A deep learning short-term traffic flow prediction method considering spatial-temporal association by Yang ZHANG, Yue HU, Dongrong XIN

“…The short-term traffic flow prediction is too dependent on the time correlation characteristics, which due to the problems that the correlation factors of the spatial correlation characteristics are too complicated and difficult to quantify.In response to this defect, a deep learning short-term traffic flow prediction method considering spatial-temporal association was proposed.Firstly, by constructing a spatial association measurement function that simultaneously considers distance, flow similarity, and speed similarity, the spatial correlation between the target road segment and the surrounding associated road segments was quantified and predicted.Then, a convolutional neural network model with long short-term memory neurons embedded was constructed.The long short-term memory neurons were used to extract the temporal correlation characteristics between the data, and the spatial correlation metric and the convolution transmission of traffic data were used to extract the spatial correlation characteristics between the data, so as to realize the traffic flow prediction considering the spatial-temporal association.The experimental results show that the proposed method can adapt to short-term forecasting under different traffic flow characteristics such as weekdays and weekends, and the prediction accuracy is better than that of the classical methods.In weekdays and weekends, the forecast bias are 10.45% and 12.35% respectively.…”
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Harnessing Syntax GCN and Multi-View Interaction for Conversational Aspect-Based Quadruple Sentiment Analysis by Chunling Wu, Houwei Kang

“…Therefore, the analysis needs to consider both the syntactic structure of individual utterances and the interactions between different views. However, previous studies often lack modeling of dialogue features and overlook the interdependence between utterances. …”
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Assessment of Driver Stress using Multimodal wereable Signals and Self-Attention Networks by Pavan Kaveti, Ganapathy Nagarajan

“…Electrocardiogram (ECG) signals (256 Hz) and respiration (RESP) signals (128 Hz) were obtained from ten subjects using textile electrodes while driving in different scenarios, namely normal driving and phone usage (calling). …”
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Hybrid-RViT: Hybridizing ResNet-50 and Vision Transformer for Enhanced Alzheimer's disease detection. by Hongjie Yan, Vivens Mubonanyikuzo, Temitope Emmanuel Komolafe, Liang Zhou, Tao Wu, Nizhuan Wang

“…The proposed Hybrid-RViT model integrates the pre-trained convolutional neural network (ResNet-50) with the Vision Transformer (ViT) to classify brain MRI images across different stages of AD. …”
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AI-Driven Solutions for Early Detection of Plant Diseases by Saha Laboni, Lalmawipuii R.

“…A CNN model is developed and trained in this research on a large annotated dataset of high-resolution plant images from different agricultural environments of healthy and diseased plants. …”
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Deep Learning Based DDoS Attack Detection by Xu Ziyi

“…The classification resulting from this model yielded high accuracy with robust results for different attack scenarios. Results reflect the potential superiority of the given model in detecting DDoS attacks. …”
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AS-Faster-RCNN: An Improved Object Detection Algorithm for Airport Scene Based on Faster R-CNN by Zhige He, Yuanqing He

“…The most important part of this is the capability of discriminate the different type of objects correctly. However, the existing detection models have the problems of degradation, lacking of detection capability for deformed and small objects and single feature extraction, causing low detection accuracy. …”
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Text classification model of rare earths patents based on ERNE-CAB-CNN by Liao Liefa, Shi Lijiao

“…Combined with ERNIE and Convolutional Neural Network (CNN), an innovative model ERNE-CAB-CNN for rare earth patent text classification is constructed. …”
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A Deep Learning Approach Toward Analyzing the Cross-Lingual Acoustic-Phonetic Similarities in Multilingual Speech Emotion Recognition by Syeda Tamanna Alam Monisha, Sadia Sultana

“…The experimental results reveal that the models can recognize emotions of multiple language speech of the same linguistic family better than language speech from different families. The DCNN model achieved the highest multilingual emotion recognition accuracy of 83% for Indo-Aryan languages, 79% for Germanic languages, and 73% when both language families were combined. …”
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Universal slip detection of robotic hand with tactile sensing by Chuangri Zhao, Yang Yu, Zeqi Ye, Ziyang Tian, Yifan Zhang, Ling-Li Zeng

“…Second, according to the principle of deep double descent, we designed a lightweight universal slip detection convolutional network for different grasp types (USDConvNet-DG) to classify grasp states (no-touch, slipping, and stable grasp). …”
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TGF-Net: Transformer and gist CNN fusion network for multi-modal remote sensing image classification. by Huiqing Wang, Huajun Wang, Linfen Wu

“…Meanwhile, the transformer-based spectral feature extraction module (TSFEM) was designed by combining the different characteristics of remote sensing images and considering the problem of orderliness of the sequence between hyperspectral image (HSI) channels. …”
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Cross-attention swin-transformer for detailed segmentation of ancient architectural color patterns by Lv Yongyin, Yu Caixia

“…The results highlight the model's ability to generalize well across different tasks and provide robust segmentation, even in challenging scenarios. …”
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ReLU, Sparseness, and the Encoding of Optic Flow in Neural Networks by Oliver W. Layton, Siyuan Peng, Scott T. Steinmetz

“…The present study investigates the influence of different activation functions—ReLU, leaky ReLU, GELU, and Mish—on the accuracy, robustness, and encoding properties of convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs) trained to estimate self-motion from optic flow. …”
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DCFE-YOLO: A novel fabric defect detection method. by Lei Zhou, Bingya Ma, Yanyan Dong, Zhewen Yin, Fan Lu

“…Finally, the feature fusion network integrates Partial Convolution and Efficient Multi-scale Attention, optimizing the fusion of information across different feature levels and spatial scales, which enhances the richness and accuracy of feature representations while reducing computational complexity. …”
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Parallax-Tolerant Weakly-Supervised Pixel-Wise Deep Color Correction for Image Stitching of Pinhole Camera Arrays by Yanzheng Zhang, Kun Gao, Zhijia Yang, Chenrui Li, Mingfeng Cai, Yuexin Tian, Haobo Cheng, Zhenyu Zhu

“…In the first stage, based on the differences between high-dimensional feature vectors extracted by a convolutional module, a parallax-tolerant color correction network with dynamic loss weights is utilized to adaptively compensate for color differences in overlapping regions. …”
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Assessing the performance of domain-specific models for plant leaf disease classification: a comprehensive benchmark of transfer-learning on open datasets by David J. Richter, Kyungbaek Kim

“…There exist many different highly-capable models at this time. There also exists a range of plant leaf disease classification image datasets containing different plants and diseases. …”
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Research on Abnormal Ship Brightness Temperature Detection Based on Infrared Image Edge-Enhanced Segmentation Network by Xiaobin Hong, Guanqiao Chen, Yuanming Chen, Ruimou Cai

“…Statistical analysis reveals that the proportion of ship temperature differences predicted by the XGBoost model exceeding 2 is less than 0.020%. …”
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OcularAge: A Comparative Study of Iris and Periocular Images for Pediatric Age Estimation by Naveenkumar G. Venkataswamy, Poorna Ravi, Stephanie Schuckers, Masudul H. Imtiaz

“…We utilized a longitudinal dataset comprising more than 21,000 near-infrared (NIR) images, collected from 288 pediatric subjects over eight years using two different imaging sensors. A multi-task deep learning framework was employed to jointly perform age prediction and age-group classification, enabling a systematic exploration of how different convolutional neural network (CNN) architectures, particularly those adapted for non-square ocular inputs, capture the complex variability inherent in pediatric eye images. …”
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Particle swarm optimization with YOLOv8 for improved detection performance of tomato plants by Sarah M. Ayyad, Nada M. Sallam, Samah A. Gamel, Zainab H. Ali

“…Within the past few years, applying deep learning in detecting and classifying tomatoes into different classes has gained popularity. This study aims to build a new framework for the efficient automated harvesting of tomato plants based on deep learning. …”
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Prediction of Shield Tunneling Attitude Based on WM-CTA Method by GAO Su, CHEN Cheng

“…The preprocessing module, composed of Wavelet Transform (WT) and the Maximum Information Coefficient (MIC) algorithms, was used to perform noise reduction and parameter correlation analysis on the raw data, thereby generating enhanced inputs. The Convolutional Neural Network (CNN) integrated with a channel-wise attention mechanism explored parameter weight differences and extracted local data features. …”
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