The Study of Roadside Visual Perception in Internet of Vehicles Based on Improved YOLOv5 and CombineSORT by LI Xiaohui, YANG Jie, XIA Qin

“…On the contrary, the algorithms applying YOLOv5, YOLOX, YOLOv7 and the paper's improved YOLOv5 achieved the recall rates from 95.26% to 96.28%, while algorithms applying DeepSORT, StrongSORT, Bot-SORT and CombineSORT achieved the MOTA values from 0.887 to 0.901. But most of them had the time cost exceeding 80ms, making them could not perform real-time calculations. …”
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Lightweight Apple Leaf Disease Detection Algorithm Based on Improved YOLOv8 by LUO Youlu, PAN Yonghao, XIA Shunxing, TAO Youzhi

“…[Objective]As one of China's most important agricultural products, apples hold a significant position in cultivation area and yield. …”
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Cytopathological quantification of NORs using artificial intelligence to oral cancer screening by Tatiana Wannmacher LEPPER, Luara Nascimento do AMARAL, Ana Laura Ferrares ESPINOSA, Igor Cavalcante GUEDES, Maikel Maciel RÖNNAU, Natália Batista DAROIT, Alex Nogueira HAAS, Fernanda VISIOLI, Manuel Menezes de OLIVEIRA NETO, Pantelis Varvaki RADOS

“…Abstract Oral squamous cell carcinoma (OSCC) remains the most prevalent neoplasm of the head and neck. In recent decades, the incidence and prevalence of OSCC have not significantly changed, highlighting the critical need to develop and implement new risk assessment measures. …”
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An Anchor-Free Method Based on Transformers and Adaptive Features for Arbitrarily Oriented Ship Detection in SAR Images by Bingji Chen, Chunrui Yu, Shuang Zhao, Hongjun Song

“…Ship detection is a crucial application of synthetic aperture radar (SAR). Most recent studies have relied on convolutional neural networks (CNNs). …”
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xLSTM Interaction Multilevel SSM-Assisted Decoding Network for Remote Sensing Image Change Detection by Chunpeng Wu, Shuli Cheng, Anyu Du, Liejun Wang, Wenbin Tang

“…With the advancements of convolutional neural networks (CNNs) and Transformers in deep learning, the accuracy of RSCD has significantly improved. …”
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Review of Recent Advances in Remote Sensing and Machine Learning Methods for Lake Water Quality Management by Ying Deng, Yue Zhang, Daiwei Pan, Simon X. Yang, Bahram Gharabaghi

“…This review also discusses the effectiveness of these models in predicting various water quality parameters, offering insights into the most appropriate model–satellite combinations for different monitoring scenarios. …”
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Automated Models for Predicting Software Defects in Hybrid Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) Parallel Programs Using Deep Learning by Amani Saad Althiban, Hajar M. Alharbi, Lama A. Al Khuzayem, Fathy Elbouraey Eassa

“…The results reveal that Clang-token-based representation provided the most effective input for defect prediction, enabling CNN models to achieve an accuracy of 97%. …”
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Plant Leaf Disease Detection Using Deep Learning: A Multi-Dataset Approach by Manjunatha Shettigere Krishna, Pedro Machado, Richard I. Otuka, Salisu W. Yahaya, Filipe Neves dos Santos, Isibor Kennedy Ihianle

“…Detecting plant diseases accurately in diverse and uncontrolled environments remains challenging, as most current detection methods rely heavily on lab-captured images that may not generalise well to real-world settings. …”
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A Novel Open Circuit Fault Diagnosis for a Modular Multilevel Converter with Modal Time-Frequency Diagram and FFT-CNN-BIGRU Attention by Ziyuan Zhai, Ning Wang, Siran Lu, Bo Zhou, Lei Guo

“…Fault diagnosis is one of the most important issues for a modular multilevel converter (MMC). …”
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RainHCNet: Hybrid High-Low Frequency and Cross-Scale Network for Precipitation Nowcasting by Lei Wang, Zheng Wang, Wenjun Hu, Cong Bai

“…Recent advancements in deep learning have led to the development of radar echo extrapolation methods. However, most convolutional neural network-based methods focus primarily on high-frequency information, neglecting essential low-frequency cues necessary for forecasting high-intensity rainfall. …”
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Satellite Image Time-Series Classification with Inception-Enhanced Temporal Attention Encoder by Zheng Zhang, Weixiong Zhang, Yu Meng, Zhitao Zhao, Ping Tang, Hongyi Li

“…Thirdly, the proposed IncepTAE is more lightweight due to the use of group convolutions. IncepTAE achieves 95.65% and 97.84% overall accuracy on two challenging datasets, TimeSen2Crop and Ghana. …”
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Enhancing Learning-Based Cross-Modality Prediction for Lossless Medical Imaging Compression by Daniel S. Nicolau, Lucas A. Thomaz, Luis M. N. Tavora, Sergio M. M. Faria

“…Additionally, this approach allows to reduce the computational complexity by almost half in comparison to selecting the most compression-efficient after testing both schemes.…”
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Deep learning identification of reward-related neural substrates of preadolescent irritability: A novel 3D CNN application for fMRI by Johanna C. Walker, Conner Swineford, Krupali R. Patel, Lea R. Dougherty, Jillian Lee Wiggins

“…Regression activation mapping (RAM) was employed to extract feature maps of brain regions most predictive of irritability severity from the model. …”
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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

“…Saliency maps were applied to identify the most critical EEG electrodes, potentially reducing the number needed without sacrificing accuracy. …”
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Optimizing Traffic Speed Prediction Using a Multi-Objective Genetic Algorithm-Enhanced RNN for Intelligent Transportation Systems by C. Swetha Priya, F. Sagayaraj Francis

“…Many existing approaches integrate Convolutional Neural Networks (CNNs) and variants of Recurrent Neural Networks (RNNs) to analyze spatially correlated traffic data over time. …”
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A deep learning model for predicting systemic lupus erythematosus-associated epitopes by Jiale He, Zixia Liu, Xiaopo Tang

“…Notably, ablation studies revealed that the CNN component had the most substantial influence on performance, while the custom fusion mechanism yielded better integration of features than conventional strategies. …”
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PRDAGE: a prescription recommendation framework for traditional Chinese medicine based on data augmentation and multi-graph embedding by Zhihua Wen, Yunchun Dong, Lihong Peng, Longxin Zhang, Junfeng Yan

“…However, the semantic information inherent in both symptoms and herbs has received limited attention. Furthermore, most datasets in the field of TCM suffer from limited data volumes, which can adversely impact model training. …”
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Leveraging data analytics for detection and impact evaluation of fake news and deepfakes in social networks by Tony Mathew Abraham, Tao Wen, Ting Wu, Yu-wang Chen

“…Despite many advantages social media offers, one of the most significant challenges is the rapid rise of fake news and AI-generated deepfakes across these social networks. …”
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Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach. by Hamidreza Moradi, H Timothy Bunnell, Bradley S Price, Maryam Khodaverdi, Michael T Vest, James Z Porterfield, Alfred J Anzalone, Susan L Santangelo, Wesley Kimble, Jeremy Harper, William B Hillegass, Sally L Hodder, National COVID Cohort Collaborative (N3C) Consortium

“…<h4>Methods</h4>Gradient Boosted Decision Tree, Deep and Convolutional Neural Network classifiers were implemented and trained on the National COVID Cohort Collaborative (N3C) data repository to predict the patients' outcome of death or discharge. …”
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Technical study on the efficiency and models of weed control methods using unmanned ground vehicles: A review by Evans K. Wiafe, Kelvin Betitame, Billy G. Ram, Xin Sun

“…Finally, trials of most UGVs have limited documentation or lack extensive trials under various conditions, such as varying soil types, crop fields, topography, field geometry, and annual weather conditions. …”
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