Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model by Tingtao Wu, Lei Xu, Ziwei Pan, Ruinan Cai, Jin Dai, Shuang Yang, Xihao Zhang, Xi Zhang, Nengcheng Chen

“…This article proposes a spatiotemporal prediction framework that integrates causal inference with deep learning, termed the causal-guided spatiotemporal Swin transformer (Causal ST-SwinT). …”
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A Comprehensive Deep Learning System With MGRF Modeling for Predicting Breast Cancer Response to Neoadjuvant Chemotherapy by Ahmed Sharafeldeen, Fatma Taher, Norah Saleh Alghamdi, Eman Alnaghy, Reham Alghandour, Khadiga M. Ali, Sameh Shamaa, Abdelrahman Gamal, Mohammed Ghazal, Sohail Contractor, Ayman El-Baz

“…This study introduces a novel deep learning-based framework that integrates multi-parametric magnetic resonance imaging (MRI) (i.e., T1, T2, STIR, and DWI), along with clinical and molecular subtype markers, to classify tumor response into pathological complete response (pCR), partial response (PR), and stable disease (SD). …”
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Precise prediction of choke oil rate in critical flow condition via surface data by Qing Wang, Muntadher Abed Hussein, Bhavesh Kanabar, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Mohammad Mahtab Alam, Hojjat Abbasi

“…The goal is to construct a robust framework to proficiently calculate the oil production rates of wells while taking into account tubing head pressure (THP), choke size, BS&W, gas oil ratio (GOR), and oil API. …”
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A Dual-Technology Approach: Handheld NIR Spectrometer and CNN for <i>Fritillaria</i> spp. Quality Control by Fengling Li, Wen Lei, Juan Li, Xiaoting Wang, Jingyu Su, Tangnuer Sahati, Xiahenazi Aierkenjiang, Ruyi Tian, Weihong Zhou, Jixiong Zhang, Jingjing Xia

“…., a handheld near-infrared spectrometer was combined with a convolutional neural network (CNN) to establish an efficient and convenient quality assessment method. …”
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Efficient Robot Localization Through Deep Learning-Based Natural Fiduciary Pattern Recognition by Ramón Alberto Mena-Almonte, Ekaitz Zulueta, Ismael Etxeberria-Agiriano, Unai Fernandez-Gamiz

“…By integrating advanced deep learning frameworks with natural scene analysis, the proposed algorithm facilitates robust, real-time localization in dynamic and unstructured settings. …”
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Hands-On Introduction to Quantum Machine Learning by Muhammad Ismail, Mohamed Shaban, Samuel Yen-Chi Chen

“…Further reading and software packages and frameworks are shared with the audience.…”
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Mul-material decomposition method for sandstone spectral CT images based on I-MultiEncFusion-Net by Yanfang Wu, Yanfang Wu, Yanfang Wu, Ran Zhang, Ran Zhang, Ran Zhang, Huihua Kong, Huihua Kong, Huihua Kong, Ping Chen, Ping Chen, Ping Chen, Yu Zou

“…However, traditional convolutional neural networks (CNNs) demonstrate limited effectiveness in capturing non-local features. …”
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Automating tephra fall building damage assessment using deep learning by E. Tennant, S. F. Jenkins, V. Miller, R. Robertson, B. Wen, S.-H. Yun, B. Taisne

“…In this work, we established a UAV-appropriate tephra fall building damage state framework and used it to label <span class="inline-formula">∼50 000</span> building bounding boxes around <span class="inline-formula">∼2000</span> individual buildings in 2811 optical images collected during surveys conducted after the 2021 eruption of La Soufrière volcano, St Vincent and the Grenadines. …”
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GAN-enhanced deep learning for improved Alzheimer's disease classification and longitudinal brain change analysis by Purushottam Pandey, Surbhi Bhatia Khan, Surbhi Bhatia Khan, Surbhi Bhatia Khan, Jyoti Pruthi, Eid Albalawi, Ali Algarni, Ahlam Almusharraf

“…Furthermore, brain simulation models are integrated into this framework to provide deeper insights into the underlying neural mechanisms of AD. …”
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Extracellular Vesicle Proteome Analysis Improves Diagnosis of Recurrence in Triple‐Negative Breast Cancer by Ju‐Yong Hyon, Min Woo Kim, Kyung‐A Hyun, Yeji Yang, Seongmin Ha, Jee Ye Kim, Young Kim, Sunyoung Park, Hogyeong Gawk, Heaji Lee, Suji Lee, Sol Moon, Eun Hee Han, Jin Young Kim, Ji Yeong Yang, Hyo‐Il Jung, Seung Il Kim, Young‐Ho Chung

“…To enhance biomarker selection robustness, we implemented a hybrid machine learning framework integrating LsBoost, convolutional neural networks, and support vector machines. …”
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Estimation of Ca2+ wet deposition in the Northern Hemisphere by use of CNN deep-learning model by Wanying Chen, Xingcheng Lu, Chaofan Xian, Xu Sun, Yiang Chen, Mingyun Hu, Geng Li, Jimmy C.H. Fung

“…This study addresses this gap by developing a convolutional neural network (CNN) framework to estimate Ca2+ wet deposition across the Northern Hemisphere from 2000 to 2022, with a high spatial resolution of 0.5° x 0.5°. …”
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Spatial Localization of Broadleaf Species in Mixed Forests in Northern Japan Using UAV Multi-Spectral Imagery and Mask R-CNN Model by Nyo Me Htun, Toshiaki Owari, Satoshi N. Suzuki, Kenji Fukushi, Yuuta Ishizaki, Manato Fushimi, Yamato Unno, Ryota Konda, Satoshi Kita

“…A Mask Region-based Convolutional Neural Network (Mask R-CNN) framework was employed to recognize and classify single tree crowns based on annotated training data. …”
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UAV Hyperspectral Remote Sensing Image Classification: A Systematic Review by Zhen Zhang, Lehao Huang, Qingwang Wang, Linhuan Jiang, Yemao Qi, Shunyuan Wang, Tao Shen, Bo-Hui Tang, Yanfeng Gu

“…This article provides an in-depth and systematic review of UAV HSI classification techniques, systematically examining the evolution from traditional machine learning approaches, such as sparse coding, compressed sensing, and kernel methods, to cutting-edge deep learning frameworks, including convolutional neural networks, Transformer models, recurrent neural networks, graph convolutional networks, generative adversarial networks, and hybrid models. …”
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Deep learning with data transformation improves cancer risk prediction in oral precancerous conditions by John Adeoye, Yuxiong Su

“…Conclusion: Tabular-to-2D image data transformation may improve the use of structured input features for developing optimal intelligent models for oral cancer risk prediction in OL and OLD using convolutional networks. This approach may have the potential to robustly handle structured data in multimodal DL frameworks for oncological outcome prediction.…”
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Computer-vision based automatic rider helmet violation detection and vehicle identification in Indian smart city scenarios using NVIDIA TAO toolkit and YOLOv8 by Uttam U. Deshpande, Goh Kah Ong Michael, Sufola Das Chagas Silva Araujo, Vaidehi Deshpande, Rudragoud Patil, Ramchandra Alias Ameet Chate, Varun R. Tandur, Supreet S. Goudar, Shreya Ingale, Vaishnavi Charantimath

“…In the first stage, we utilized a highly efficient, robust, and accurate object identification DetectNet (Model 1) framework developed by NVIDIA, and it uses the ResNet18 Convolutional Neural Network (CNN) architecture as part of the Transfer Learning Toolkit known as TAO (Train, Adapt, Optimize). …”
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Building a machine learning-assisted echocardiography prediction tool for children at risk for cancer therapy-related cardiomyopathy by Lindsay A. Edwards, Christina Yang, Surbhi Sharma, Zih-Hua Chen, Lahari Gorantla, Sanika A. Joshi, Nicolas J. Longhi, Nahom Worku, Jamie S. Yang, Brandy Martinez Di Pietro, Saro Armenian, Aarti Bhat, William Border, Sujatha Buddhe, Nancy Blythe, Kayla Stratton, Kasey J. Leger, Wendy M. Leisenring, Lillian R. Meacham, Paul C. Nathan, Shanti Narasimhan, Ritu Sachdeva, Karim Sadak, Eric J. Chow, Patrick M. Boyle

“…We used holdout subsets in a 10-fold cross-validation framework and standard metrics to assess model performance (e.g., F1-score, area under the precision-recall curve [AUPRC]). …”
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Category semantic and global relation distillation for object detection by Yanpeng LIANG, Zhonggui MA, Zongjie WANG, Zhuo LI

“…To settle these questions, this study proposes a novel attention-based knowledge distillation framework for object detection, striking a better balance between efficiency and accuracy. …”
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Driver Drowsiness Detection Using Swin Transformer and Diffusion Models for Robust Image Denoising by Samy Abd El-Nabi, Ahmed F. Ibrahim, El-Sayed M. El-Rabaie, Osama F. Hassan, Naglaa F. Soliman, Khalil F. Ramadan, Walid El-Shafai

“…This study presents a robust and scalable driver drowsiness detection framework that integrates a Swin Transformer-based deep learning model with a diffusion model for image denoising. …”
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AI-driven pharmacovigilance: Enhancing adverse drug reaction detection with deep learning and NLP by Dr. Bharti Khemani, Dr. Sachin Malave, Samyukta Shinde, Mandvi Shukla, Razzaq Shikalgar, Harshita Talwar

“…This study proposes a hybrid AI-driven framework that integrates structured (e.g., patient demographics, lab results) and unstructured data (e.g., clinical notes) to detect ADRs using advanced deep learning and NLP methods. …”
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Forecasting Short- and Long-Term Wind Speed in Limpopo Province Using Machine Learning and Extreme Value Theory by Kgothatso Makubyane, Daniel Maposa

“…To the best of our knowledge, this is the first study to successfully combine EVT and machine learning for short- and long-term wind speed forecasting, providing a novel framework for reliable wind energy planning.…”
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