GCT-GF: A generative CNN-transformer for multi-modal multi-temporal gap-filling of surface water probability by Yanjiao Song, Linyi Li, Yun Chen, Junjie Li, Zhe Wang, Zhen Zhang, Xi Wang, Wen Zhang, Lingkui Meng

“…The GCT-GF employs a coarse-to-fine structure: information from different time points is initially aggregated using a branched gated inpainting module, followed by refinement and alignment of the coarse output under target SAR guidance. …”
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A Modified MobileNetv3 Coupled With Inverted Residual and Channel Attention Mechanisms for Detection of Tomato Leaf Diseases by Rubina Rashid, Waqar Aslam, Romana Aziz, Ghadah Aldehim

“…This research focuses on enhancing the efficiency and accuracy of tomato leaf disease detection by modifying mobile-based Convolutional Neural Networks (CNNs). This model employs two parallel network streams based on the core principles of MobileNetv3, utilizing inverted residual blocks (IRBs) to improve accuracy at both low and high-level features, operating across different image dimensions. …”
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STSA‐Based Early‐Stage Detection of Small Brain Tumors Using Neural Network by Nafiul Hasan, Md. Masud Rana, Md Mahmudul Hasan, AKM Azad, Dil Afroz, Md Mostafizur Rahman Komol, Mousumi Aktar, Mohammad Ali Moni

“…The proposed methodology was benchmarked against Support Vector Machine (SVM), K‐Nearest Neighbor (KNN), Random Forest Classifier (RFC), and Graph Convolutional Neural Network (GCN), demonstrating superior classification performance across different tumor sizes. …”
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LIU-NET: lightweight Inception U-Net for efficient brain tumor segmentation from multimodal 3D MRI images by Gul e Sehar Shahid, Jameel Ahmad, Chaudary Atif Raza Warraich, Amel Ksibi, Shrooq Alsenan, Arfan Arshad, Rehan Raza, Zaffar Ahmed Shaikh

“…LIU-Net balances model complexity and computational load to provide consistent performance and uses Inception blocks to capture features at different scales, which makes it relatively lightweight. …”
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CNN–Transformer Hybrid Architecture for Underwater Sonar Image Segmentation by Juan Lei, Huigang Wang, Zelin Lei, Jiayuan Li, Shaowei Rong

“…FLSSNet is built upon a CNN and Transformer backbone network, integrating four core submodules to address various technical challenges: (1) The asymmetric dual encoder–decoder (ADED) is capable of simultaneously extracting features from different modalities and systematically modeling both local contextual information and global spatial structure. (2) The Transformer feature converter (TFC) module optimizes the multimodal feature fusion process through feature transformation and channel compression. (3) The long-range correlation attention (LRCA) module enhances CNN’s ability to model long-range dependencies through the collaborative use of convolutional kernels, selective sequential scanning, and attention mechanisms, while effectively suppressing noise interference. (4) The recursive contour refinement (RCR) model refines edge contour information through a layer-by-layer recursive mechanism, achieving greater precision in boundary details. …”
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Deep-Learning-Based Computer-Aided Grading of Cervical Spinal Stenosis from MR Images: Accuracy and Clinical Alignment by Zhiling Wang, Xinquan Chen, Bin Liu, Jinjin Hai, Kai Qiao, Zhen Yuan, Lianjun Yang, Bin Yan, Zhihai Su, Hai Lu

“…<b>Objective:</b> This study aims to apply different deep learning convolutional neural network algorithms to assess the grading of cervical spinal stenosis and to evaluate their consistency with clinician grading results as well as clinical manifestations of patients. …”
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Synergistic hyperspectral and SAR imagery retrieval of mangrove leaf area index using adaptive ensemble learning and deep learning algorithms by Jun Sun, Weiguo Jiang, Bolin Fu, Hang Yao, Huajian Li

“…Finally, the outputs of the AELR and DNNR models were interpreted, and the interactions between different image features were clarified to select the sensitive spectral ranges and vegetation indexes for estimating the mangrove LAI using SHAP (Shapley additive explanation). …”
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Multi-Source Attention U-Net: A Novel Deep Learning Framework for the Land Use and Soil Salinization Classification of Keriya Oasis in China with RADARSAT-2 and Landsat-8 Data by Yang Xiang, Ilyas Nurmemet, Xiaobo Lv, Xinru Yu, Aoxiang Gu, Aihepa Aihaiti, Shiqin Li

“…Furthermore, Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and deep learning methods including U-Net and MSA-U-Net were employed to identify the different degrees of salinized soil. The results indicated that the MS + SAR dataset outperformed the MS dataset, with the inclusion of the SAR band resulting in an Overall Accuracy (OA) increase of 1.94–7.77%. …”
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Multimodal Emotion Recognition Based on Facial Expressions, Speech, and EEG by Jiahui Pan, Weijie Fang, Zhihang Zhang, Bingzhi Chen, Zheng Zhang, Shuihua Wang

“…Although previous attempts to classify emotions have achieved high performance, several challenges remain open: 1) How to effectively recognize emotions using different modalities remains challenging. 2) Due to the increasing amount of computing power required for deep learning, how to provide real-time detection and improve the robustness of deep neural networks is important. …”
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HAMF: A Novel Hierarchical Attention-Based Multi-Modal Fusion Model for Parkinson&#x2019;s Disease Classification and Severity Prediction by Anitha Rani Palakayala, P. Kuppusamy, D. Kothandaraman, Gunakala Archana, Jaideep Gera

“…This leads to richer feature extraction, besides fusing different data modalities with accurate integration. …”
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Decoupled pixel-wise correction for abdominal multi-organ segmentation by Xiangchun Yu, Longjun Ding, Dingwen Zhang, Jianqing Wu, Miaomiao Liang, Jian Zheng, Wei Pang

“…These modules are designed to counteract the challenges posed by the high inter-class similarity among different organs when performing multi-organ segmentation. …”
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Seasonal quantile forecasting of solar photovoltaic power using Q-CNN-GRU by Louiza Ait Mouloud, Aissa Kheldoun, Samira Oussidhoum, Hisham Alharbi, Saud Alotaibi, Thabet Alzahrani, Takele Ferede Agajie

“…This paper presents a novel approach to probabilistic solar power forecasting by combining Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) into a hybrid Quantile-CNN-GRU model. …”
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Approximated 2-Bit Adders for Parallel In-Memristor Computing With a Novel Sum-of-Product Architecture by Christian Simonides, Dominik Gausepohl, Peter M. Hinkel, Fabian Seiler, Nima Taherinejad

“…There is a wide range of logic forms compatible with memristive IMC, each offering different advantages. We present a novel mixed-logic solution that utilizes properties of the sum-of-product (SOP) representation and propose a full-adder circuit that works efficiently in 2-bit units. …”
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Multiscale Feature Reconstruction and Interclass Attention Weighting for Land Cover Classification by Zongqian Zhan, Zirou Xiong, Xin Huang, Chun Yang, Yi Liu, Xin Wang

“…In recent years, many serial deep-learning architectures (features are delivered through a single path, such as in <italic>ResNet</italic>, <italic>MobileNet</italic>, and <italic>Segformer</italic>) based on convolutional neural networks and attention mechanisms have been widely explored in land cover classification. …”
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Classification of single tree decay stages from combined airborne LiDAR data and CIR imagery by Tsz-Chung Wong, Abubakar Sani-Mohammed, Jinhong Wang, Puzuo Wang, Wei Yao, Marco Heurich

“…This study, for the first time, automatically categorizing individual coniferous trees (Norway spruce) into five decay stages (live, declining, dead, loose bark, and clean) from combined Airborne Laser Scanning (ALS) point clouds and color infrared (CIR) images using three different ML methods − 3D point cloud-based deep learning (KPConv), Convolutional Neural Network (CNN), and Random Forest (RF). …”
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A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoes by J. Li, A. Geiss, Z. Feng, L. R. Leung, Y. Qian, W. Cui, W. Cui

“…The dataset consists of two subsets based on different gust speed data sources and is analyzed to document the climatology of derechos in the United States. …”
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A lightweight mechanism for vision-transformer-based object detection by Yanming Ye, Qiang Sun, Kailong Cheng, Xingfa Shen, Dongjing Wang

“…XFA simplifies the attention mechanism’s computational process and reduces complexity through L2 normalization and two one-dimensional convolutions applied in different directions. This design reduces the computational complexity from quadratic to linear while preserving spatial context awareness. …”
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Truth be told: a multimodal ensemble approach for enhanced fake news detection in textual and visual media by Rami Mohawesh, Islam Obaidat, Ahmed Abdallah AlQarni, Ali Abdulaziz Aljubailan, Moy’awiah A. Al-Shannaq, Haythem Bany Salameh, Ali Al-Yousef, Ahmad A. Saifan, Suboh M. Alkhushayni, Sumbal Maqsood

“…This paper presents (Verifiable Fake News Detection), a framework tailored to detect fake news in articles that incorporate both textual and visual content. employs a multi-modal ensemble approach, an integration technique that combines various models and data sources for a holistic analysis, to aggregate feature vectors from different media sources within a news article and effectively classify its credibility. …”
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Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies by Md. Kabin Hasan Kanchon, Mahir Sadman, Kaniz Fatema Nabila, Ramisa Tarannum, Riasat Khan

“…Next, the text content of the electronic documents is modified by employing different natural language processing (NLP) techniques, including named entity recognition of spaCy, knowledge graph, generative pre-trained transformer 3 (GPT-3), and text-to-text transfer transformer (T5) model, to accommodate diverse learning styles. …”
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Estimation of Potato Growth Parameters Under Limited Field Data Availability by Integrating Few-Shot Learning and Multi-Task Learning by Sen Yang, Quan Feng, Faxu Guo, Wenwei Zhou

“…Independent spatiotemporal validation further confirmed the potential of MTL-MMOE in estimating LAI and AGB across different years and locations (R² = 0.37~0.52). …”
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