Linguistic-visual based multimodal Yi character recognition by Haipeng Sun, Xueyan Ding, Zimeng Li, Jian Sun, Hua Yu, Jianxin Zhang

“…Abstract The recognition of Yi characters is challenged by considerable variability in their morphological structures and complex semantic relationships, leading to decreased recognition accuracy. …”
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GRU2-Net: Global response double U-shaped network for lesion segmentation in ultrasound images by Xiaokai Jiang, Xuewen Ding, Jinying Ma, Chunyu Liu, Xinyi Li

“…To improve global context modeling, this paper proposes the Global Response Transformer Block in the bottleneck, enabling the network to capture long-range dependencies and structural variability in lesion appearance. By modeling interactions across distant regions, the block more effectively captures the variability in lesion shape, size, and location, enhancing segmentation accuracy for complex and irregular structures in ultrasound images. …”
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Long Term Predictability of Southern Ocean Surface Nutrients Using Explainable Neural Networks by Gian Giacomo Navarra, Curtis Deutsch, Antonios Mamalakis, Andrew Margolskee, Graeme MacGilchrist

“…Abstract The Southern Ocean is a region of high surface nutrient content, reflecting an inefficient biological carbon pump. The variability, predictability, and causes of changes in these nutrient levels on interannual to decadal time scales remain unclear. …”
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Modeling energy consumption indexes of an industrial cement ball mill for sustainable production by Saeed Chehreh Chelgani, Rasoul Fatahi, Ali Pournazari, Hamid Nasiri

“…To fill the gap, this study developed a CL by examining different AI models (Random Forest, Support Vector Regression, Convolutional Neural Network, extreme gradient boosting, CatBoost, and SHapley Additive exPlanations) for modeling energy consumption indexes of a close ball mill circuit in a cement plant to address the effectiveness of operating variables. …”
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Distinguishable IQ Feature Representation for Domain-Adaptation Learning of WiFi Device Fingerprints by Abdurrahman Elmaghbub, Bechir Hamdaoui

“…., day/time, location, channel, etc.) changes and variability. This work proposes a novel IQ data representation and feature design, termed Double-Sided Envelope Power Spectrum or <monospace>EPS</monospace>, that is proven to significantly overcome the domain adaptation challenges associated with WiFi transmitter fingerprinting. …”
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RDM-YOLO: A Lightweight Multi-Scale Model for Real-Time Behavior Recognition of Fourth Instar Silkworms in Sericulture by Jinye Gao, Jun Sun, Xiaohong Wu, Chunxia Dai

“…Current manual observation paradigms face critical limitations in temporal resolution, inter-observer variability, and scalability. This study presents RDM-YOLO, a computationally efficient deep learning framework derived from YOLOv5s architecture, specifically designed for the automated detection of three essential behaviors (resting, wriggling, and eating) in fourth instar silkworms. …”
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Analyzing spatiotemporal variation in suspended particulate matter in lakes using remote sensing by WEI Junyan, ZHAO Yiming, HAO Yanling, JIA Xiaoxue, MA Xinyan

“…The most influential variable was the B4·B5, followed by B4, B4+B5, and B5·(B4+B5). ③ From 2017 to 2023, annual average SPM concentrations ranged from 8.43 to 11.68 mg/L, showing a slight downward trend. …”
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A Robust Hybrid CNN+ViT Framework for Breast Cancer Classification Using Mammogram Images by Vasudha Rani Patheda, Gunda Laxmisai, B. V. Gokulnath, S. P. Siddique Ibrahim, S. Selva Kumar

“…This research addresses the variability and potential oversight in radiologists&#x2019; manual mammogram interpretations, aiming to enhance classification accuracy by combining Convolution Neural Networks (CNNs) and Vision Transformers (ViTs). …”
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The best angle correction of basketball shooting based on the fusion of time series features and dual CNN by Meicai Xiao

“…However, the current method is limited by the variability of the shape base, ignoring dynamic features and visual information, and there are some problems in the process of feature extraction and correction of related actions. …”
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A Machine Learning Model for Procurement of Secondary Reserve Capacity in Power Systems with Significant vRES Penetrations by João Passagem dos Santos, Hugo Algarvio

“…The growing investment in variable renewable energy sources is changing how electricity markets operate. …”
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H-ConvLSTM to Estimate Reference Evapotranspiration From Air Temperature and Relative Humidity by Abdul Haris, M. Marimin, Sri Wahjuni, Budi Indra Setiawan

“…These three algorithms have been extensively evaluated and validated due to their ability to predict and estimate ETo data using temperature (T), relative humidity (RH), and solar radiation (Rs) as variables. This paper presents a comparison of the results of the three algorithms using only two variables, namely temperature and relative humidity, without the inclusion of solar radiation variables. …”
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Feature Fusion to Improve YOLOv8 for Segmenting and Classifying Aerial Images of Tree Crowns by Ziyi Sun, Bing Xue, Mengjie Zhang, Jan Schindler

“…In varied rural landscapes, canopy imagery often includes a mix of tiny, small, and medium tree objects scattered across diverse terrains, from standalone trees to densely clustered forest stands. This variability poses significant challenges to traditional instance segmentation methods. …”
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Enhancing Real-Time Aerial Image Object Detection with High-Frequency Feature Learning and Context-Aware Fusion by Xin Ge, Liping Qi, Qingsen Yan, Jinqiu Sun, Yu Zhu, Yanning Zhang

“…Aerial image object detection faces significant challenges due to notable scale variations, numerous small objects, complex backgrounds, illumination variability, motion blur, and densely overlapping objects, placing stringent demands on both accuracy and real-time performance. …”
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RSDCNet: An efficient and lightweight deep learning model for benign and malignant pathology detection in breast cancer by Yuan Liu, Haipeng Li, Zhu Zhu, Chen Chen, Xiaojing Zhang, Gongsheng Jin, Hongtao Li

“…Traditional diagnostic methods, reliant on manual interpretation, are not only time-intensive and subjective but also susceptible to variability based on the pathologist's expertise and workload. …”
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Automatic detection of optic canal fractures and recognition and segmentation of anatomical structures in the orbital apex based on artificial intelligence by Yu-Lin Li, Yu-Hao Li, Mu-Yang Wei, Guang-Yu Li

“…However, diagnosing OCF can be challenging for inexperienced clinicians due to atypical OCF changes in imaging studies and variability in optic canal anatomy. This study aimed to develop an artificial intelligence (AI) image recognition system for OCF to assist in diagnosing OCF and segmenting important anatomical structures in the orbital apex.MethodsUsing the YOLOv7 neural network, we implemented OCF localization and assessment in CT images. …”
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Intelligent model for forecasting fluctuations in the gold price by Mahdieh Tavassoli, Mahnaz Rabeei, Kiamars Fathi Hafshejani

“…Purpose: The present study aims to identify the most important variables affecting the fluctuations of gold prices. …”
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A MFR Work Modes Recognition Method Based on Dual-Scale Feature Extraction by Zhiyuan Li, Xuan Fu, Chengjian Mo, Jianlong Tang, Ronghua Guo, Wenbo Li

“…The recognition method first obtains the variable-length sequence processing capability through pulse sequence segmentation. …”
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Fault Classification of 3D-Printing Operations Using Different Types of Machine and Deep Learning Techniques by Satish Kumar, Sameer Sayyad, Arunkumar Bongale

“…The ML models such as k-nearest neighbor (KNN), decision tree (DT), extra trees (ET), and random forest (RF) with convolutional neural network (CNN) as a DL model are used to classify the variable operation printing parameters. …”
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Identification of Low‐Value Defects in Infrared Images of Porcelain Insulators Based on STCE‐YOLO Algorithm by Shaotong Pei, Weiqi Wang, Chenlong Hu, Keyu Li, Haichao Sun, Mianxiao Wu, Bo Lan

“…And the multiple attention mechanism improved to the third generation of variability convolution is used to detect the head to improve the accuracy of the algorithm's target localization. …”
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Machine learning-based state of charge estimation: A comparison between CatBoost model and C-BLSTM-AE model by Abderrahim Zilali, Mehdi Adda, Khaled Ziane, Maxime Berger

“…The C-BLSTM-AE model achieves a low Mean Absolute Error (MAE) of 0.52 % under fixed ambient temperature conditions and maintains a MAE of 1.03 % for variable ambient temperatures. The CatBoost model achieves a MAE of 0.69 % with fixed temperature settings and a MAE of 1.09 % under variable temperature conditions.…”
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