to a key conclusion the self-healing process of PVB can be divided into two parts: the self-healing of the obstructed region and the damage in the unstructured region. …”
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Study on the quantitative analysis of Tilianin based on Raman spectroscopy combined with deep learning. by Wen Jiang, Wei Liu, Xiaotong Xin, Wei Zhang, Junhui Chen, Jieyu Liu, Yanqi Ma, Cheng Chen, Xiaomei Pan

“…The structure of this model not only focuses on the deep and shallow features of the spectrum, but also the information between different channels, and the self-attention mechanism further extracts the features and outputs the predicted values of Tilianin concentration through the fully connected layer. …”
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Kidney Stone Detection based on Improved YOLOv7 with Attention Module and Super Resolution Techniques Under Limited Training Samples by Minh Tai Pham Nguyen, Viet Tuan Le, Huu Thanh Duong, Vinh Truong Hoang

“…Due to the two main problems above, this study is carried out with two contributions. First, fusing different attention modules to YOLOv7 architecture that shall bring higher performance of kidney stone detection task. …”
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Achromatic 3D Multi‐Color Orbital Angular Momentum Holography by Hang Su, Baoli Li, Yike Bai, Yiqi Ye, Yibo Dong, Xinyuan Fang

“…Abstract Orbital angular momentum (OAM) holography represents a transformative technique for enhancing the information channel capacity of holographic systems through the convolution of theoretical infinite OAM states with discrete sampling target images. …”
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Multi-Feature Fusion for Enhanced Feature Representation in Automatic Modulation Recognition by Jiuxiao Cao, Rui Zhu, Lingfeng Wu, Jun Wang, Guohao Shi, Peng Chu, Kang Zhao

“…To further enhance recognition performance, this paper proposes a novel multi-feature input fusion network. By utilizing different representations and processing methods of the signal, the proposed approach designs distinct feature extraction networks tailored to specific processed signals, leveraging the characteristics of convolution kernels with varying sizes and receptive fields. …”
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Investigation of cracking behavior in asphalt pavement using digital image processing technology by Jie Jiang, Jie Jiang, Kui Xu, Yanbin Song, Ling Gao, Jinzhu Zhang

“…IntroductionRoad hazards and some traffic accidents are often caused by damage of the performance of the pavement due to cracking of asphalt concrete.MethodsA simulation test was designed to investigate asphalt pavement cracking under different temperature conditions (15°C, 0°C, −5°C, −15°C) with a fixed traffic load strip. …”
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Adaptive Threshold Algorithm for Outlier Elimination in 3D Topography Data of Metal Additive Manufactured Surfaces Obtained from Focus Variation Microscopy by Xin Xu, Tobias Pahl, Sebastian Hagemeier, Peter Lehmann

“…This paper introduces a method for deriving self-adaptive thresholds from the convolution result and compares the effects of different operators in creating self-adaptive thresholds. …”
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Interaction-Temporal GCN: A Hybrid Deep Framework For Covid-19 Pandemic Analysis by Zehua Yu, Xianwei Zheng, Zhulun Yang, Bowen Lu, Xutao Li, Maxian Fu

“…Therefore, we propose a novel framework, the Interaction-Temporal Graph Convolution Network (IT-GCN), to analyze pandemic data. …”
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Non-Destructive Detection Method of Apple Watercore: Optimization Using Optical Property Parameter Inversion and MobileNetV3 by Zihan Chen, Haoyun Wang, Jufei Wang, Huanliang Xu, Ni Mei, Sixu Zhang

“…To determine whether different batches of apples are suitable for long-term storage or long-distance transportation, and to classify the apples according to quality level to enhance the economic benefits of the apple industry, it is essential to conduct non-destructive testing for watercore. …”
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OncoFusion: Multi-Model Approach for Generalized and Ovarian Cancer Detection with Stacked Ensembles by Zaware Sarika, Neharkar Rutuja, Sayyad Ayesha, Patil Aarya, Mulla Salma

“…We investigated Convolutional Neural Networks (CNN) as well as more conventional machine learning models like Random Forests and XGBoost for the analyzing images. …”
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A Multi-Frame Super-Resolution Reconstruction Method Based on Optical Flow for Space Noncooperative Objects by Heng Deng, Zhao Zhang, Dong Zhou, Lingyu Ma

“…This algorithm employs a multi-level wavelet convolutional network (MWCNN) for feature extraction and uses SpyNet to obtain multi-level optical flow between different frames. …”
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Local Back-Propagation for Forward-Forward Networks: Independent Unsupervised Layer-Wise Training by Taewook Hwang, Hyein Seo, Sangkeun Jung

“…However, the mechanism of BP is fundamentally different from how the human brain processes learning. …”
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Impact of Weather Predictions on COVID-19 Infection Rate by Using Deep Learning Models by Yogesh Gupta, Ghanshyam Raghuwanshi, Abdullah Ali H. Ahmadini, Utkarsh Sharma, Amit Kumar Mishra, Wali Khan Mashwani, Pinar Goktas, Shokrya S. Alshqaq, Oluwafemi Samson Balogun

“…It is concluded that the convolutional neural network performs better than the competitive model.…”
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A Novel Method for Traceability of Crude Oil Leakage on Offshore Platforms Based on Improved YOLOv5 Model Multi-Target Identification and Correlation Analysis by Zhenghua Wang, Shihai Zhang, Chongnian Qu, Zongyi Zhang, Feng Sun

“…In response to practical issues such as the complex structure of offshore platform scenes and the overlap of associated oil spill targets, a deformable convolution DCNv2 module is introduced to improve the model’s recognition accuracy for multi-shaped targets. …”
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Few-shot bearing fault diagnosis method based on an EEMD parallel neural network and a relation network by Cunsheng Zhao, Bo Tong, Chao Zhou, Qingrong Fan

“…Bearing fault diagnosis presents challenges, such as insufficient fault samples and significant fault data distribution variation in different bearing operating conditions. These problems cause traditional deep learning models to show poor generality and accuracy during bearing fault diagnosis. …”
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