Leaky ReLU-ResNet for Plant Leaf Disease Detection: A Deep Learning Approach by Smitha Padshetty, Ambika

“…Experimental evaluations were performed on affected plant leaf disease images from the Plant Village dataset, utilizing performance evaluation metrics to assess the proposed model. …”
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Research on Vehicle Lane Change Intent Recognition Based on Transformers and Bidirectional Gated Recurrent Units by Dan Zhou, Yujie Chen, Kexing Fan, Qi Bai, Yong Luo, Guodong Xie

“…The performance of the Model_TA model was trained and validated on the I-80 dataset in NGSIM. …”
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Fusion of multi-scale and context for small target detection algorithm of unmanned aerial vehicle rescue by LIU Yuan, ZHAO Jing, JIANG Guoping, XU Fengyu, LU Ningyun

“…Firstly, context enhancement module was designed for small target feature information, which effectively enhanced the ability of the model to process small targets by enhancing the contextual information of the feature layer. …”
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Predicting the Recurrence of Differentiated Thyroid Cancer Using Whale Optimization-Based XGBoost Algorithm by Keshika Shrestha, H. M. Jabed Omur Rifat, Uzzal Biswas, Jun-Jiat Tiang, Abdullah-Al Nahid

“…<b>Conclusions:</b> Furthermore, we have compared our work with other innovative works and validated the performance of our model for the prediction of DTC recurrence.…”
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MSLI-Net: retinal disease detection network based on multi-segment localization and multi-scale interaction by Zhenjia Qi, Jin Hong, Jilan Cheng, Guoli Long, Hanyu Wang, Siyue Li, Shuangliang Cao

“…Additionally, a multi-segmented lesion localization module (LLM) is integrated within each branch of a modified feature pyramid network (FPN) to effectively extract critical features while suppressing background noise through parallel branch refinement, and a wavelet subband spatial attention module (WSSA) is designed to significantly improve the model’s overall performance in noise suppression by collaboratively processing and exchanging information between the low- and high-frequency subbands extracted through wavelet decomposition.ResultsExperimental evaluation on the OCT-C8 dataset demonstrates that MSLI-Net achieves 96.72% accuracy in retinopathy classification, underscoring its strong discriminative performance and promising potential for clinical application.ConclusionThis model provides new research ideas for the early diagnosis of retinal diseases and helps drive the development of future high-precision medical imaging-assisted diagnostic systems.…”
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SIP-IFVM: A Time-evolving Coronal Model with an Extended Magnetic Field Decomposition Strategy by Haopeng Wang, Liping Yang, Stefaan Poedts, Andrea Lani, Yuhao Zhou, Yuhang Gao, Luis Linan, Jiakun Lv, Tinatin Baratashvili, Jinhan Guo, Rong Lin, Zhan Su, Caixia Li, Man Zhang, Wenwen Wei, Yun Yang, Yucong Li, Xinyi Ma, Edin Husidic, Hyun-Jin Jeong, Mahdi Najafi-Ziyazi, Juan Wang, Brigitte Schmieder

“…The results show that this coronal model effectively captures observational features and performs more than 80 times faster than real-time evolutions using only 192 CPU cores, making it well suited for practical applications in simulating the time-evolving corona.…”
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Implementation of MF block in CNN for advanced REB fault diagnosis by M. Pandiyan, Narendiranath Babu T.

“…This work focuses on vibration signals sampled at 12,800 Hz and 5120 Hz as input data to perform the fault diagnosis of bearings. Further, the Multi Feature (MF) block has been used in the architecture of the C-CNN model for better accuracy. …”
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Small object detection algorithm based on improved YOLOv10 for traffic sign by Yukang Zou, Scarlett Liu

“…These improvements are achieved with only a slight increase in parameters, demonstrating the model’s superiority in terms of accuracy, robustness, and real-time performance. …”
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Predicting cardiovascular risk with hybrid ensemble learning and explainable AI by Pooja Shah, Madhu Shukla, Neel H. Dholakia, Himanshu Gupta

“…They build further on model interpretability through explainable AI methods so that clinicians can observe the involvement of each feature in generating the predictions. …”
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Leveraging a foundation model zoo for cell similarity search in oncological microscopy across devices by Gabriel Kalweit, Gabriel Kalweit, Anusha Klett, Paula Silvestrini, Paula Silvestrini, Jens Rahnfeld, Jens Rahnfeld, Mehdi Naouar, Mehdi Naouar, Yannick Vogt, Yannick Vogt, Diana Infante, Diana Infante, Rebecca Berger, Jesús Duque-Afonso, Tanja Nicole Hartmann, Marie Follo, Marie Follo, Elitsa Bodurova-Spassova, Elitsa Bodurova-Spassova, Michael Lübbert, Michael Lübbert, Roland Mertelsmann, Roland Mertelsmann, Roland Mertelsmann, Joschka Boedecker, Joschka Boedecker, Joschka Boedecker, Evelyn Ullrich, Evelyn Ullrich, Evelyn Ullrich, Maria Kalweit, Maria Kalweit

“…This especially holds true for recognizing specific cell types and states in response to treatments.ObjectiveWe aim to develop an unsupervised approach using general vision foundation models trained on diverse and extensive imaging datasets to extract rich visual features for cell-analysis across devices, including both stained and unstained live cells. …”
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Improved Patch-Mix Transformer and Contrastive Learning Method for Sound Classification in Noisy Environments by Xu Chen, Mei Wang, Ruixiang Kan, Hongbing Qiu

“…Furthermore, a novel contrastive learning scheme is introduced to quantify loss and improve model performance, synergizing well with the Transformer model. …”
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Predicting determinants of unimproved water supply in Ethiopia using machine learning analysis of EDHS-2019 data by Jember Azanaw, Mihret Melese, Eshetu Abera Worede

“…To examine the significance of features in tree-based models, permutation importance and SHAP values were utilized. …”
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UnetTransCNN: integrating transformers with convolutional neural networks for enhanced medical image segmentation by Yi-Hang Xie, Bo-Song Huang, Fan Li, Fan Li

“…Multi-scale skip connections and adaptive global-local coupling units are incorporated to facilitate effective feature fusion across resolutions. Experiments were conducted on the BTCV and MSD public datasets for multi-organ and tumor segmentation.ResultsUnetTransCNN achieves state-of-the-art performance with an average Dice score of 85.3%, outperforming existing CNN- and transformer-based models on both large and small organ structures. …”
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Robust UAV Target Tracking Algorithm Based on Saliency Detection by Hanqing Wu, Weihua Wang, Gao Chen, Xin Li

“…Using saliency detection methods, the DCF tracker is optimized in three aspects to enhance the robustness of the tracker in complex scenes: feature fusion, filter-model construct, and scale-estimation methods improve. …”
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Remaining Available Energy Prediction for Energy Storage Batteries Based on Interpretable Generalized Additive Neural Network by Ji Qi, Pengrui Li, Yifan Dong, Zhicheng Fu, Zhanguo Wang, Yong Yi, Jie Tian

“…Finally, the model is trained and validated on the feature dataset. …”
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An efficient method for identifying surface damage in hydraulic concrete buildings by Libo Yang, Dawei Zhu, Xuemei Liu

“…To this end, we propose a robust discriminative feature selection model to identify the most salient features, thereby enhancing the performance of apparent damage recognition in hydraulic structures while concurrently reducing the inference time. …”
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Detection of Seed Potato Sprouts Based on Improved YOLOv8 Algorithm by Yufei Li, Qinghe Zhao, Zifang Zhang, Jinlong Liu, Junlong Fang

“…In this paper, a lightweight deep learning algorithm, YOLOv8_EBG, is proposed to both improve the detection performance and reduce the model parameters. The ECA attention mechanism was introduced in the backbone and neck of the model to more accurately extract and fuse sprouting features. …”
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Machine‐Learning Prediction of Bleeding After Endoscopic Submucosal Dissection for Early Gastric Cancer: A Multicenter Study by Hiroki Maruyama, Kazuya Takahashi, Kosuke Kojima, Nao Nakajima, Hiroki Sato, Ken‐ichi Mizuno, Soichi Sugitani, Shuji Terai

“…Feature importance analysis was performed to aid interpretation. …”
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Optimizing Stroke Recognition With MediaPipe and Machine Learning: An Explainable AI Approach for Facial Landmark Analysis by Reshad Ul Karim, Sammam Mahdi, Abrar Samin, Aniqua Nusrat Zereen, M. Abdullah-Al-Wadud, Jia Uddin

“…Feature importance analysis, facilitated by XAI, identified key facial regions&#x2014;such as the eyes, cheeks, and lips&#x2014;as critical indicators of stroke, significantly improving the model&#x2019;s diagnostic precision. …”
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Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context-Aware, and Adaptive M-Learning Model by Muhammad Adnan, Duaa H. AlSaeed, Heyam H. Al-Baity, Abdur Rehman

“…The M-learning model was based on the artificial neural network (ANN) algorithm with the aim to predict learners’ performance and classify them into five performance groups, whereas the random forest (RF) algorithm was used to determine each feature’s importance in the creation of the M-learning model. …”
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