Jewelry Art Modeling Design Method Based on Computer-Aided Technology by Yuan Li, Hai Wen

“…In order to improve the effect of jewelry art modeling design, this paper applies computer-aided technology to jewelry art modeling design. …”
Get full text

Modelling of Modulus of Elasticity of Low-Calcium-Based Geopolymer Concrete Using Regression Analysis by Ali A. Khalaf, Katalin Kopecskó

“…Despite the unremitting efforts to model the modulus of elasticity of low-calcium-based geopolymer concrete, the state-of-the-art models need much improvement to reduce the error signals and increase the reliability. …”
Get full text

Experimental Porcine Toxoplasma gondii Infection as a Representative Model for Human Toxoplasmosis by Julia Nau, Silvia Kathrin Eller, Johannes Wenning, Katrin Henrike Spekker-Bosker, Horst Schroten, Christian Schwerk, Andrea Hotop, Uwe Groß, Walter Däubener

“…Porcine infections are currently not the state-of-the-art model to study human diseases. Nevertheless, the course of human and porcine toxoplasmosis is much more comparable than that of human and murine toxoplasmosis. …”
Get full text

A novel group tour trip recommender model for personalized travel systems by Mohammed Alatiyyah

“…Experimental results show that GTTRM significantly improves satisfaction levels for individual group members, outperforming state-of-the-art models in terms of both subgroup management and optimization efficiency.…”
Get full text

Modality Specific CBAM-VGGNet Model for the Classification of Breast Histopathology Images via Transfer Learning by Areesha Ijaz, Basit Raza, Iqra Kiran, Abdul Waheed, Aadil Raza, Habib Shah, Sulaiman Aftan

“…The proposed CBAM ensemble model has outperformed state-of-the-art models with an accuracy of 98.96% and 97.95% F1-score on 400X data of the BreakHis dataset.…”
Get full text

Deep learning Chinese input method with incremental vocabulary selection by Huajian REN, Xiulan HAO, Wenjing XU

“…The core task of an input method is to convert the keystroke sequences typed by users into Chinese character sequences.Input methods applying deep learning methods have advantages in learning long-range dependencies and solving data sparsity problems.However, the existing methods still have two shortcomings: the separation structure of pinyin slicing in conversion leads to error propagation, and the model is complicated to meet the demand for real-time performance of the input method.A deep-learning input method model incorporating incremental word selection methods was proposed to address these shortcomings.Various softmax optimization methods were compared.Experiments on People’s Daily data and Chinese Wikipedia data show that the model improves the conversion accuracy by 15% compared with the current state-of-the-art model, and the incremental vocabulary selection method makes the model 130 times faster without losing conversion accuracy.…”
Get full text

Efficient Image Inpainting for Handwritten Text Removal Using CycleGAN Framework by Somanka Maiti, Shabari Nath Panuganti, Gaurav Bhatnagar, Jonathan Wu

“…Although state-of-the-art models are effective, they often fail to inpaint complex missing areas, especially when handwritten occlusions are present in the image. …”
Get full text

Recurrent Fourier-Kolmogorov Arnold Networks for photovoltaic power forecasting by Desheng Rong, Zhongbao Lin, Guomin Xie

“…Comparative experiments with baseline and state-of-the-art models further underscore the efficiency of RFKAN. …”
Get full text

ABioNER: A BERT-Based Model for Arabic Biomedical Named-Entity Recognition by Nada Boudjellal, Huaping Zhang, Asif Khan, Arshad Ahmad, Rashid Naseem, Jianyun Shang, Lin Dai

“…The model performance was compared with two state-of-the-art models (namely, AraBERT and multilingual BERT cased), and it outperformed both models with 85% F1-score.…”
Get full text

The Process‐Oriented Understanding on the Reduced Double‐ITCZ Bias in the High‐Resolution CESM1 by Enze Dong, Fengfei Song, Lixin Wu, Lu Dong, Shengpeng Wang, Fukai Liu, Hong Wang

“…Here, by comparing a high‐ and low‐resolution state‐of‐the‐art model CESM1, it is found that the double‐ITCZ bias is largely reduced in the high‐resolution CESM1. …”
Get full text

Enhanced YOLOv5s for PCB Defect Detection with Coordinate Attention and Internal Convolution by Zhijun Xiao

“…Experiments on the PCB defect dataset demonstrate that the proposed CA-CBAM-IOYOLOv5s model achieves higher accuracy (97.8%), recall (98.6%), and F1 score (98.3%) compared to the basic YOLOv5s and other state-of-the-art models. The model also shows excellent performance in detecting various types of PCB defects, with an average detection accuracy of 98.45% and an average detection time of 0.114 seconds. …”
Get full text

An Ensemble for Automatic Time Series Forecasting With K-Nearest Neighbors by Maria P. Frias, Francisco Martinez

“…The forecast accuracy of the ensemble is similar to state-of-the-art models. Furthermore, this paper also tests the effectiveness of some recent approaches for dealing with trending time series when using the k-nearest neighbors algorithm.…”
Get full text

Yogasana classification using Deep Neural Network: A Unique Approach by Rishi Raj, Rajesh Mukherjee, Bidesh Chakraborty

“…The model attains an impressive validation accuracy of 99%, surpassing the performance of all other contemporary state-of-the-art models. …”
Get full text

Improvement in the prediction power of an astrocyte genome-scale metabolic model using multi-omic data by Andrea Angarita-Rodríguez, Andrea Angarita-Rodríguez, Andrea Angarita-Rodríguez, Nicolás Mendoza-Mejía, Nicolás Mendoza-Mejía, Janneth González, Jason Papin, Jason Papin, Jason Papin, Andrés Felipe Aristizábal, Andrés Pinzón

“…This method facilitates the reconstruction of context-specific models grounded in multi-omics data, enhancing their biological relevance and predictive capacity.ResultsUsing this approach, we successfully reconstructed an astrocyte GEM with improved prediction capabilities compared to state-of-the-art models available in the literature.DiscussionThese advancements underscore the potential of multi-omic inte-gration to refine metabolic modeling and its critical role in studying neurodegeneration and developing effective therapies.…”
Get full text

FArSS: Fast and Efficient Semantic Question Similarity in Arabic by Mohamed Alkaoud

“…With strategic data augmentation, our model achieves an F1-score of 0.928, closely competing with state-of-the-art models that rely on advanced architectures employing self-attention mechanisms. …”
Get full text

Lightweight malicious domain name detection model based on separable convolution by Luhui YANG, Huiwen BAI, Guangjie LIU, Yuewei DAI

“…The application of artificial intelligence in the detection of malicious domain names needs to consider both accuracy and calculation speed,which can make it closer to the actual application.Based on the above considerations,a lightweight malicious domain name detection model based on separable convolution was proposed.The model uses a separable convolution structure.It first applies depthwise convolution on every input channel,and then performs pointwise convolution on all output channels.This can effectively reduce the parameters of convolution process without impacting the effectiveness of convolution feature extraction,and realize faster convolution process while keeping high accuracy.To improve the detection accuracy considering the imbalance of the number and difficulty of positive and negative samples,a focal loss function was introduced in the training process of the model.The proposed algorithm was compared with three typical deep-learning-based detection models on a public data set.Experimental results denote that the proposed algorithm achieves detection accuracy close to the state-of-the-art model,and can significantly improve model inference speed on CPU.…”
Get full text

CoLR: Classification-Oriented Local Representation for Image Recognition by Tan Guo, Lei Zhang, Xiaoheng Tan, Liu Yang, Zhiwei Guo, Fupeng Wei

“…Extensive experiments verify the superiority of CoLR in comparison with some state-of-the-art models.…”
Get full text

MCF-DTI: Multi-Scale Convolutional Local–Global Feature Fusion for Drug–Target Interaction Prediction by Jihong Wang, Ruijia He, Xiaodan Wang, Hongjian Li, Yulei Lu

“…Experimental results on the Davis dataset demonstrate that MCF-DTI achieves an AUC of 0.9746 and an AUPR of 0.9542, outperforming other state-of-the-art models. Our case study demonstrates that our model effectively validated several known drug–target relationships in lung cancer and predicted the therapeutic potential of certain preclinical compounds in treating lung cancer. …”
Get full text

Risk-Aware Stochastic Vehicle Trajectory Prediction With Spatial-Temporal Interaction Modeling by Yuxiang Feng, Qiming Ye, Eduardo Candela, Jose Javier Escribano-Macias, Bo Hu, Yiannis Demiris, Panagiotis Angeloudis

“…It also achieves an improvement of over 8% in prediction accuracy when compared with the state-of-the-art model.…”
Get full text

Hierarchical Contrastive Representation for Accurate Evaluation of Rehabilitation Exercises via Multi-View Skeletal Representations by Zhejun Kuang, Jingrui Wang, Dawen Sun, Jian Zhao, Lijuan Shi, Yusheng Zhu

“…This new approach yields substantial improvements over existing state-of-the-art models, achieving over a 30% reduction in mean absolute deviation on both the KIMORE and UIPRMD datasets. …”
Get full text