A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships. by Alexander Kroll, Sahasra Ranjan, Martin J Lercher

“…The resulting predictions outperform recently published state-of-the-art models for predicting protein-small molecule interactions across three diverse tasks: predicting kinase inhibitions; inferring potential substrates for enzymes; and predicting Michaelis constants KM. …”
Get full text

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. …”
Get full text

Improving spleen segmentation in ultrasound images using a hybrid deep learning framework by Ali Karimi, Javad Seraj, Fatemeh Mirzadeh Sarcheshmeh, Kasra Fazli, Amirali Seraj, Parisa Eslami, Mohamadreza Khanmohamadi, Helia Sajjadian Moosavi, Hadi Ghattan Kashani, Abdoulreza Sajjadian Moosavi, Masoud Shariat Panahi

“…Our method has been validated on this dataset, and the experimental results show that it outperforms existing state-of-the-art models. Specifically, our approach achieved a mean Intersection over Union (mIoU) of 94.17% and a mean Dice (mDice) score of 96.82%, surpassing models such as Splenomegaly Segmentation Network (SSNet), U-Net, and Variational autoencoder based methods. …”
Get full text

Dual intent view contrastive learning for knowledge aware recommender systems by Jianhua Guo, Zhixiang Yin, Shuyang Feng, Donglin Yao, Shaopeng Liu

“…Experimental results on three benchmark datasets demonstrate that DIVCL outperforms state-of-the-art models, showcasing its superior performance. The implementation is available at: https://github.com/yzxx667/DIVCL .…”
Get full text

Unified Quantile Regression Deep Neural Network with Time-Cognition for Probabilistic Residential Load Forecasting by Zhuofu Deng, Binbin Wang, Heng Guo, Chengwei Chai, Yanze Wang, Zhiliang Zhu

“…With ablation experiments, the proposed model achieved the best results in the AQS, AACE, and inversion error, and especially the average of the AACE is grown by 34.71%, 75.22%, and 32.44% compared with QGBRT, QCNN, and QLSTM, respectively, indicating that our method has excellent reliability and robustness rather than the state-of-the-art models obviously. Meanwhile, great performances of efficient time response demonstrate that our proposed work has promising prospects in practical applications.…”
Get full text

Future increase in compound soil drought-heat extremes exacerbated by vegetation greening by Jun Li, Yao Zhang, Emanuele Bevacqua, Jakob Zscheischler, Trevor F. Keenan, Xu Lian, Sha Zhou, Hongying Zhang, Mingzhu He, Shilong Piao

“…Here, using a suite of state-of-the-art model simulations, we show that the projected vegetation greening will increase the frequency of global compound soil drought-heat events, equivalent to 12–21% of the total increment at the end of 21st century. …”
Get full text

SE-HCL: Schema Enhanced Hybrid Curriculum Learning for Multi-Turn Text-to-SQL by Yiyun Zhang, Sheng'an Zhou, Gengsheng Huang

“…Our experiments show that our proposed method improves SQL-generated performance over previous state-of-the-art models on SparC and CoSQL, especially for hard and long-turn questions.…”
Get full text

Combining Region-Guided Attention and Attribute Prediction for Thangka Image Captioning Method by Fujun Zhang, Wendong Kang, Wenjin Hu

“…On the COCO dataset in the natural domain, RGFEAP achieves performance comparable to other state-of-the-art models, showcasing its strong adaptability.…”
Get full text

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. …”
Get full text

Developing a TinyML Image Classifier in an Hour by Riccardo Berta, Ali Dabbous, Luca Lazzaroni, Danilo Pietro Pau, Francesco Bellotti

“…In all cases, the tool was able to build microcontroller-deployment ready, beyond the state-of-the-art models, within 1 h on Google Colab CPUs.…”
Get full text

Fine-Grained Classification via Hierarchical Feature Covariance Attention Module by Yerim Jung, Nur Suriza Syazwany, Sujeong Kim, Sang-Chul Lee

“…Our method outperforms the state-of-the-art models by a margin of 0.4%, 1.1%, and 1.4%.…”
Get full text

Attention-Aware Heterogeneous Graph Neural Network by Jintao Zhang, Quan Xu

“…Experimental results on three widely used datasets showed that the AHNN model could significantly outperform the state-of-the-art models.…”
Get full text

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.…”
Get full text

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;. …”
Get full text

Multimodal Autism Spectrum Disorder Method Using GCN With Dual Transformers by Tianming Song, Zhe Ren, Jian Zhang, Yawei Qu, Yingying Cui, Zhengda Liang

“…The experimental results reveal that our approach significantly outperforms existing baseline and state-of-the-art models. The method achieves 79.47% of accuracy, 78.97% precision, 82.11% recall, and 0.85 of AUC metrics. …”
Get full text

M2Caps: learning multi-modal capsules of optical and SAR images for land cover classification by Haodi Zhang, Anzhu Yu, Kuiliang Gao, Xuanbei Lu, Xuefeng Cao, Wenyue Guo, Weiqi Lian

“…M²Caps outperformed state-of-the-art models, improving mean intersection over union (mIoU) by 2.86% – 12.9% on the WHU-OPT-SAR dataset and 3.91% – 12.3% on the GF-2 and GF-3 Pohang datasets, demonstrating its effectiveness in high-precision LCC in complex environments.…”
Get full text

Enhancing zero-shot stance detection via multi-task fine-tuning with debate data and knowledge augmentation by Qinlong Fan, Jicang Lu, Yepeng Sun, Qiankun Pi, Shouxin Shang

“…Our model outperforms current state-of-the-art models on these two datasets, demonstrating the superiority of multi-task fine-tuning with debate data and knowledge augmentation.…”
Get full text

Dual Attention Dual-Resolution Networks for Real-Time Semantic Segmentation of Street Scenes by Baofeng Ye, Renzheng Xue

“…Our approach outperforms most state-of-the-art models while requiring less computational power.…”
Get full text

Accessible AI Diagnostics and Lightweight Brain Tumor Detection on Medical Edge Devices by Akmalbek Abdusalomov, Sanjar Mirzakhalilov, Sabina Umirzakova, Abror Shavkatovich Buriboev, Azizjon Meliboev, Bahodir Muminov, Heung Seok Jeon

“…The modified RetinaNet achieves an average precision (AP) of 32.1, surpassing state-of-the-art models in small tumor detection (AP_S: 14.3) and large tumor localization (AP_L: 49.7). …”
Get full text

Masked and unmasked Face Recognition Model Using Deep Learning Techniques. A case of Black Race. by Mabiriz,I, Vicent, Ampaire, Ray Brooks, Muhoza, B. Gloria

“…However, the state-of-the-art models are not generalizable across populations and probably will not work in the Ugandan context because they have not been implemented with capabilities to eliminate racial discrimination in face recognition. …”
Get full text