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. …”
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Bi-directional LSTM-CNNs-CRF for Italian Sequence Labeling and Multi-Task Learning by Pierpaolo Basile, Pierluigi Cassotti, Lucia Siciliani, Giovanni Semeraro

“…In this paper, we propose a Deep Learning architecture for several Italian Natural Language Processing tasks based on a state of the art model that exploits both word- and character-level representations through the combination of bidirectional LSTM, CNN and CRF. …”
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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.…”
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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.…”
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The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection by Momina Liaqat Ali, Zhou Zhang

“…This paper provides a comprehensive review of the YOLO (You Only Look Once) framework up to its latest version, YOLO 11. As a state-of-the-art model for object detection, YOLO has revolutionized the field by achieving an optimal balance between speed and accuracy. …”
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CPLLM: Clinical prediction with large language models. by Ofir Ben Shoham, Nadav Rappoport

“…We compared our results to various baselines, including Retain and Med-BERT, the latter of which is the current state-of-the-art model for disease prediction using temporal structured EHR data. …”
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Analysis of the impact of deep learning know-how and data in modelling neonatal EEG by Aengus Daly, Gordon Lightbody, Andriy Temko

“…A novel developed architecture is presented that outperforms the current state-of-the-art model for the task of neonatal seizure detection. …”
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Decomposition-Based Multistep Sea Wind Speed Forecasting Using Stacked Gated Recurrent Unit Improved by Residual Connections by Jupeng Xie, Huajun Zhang, Linfan Liu, Mengchuan Li, Yixin Su

“…The experiment results on three different sea areas show that the performance of this model surpasses those of a state-of-the-art model, several benchmarks, and decomposition-based models.…”
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Auxiliary Task Graph Convolution Network: A Skeleton-Based Action Recognition for Practical Use by Junsu Cho, Seungwon Kim, Chi-Min Oh, Jeong-Min Park

“…AT-GCN outperforms the original State-of-the-Art model on the NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets while maintaining the same inference time. …”
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Secured DICOM medical image transition with optimized chaos method for encryption and customized deep learning model for watermarking by R. Abirami, C. Malathy

“…In comparison to the state-of-the-art model, the suggested model performs better in every respect. …”
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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. …”
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DNNobfus: a study on obfuscation-based edge-side model protection framework by SONG Feiyang, ZHAO Xinmiao, YAN Fei, CHENG Binlin, ZHANG Liqiang, YANG Xiaolin, WANG Yang

“…The experimental findings demonstrate that the obfuscation framework, named DNNobfus, significantly diminishes the accuracy of state-of-the-art model decompilation tools in identifying model operator types and network connections to 21.63% and 48.24%, respectively. …”
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Building extraction from unmanned aerial vehicle imagery using Mask-RCNN (case study: Institut Teknologi Sepuluh Nopember, Surabaya) by Ramadhani Anisa, Alya Nurul Fitri

“…In this paper, a dataset consisting of aerial photography images acquired by aircraft in the urban and educational area of Institut Teknologi Sepuluh Nopember Surabaya to explore the potential of using Mask R-CNN, the art model, for instance, segmentation to automatically detect building footprints, which are essential attributes that define the urban fabric (which is critical to accelerating land cover updates with high highly accurate in terms of area and spatial assessment). …”
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CLAIRE: a contrastive learning-based predictor for EC number of chemical reactions by Zishuo Zeng, Jin Guo, Jiao Jin, Xiaozhou Luo

“…Remarkably, CLAIRE significantly outperformed the state-of-the-art model by 3.65 folds and 1.18 folds, respectively. …”
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Low-Rank Adaptation of Pre-Trained Large Vision Models for Improved Lung Nodule Malignancy Classification by Benjamin P. Veasey, Amir A. Amini

“…<italic>Results:</italic> The best LoRA-adapted model achieved a 3&#x0025; increase in ROC AUC over the state-of-the-art model, utilized 89.9&#x0025; fewer parameters, and reduced training times by 36.5&#x0025;. …”
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Attention-enhanced corn disease diagnosis using few-shot learning and VGG16 by Ruchi Rani, Jayakrushna Sahoo, Sivaiah Bellamkonda, Sumit Kumar

“…Thus, Few Shot Learning is the state-of-the-art model in machine learning, which requires minimum examples to train the model for generalization. …”
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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. …”
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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. …”
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Machine learning based intrusion detection framework for detecting security attacks in internet of things by V. Kantharaju, H. Suresh, M. Niranjanamurthy, Syed Immamul Ansarullah, Farhan Amin, Amerah Alabrah

“…We have compared our proposed framework using state of the art model and efficiency of 23.19%, 27.55%, and 18.35% higher accuracy and 14.46%, 26.76%, and 13.65% lower computational time compared to traditional models.…”
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Towards the development of offshore wind farms in the Mediterranean Sea: A techno‐economic analysis including green hydrogen production during curtailments by Riccardo Travaglini, Francesco Superchi, Francesco Lanni, Giovanni Manzini, Laura Serri, Alessandro Bianchini

“…In comparison to the pioneering studies to date, a more detailed computational model is used, able to account for several critical factors like a better description of metocean conditions, constraints on grid capacity, and a state‐of‐the‐art model to define the farm layout. Concerning hydrogen production, a comparison between the statistical approach, which is commonly used in the field, and a fully time‐dependent method is performed. …”
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