A capsule network framework for flood mapping integrating remote sensing fusion techniques by Pouya Ahmadi, Mohammad Javad Valadan Zoej, Mehdi Mokhtarzade, Nazila Kardan Halvaie, Ebrahim Ghaderpour

“…Flooding is one of the most serious natural hazards worldwide, causing significant and often irreversible damage to economies, infrastructure, and human health in urban areas. …”
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A Multi-Spatial-Scale Ocean Sound Speed Profile Prediction Model Based on a Spatio-Temporal Attention Mechanism by Shuwen Wang, Ziyin Wu, Shuaidong Jia, Dineng Zhao, Jihong Shang, Mingwei Wang, Jieqiong Zhou, Xiaoming Qin

“…Hence, this paper proposes a new data-driven method called STA-Conv-LSTM that combines convolutional long short-term memory (Conv-LSTM) and spatio-temporal attention (STA) to predict SSPs. …”
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Exploring vision transformers and XGBoost as deep learning ensembles for transforming carcinoma recognition by Akella Subrahmanya Narasimha Raju, K. Venkatesh, B. Padmaja, CH. N. Santhosh Kumar, Pattabhi Rama Mohan Patnala, Ayodele Lasisi, Saiful Islam, Abdul Razak, Wahaj Ahmad Khan

“…Abstract Early detection of colorectal carcinoma (CRC), one of the most prevalent forms of cancer worldwide, significantly enhances the prognosis of patients. …”
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Artificial intelligence−assisted radiation imaging pathways for distinguishing uterine fibroids and malignant lesions in patients presenting with cancer pain: a literature review by Chengfeng Cai, Wenhui Hu, Haimei Zhou, Xian Zhang, Rongfei Ren, Yilin Liu, Facui Ye

“…Uterine fibroids (leiomyomas) are the most common benign uterine tumours, affecting a significant portion of women, and often present with symptoms similar to malignant tumours, such as leiomyosarcoma or endometrial carcinoma, particularly in patients with cancer-related pelvic pain. …”
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FUSCANet: Enhancing Skin Disease Classification Through Feature Fusion and Spatial-Channel Attention Mechanisms by Qinyang Liu, Xuan Wang, Hongjiu Liu, Xiangzhen Zang, Lei Li, Zhanlin Ji, Ivan Ganchev

“…Lastly, a newly designed Enhanced Convolutional Block Attention Module (ECBAM) increases the model’s focus on important information, allowing for the final extraction and summarization of features. …”
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Consistency Regularization for Semi-Supervised Semantic Segmentation of Flood Regions From SAR Images by G. Savitha, S. Girisha, Pundarika Sughosh, Dasharathraj K. Shetty, Jayaraj Mymbilly Balakrishnan, Rahul Paul, Nithesh Naik

“…As one of the most powerful natural catastrophes, floods pose serious risks to people’s lives, the integrity of infrastructure, and agricultural landscapes, which increases the toll they take on the economy and society. …”
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Exploration of machine learning approaches for automated crop disease detection by Annu Singla, Ashima Nehra, Kamaldeep Joshi, Ajit Kumar, Narendra Tuteja, Rajeev K. Varshney, Sarvajeet Singh Gill, Ritu Gill

“…This systematic review provides a comprehensive analysis after text mining the most recent literature resources of the last half a decade. …”
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YOLOv8-Scm: an improved model for citrus fruit sunburn identification and classification in complex natural scenes by Guoxun Cong, Guoxun Cong, Xinghong Chen, Xinghong Chen, Zongyu Bing, Zongyu Bing, Wenhuan Liu, Wenhuan Liu, Xiangling Chen, Qun Wu, Zheng Guo, Zheng Guo, Yongqiang Zheng, Yongqiang Zheng

“…Citrus ranks among the most widely cultivated and economically vital fruit crops globally, with southern China being a major production area. …”
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Time-Distributed Vision Transformer Stacked With Transformer for Heart Failure Detection Based on Echocardiography Video by Mgs M. Luthfi Ramadhan, Adyatma W. A. Nugraha Yudha, Muhammad Febrian Rachmadi, Kevin Moses Hanky Jr Tandayu, Lies Dina Liastuti, Wisnu Jatmiko

“…Existing studies have employed artificial intelligence to help with heart failure screening and diagnosis processes based on echocardiography videos. Specifically, most existing studies use a convolutional neural network that only captures the local context of an image hindering it from learning the global context of an image. …”
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Global surface eddy mixing ellipses: spatio-temporal variability and machine learning prediction by Tian Jing, Ru Chen, Chuanyu Liu, Chunhua Qiu, Chunhua Qiu, Cuicui Zhang, Mei Hong

“…We also assessed the predictability of global mixing ellipses using machine learning algorithms, including Spatial Transformer Networks (STN), Convolutional Neural Network (CNN) and Random Forest (RF), with mean-flow and eddy- properties as features. …”
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MWFNet: A multi-level wavelet fusion network for hippocampal subfield segmentation by Xinwei Li, Linjin Wang, Weijian Tao, Hongying Meng, Haiming Li, Jiangtao He, Yue Zhao, Jun Hu, Zhangyong Li

“…Additionally, we developed a Multi-scale Attention Residual Block (MARB) that leverages convolutional kernels of different sizes to facilitate multi-scale feature extraction. …”
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Novel Quanvolutional Neural Network-Based COVID-19 Diagnosis From Raman Spectroscopic Signals of Human Serum by Shivanya Shomir Dutta, Sahil Sandeep, S. Sridevi, Rajiv Mistry, Jonathan Ortega, Gorkem Kar

“…COVID-19 has proven to be one of the most devastating pandemics of the 21st century, causing widespread mortality and overwhelming healthcare systems globally. …”
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Deep learning-based accurate detection of insects and damage in cruciferous crops using YOLOv5 by Sourav Chakrabarty, Pathour Rajendra Shashank, Chandan Kumar Deb, Md. Ashraful Haque, Pradyuman Thakur, Deeba Kamil, Sudeep Marwaha, Mukesh Kumar Dhillon

“…With the rapid development of convolutional neural networks, automatic detection techniques for identifying insects using digital images have shown impressive performances in agriculture. …”
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Pedestrian Crossing Direction Prediction at Intersections for Pedestrian Safety by Younggun Kim, Mohamed Abdel-Aty, Keechoo Choi, Zubayer Islam, Dongdong Wang, Shaoyan Zhai

“…Pedestrians are among the most vulnerable road users, with significant risks arising at intersections due to potential conflicts with vehicular traffic. …”
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Hybrid Wavelet-Attention Model for Detecting Changes in High-Resolution Remote Sensing Images by Lhuqita Fazry, MGS M. Luthfi Ramadhan, Alif Wicaksana Ramadhan, Muhammad Febrian Rachmadi, Aprinaldi Jasa Mantau, Lukito Edi Nugroho, Chi-Hung Chi, Wisnu Jatmiko

“…The emergence of a Vision Transformer (ViT) as a new standard in Computer Vision, replacing Convolutional Neural Network (CNN), also shifts the role of CNN in the field of remote sensing. …”
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MobilitApp: A Deep Learning-Based Tool for Transport Mode Detection to Support Sustainable Urban Mobility by Gerard Caravaca Ibanez, Luis J. de la Cruz Llopis, Adrian Catalin Diaconeasa, Alberto Bazan Guillen, Monica Aguilar Igartua

“…Our approach leverages a hierarchical model combining convolutional neural networks (CNNs) for feature extraction and long short-term memory (LSTM) layers for temporal processing, enhanced by skip connections. …”
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MFEM-CIN: A Lightweight Architecture Combining CNN and Transformer for the Classification of Pre-Cancerous Lesions of the Cervix by Peng Chen, Fobao Liu, Jun Zhang, Bing Wang

“…<italic>Goal:</italic> Cervical cancer is one of the most common cancers in women worldwide, ranking among the top four. …”
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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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Computerized Diagnosis Methods for Pediatric Cardiology: A Review by R. Shantha Selvakumari, Kumaradevan Punithakumar, D. Selvathi, S. Chrisilla, R. Sri Abinaya, Nilanjan Ray

“…DL methods use Convolutional Neural Networks (CNN) as the primary classifier and U-Net architectures are preferred for segmentation studies in the reviewed papers. …”
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Segmentation-Guided Deep Learning for Glioma Survival Risk Prediction with Multimodal MRI by Jianhong Cheng, Hulin Kuang, Songhan Yang, Hailin Yue, Jin Liu, Jianxin Wang

“…Glioma survival risk prediction is of great significance for the individualized treatment and assessment programs. Currently, most deep learning based survival prediction paradigms rely on invasive and expensive histopathology and genomics methods. …”
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