InfectA-Chat, an Arabic Large Language Model for Infectious Diseases: Comparative Analysis by Yesim Selcuk, Eunhui Kim, Insung Ahn

“…Among the state-of-the-art models, InfectA-Chat achieved a leading performance of 23.78%, competing closely with the GPT-4 model. …”
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A Hybrid Transformer Architecture for Multiclass Mental Illness Prediction Using Social Media Text by Adnan Karamat, Muhammad Imran, Muhammad Usman Yaseen, Rasool Bukhsh, Sheraz Aslam, Nouman Ashraf

“…The results reveal outstanding performance of the proposed architecture with an overall accuracy of 92% and an F1-score of 92%, surpassing state-of-the-art models in comparison. This study underscores the necessity for further research in this field and illustrates the potential of advanced technologies to address mental health issues in contemporary society.…”
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Edge-centric optimization: a novel strategy for minimizing information loss in graph-to-text generation by Zheng Yao, Jingyuan Li, Jianhe Cen, Shiqi Sun, Dahu Yin, Yuanzhuo Wang

“…Experimental results reveal that TriELMR exhibits exceptional performance across various benchmark tests, especially on the webnlgv2.0 and Event Narrative datasets, achieving BLEU-4 scores of $$66.5\%$$ 66.5 % and $$37.27\%$$ 37.27 % , respectively, surpassing the state-of-the-art models. These demonstrate the advantages of TriELMR in maintaining the accuracy of graph structural information. …”
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Enhanced CATBraTS for Brain Tumour Semantic Segmentation by Rim El Badaoui, Ester Bonmati Coll, Alexandra Psarrou, Hykoush A. Asaturyan, Barbara Villarini

“…Through the adoption of E-CATBraTS, the accuracy of the results improved significantly on two datasets, outperforming the current state-of-the-art models by a mean DSC of 2.6% while maintaining a high accuracy that is comparable to the top-performing models on the other datasets. …”
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Hybrid generative adversarial network based on frequency and spatial domain for histopathological image synthesis by Qifeng Liu, Tao Zhou, Chi Cheng, Jin Ma, Marzia Hoque Tania

“…Experiments on the Patch Camelyon dataset show superior performance over eight state-of-the-art models across five metrics. This approach advances automated histopathological image generation with potential for clinical applications.…”
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ClinClip: a Multimodal Language Pre-training model integrating EEG data for enhanced English medical listening assessment by Guangyu Sun

“…The model leverages cognitive-enhanced strategies, including EEG-based modulation and hierarchical fusion of multimodal data, to overcome the challenges faced by traditional methods.Results and discussionExperiments conducted on four datasets–EEGEyeNet, DEAP, PhyAAt, and eSports Sensors–demonstrate that ClinClip significantly outperforms six state-of-the-art models in both Word Error Rate (WER) and Cognitive Modulation Efficiency (CME). …”
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Precise Recognition and Feature Depth Analysis of Tennis Training Actions Based on Multimodal Data Integration and Key Action Classification by Weichao Yang

“…Compared to state-of-the-art models, ASE-CNN exhibits significant advantages in per-frame processing time and resource utilization efficiency, offering potential for efficient real-time feedback in resource-constrained environments. …”
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DSIA U-Net: deep shallow interaction with attention mechanism UNet for remote sensing satellite images by Naga Surekha Jonnala, Renuka Chowdary Bheemana, Krishna Prakash, Shonak Bansal, Arpit Jain, Vaibhav Pandey, Mohammad Rashed Iqbal Faruque, K. S. Al-mugren

“…When compared to state-of-the-art models, lightweight semantic segmentation models usually exhibit performance gaps. …”
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PilotCareTrans Net: an EEG data-driven transformer for pilot health monitoring by Kun Zhao, Xueying Guo

“…PilotCareTrans Net was evaluated on multiple public EEG datasets, including MODA, STEW, SJTUEmotion EEG, and Sleep-EDF, where it outperformed state-of-the-art models in key metrics.Results and discussionThe experimental results demonstrate the model's ability to not only enhance prediction accuracy but also reduce computational complexity, making it suitable for real-time applications in resource-constrained settings. …”
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Design of Super Resolution and Fuzzy Deep Learning Architecture for the Classification of Land Cover and Landsliding Using Aerial Remote Sensing Data by Junaid Ali Khan, Muhammad Attique Khan, Mohammed Al-Khalidi, Dina Abdulaziz AlHammadi, Areej Alasiry, Mehrez Marzougui, Yudong Zhang, Faheem Khan

“…The results are compared with state-of-the-art models, such as the hybrid version of VGGNet-16, Yolov4, ResNet-50, DenseNet-121, and other reported techniques. …”
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Integration of a hybrid vibration prediction model for railways into noise mapping software: methodology, assumptions and demonstration by Pieter Reumers, Geert Degrande, Geert Lombaert, David J. Thompson, Evangelos Ntotsios, Pascal Bouvet, Brice Nélain, Andreas Nuber

“…The user can select soil impedance and transfer functions from a database, pre-computed for a wide range of parameters with state-of-the-art models. An experimental database of force densities, transfer functions, free field vibration and input parameters is also provided. …”
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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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Multivariate Segment Expandable Encoder-Decoder Model for Time Series Forecasting by Yanhong Li, David C. Anastasiu

“…MSEED consistently outperforms state-of-the-art models, showing improvements in forecasting accuracy ranging from 18% to 74%.…”
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Toward a Next Generation Particle Precipitation Model: Mesoscale Prediction Through Machine Learning (a Case Study and Framework for Progress) by Ryan M. McGranaghan, Jack Ziegler, Téo Bloch, Spencer Hatch, Enrico Camporeale, Kristina Lynch, Mathew Owens, Jesper Gjerloev, Binzheng Zhang, Susan Skone

“…With a more capable representation of the organizing parameters and the target electron energy flux observations, PrecipNet achieves a >50% reduction in errors from a current state‐of‐the‐art model oval variation, assessment, tracking, intensity, and online nowcasting (OVATION Prime), better captures the dynamic changes of the auroral flux, and provides evidence that it can capably reconstruct mesoscale phenomena. …”
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Data-driven modeling of open circuit voltage hysteresis for LiFePO4 batteries with conditional generative adversarial network by Lisen Yan, Jun Peng, Zeyu Zhu, Heng Li, Zhiwu Huang, Dirk Uwe Sauer, Weihan Li

“…Experimental results demonstrate that the proposed model achieves a voltage error of less than 3.8 mV across various conditions, with accuracy improvements of 31.3–48.7% compared to three state-of-the-art models.…”
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Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model by Fırat Dişli, Mehmet Gedikpınar, Hüseyin Fırat, Abdulkadir Şengür, Hanifi Güldemir, Deepika Koundal

“…Comparative analyses with previous studies and state-of-the-art models demonstrated the superior performance of the DCNN model and image concatenation technique. …”
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DeepTGIN: a novel hybrid multimodal approach using transformers and graph isomorphism networks for protein-ligand binding affinity prediction by Guishen Wang, Hangchen Zhang, Mengting Shao, Yuncong Feng, Chen Cao, Xiaowen Hu

“…To evaluate the performance of DeepTGIN, we compared it with state-of-the-art models using the PDBbind 2016 core set and PDBbind 2013 core set. …”
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Novel deep neural network architecture fusion to simultaneously predict short-term and long-term energy consumption. by Abrar Ahmed, Safdar Ali, Ali Raza, Ibrar Hussain, Ahmad Bilal, Norma Latif Fitriyani, Yeonghyeon Gu, Muhammad Syafrudin

“…Additionally, the proposed hybrid model is compared with existing state-of-the-art models, demonstrating its superior performance in both short-term and long-term energy consumption predictions.…”
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FSDN-DETR: Enhancing Fuzzy Systems Adapter with DeNoising Anchor Boxes for Transfer Learning in Small Object Detection by Zhijie Li, Jiahui Zhang, Yingjie Zhang, Dawei Yan, Xing Zhang, Marcin Woźniak, Wei Dong

“…Experiments on the COCO and AI-TOD-V2 datasets show that FSDN-DETR achieves an approximately 20% improvement in average precision for very small objects, surpassing state-of-the-art models and demonstrating robustness and reliability for small object detection in complex environments.…”
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Parkinson’s Disease Prediction: An Attention-Based Multimodal Fusion Framework Using Handwriting and Clinical Data by Sabrina Benredjem, Tahar Mekhaznia, Abdulghafor Rawad, Sherzod Turaev, Akram Bennour, Bourmatte Sofiane, Abdulaziz Aborujilah, Mohamed Al Sarem

“…Comparative analysis against state-of-the-art models, along with an in-depth exploration of attention mechanisms, highlights the efficacy of PMMD in PD classification. …”
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