YOLOv9-GDV: A Power Pylon Detection Model for Remote Sensing Images by Ke Zhang, Ningxuan Zhang, Chaojun Shi, Qiaochu Lu, Xian Zheng, Yujie Cao, Xiaoyun Zhang, Jiyuan Yang

“…Finally, the Variable Minimum Point Distance Intersection over Union (VMPDIoU) loss is proposed to optimize the model’s loss function. …”
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Progressive Cluster-Guided Knowledge Distillation for Remote Sensing Image Scene Classification by Zhaopeng Deng, Zheng Zhou, Haoran Zhao, Xiaolin Chen, Danfeng Hong, Xin Sun

“…However, existing KD methods neglect the high interclass similarity and significant intraclass variability, as well as the imbalance of data features in RSIs. …”
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Toward long-range ENSO prediction with an explainable deep learning model by Qi Chen, Yinghao Cui, Guobin Hong, Karumuri Ashok, Yuchun Pu, Xiaogu Zheng, Xuanze Zhang, Wei Zhong, Peng Zhan, Zhonglei Wang

“…Abstract El Niño-Southern Oscillation (ENSO) is a prominent mode of interannual climate variability with far-reaching global impacts. Its evolution is governed by intricate air-sea interactions, posing significant challenges for long-term prediction. …”
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Implications of artificial intelligence in periodontal treatment maintenance: a scoping review by Raafat Musief Sarakbi, Sudhir Rama Varma, Sudhir Rama Varma, Lovely Muthiah Annamma, Vinay Sivaswamy, Vinay Sivaswamy

“…Traditional diagnostic methods in periodontology often rely on subjective clinical assessments, which can lead to variability and inconsistencies in care. Imbibing artificial intelligence (AI) facilitates a significant solution by enhancing precision metrics, treatment planning, and personalized care. …”
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Deep Learning for Video Fluoroscopic Swallowing Study Analysis: A Survey on Classification, Detection, and Segmentation Techniques by Ahmed Fakhry, Sarah Mary Antony, Eunhee Park, Jong Taek Lee

“…Classification methods utilizing convolutional neural networks achieve high accuracy, ranging from 91.7% to 95.98%, and Area Under the ROC Curve scores between 0.71 and 0.97, thus enhancing the consistency and reliability of swallowing phase identification. …”
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Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human by Jucheol Moon, Pratik Jadhav, Sangtae Choi

“…Traditional imaging techniques, including plain radiography, ultrasounds, computed tomography, and magnetic resonance imaging (MRI), play a critical role in diagnosing and monitoring these conditions, but face limitations like inter-observer variability and time-consuming assessments. Recently, deep learning (DL), a subset of artificial intelligence, has emerged as a promising tool for enhancing medical imaging analysis. …”
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Bridging technology and ecology: enhancing applicability of deep learning and UAV-based flower recognition by Marie Schnalke, Jonas Funk, Andreas Wagner, Andreas Wagner

“…Challenges remain, such as detecting flowers in dense vegetation and accounting for environmental variability.…”
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Computer-Aided Diagnosis Techniques for Brain Tumor Segmentation and Classification Using MRI by Gadicha A. B., Kale Prachi V., Dalvi G. D., Mohod M. M., Pakhale S. C., Khan S. M.

“…Additionally, the paper highlights the challenges associated with model generalization, dataset limitations, preprocessing variability, and computational resource constraints. …”
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Influence of cognitive networks and task performance on fMRI-based state classification using DNN models by Murat Kucukosmanoglu, Javier O. Garcia, Justin Brooks, Kanika Bansal

“…This study highlights the application of interpretable DNNs in revealing cognitive mechanisms associated with task performance and individual variability.…”
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Enhanced Heart Disease Classification Using Dual Attention Mechanisms and 3D-Echo Fusion Algorithm in Echocardiogram Videos by S Deepika, N. Jaisankar

“…Furthermore, our proposed method addresses challenges related to the variability and complexity of cardiac data, aligning with current research that advocates for automated learning models in interpreting echocardiographic imagery. …”
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Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study by Katarzyna Mróz, Kamil Jonak

“…However, challenges such as data variability, noise, and model interpretability remain significant. …”
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A Novel Multimodal Deep Learning Approach With Loss Function for Detection of Sleep Apnea Events by Alireza Fakhim Babaei, Jafar Tanha, Mohammad Ali Balafar, Seyedehsan Roshan

“…Detecting sleep apnea accurately and efficiently presents several challenges, including variability in physiological signals among individuals and class imbalance for apnea events. …”
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Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones by Aske G. Larsen, Line Ø. Sadolin, Trine R. Thomsen, Anderson S. Oliveira

“…Stride time predictions were highly accurate (<5% error), while stance and swing times exhibited moderate variability and double support time showed the highest errors (>20%). …”
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Application of Gated Recurrent Unit in Electroencephalogram (EEG)-Based Mental State Classification by Gst. Ayu Vida Mastrika Giri, Ngurah Agus Sanjaya ER, I Ketut Gede Suhartana

“…Due to high signal variability and sensitivity to noise, correct classification is still tricky, even with advances in the analysis of EEG signals. …”
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Spatial&#x2013;Temporal Transformer for Optimizing Human Health Through Skeleton-Based Body Sports Action Recognition by Faze Liang, Lejia Ou, Zujun Lei, Xiaohong Tu, Kai Xin

“…These components collectively enable the framework to handle complex co-movement patterns, occlusions, and variability in execution styles. We evaluate FG-STTrans on two diverse datasets: the Workout Action Video Dataset (WAVd) and the YogaVid dataset. …”
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Hand Gesture Recognition in Indian Sign Language Using Deep Learning by Harsh Kumar Vashisth, Tuhin Tarafder, Rehan Aziz, Mamta Arora, Alpana

“…Recognizing hand gestures in sign languages is a challenging task due to the high variability in hand shapes, movements, and orientations. …”
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Unsupervised Deep Clustering on Spatiotemporal Objects Extracted from 4D Point Clouds for Automatic Identification of Topographic Processes in Natural Environments by J. Wang, K. Anders

“…Subsequently, after jointly optimizing the reconstruction and clustering loss, our model generates unique clusters with high intra-cluster similarity and inter-cluster variability. We validated the proposed method on a six-month 4D dataset, acquired at Kijkduin sandy beach (The Netherlands), yielding distinctive clusters that correspond to sediment change phenomena. …”
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A Graphite Ore Grade Recognition Method Based on Improved Inception-ResNet-v2 Model by Xueyu Huang, Renjie Pan, Jionghui Wang

“…With the rapid advancement of technology, intelligent identification of graphite ore grade in graphite mines has emerged as an essential requirement. To address the variability and low timeliness of traditional manual methods and the limited accuracy of deep learning due to image complexity and feature similarity, we propose an improved Inception-ResNet-v2 model for graphite ore grade recognition. …”
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HEE-SegGAN: A holistically-nested edge enhanced GAN for pulmonary nodule segmentation. by Yong Wang, Seri Mastura Mustaza, Mohammad Syuhaimi Ab-Rahman, Siti Salasiah Mokri

“…However, this task remains challenging due to the complex morphological variability of pulmonary nodules in CT images and the limited availability of well-annotated datasets. …”
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Leveraging artificial intelligence for diagnosis of children autism through facial expressions by Mahmood A. Mahmood, Leila Jamel, Nazik Alturki, Medhat A. Tawfeek

“…Future research needs to include multiple data types as well as extend dataset variability while optimizing hybrid architecture systems to elevate diagnostic forecasting. …”
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