An Evaluation of Deep Learning Methods for Small Object Detection by Nhat-Duy Nguyen, Tien Do, Thanh Duc Ngo, Duy-Dinh Le

“…In this study, we evaluate current state-of-the-art models based on deep learning in both approaches such as Fast RCNN, Faster RCNN, RetinaNet, and YOLOv3. …”
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Recommendation System with Biclustering by Jianjun Sun, Yu Zhang

“…Experiment results demonstrate that the proposed method outperforms state-of-the-art models in terms of several aspects on three benchmark datasets.…”
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DANSK: Domain Generalization of Danish Named Entity Recognition by Kenneth Enevoldsen, Emil Trenckner Jessen, Rebekah Baglini

“…To alleviate these limitations, this paper introduces: 1) DANSK: a named entity dataset providing for high-granularity tagging as well as within-domain evaluation of models across a diverse set of domains; 2) and three generalizable models with fine-grained annotation available in DaCy 2.6.0; and 3) an evaluation of current state-of-the-art models’ ability to generalize across domains. The evaluation of existing and new models revealed notable performance discrepancies across domains, which should be addressed within the field. …”
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Automatic detection of concrete cracks from images using Adam-SqueezeNet deep learning model by Lin Wang

“…The fine-tuned CrackSN system outperforms state-of-the-art models in recent literature by correctly classifying 97.3% of the cracked patches in the image dataset. …”
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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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A Pre-Activation Residual Convolutional Network With Attention Modules for High-Resolution Segmented EEG Emotion Recognition by Ioannis Charalampous, Christos Mavrokefalidis, Kostas Berberidis

“…The suggested exploitation of the temporal dynamics of the EEG signals in emotion recognition turns out to be useful, as classification accuracies of up to 99.51% and 97.51% on SEED and SEED-IV datasets have been achieved, respectively, thus, outperforming the current state-of-the-art models.…”
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Advancing a Vision Foundation Model for Ming-Style Furniture Image Segmentation: A New Dataset and Method by Yingtong Wan, Wanru Wang, Meng Zhang, Wei Peng, He Tang

“…Our experiments demonstrate that the proposed method significantly improves the segmentation accuracy, outperforming state-of-the-art models in terms of the mean intersection over union (mIoU). …”
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Trust-Centric and Economically Optimized Resource Management for 6G-Enabled Internet of Things Environment by Osama Z. Aletri, Kamran Ahmad Awan, Abdullah M. Alqahtani

“…Results demonstrate that TEO-IoT achieves an optimal resource usage of 92.5% in Edge-IIoTset and reduces power consumption by 15.2% in IoT-23, outperforming state-of-the-art models like IDSOFT and RAT6G.…”
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Small Object Detection with Multiscale Features by Guo X. Hu, Zhong Yang, Lei Hu, Li Huang, Jia M. Han

“…Through testing, the detection accuracy of our model for small objects is 11% higher than the state-of-the-art models. In addition, we also used the model to detect aircraft in remote sensing images and achieved good results.…”
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MUFFNet: lightweight dynamic underwater image enhancement network based on multi-scale frequency by Dechuan Kong, Dechuan Kong, Yandi Zhang, Xiaohu Zhao, Yanqiang Wang, Lei Cai

“…A Multi-Scale Joint Loss framework facilitates dynamic network optimization.ResultsExperimental results demonstrate that MUFFNet outperforms existing state-of-the-art models while consuming fewer computational resources and aligning enhanced images more closely with human visual perception.DiscussionThe enhanced images generated by MUFFNet exhibit better alignment with human visual perception, making it a promising solution for improving underwater robotic vision systems.…”
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QUERY2BERT: Combining Knowledge Graph and Language Model for Reasoning on Logical Queries by Truong H. V. Phan, Phuc do

“…We tested our model on three benchmark knowledge graph datasets and showed that QUERY2BERT significantly improved accuracy and speed compared to other state-of-the-art models.…”
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Accelerating materials property prediction via a hybrid Transformer Graph framework that leverages four body interactions by Mohammad Madani, Valentina Lacivita, Yongwoo Shin, Anna Tarakanova

“…It outperforms state-of-the-art models in 8 materials property regression tasks. …”
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Improving Monocular Depth Estimation Through Knowledge Distillation: Better Visual Quality and Efficiency by Chang Yeop Lee, Dong Ju Kim, Young Joo Suh, Do Kyung Hwang

“…To validate the effectiveness of the proposed framework, extensive comparative evaluations were performed using state-of-the-art models, including AdaBins, LocalBins, BinsFormer, PixelFormer, and ZoeDepth. …”
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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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Optimizing Aspect-Based Sentiment Analysis Using BERT for Comprehensive Analysis of Indonesian Student Feedback by Ahmad Jazuli, Widowati, Retno Kusumaningrum

“…Experimental results indicate that the proposed ABSA model surpasses previous state-of-the-art models in analyzing sentiment related to specific aspects of educational evaluation. …”
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Task relevant autoencoding enhances machine learning for human neuroscience by Seyedmehdi Orouji, Vincent Taschereau-Dumouchel, Aurelio Cortese, Brian Odegaard, Cody Cushing, Mouslim Cherkaoui, Mitsuo Kawato, Hakwan Lau, Megan A. K. Peters

“…However, state-of-the-art models typically require large datasets to train, and so are prone to overfitting on human neuroimaging data that often possess few samples but many input dimensions. …”
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WEB VULNERABILITIES DETECTION USING A HYBRID MODEL OF CNN, GRU AND ATTENTION MECHANISM by Sarbast H. Ali, Arman I. Mohammed, Sarwar MA. Mustafa, Sardar Omar Salih

“…These results significantly outperform the state-of-the-art models and can strongly identify vulnerabilities in web applications. …”
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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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ViT-DualAtt: An efficient pornographic image classification method based on Vision Transformer with dual attention by Zengyu Cai, Liusen Xu, Jianwei Zhang, Yuan Feng, Liang Zhu, Fangmei Liu

“…Our results demonstrated that ViT-DualAtt achieved a classification accuracy of 97.2% ± 0.1% in pornographic image classification tasks, outperforming the current state-of-the-art model (RepVGG-SimAM) by 2.7%. Furthermore, the model achieves a pornographic image miss rate of only 1.6%, significantly reducing the risk of pornographic image dissemination on internet platforms.…”
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Enhancing the performance of SSVEP-based BCIs by combining task-related component analysis and deep neural network by Qingguo Wei, Chang Li, Yijun Wang, Xiaorong Gao

“…The performance of the proposed method is validated on two SSVEP BCI datasets and compared with that of eTRCA, sbCNN and other state-of-the-art models. Experimental results indicate that the proposed method significantly outperform the compared algorithms, and thus helps to promote the practical application of SSVEP- BCI systems.…”
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