Enhanced Heart Disease Classification Using Dual Attention Mechanisms and 3D-Echo Fusion Algorithm in Echocardiogram Videos by S Deepika, N. Jaisankar

“…An RNN models the temporal dynamics of heart valve motion, while the Dual Attention Model refines the classification process by enabling the model to focus on the most relevant features. …”
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ABDviaMSIFAT: Abnormal Crowd Behavior Detection Utilizing a Multi-Source Information Fusion Technique by Ali Ahmad Hamid, S. Amirhassan Monadjemi, Bijan Shoushtarian

“…In the first pipeline, we utilize a depth-wise Separable Convolutional Neural Network (DWS-CNN) that provides reduced filtering compared to standard CNNs. …”
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Machine Learning for Fire Safety in the Built Environment: A Bibliometric Insight into Research Trends and Key Methods by Mehmet Akif Yıldız

“…Convolutional neural networks, artificial neural networks, support vector machines, deep neural networks, you only look once, deep learning, and decision trees were prominent as machine learning categories. …”
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Neural models for detection and classification of brain states and transitions by Arnau Marin-Llobet, Arnau Manasanch, Leonardo Dalla Porta, Melody Torao-Angosto, Maria V. Sanchez-Vives

“…Abstract Exploring natural or pharmacologically induced brain dynamics, such as sleep, wakefulness, or anesthesia, provides rich functional models for studying brain states. …”
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Enhanced Interpretable Forecasting of Cryptocurrency Prices Using Autoencoder Features and a Hybrid CNN-LSTM Model by Wajeeha Badar, Shabana Ramzan, Ali Raza, Norma Latif Fitriyani, Muhammad Syafrudin, Seung Won Lee

“…These results show that the proposed model is a good financial forecast method since it effectively reflects the complex dynamics of primary changes in the price of Bitcoin. …”
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Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding by Yu Song, Yu Song, Liyuan Han, Tielin Zhang, Bo Xu, Bo Xu

“…Spiking Neural Networks (SNNs), with their neuronal dynamics and spike-based signal processing, are inherently well-suited for this task. …”
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A CNN-Transformer Fusion Model for Proactive Detection of Schizophrenia Relapse from EEG Signals by Sana Yasin, Muhammad Adeel, Umar Draz, Tariq Ali, Mohammad Hijji, Muhammad Ayaz, Ashraf M. Marei

“…In this study, we propose a CNN-Transformer fusion model that leverages the complementary strengths of Convolutional Neural Networks (CNNs) and Transformer-based architectures to process electroencephalogram (EEG) signals enriched with clinical and sentiment-derived features. …”
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From Envelope Spectra to Bearing Remaining Useful Life: An Intelligent Vibration-Based Prediction Model with Quantified Uncertainty by Haobin Wen, Long Zhang, Jyoti K. Sinha

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Impact of Safety Signage Placement on Evacuation Behavior in Virtual Fire Scenarios Based on EDA Data by Wenqi Song, Xu Feng, Yu Song

“…Three variables are evaluated, signage height (1m, 0.5m, and 0m), spacing (5m and 10m), and presence of active fire, using a hybrid classification model that integrates an im-proved convolutional neural network (CNN), a Transformer-based sequence encoder, and a multi-layer spiking neural network. …”
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MEMPSEP‐II. Forecasting the Properties of Solar Energetic Particle Events Using a Multivariate Ensemble Approach by Maher A. Dayeh, Subhamoy Chatterjee, Andrés Muñoz‐Jaramillo, Kimberly Moreland, Hazel M. Bain, Samuel T. Hart

“…The complex, intertwined dynamics of SEP sources, acceleration, and transport make their forecasting very challenging. …”
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Multi-feature stock price prediction by LSTM networks based on VMD and TMFG by Zhixin Zhang, Qingyang Liu, Yanrong Hu, Hongjiu Liu

“…Abstract The stock market is characterized by its high nonlinearity and complexity, making traditional methods ineffective in capturing its nonlinear features and complex market dynamics. This paper proposes a novel stock price forecasting model—the Variational Mode Decomposition—Triangulated Maximally Filtered Graph—Long Short-Term Memory (VMD–TMFG–LSTM) combined model—aimed at improving prediction accuracy, stability, and computational efficiency. …”
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A proximal policy optimization based deep reinforcement learning framework for tracking control of a flexible robotic manipulator by Joshi Kumar V, Vinodh Kumar Elumalai

“…This paper puts forward a policy feedback based deep reinforcement learning (DRL) control scheme for a partially observable system by leveraging the potentials of proximal policy optimization (PPO) algorithm and convolutional neural network (CNN). Although several DRL algorithms have been investigated for a fully observable system, there has been limited studies on devising a DRL control for a partially observable system with uncertain dynamics. …”
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Thermal error modeling of slant bed CNC lathe spindle based on BiLSTM with data augmentation and grey wolf optimizer algorithm by Musab Alataiqeh, Hu Shi, Qiangqiang Qu, Xuesong Mei, Haitao Wang

“…In comparison to traditional methods, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and standalone BiLSTM, the GWO-BiLSTM-SMOTE model demonstrates predictive accuracy, achieving R2 of 0.95384 and 0.95004 at various rotation speeds. …”
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Learning-Assisted Multi-IMU Proprioceptive State Estimation for Quadruped Robots by Xuanning Liu, Yajie Bao, Peng Cheng, Dan Shen, Zhengyang Fan, Hao Xu, Genshe Chen

“…Specifically, one body IMU and four additional IMUs attached to each calf link of the robot are used for sensing the dynamics of the body and legs, in addition to joint encoders. …”
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Research on freeze-thaw displacement prediction model of sandy soil based on attention mechanism CNN-BiGRU by Zecheng Wang, Dongwei Li, Zhengbin Dong, Zhiwen Jia, Chaochao Zhang

“…Traditional methods struggle with nonlinear complexities in freeze-thaw dynamics. This study develops an attention-based CNN-BiGRU model that synergizes convolutional neural networks for spatial feature extraction, bidirectional gated recurrent units for temporal dependency modeling, and attention mechanisms for critical time-step weighting. …”
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Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction by Yujie Shen, Shuxia Ye, Yongwei Zhang, Liang Qi, Qian Jiang, Liwen Cai, Bo Jiang

“…Based on the ship resistance sample data obtained from computational fluid dynamics (CFD) simulation, this study uses a machine learning method to realize the fast prediction of ship resistance corresponding to different bulbous bows. …”
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Deep Mining on the Formation Cycle Features for Concurrent SOH Estimation and RUL Prognostication in Lithium-Ion Batteries by Dongchen Yang, Weilin He, Xin He

“…Full lifecycle data of batteries, gathered under varying pressures during formation, are used to predict RUL using convolutional neural networks (CNN) and Gaussian process regression (GPR). …”
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Effects of Automatic Hyperparameter Tuning on the Performance of Multi‐Variate Deep Learning‐Based Rainfall Nowcasting by Amirmasoud Amini, Mehri Dolatshahi, Reza Kerachian

“…Developing an accurate rainfall nowcasting model could provide insights into rainfall dynamics and ultimately could prevent significant damages. …”
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Decoding vocal indicators of stress in laying hens: A CNN-MFCC deep learning framework by Suresh Neethirajan

“…This study leverages advanced Convolutional Neural Networks (CNNs) combined with Mel Frequency Cepstral Coefficients (MFCCs) to decode intricate vocalization patterns in laying hens experiencing acute environmental stress. …”
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Enhancing Stock Price Forecasting with CNN-BiGRU-Attention: A Case Study on INDY by Madilyn Louisa, Gumgum Darmawan, Bertho Tantular

“…The stock price of PT Indika Energy Tbk (INDY) reflects the dynamics of Indonesia’s energy sector, which is heavily influenced by global coal price fluctuations, national energy policies, and geopolitical conditions. …”
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