Maritime Small Object Detection Algorithm in Drone Aerial Images Based on Improved YOLOv8 by Peng Ling, Yihong Zhang, Shuai Ma

“…When compared to state-of-the-art models, AB2D-YOLO model is conducive to the deployment of maritime UAV.…”
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RETRACTED: Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis by S. M. Nuruzzaman Nobel, S M Masfequier Rahman Swapno, Md. Ashraful Hossain, Mejdl Safran, Sultan Alfarhood, Md. Mohsin Kabir, M. F. Mridha

“…We proposed a new Attention Embedder, a state-of-the-art model with effective results in ovarian cancer subtype classification and outlier detection. …”
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Investigating the Working Efficiency of Typical Work in High-Altitude Alpine Metal Mining Areas Based on a SeqGAN-GABP Mixed Algorithm by Ning Hua, He Huang, Xinhong Zhang

“…Finally, three high-altitude alpine metal mines in Xinjiang were selected as representative examples to verify the proposed framework by comparing it with other state-of the art models (multiple linear regression prediction model, backpropagation (BP) neural network model, and genetic algorithm back propagation (GA-BP) neural network model). …”
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A Deep Learning-Based Approach to Strawberry Grasping Using a Telescopic-Link Differential Drive Mobile Robot in ROS-Gazebo for Greenhouse Digital Twin Environments by Rajmeet Singh, Lakmal Seneviratne, Irfan Hussain

“…It is compared to state-of-the-art models and deployed on a telescopic arm-based robotic platform, which is simpler to control than an articulated arm for strawberry harvesting and grasping tasks.…”
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SSMM-DS: A semantic segmentation model for mangroves based on Deeplabv3+ with swin transformer by Zhenhua Wang, Jinlong Yang, Chuansheng Dong, Xi Zhang, Congqin Yi, Jiuhu Sun

“…Using GF-1 and GF-6 images, taking mean precision (mPrecision), mean intersection over union (mIoU), floating-point operations (FLOPs), and the number of parameters (Params) as evaluation metrics, we evaluate SSMM-DS against state-of-the-art models, including FCN, PSPNet, OCRNet, uPerNet, and SegFormer. …”
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Need for judicious selection of runoff inputs in a global flood model by Jayesh Parmar, Mohit Prakash Mohanty, Subhankar Karmakar

“…To highlight these implications, the present study examines GFM simulations forced with eight state-of-the-art model runoff datasets, including LSMs, GHMs, and reanalysis observations, uncovering unsafe inter-model flood depth variation (IMDV). …”
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Advanced Algorithmic Model for Real-Time Multi-Level Crop Disease Detection Using Neural Architecture Search by Slimani Hicham, El Mhamdi Jamal, Jilbab Abdelilah

“…Among the evaluated models, our NAS-based model emerges as the top performer, highlighting the importance and effectiveness of this method in developing state-of-the-art models. It achieves a mean average precision of 94.10% and an impressive overall recall of 96.96%. …”
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MDFGNN-SMMA: prediction of potential small molecule-miRNA associations based on multi-source data fusion and graph neural networks by Jianwei Li, Xukun Zhang, Bing Li, Ziyu Li, Zhenzhen Chen

“…Conclusions The performance of MDFGNN-SMMA was assessed using 10-fold cross-validation, demonstrating superior compared to the four state-of-the-art models in terms of both AUC and AUPR. Moreover, the experimental results of an independent test set confirmed the model’s generalization capability. …”
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Enhancing Cervical Cancer Classification: Through a Hybrid Deep Learning Approach Integrating DenseNet201 and InceptionV3 by Abhiram Sharma, R. Parvathi

“…Comprehensive evaluation metrics, including accuracy, precision, recall, and F1-score, indicate that the proposed model achieves an accuracy of 96.54%, 95.91% Presicion, 96.44% Recall and 96.17% F1 Score surpassing state-of-the-art models such as ResNet-50, DenseNet-201, InceptionV3, and Xception. …”
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SFSCDNet: A Deep Learning Model With Spatial Flow-Based Semantic Change Detection From Bi-Temporal Satellite Images by K. S. Basavaraju, N. Sravya, Vibha Damodara Kevala, Shilpa Suresh, Shyam Lal

“…These results represent substantial improvements over previous state-of-the-art models, including a 0.26% increase in overall accuracy, a 2.21% increase in mean Intersection over Union, a 2.62% enhancement in Separated Kappa, and a 3.6% improvement in F1-score for semantic change detection compared to the best-performing models. …”
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The art of being healthy: a qualitative study to develop a thematic framework for understanding the relationship between health and the arts by Matthew Knuiman, Peter Wright, Michael Rosenberg, Christina R Davies

“…This framework expands on current knowledge, further defines the health–arts relationship and is a step towards the conceptualisation of a causal health–arts model.…”
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DSC-SeNet: Unilateral Network with Feature Enhancement and Aggregation for Real-Time Segmentation of Carbon Trace in the Oil-Immersed Transformer by Liqing Liu, Hongxin Ji, Junji Feng, Xinghua Liu, Chi Zhang, Chun He

“…Experimental results showed that the proposed DSC-SeNet outperformed state-of-the-art models with a good balance between segmentation accuracy and inference speed. …”
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Deep unsupervised clustering for prostate auto-segmentation with and without hydrogel spacer by Hengrui Zhao, Biling Wang, Michael Dohopolski, Ti Bai, Steve Jiang, Dan Nguyen

“…Additionally, CLIP-UNet outperforms other state-of-the-art models with or without cluster information. Conclusion. …”
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Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches by Mohsin Akram, Muhammad Adnan, Syed Farooq Ali, Jameel Ahmad, Amr Yousef, Tagrid Abdullah N. Alshalali, Zaffar Ahmed Shaikh

“…Our experiments on a combined dataset (APTOS 2019 + DDR) with pre-processed images showed that the Bayesian-augmented DenseNet-121 outperforms state-of-the-art models in test accuracy, achieving 97.68% for the Monte Carlo Dropout model, 94.23% for Mean Field Variational Inference, and 91.44% for the Deterministic model. …”
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MFBTFF-Net: A Novel Multi-Frequency Brightness Temperature Feature Fusion Network for Global Lunar Surface Oxides Abundance Estimation With Chang'e-2 Lunar Microwave Sounder... by Yu Li, Zifeng Yuan, Sarah Mazhar, Zhiguo Meng, Yuanzhi Zhang, Jinsong Ping, Ferdinando Nunziata

“…%) on estimating Al₂O₃, FeO, and TiO₂, which outperformed the state-of-the-art models by at least 0.0674, 0.6217, and 0.0578, respectively. …”
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InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks by Mahmood Kalemati, Mojtaba Zamani Emani, Somayyeh Koohi

“…Previous state-of-the-art models, like transformers and graph-based approaches, face scalability and resource efficiency challenges. …”
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A Physics-Based Hyper Parameter Optimized Federated Multi-Layered Deep Learning Model for Intrusion Detection in IoT Networks by Chirag Jitendra Chandnani, Vedik Agarwal, Shlok Chetan Kulkarni, Aditya Aren, D. Geraldine Bessie Amali, Kathiravan Srinivasan

“…The proposed Fed-MLDL with Fed-RIME optimization outperforms existing state-of-the-art models on the CIC-IoT23 dataset.…”
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Snake-DETR: a lightweight and efficient model for fine-grained snake detection in complex natural environments by Heng Wang, Shuai Zhang, Cong Zhang, Zheng Liu, Qiuxian Huang, Xinyi Ma, Yiming Jiang

“…Compared to other state-of-the-art models, Snake-DETR achieved an accuracy of 97.66%, a recall rate of 93.92%, mAP@0.5 of 95.23%, and mAP@0.5:0.95 of 72.15%, all outperforming other algorithms in the comparative tests. …”
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Assessing tick attachments to humans with citizen science data: spatio-temporal mapping in Switzerland from 2015 to 2021 using spatialMaxent by Lisa Bald, Nils Ratnaweera, Tomislav Hengl, Patrick Laube, Jürg Grunder, Werner Tischhauser, Netra Bhandari, Dirk Zeuss

“…The maps were created using a state-of-the-art modeling approach with the software extension spatialMaxent, which accounts for spatial autocorrelation when creating Maxent models. …”
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Attention-Driven Hybrid Ensemble Approach With Bayesian Optimization for Accurate Energy Forecasting in Jeju Island’s Renewable Energy System by Muhammad Ali Iqbal, Joon-Min Gil, Soo Kyun Kim

“…ABHEF integrates state-of-the-art models—ConvBiLSTM (Convolutional Bidirectional Long Short-Term Memory), ETCN (Enhanced Temporal Convolutional Network), TFT (Temporal Fusion Transformer), and DAT (Dual Attention Transformer)—to capture both short-term fluctuations and long-term trends in energy data. …”
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