A study on early diagnosis for fracture non-union prediction using deep learning and bone morphometric parameters by Hui Yu, Qiyue Mu, Zhi Wang, Yu Guo, Jing Zhao, Guangpu Wang, Qingsong Wang, Xianghong Meng, Xiaoman Dong, Shuo Wang, Jinglai Sun

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Drug-target binding affinity prediction based on power graph and word2vec by Jing Hu, Shuo Hu, Minghao Xia, Kangxing Zheng, Xiaolong Zhang

“…Abstract Background Drug and protein targets affect the physiological functions and metabolic effects of the body through bonding reactions, and accurate prediction of drug-protein target interactions is crucial for drug development. In order to shorten the drug development cycle and reduce costs, machine learning methods are gradually playing an important role in the field of drug-target interactions. …”
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SE-TFF: Adaptive Tourism-Flow Forecasting Under Sparse and Heterogeneous Data via Multi-Scale SE-Net by Jinyuan Zhang, Tao Cui, Peng He

“…Accurate and timely forecasting of cross-regional tourist flows is essential for sustainable destination management, yet existing models struggle with sparse data, complex spatiotemporal interactions, and limited interpretability. This paper presents SE-TFF, a multi-scale tourism-flow forecasting framework that couples a Squeeze-and-Excitation (SE) network with reinforcement-driven optimization to adaptively re-weight environmental, economic, and social features. …”
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DynaOOD-Net: Dynamic Progressive Feature Fusion and Energy Balancing for Robust Out-of-Distribution Detection by Jiting Zhou, Zhihao Zhou, Pu Zhang

“…Additionally, Dynamic Progressive Feature Pyramid Network-Lite (DP-FPN-Lite) is proposed, which reduces computational and storage overhead by simplifying the network architecture while preserving the core concepts of DP-FPN. …”
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Learning place cells and remapping by decoding the cognitive map by Markus Borud Pettersen, Vemund Schøyen, Anders Malthe-Sørenssen, Mikkel E Lepperød

“…Motivated by the notion of a cognitive map, the network’s position is estimated directly from its learned representations. …”
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Intersections of Big Data and IoT in Academic Publications: A Topic Modeling Approach by Diana-Andreea Căuniac, Andreea-Alexandra Cîrnaru, Simona-Vasilica Oprea, Adela Bâra

“…It delves into the efficiency of IoT networks with terms like “accuracy”, “power” and “performance” standing out.…”
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Predicting imitative performance through cortico-cerebellar circuits: A multivariate and effective connectivity study by Antonino Errante, Giuseppe Ciullo, Settimio Ziccarelli, Alessandro Piras, Cristina Russo, Leonardo Fogassi

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Collaborative multiview time series modeling for vehicle maintenance demand prediction by Fanghua Chen, Deguang Shang, Gang Zhou, Ke Ye, Fujie Ren, Guofang Wu

“…Leveraging the interdependencies among vehicle maintenance projects across various time periods, we employ a temporal dependency learning approach utilizing a multi-attention mechanism. To enhance the interaction between distinct time points and temporal dependencies, we developed a dependency-aware learning algorithm that effectively integrates and weighs the information and dependencies at each time step, thereby improving the model’s ability to capture the complex relationships among maintenance projects over time. …”
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Physics-Informed Deep Learning for Musculoskeletal Modeling: Predicting Muscle Forces and Joint Kinematics From Surface EMG by Jie Zhang, Yihui Zhao, Fergus Shone, Zhenhong Li, Alejandro F. Frangi, Sheng Quan Xie, Zhi-Qiang Zhang

“…Convolutional neural network (CNN) is employed as the deep neural network to implement the proposed framework. …”
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Learning to represent causality in recommender systems driven by large language models (LLMs) by Serge Stéphane Aman, Tiemoman Kone, Behou Gerald N’guessan, Kouadio Prosper Kimou

“…The Bayesian network captures causal dependencies among user-item interactions, while the LLM injects contextual semantics from user reviews and product descriptions. …”
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A Spatiotemporal Feature Extraction Technique Using Superlet-CNN Fusion for Improved Motor Imagery Classification by Neha Sharma, Manoj Sharma, Amit Singhal, Nuzhat Fatema, Vinay Kumar Jadoun, Hasmat Malik, Asyraf Afthanorhan

“…This study presents an enhanced method for the precise recognition of MI tasks using EEG data, to facilitate more intuitive interactions between individuals with mobility challenges and their environment. …”
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HD-6mAPred: a hybrid deep learning approach for accurate prediction of N6-methyladenine sites in plant species by Huimin Li, Wei Gao, Yi Tang, Xiaotian Guo

“…Methods We proposed HD-6mAPred, a hybrid deep learning model that combines bidirectional gated recurrent unit (BiGRU), convolutional neural network (CNN) and attention mechanism, along with various DNA sequence coding schemes. …”
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Multi-Objective Optimization of Injection Parameters and Energy Consumption Based on ANN-Differential Evolution by Devic Oktora, Yu-Hung Ting, Sukoyo

“…Unlike classic linear modeling techniques like simple regression, many machine learning (ML) models have the ability to adjust to the nonlinear behaviors and interactions between input and output parameters. …”
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Hybrid learning in post-pandemic higher education systems: an analysis using SEM and DNN by Alvin Muhammad ‘Ainul Yaqin, Ahmad Kamil Muqoffi, Sigit Rahmat Rizalmi, Faishal Arham Pratikno, Remba Yanuar Efranto

“…The DNN demonstrated its ability to capture complex, nonlinear relationships and provide actionable insights into factors driving student interests. …”
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PSYCHOLOGICAL GUIDING OF STUDENTS’ INDIVIDUAL EDUCATIONAL TRAJECTORIES IN VOCATIONAL SCHOOL by Evald F. Zeer, Oksana S. Popova

“…The requirement for the new methodology of vocational training based on network interaction of members of education is highlighted. …”
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Multi-Task Prediction Method Based on GGCN for Object Centric Event Logs by Li Ke, Fang Huan, Xu Yifei, Shao Chifeng

“…Existing process prediction methods are primarily based on flattened event logs, which overlook multi-object interactions and complex dependencies, thereby limiting their ability to model complex processes. …”
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Prediction of heat transfer in a rotor-stator cavity cooled by multiple jets: Integration of CFD models and machine learning for performance optimization by Abdellatif EL hannaoui, Rachid Boutarfa

“…To refine predictions and optimize model performance, artificial neural networks (ANN) were integrated to capture the nonlinear and multivariate relationships between input parameters and heat transfer. …”
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The impact of knowledge absorptive capacity on the success of knowledge transfers in alliances: The mediating role of interorganizational link strength by Hugo E. Martínez-Ardila, Monica Duenas, Luis Becerra

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Attention Mechanism and Weighted Trend Loss for Wind Speed Correction by Liu Xu, Yang Hao, Liang Xiaoyun, Chen Jing, Li Qiaoping, Li Ruqing, Chen Min

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A Wi-Fi sensing method for complex continuous human activities based on CNN-BiGRU by Yang LIU, Anming DONG, Jiguo YU, Kai ZHAO, You ZHOU

“…Human activity sensing based on Wi-Fi channel state information (CSI) has an important application prospect in future intelligent interaction scenarios such as virtual reality, intelligent games, and the metaverse.Accurate sensing of complex and continuous human activities is an important challenge for Wi-Fi sensing.Convolutional neural network (CNN) has the ability of spatial feature extraction but is poor at modeling the temporal features of the data.While long short-term memory (LSTM) network or gated recurrent unit (GRU) network, which are suitable for modeling time-series data, neglect learning spatial features of data.In order to solve this problem, an improved CNN that integrates bidirectional gated recurrent unit (BiGRU) network was proposed.The bi-directional feature extraction ability of BiGRU was used to capture the correlation and dependence of the front and back information in the time series data.The extraction of the spatiotemporal features of the time series CSI data was realized, and then the mapping relationship between the action and the CSI data was present.Thus the recognition accuracy of the complex continuous action was improved.The proposed network structure was tested with basketball actions.The results show that the recognition accuracy of this method is above 95% under various conditions.Compared with the traditional multi-layer perceptron (MLP), CNN, LSTM, GRU, and attention based bidirectional long short-term memory (ABLSTM) baseline methods, the recognition accuracy has been improved by 1%～20%.…”
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