Reducing lead requirements for wearable ECG: Chest lead reconstruction with 1D-CNN and Bi-LSTM by Kazuki Hebiguchi, Hiroyoshi Togo, Akimasa Hirata

“…Leveraging the PTB-XL ECG dataset, we preprocessed the signals to eliminate noise and trained a model integrating 1D convolutional layers with a Bi-directional Long Short-Term Memory (Bi-LSTM) architecture. …”
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Enhancing chronic wound assessment through agreement analysis and tissue segmentation by Ana C. Morgado, Rafaela Carvalho, Ana Filipa Sampaio, Maria J. M. Vasconcelos

“…However, the current manual process of tissue segmentation and quantification, which is an indicator of the healing progress, is time-consuming and subject to variability, so automated methods that can effectively monitor wound healing are required. …”
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Accurate Sugarcane Detection and Row Fitting Using SugarRow-YOLO and Clustering-Based Spline Methods for Autonomous Agricultural Operations by Guiqing Deng, Fangyue Zhou, Huan Dong, Zhihao Xu, Yanzhou Li

“…In addition to addressing the problem of large variability in row spacing and plant spacing of sugarcane, this paper introduces the DBSCAN clustering algorithm and combines it with a smooth spline curve to fit the crop rows in order to realize the accurate extraction of crop rows. …”
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Noninvasive Continuous Glucose Monitoring Using Multimodal Near-Infrared, Temperature, and Pressure Signals on the Earlobe by Jongdeog Kim, Bong Kyu Kim, Mi-Ryong Park, Hyoyoung Cho, Chul Huh

“…Continuous glucose data capturing pre- and postprandial variations were analyzed using a 1D convolutional neural network (Conv1D). For three subjects under stable physiological conditions, the model achieved 97.0% Clarke error grid (CEG) A-Zone accuracy and a mean absolute relative difference (MARD) of 5.2%. …”
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AI-driven smart agriculture using hybrid transformer-CNN for real time disease detection in sustainable farming by Zhuo Zeng, Tariq Mahmood, Yu Wang, Amjad Rehman, Muhammad Akram Mujahid

“…This study introduces the AttCM-Alex model, a novel deep-learning framework designed to boost the detection and classification of plant diseases under challenging environmental conditions. By integrating convolutional operations with self-attention mechanisms, AttCM-Alex effectively addresses the variability in light intensity and image noise, ensuring robust performance. …”
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Advances in ECG and PCG-based cardiovascular disease classification: a review of deep learning and machine learning methods by Asmaa Ameen, Ibrahim Eldesouky Fattoh, Tarek Abd El-Hafeez, Kareem Ahmed

“…A pressing necessity exists for a study on the variability of these factors and their impact on cardiovascular disease (CVD). …”
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Randomization-Driven Hybrid Deep Learning for Diabetic Retinopathy Detection by A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, M. Murugappan

“…Despite advances, existing diagnostic methods face challenges such as resource dependency, variability in accuracy, and limited accessibility, especially in underserved regions. …”
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I-AIR: intention-aware travel itinerary recommendation via multi-signal fusion and spatiotemporal constraints by Xiao Cui, Zhihua Wang, Ping Li, Qiang Xu

“…The model combines a multi-head self-attention transformer to capture the sequential and temporal dynamics of user behavior, with a graph convolutional network (GCN) that models complex co-visitation patterns among POIs. …”
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Deep Learning Techniques for Lung Cancer Diagnosis with Computed Tomography Imaging: A Systematic Review for Detection, Segmentation, and Classification by Kabiru Abdullahi, Kannan Ramakrishnan, Aziah Binti Ali

“…However, challenges persist, including dataset scarcity, annotation variability, and population generalizability. Hybrid architectures, such as convolutional neural networks (CNNs) and transformers, show promise in improving nodule localization. …”
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DermViT: Diagnosis-Guided Vision Transformer for Robust and Efficient Skin Lesion Classification by Xuejun Zhang, Yehui Liu, Ganxin Ouyang, Wenkang Chen, Aobo Xu, Takeshi Hara, Xiangrong Zhou, Dongbo Wu

“…Currently, skin lesion classification faces challenges such as lesion–background semantic entanglement, high intra-class variability, artifactual interference, and more, while existing classification models lack modeling of physicians’ diagnostic paradigms. …”
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Thyroid nodule segmentation in ultrasound images using transformer models with masked autoencoder pre-training by Yi Xiang, Rajendra Acharya, Quan Le, Jen Hong Tan, Chiaw-Ling Chng

“…Unlike traditional convolutional neural networks (CNNs), transformers capture global context from the first layer, enabling more comprehensive image representation, which is crucial for identifying subtle nodule boundaries. …”
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A novel deep learning method for large-scale analysis of bone marrow adiposity using UK Biobank Dixon MRI data by David M. Morris, Chengjia Wang, Giorgos Papanastasiou, Calum D. Gray, Wei Xu, Samuel Sjöström, Sammy Badr, Julien Paccou, Scott IK Semple, Tom MacGillivray, William P. Cawthorn

“…Bone marrow (BM) segmentation was automated using a new lightweight attention-based 3D U-Net convolutional neural network that improved segmentation of small structures from large volumetric data. …”
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Deep learning (DL)‐based advancements in prostate cancer imaging: Artificial intelligence (AI)‐based segmentation of 68Ga‐PSMSA PET for tumor volume assessment by Sharjeel Usmani, Khulood Al Riyami, Subash Kheruka, Shah P Numani, Rashid al Sukaiti, Maria Ahmed, Nadeem Pervez

“…This review discusses the principles underlying AI‐based segmentation algorithms, including convolutional neural networks, and their applications in PC imaging. …”
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Privacy–preserving dementia classification from EEG via hybrid–fusion EEGNetv4 and federated learning by Muhammad Umair, Muhammad Shahbaz Khan, Muhammad Hanif, Wad Ghaban, Ibtehal Nafea, Sultan Noman Qasem, Sultan Noman Qasem, Faisal Saeed

“…Five convolutional neural networks (EEGNetv1, EEGNetv4, EEGITNet, EEGInception, EEGInceptionERP) have been evaluated on resting-state EEG dataset comprising 88 subjects. …”
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A Two-Stage Deep Fusion Integration Framework Based on Feature Fusion and Residual Correction for Gold Price Forecasting by Cihai Qiu, Yitian Zhang, Xunrui Qian, Chuhang Wu, Jiacheng Lou, Yang Chen, Yansong Xi, Weijie Zhang, Zhenxi Gong

“…Nonetheless, traditional single prediction models usually suffer from limited predictive performance and fail to capture complex variability of market behavior. Aiming to solve these limitations, an innovative two-stage hybrid deep integration framework that combines feature extraction and residual correction techniques is proposed with a view to predicting the gold price more accurately. …”
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Automated lung cancer detection using novel genetic TPOT feature optimization with deep learning techniques by Mohamed Hammad, Mohammed ElAffendi, Muhammad Asim, Ahmed A. Abd El-Latif, Radwa Hashiesh

“…Deep learning, particularly convolutional neural networks (CNNs), offers an automated alternative capable of learning intricate patterns from medical images. …”
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PM2.5 Forecasting at U.S. Embassies and Consulates Worldwide Using NASA Model Powered by Machine Learning by Junhyeon Seo, Alqamah Sayeed, Seohui Park, John Kerekes, Stephanie M. Christel, Mary T. Tran, Pawan Gupta

“…Multi‐channel input data was prepared using the Goddard Earth Observing System forward processing for meteorology and aerosol forecasts over 72 hr. An advanced convolutional neural network addressed high‐dimensional data and nonlinearities between inputs and outputs. …”
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An Effective Deep Neural Network Architecture for EEG-Based Recognition of Emotions by Khadidja Henni, Neila Mezghani, Amar Mitiche, Lina Abou-Abbas, Amel Benazza-Ben Yahia

“…Current research in EEG-based emotion recognition faces significant challenges due to the high-dimensionality and variability of EEG signals, which complicate accurate classification. …”
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TDFNet: twice decoding V-Mamba-CNN Fusion features for building extraction by Wenlong Wang, Peng Yu, Mengmeng Li, Xiaojing Zhong, Yuanrong He, Hua Su, Yunxuan Zhou

“…Therefore, methods integrating convolutional neural networks (CNNs) and visual transformers (ViTs) are popular nowadays. …”
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Spatiotemporal Forecasting of Solar and Wind Energy Production: A Robust Deep Learning Model with Attention Framework by Md. Shadman Abid, Razzaqul Ahshan, Mohammed Al-Abri, Rashid Al Abri

“…The variability in the spatiotemporal distribution of power generation is a significant challenge for accurately predicting renewable energy production patterns. …”
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