Online Traffic Crash Risk Inference Method Using Detection Transformer and Support Vector Machine Optimized by Biomimetic Algorithm by Bihui Zhang, Zhuqi Li, Bingjie Li, Jingbo Zhan, Songtao Deng, Yi Fang

Get full text

High-Resolution Direction of Arrival Estimation of Underwater Multitargets Using Swarming Intelligence of Flower Pollination Heuristics by Nauman Ahmed, Huigang Wang, Shanshan Tu, Norah A.M. Alsaif, Muhammad Asif Zahoor Raja, Muhammad Kashif, Ammar Armghan, Yasser S. Abdalla, Wasiq Ali, Farman Ali

“…Moreover, acoustic waves impinging from the far-field multitarget are evaluated using the different number of hydrophones of uniform linear array (ULA). The measuring parameters like robustness against noise and element quantity, estimation accuracy, computation complexity, various numbers of hydrophones, variability analysis, frequency distribution and cumulative distribution function of root mean square error (RMSE), and resolution ability are applied for analyzing the performance of the proposed model with additive white Gaussian noise (AWGN). …”
Get full text

Sub-milliwatt threshold power and tunable-bias all-optical nonlinear activation function using vanadium dioxide for wavelength-division multiplexing photonic neural networks by Jorge Parra, Juan Navarro-Arenas, Pablo Sanchis

“…Our proposed device features a sub-milliwatt threshold power, a footprint of 5 μm, and an ELU-like activation function. …”
Get full text

Optimizing protein-ligand docking through machine learning: algorithm selection with AutoDock Vina by Ala’ Omar Hasan Zayed

“…Abstract Context Understanding protein-ligand interactions is fundamental to drug design, where optimizing docking parameter selection can potentially enhance computational efficiency and resource allocation in virtual screening. …”
Get full text

Rethinking spatial-temporal contrastive learning for Urban traffic flow forecasting: multi-level augmentation framework by Lin Pan, Qianqian Ren, Zilong Li, Xingfeng Lv

Get full text

Sweet Potato Yield Prediction Using Machine Learning Based on Multispectral Images Acquired from a Small Unmanned Aerial Vehicle by Kriti Singh, Yanbo Huang, Wyatt Young, Lorin Harvey, Mark Hall, Xin Zhang, Edgar Lobaton, Johnie Jenkins, Mark Shankle

Get full text

Lightweight Detection and Counting of Maize Tassels in UAV RGB Images by Hang Yang, Jiaji Wu, Yi Lu, Yuning Huang, Pinwei Yang, Yurong Qian

Get full text

Conformal On-Body Antenna System Integrated with Deep Learning for Non-Invasive Breast Cancer Detection by Marwa H. Sharaf, Manuel Arrebola, Khalid F. A. Hussein, Asmaa E. Farahat, Álvaro F. Vaquero

“…A core innovation of this work is the development of the Attention-Based Feature Separation (ABFS) model, which dynamically identifies optimal frequency sub-bands and disentangles discriminative features tailored to each tumor parameter. …”
Get full text

Prediction of bone oligometastases in breast cancer using models based on deep learning radiomics of PET/CT imaging by Guoxiu Lu, Guoxiu Lu, Ronghui Tian, Wei Yang, Jiayi Zhao, Wenjing Chen, Zijie Xiang, Shanhu Hao, Guoxu Zhang

“…Radiomic features were extracted from computed tomography (CT), positron emission tomography (PET), and fused PET/CT images using PyRadiomics embedded in the uAI Research Portal. …”
Get full text

Flood Image Classification using Convolutional Neural Networks by Olusogo Julius Adetunji, Ibrahim Adepoju Adeyanju, Adebimpe Omolayo Esan, Adedayo Aladejobi Sobowale Sobowale

Get full text

AE-XGBoost: A Novel Approach for Machine Tool Machining Size Prediction Combining XGBoost, AE and SHAP by Mu Gu, Shuimiao Kang, Zishuo Xu, Lin Lin, Zhihui Zhang

“…In this study, XGBoost was used to establish an evaluation system for the actual machining size of computer numerical control (CNC) machine tools. The XGBoost model was combined with SHAP approximation to effectively capture local and global features in the data using autoencoders and transform the preprocessed data into more representative feature vectors. …”
Get full text

Classification of Fritillaria thunbergii appearance quality based on machine vision and machine learning technology by DONG Chengye, LI Dongfang, FENG Huaiqu, LONG Sifang, XI Te, ZHOU Qin’an, WANG Jun

“…The tune-up in this study enhanced the detection performance of the model without increasing the number of parameters, computational complexity, or major changes to the original model. …”
Get full text

BinaryViT: Binary Vision Transformer for Hyperspectral Image Classification by Xiang Hu, Taolin Liu, Zhe Guo, Yuxiang Tang, Yuanxi Peng, Tong Zhou

“…As a key technology for lightweight deep models, binary quantization achieves significant parameter compression and computational acceleration by binarizing activations and weights. …”
Get full text

The applications of CT with artificial intelligence in the prognostic model of idiopathic pulmonary fibrosis by Zeyu Chen, Zheng Lin, Zihan Lin, Qi Zhang, Haoyun Zhang, Haiwen Li, Qing Chang, Jianqi Sun, Feng Li

“…Recently, several studies attempted to build prognostic models by extracting predictive variates from pulmonary function data, basic information, or chest computed tomography (CT) and CT-derived parameters with clinical characteristics. …”
Get full text

Supervised and unsupervised machine learning approaches for prediction and geographical discrimination of Iranian saffron ecotypes based on flower-related and phytochemical attribu... by Seid Mohammad Alavi-Siney, Jalal Saba, Alireza Fotuhi Siahpirani, Jaber Nasiri

“…Based on the results of Leave-One-Out Cross-Validation (LOOCV), various prediction values were computed for all 10 classifiers of LDA, QDA, FDA, MDA, RDA, Naive Bayes, Decision Tree, Linear SVM, Radial SVM, and Random Forest in terms of accuracy, sensitivity and specificity parameters. …”
Get full text

LSEVGG: An attention mechanism and lightweight-improved VGG network for remote sensing landscape image classification by Yao Lu

“…Specifically, our approach incorporates depthwise separable convolutions and Squeeze-and-Excitation (SE) attention modules to create a model that is both computationally efficient and highly effective for landscape feature extraction. …”
Get full text

Efficient intelligent fault diagnosis method and graphical user interface development based on fusion of convolutional networks and vision transformers characteristics by Chaoquan Mo, Ke Huang, Houxin Ji, Wenhan Li, Kaibo Xu

“…This method combines the local feature extraction capability of CNNs with the global dependency capturing ability of ViTs, while maintaining computational efficiency. …”
Get full text

Research on lightweight malware classification method based on image domain by SUN Jingzhang, CHENG Yinan, ZOU Binghui, QIAO Tonghua, FU Sizheng, ZHANG Qi, CAO Chunjie

“…This mechanism guided the model to focus on more informative features, while depthwise separable convolution further reduced the number of model parameters. …”
Get full text

Data Integration Based on UAV Multispectra and Proximal Hyperspectra Sensing for Maize Canopy Nitrogen Estimation by Fuhao Lu, Haiming Sun, Lei Tao, Peng Wang

“…However, the CE of the integrated model decreased by 1.93% and 1.68%, respectively. Key features included multispectral red-edge indices (NREI, NDRE, CI) and texture parameters (R1m), alongside hyperspectral indices (SR, PRI) and spectral parameters (SDy, Rg) exhibited varying directional impacts on CNC estimation using RF. …”
Get full text

HUMAn, a Real-Time Evolutive Patient Model for Major Incident Simulation: Development and Validation Study by Maxence Laurent, Arnaud Jaccard, Laurent Suppan, Elio Erriquez, Xavier Good, Eric Golay, Dominique Jaccard, Mélanie Suppan

“…During the relevance phase, the team defined the needed features of the model: clinically realistic, able to compute the evolution of prehospital vital signs, yet simple enough to allow real-time computation for several simulated patients on regular computers or tablets. …”
Get full text