TrioConvTomatoNet-BiLSTM: An Efficient Framework for the Classification of Tomato Leaf Diseases in Real Time Complex Background Images by S. Ledbin Vini, P. Rathika

“…Abstract Tomatoes are the most valuable vegetable worldwide that suffer from leaf diseases, which affect long-term tomato protection. …”
Get full text

ED‐Autoformer: A New Model for Precise Global TEC Forecast by Jiawei Zhou, Hongtao Cai, Xu Yan, Hong‐wen Xu, Kun Hu, Chao Xiong

“…For the geomagnetic storm on 20 September 2015, the RMSE remained below 3.50 TECu for most periods, peaking at 5.50 TECu during the main phase. …”
Get full text

Integrating Deep Learning and Transcriptomics to Assess Livestock Aggression: A Scoping Review by Roland Juhos, Szilvia Kusza, Vilmos Bilicki, Zoltán Bagi

“…The main developments include convolutional neural network (CNN)-based object detection and pose estimation systems, together with the transcriptomic identification of molecular pathways that link to aggression and stress. …”
Get full text

AI-based estimation of forest plant community composition from UAV imagery by Lindo Nepi, Giacomo Quattrini, Simone Pesaresi, Adriano Mancini, Roberto Pierdicca

“…Following a rigorous training and evaluation process, the ViT-H14 model was identified as the most effective approach, demonstrating an accuracy of over 0.93. …”
Get full text

Nondestructive Detection of Rice Milling Quality Using Hyperspectral Imaging with Machine and Deep Learning Regression by Zhongjie Tang, Shanlin Ma, Hengnian Qi, Xincheng Zhang, Chu Zhang

“…Most multi-task models demonstrated a higher prediction accuracy compared with their corresponding single-task models. …”
Get full text

A novel approach for multiclass sentiment analysis on Chinese social media with ERNIE-MCBMA by Youyang Sun, Ziyi Yu, Yuyu Sun, Yaqing Xu, Boming Song

“…Abstract Weibo, one of the most widely used social media platforms in China, sees a vast number of users expressing their opinions and emotional tendencies. …”
Get full text

Progressive multi-scale multi-attention fusion for hyperspectral image classification by Hu Wang, Sixiang Quan, Jun Liu, Hai Xiao, Yingying Peng, Zhihui Wang, Huali Li

“…Although hyperspectral image classification methods based on convolutional neural networks (CNN) have noticeably improved performance, there are still certain shortcomings in the extraction of detailed and local features. …”
Get full text

GCAFlow: Multi-Scale Flow-Based Model with Global Context-Aware Channel Attention for Industrial Anomaly Detection by Lin Liao, Congde Lu, Yujie Gao, Hao Yu, Biao Cai

“…Recent work has largely focused on feature fusion strategies. However, most of the flow-based methods emphasize spatial information while neglecting the critical role of channel-wise features. …”
Get full text

Surface and Subsurface Soil Moisture Estimation Using Fusion of SMAP, NLDAS-2, and SOLUS100 Data with Deep Learning by Saman Rabiei, Ebrahim Babaeian, Sabine Grunwald

“…The model also performed in estimating subsurface SM than surface SM for most land-cover types. Incorporating SMAP ancillary data and SOLUS100 digital soil maps into the ConvLSTM improved the spatial and temporal estimation of surface and subsurface SM. …”
Get full text

Multimodal contrastive learning for enhanced explainability in pediatric brain tumor molecular diagnosis by Sara Ketabi, Matthias W. Wagner, Cynthia Hawkins, Uri Tabori, Birgit Betina Ertl-Wagner, Farzad Khalvati

“…Abstract Despite the promising performance of convolutional neural networks (CNNs) in brain tumor diagnosis from magnetic resonance imaging (MRI), their integration into the clinical workflow has been limited. …”
Get full text

A Novel Metaheuristic-Based Methodology for Attack Detection in Wireless Communication Networks by Walaa N. Ismail

“…The framework used involves developing a lightweight model based on a convolutional neural network with 11 layers, referred to as CSO-2D-CNN, which demonstrates fast learning rates and excellent generalization capabilities. …”
Get full text

Effects of Automatic Hyperparameter Tuning on the Performance of Multi‐Variate Deep Learning‐Based Rainfall Nowcasting by Amirmasoud Amini, Mehri Dolatshahi, Reza Kerachian

“…In this paper, deep neural networks (DNNs) and numerical weather predictions (NWPs) are applied for rainfall and runoff forecasting in an urban catchment with a complex drainage system. DNNs are among the most accurate models for rainfall nowcasting. However, the design and training of DNNs are usually complicated. …”
Get full text

Brain-Inspired Architecture for Spiking Neural Networks by Fengzhen Tang, Junhuai Zhang, Chi Zhang, Lianqing Liu

“…Spiking neural networks (SNNs), using action potentials (spikes) to represent and transmit information, are more biologically plausible than traditional artificial neural networks. However, most of the existing SNNs require a separate preprocessing step to convert the real-valued input into spikes that are then input to the network for processing. …”
Get full text

Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates by Chayanon Sub-r-pa, Rung-Ching Chen

“…Ablation studies reveal that the number of keypoints (K) impacts the performance, with K = 3 providing the most suitable balance for the overall alignment accuracy, although the performance varies across different template geometries. …”
Get full text

Multi-model Approach for Tree Detection and Classification in Wallonia Region (Belgium) by N. Dimarco, B. Bartiaux, L. Andreani, R. Schlögel

“…A Faster R-CNN model trained for tree detection achieved a F1 score of 0.828 and a mAP@50 of 0.827, effectively locating tree crowns under varying illumination and phenological conditions. Meanwhile, a convolutional neural network (CNN) for species classification attained an overall accuracy of 0.937, accurately distinguishing most species and age classes. …”
Get full text

Multilevel Feature Gated Fusion Based Spatial and Frequency Domain Attention Network for Joint Classification of Hyperspectral and LiDAR Data by Cuiping Shi, Zhipeng Zhong, Shihang Ding, Yeqi Lei, Liguo Wang, Zhan Jin

“…First, extract multilevel convolutional features from hyperspectral and LiDAR images and adaptively fuse them through a gating mechanism. …”
Get full text

Deep Learning-Based Pulmonary Nodule Screening: A Narrative Review by Abhishek Mahajan, Ujjwal Agarwal, Rajat Agrawal, Aditi Venkatesh, Shreya Shukla, K S. S. Bharadwaj, M L. V. Apparao, Vivek Pawar, Vivek Poonia

“…Given its capacity to generate three-dimensional pictures, computed tomography is the most effective means of detecting lung nodules with more excellent resolution of detected nodules. …”
Get full text

UniFlow: Unified Normalizing Flow for Unsupervised Multi-Class Anomaly Detection by Jianmei Zhong, Yanzhi Song

“…Multi-class anomaly detection is more efficient and less resource-consuming in industrial anomaly detection scenes that involve multiple categories or exhibit large intra-class diversity. However, most industrial image anomaly detection methods are developed for one-class anomaly detection, which typically suffer significant performance drops in multi-class scenarios. …”
Get full text

Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China by Zeqiang Chen, Lei Wu, Nengcheng Chen, Ke Wan

“…The results show that the normalized difference vegetation index, the enhanced vegetation index, and the leaf area index play a dominant role at most sites. This study provides new ideas and methods for analyzing the intricate relationship between NEE and environmental factors by introducing the SHAP interpretable model. …”
Get full text

Intelligent Fault Diagnosis of Inter-Turn Short Circuit Faults in PMSMs for Agricultural Machinery Based on Data Fusion and Bayesian Optimization by Mingsheng Wang, Wuxuan Lai, Hong Zhang, Yang Liu, Qiang Song

“…Inter-turn short circuit (ITSC) faults are among the most common failures in PMSMs, and early diagnosis of these faults is crucial for enhancing the safety and reliability of motor operation. …”
Get full text