Enhancing Autism Spectrum Disorder Classification with Lightweight Quantized CNNs and Federated Learning on ABIDE-1 Dataset by Simran Gupta, Md. Rahad Islam Bhuiyan, Sadia Sultana Chowa, Sidratul Montaha, Rashik Rahman, Sk. Tanzir Mehedi, Ziaur Rahman

“…We propose a federated learning (FL) framework to ensure data privacy, which allows decentralized training across different data centers without compromising local data security. …”
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Forward Predicting Chromatic-Optical Parameters of the Mixed Light of White-Red Light-Emitting Diode Configurations Based on Deep Learning Algorithms by Songsheng Lin, Huanting Chen, Yin Zheng, Quanji Xie, Xuehua Shen, Huichuan Lin, Shuo Lin, Yan Li

“…This paper presents a novel deep learning framework that integrates experimental measurements with advanced modeling techniques to predict key optical parameters, including luminous flux, correlated color temperature (CCT), and chromaticity coordinates of white-red light-emitting diodes (LED) configurations under diverse operating conditions. …”
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Deep Multi-Modal Skin-Imaging-Based Information-Switching Network for Skin Lesion Recognition by Yingzhe Yu, Huiqiong Jia, Li Zhang, Suling Xu, Xiaoxia Zhu, Jiucun Wang, Fangfang Wang, Lianyi Han, Haoqiang Jiang, Qiongyan Zhou, Chao Xin

“…To address this, we propose a deep learning framework, Multi-Modal Skin-Imaging-based Information-Switching Network (MDSIS-Net), for end-to-end skin lesion recognition. …”
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Deep learning-based diffusion MRI tractography: Integrating spatial and anatomical information by Yiqiong Yang, Yitian Yuan, Baoxing Ren, Ye Wu, Yanqiu Feng, Xinyuan Zhang

“…To improve the accuracy of streamline propagation predictions, we introduce a novel deep learning framework that integrates image-domain spatial information and anatomical information along tracts, with the former extracted through convolutional layers and the latter modeled via a Transformer-decoder. …”
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Research on multi class pests identification and detection based on fusion attention mechanism with Mask-RCNN-CBAM by Xingwang Wang, Xingwang Wang, Xingwang Wang, Can Hu, Xufeng Wang, Hainie Zha, Xueyong Chen, Shanshan Yuan, Jing Zhang, Jianfeng Liao, Zhangying Ye

“…This study addresses challenges in agricultural pest detection, such as false positives and missed detections in complex environments, by proposing an enhanced Mask-RCNN model integrated with a Convolutional Block Attention Module (CBAM). The framework combines three innovations: (1) a CBAM attention mechanism to amplify pest features while suppressing background noise; (2) a feature-enhanced pyramid network (FPN) for multi-scale feature fusion, enhancing small pest recognition; and (3) a dual-channel downsampling module to minimize detail loss during feature propagation. …”
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Development of an Efficient Grading Model for Maize Seedlings Based on Indicator Extraction in High-Latitude Cold Regions of Northeast China by Song Yu, Yuxin Lu, Yutao Zhang, Xinran Liu, Yifei Zhang, Mukai Li, Haotian Du, Shan Su, Jiawang Liu, Shiqiang Yu, Jiao Yang, Yanjie Lv, Haiou Guan, Chunyu Zhang

“…It provides a robust framework for evaluating seedling quality under low-temperature stress.…”
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Ensemble Streamflow Simulations in a Qinghai–Tibet Plateau Basin Using a Deep Learning Method with Remote Sensing Precipitation Data as Input by Jinqiang Wang, Zhanjie Li, Ling Zhou, Chi Ma, Wenchao Sun

“…These findings highlight that the proposed 1D CNN ensemble simulation framework has great potential to improve streamflow estimations using remote sensing precipitation data as input and may provide new insight into how deep learning methods advance the application of remote sensing in hydrological research.…”
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Deep Learning-Based Super-Resolution of Remote Sensing Images for Enhanced Groundwater Quality Assessment and Environmental Monitoring in Urban Areas by Peng Shu, Rana Waqar Aslam, Iram Naz, Bushra Ghaffar, Dmitry E. Kucher, Abdul Quddoos, Danish Raza, M. Abdullah-Al-Wadud, Rana Muhammad Zulqarnain

“…This study presents a novel deep learning-based super-resolution framework for enhancing remote sensing imagery to assess groundwater quality and environmental conditions in Lahore, Pakistan. …”
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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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