Development of a diagnostic classification model for lateral cephalograms based on multitask learning by Qiao Chang, Shaofeng Wang, Fan Wang, Beiwen Gong, Yajie Wang, Feifei Zuo, Xianju Xie, Yuxing Bai

“…Eight clinical classifications were employed, including sagittal and vertical skeletal facial patterns, maxillary and mandibular anteroposterior positions, inclinations of upper and lower incisors, as well as their anteroposterior positions. …”
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Enhancing LoRa-Based Outdoor Localization Accuracy Using Machine Learning by Nur Kelesoglu, Marzena Halama, Anna Strzoda

“…This architecture integrates the strengths of Deep Learning and tree-based models, aiming to capture both temporal signal patterns and structured input correlations for improved localization accuracy. …”
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Credit Scoring Prediction Using Deep Learning Models in the Financial Sector by Xi Shi, Dingfen Tang, Yike Yu

“…Existing approaches often struggle with integrating structured numerical records and unstructured user behavior signals, limiting their ability to capture meaningful temporal and non-linear patterns. In the swiftly transforming domain of computational science, the incorporation of sophisticated machine learning algorithms has emerged as a critical driver in addressing these challenges. …”
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Enhancing Time Series Product Demand Forecasting With Hybrid Attention-Based Deep Learning Models by Xuguang Zhang, Pan Li, Xu Han, Yongbin Yang, Yiwen Cui

“…This paper presents a novel approach to time series forecasting by leveraging advanced deep learning techniques, specifically focusing on hybrid models that combine attention mechanisms with traditional recurrent neural networks. Our proposed method, the Hybrid Attention-based Long Short-Term Memory (HA-LSTM) network, integrates multi-head self-attention modules with LSTM layers to capture both long-term dependencies and local temporal patterns in time series data. …”
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Optimizing HVAC energy efficiency in low-energy buildings: a comparative analysis of reinforcement learning control strategies under Tehran climate conditions by Mohammad Anvar Adibhesami, Amir Hassanzadeh

“…We conducted comprehensive simulation assessments using the EnergyPlus and HoneybeeGym platforms to evaluate two distinct reinforcement learning models: traditional Q-learning (Model A) and deep reinforcement learning (DRL) with neural networks (Model B). …”
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Chinese Paper-Cutting Style Transfer via Vision Transformer by Chao Wu, Yao Ren, Yuying Zhou, Ming Lou, Qing Zhang

“…To further embody the symmetrical structures and hollowed hierarchical patterns intrinsic to Chinese paper-cutting, the multi-level feature contrastive learning module is designed based on a contrastive learning strategy. …”
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Integration of whole genome resequencing and transcriptome sequencing to identify candidate genes for tall and short traits in Baicheng Fatty chickens by Jiaqi Li, Kaixu Chen, Mengting Zhu, Jingdong Bi, Honggang Tang, Weiyi Gao

“…These genes may influence the growth and developmental patterns of skeletal structures, though their regulatory mechanisms require further investigation. …”
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Interpretable Deep Learning Models for Arrhythmia Classification Based on ECG Signals Using PTB-X Dataset by Ahmed E. Mansour Atwa, El-Sayed Atlam, Ali Ahmed, Mohamed Ahmed Atwa, Elsaid Md. Abdelrahim, Ali I. Siam

“…Deep learning (DL) methods are effective in ECG analysis due to their ability to learn complex patterns from raw signals. <b>Methods</b>: This study introduces two models: a custom convolutional neural network (CNN) with a dual-branch architecture for processing ECG signals and demographic data (e.g., age, gender), and a modified VGG16 model adapted for multi-branch input. …”
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Influence of boundary conditions and blood rheology on indices of wall shear stress from IVUS-based patient-specific stented coronary artery simulations by R. Patrick McCarthy, Peter J. Mason, David S. Marks, John F. LaDisa

“…Coronary stenting results in altered arterial geometry, local blood flow patterns, and wall shear stress (WSS), all of which can influence restenosis. …”
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Seq2Seq-based GRU autoencoder for anomaly detection and failure identification in coal mining hydraulic support systems by Kai Zhan, Cong Wang, Xigui Zheng, Chao Kong, Guangming Li, Wei Xin, Longhe Liu

“…Our proposed Recurrent Reconstruction Network model demonstrated excellent performance in complex coal mine hydraulic support data, effectively identifying anomalous regions and potential equipment failure characteristics while revealing potential deviations between model predictions and actual data, demonstrating its superior learning capability for periodic data patterns and equipment failure characteristics. …”
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Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanism by Aarthi Chelladurai, D.P. Manoj Kumar, S. S. Askar, Mohamed Abouhawwash, Mohamed Abouhawwash

“…This can involve leveraging the relationships and patterns observed within the dataset. The T-LSTM is based on the transductive learning approach and the scaled dot product attention evaluates the weights of each step based on the hidden state and image patches which helps in effective classification. …”
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Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates by Ocheme Anthony Ekle, William Eberle, Jared Christopher

“…Real-time anomaly detection in large, dynamic graph networks is crucial for real-world applications such as network intrusion prevention, fraud transaction identification, fake news detection in social networks, and uncovering abnormal communication patterns. …”
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Pan-genome-scale metabolic modeling of Bacillus subtilis reveals functionally distinct groups by Maxwell Neal, William Brakewood, Michael Betenbaugh, Karsten Zengler

“…Using the model and phenotypic predictions, we divide B. subtilis strains into five groups with distinct patterns of behavior that correlate across these features. …”
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Cheating Detection in Online Exams Using Deep Learning and Machine Learning by Bahaddin Erdem, Murat Karabatak

“…This study aims to identify the best deep learning and machine learning models to identify the unethical behavior patterns of learners using distance education exam data of an educational institution. …”
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DSAT: a dynamic sparse attention transformer for steel surface defect detection with hierarchical feature fusion by Shouluan Wu, Hui Yang, Liefa Liao, Chao Song, Yating Fang, Jianglong Fu, Tan Li

“…These defects exhibit diverse morphological characteristics and complex patterns, which pose substantial challenges to traditional detection models, particularly regarding multi-scale feature extraction and information retention across network depths. …”
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Comorbidities in the diseasome are more apparent than real: What Bayesian filtering reveals about the comorbidities of depression. by Peter Marx, Peter Antal, Bence Bolgar, Gyorgy Bagdy, Bill Deakin, Gabriella Juhasz

“…Comorbidity patterns have become a major source of information to explore shared mechanisms of pathogenesis between disorders. …”
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Lightweight hybrid transformers-based dyslexia detection using cross-modality data by Abdul Rahaman Wahab Sait, Yazeed Alkhurayyif

“…DL architectures, including convolutional neural networks (CNNs) and vision transformers (ViTs), encounter challenges in extracting meaningful patterns from cross-modality data. …”
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Tension and compression behaviors of Moso bamboo (Phyllostachys pubescens) for structural use by Qunying Mou, Siyang Ji, Peng Li, Xianjun Li, Zhangjing Chen, Lin He, Xiazhen Li

“…The compression strengths parallel to the grain increased from the bottom to the top of the stem, whereas a decreasing pattern was observed in terms of the strength variation perpendicular to the grain. …”
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Incidence of primary care chest pain consultations during the COVID-19 pandemic: an interrupted time series analysis with routine care data by Simone van den Bulk, Jasper W. A. van Egeraat, Annelieke H. J. Petrus, Mattijs E. Numans, Tobias N. Bonten

“…However, the pattern of chest pain in primary care is not clear yet. …”
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Mapping soil salinity in irrigated areas using hyperspectral UAV imagery by ZHOU Shixun, YIN Juan, WANG Juntao, CHANG Buhui, YANG Zhen

“…Four models, including multiple linear stepwise regression (MLSR), partial least squares regression (PLSR), support vector machine regression (SVR), and backpropagation neural network (BPNN), were evaluated for their accuracy to estimate soil salinity. …”
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