Predicting chronic pain using wearable devices: a scoping review of sensor capabilities, data security, and standards compliance by Johannes C. Ayena, Johannes C. Ayena, Amina Bouayed, Amina Bouayed, Myriam Ben Arous, Myriam Ben Arous, Youssef Ouakrim, Youssef Ouakrim, Karim Loulou, Karim Loulou, Darine Ameyed, Isabelle Savard, Leila El Kamel, Neila Mezghani, Neila Mezghani

“…Data extraction focused on device types, sensor quality, compliance with health standards, and the predictive algorithms employed.ResultsWearable devices show promise in correlating physiological markers with CP, but few studies integrate predictive models. …”
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A Multi-Branch Convolution and Dynamic Weighting Method for Bearing Fault Diagnosis Based on Acoustic–Vibration Information Fusion by Xianming Sun, Yuhang Yang, Changzheng Chen, Miao Tian, Shengnan Du, Zhengqi Wang

“…To address these challenges, we propose a dynamic weighted multimodal fault diagnosis model based on the fusion of acoustic and vibration information. …”
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Programmed cell death signatures-driven microglial transformation in Alzheimer’s disease: single-cell transcriptomics and functional validation by Mi-Mi Li, Ying-Xia Yang, Ya-Li Huang, Shu-Juan Wu, Wan-Li Huang, Li-Chao Ye, Ying-Ying Xu

“…An integrated machine learning framework, combining 12 algorithms was used to construct a PCDS model. The performance of PCDS was validated using multiple independent cohorts. …”
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Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder by Shagufta Almas, Fazli Wahid, Sikandar Ali, Ahmed Alkhyyat, Kamran Ullah, Jawad Khan, Youngmoon Lee

“…Leveraging a comprehensive dataset from KAGGLE containing 35,126 retinal fundus images representing one healthy (normal) stage and four DR stages, our proposed model demonstrates superior accuracy compared to existing deep learning algorithms. …”
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A Robust Multispectral Reconstruction Network from RGB Images Trained by Diverse Satellite Data and Application in Classification and Detection Tasks by Xiaoning Zhang, Zhaoyang Peng, Yifei Wang, Fan Ye, Tengying Fu, Hu Zhang

“…Then, the EuroSAT multispectral satellite images for eight land cover types were selected to produce natural RGB using Model-TN as training image pairs, which were input into a residual network integrating channel attention mechanisms to train the multispectral images reconstruction model, Model-NM. …”
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Federated Learning Based on an Internet of Medical Things Framework for a Secure Brain Tumor Diagnostic System: A Capsule Networks Application by Roman Rodriguez-Aguilar, Jose-Antonio Marmolejo-Saucedo, Utku Köse

“…By utilizing specific brain MRI datasets, the model enables multiple local capsule networks (CapsNet) to achieve improved classification results. …”
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Duck Egg Crack Detection Using an Adaptive CNN Ensemble with Multi-Light Channels and Image Processing by Vasutorn Chaowalittawin, Woranidtha Krungseanmuang, Posathip Sathaporn, Boonchana Purahong

“…In current practice, human inspectors use standard white light for crack detection, and many researchers have focused primarily on improving detection algorithms without addressing lighting limitations. …”
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FA-Unet: A Deep Learning Method with Fusion of Frequency Domain Features for Fruit Leaf Disease Identification by Xiaowei Li, Wenlin Wu, Fenghua Zhu, Shenhao Guan, Wenliang Zhang, Zheng Li

“…However, deep learning models often perform poorly in complex backgrounds, and in some cases, they even outperform traditional algorithms. …”
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Research on Methods for the Recognition of Ship Lights and the Autonomous Determination of the Types of Approaching Vessels by Xiangyu Gao, Yuelin Zhao

“…Firstly, to address the challenges of the small target characteristics of ship lights and complex environmental interference, an improved YOLOv8 model is developed: The dilation-wise residual (DWR) module is introduced to optimize the feature extraction capability of the C2f structure. …”
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BoneCLIP-XGBoost: A Multimodal Approach for Bone Fracture Diagnosis by Zhihao Su, Yuhuai Zhou, Jianqun Zhou, Hanhua Cao, Huanping Zhang

“…The integration of visual and textual data in medical diagnostics holds significant potential for improving the accuracy and reliability of clinical decision-making. …”
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Overbounding of Near Real-Time Estimated Ionospheric Gradient Slope in Low-Latitude Regions by Maria Caamano, Jose Miguel Juan, Jaume Sanz, Sam Pullen

“…This paper proposes to expand this approach by including the derivation of an uncertainty model for the estimated gradient slope, allowing the threat model to be substituted with the near real-time estimated and overbounded gradient slope in current GBAS algorithms. …”
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Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning Approach by Doaa A. Abdo, A. A. Salama, Alaa A. Abdelmegaly, Hanan Khadari Mahdi Mahmoud

“…Our research showed that our proposed approach, which used neutrosophic sets, improved imputation accuracy and strengthened model reliability. …”
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Applying a Convolutional Vision Transformer for Emotion Recognition in Children with Autism: Fusion of Facial Expressions and Speech Features by Yonggu Wang, Kailin Pan, Yifan Shao, Jiarong Ma, Xiaojuan Li

“…Typically using single-modal data to analyze the emotional states of children with autism, previous research has found that the accuracy of recognition algorithms must be improved. Our study creates datasets on the facial and speech emotions of children with autism in their natural states. …”
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Prediction of High-ozone Events Using GAM, SMOTE, and Tail Dependence Approaches in Texas (2005–2019) by Benjamin Brown-Steiner, Xiong Zhou, Matthew J. Alvarado, Brook T. Russell

“…Abstract We test three methods for ozone prediction in the El Paso (ELP) and Houston-Galveston-Brazoria (HGB) regions of Texas from 2005–2019: (1) a Generalized Additive Model (GAMs) approach; (2) a GAM approach with the addition of the Synthetic Minority Over-sampling TEchnique (SMOTE) and (3) a tail dependence modeling approach based in extreme value theory (EVT). …”
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River Surface Space–Time Image Velocimetry Based on Dual-Channel Residual Network by Ling Gao, Zhen Zhang, Lin Chen, Huabao Li

“…An adaptive threshold Sobel operator in the edge channel improves the model’s ability to extract edge features in STI. …”
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The Potential of Unsupervised Induction of Harmonic Syntax for Jazz by Ruben Cartuyvels, John Koslovsky, Marie-Francine Moens

“…Hierarchical structures describing a syntax of harmony have long been studied and proposed by music theorists, but algorithms that model these structures either require costly expert annotations for training or are based on music theorists' predispositions about harmonic syntax. …”
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Conformal Segmentation in Industrial Surface Defect Detection with Statistical Guarantees by Cheng Shen, Yuewei Liu

“…Some machine learning algorithms and artificial intelligence models for defect detection, such as Convolutional Neural Networks (CNNs), present outstanding performance, but they are often data-dependent and cannot provide guarantees for new test samples. …”
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Landslide Identification from Post-Earthquake High-Resolution Remote Sensing Images Based on ResUNet–BFA by Zhenyu Zhao, Shucheng Tan, Yiquan Yang, Qinghua Zhang

“…The experimental results indicate that the ResUNet–BFA model outperforms widely used algorithms in extracting landslide boundaries and details, resulting in fewer misclassifications and omissions. …”
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IDENTIFYING IMPORTANT GENES IN OVARIAN CANCER FROM HIGH-DIMENSIONAL MICROARRAY DATA USING SIFS-CART METHOD by Ni Kadek Emik Sapitri, Umu Sa'adah, Nur Shofianah

“…Many studies only focus on improving the machine learning classification algorithms to achieve higher performance. …”
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Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture by Junaid Aftab, Muhammad Attique Khan, Sobia Arshad, Shams ur Rehman, Dina Abdulaziz AlHammadi, Yunyoung Nam

“…GradCAM, an explainable artificial intelligence (XAI) approach, analyzes custom models. The proposed architecture was tested on five publically available datasets of different imaging modalities and obtained improved accuracy of 98.6 (INBreast), 95.3 (KVASIR), 94.3 (ISIC2018), 95.0 (Lung Cancer), and 98.8% (Oral Cancer), respectively. …”
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