AI-Driven Drought Monitoring: Advanced Machine Learning Techniques for Early Prediction by Vij Priya, Tiwari Ankita

“…The study employs Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to capture complex spatial and temporal patterns, enabling more accurate and timely drought forecasting compared to traditional approaches. …”
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A Copula-Driven CNN-LSTM Framework for Estimating Heterogeneous Treatment Effects in Multivariate Outcomes by Jong-Min Kim

“…In this study, we propose a novel deep learning framework integrating empirical copula transformations with a CNN-LSTM (Convolutional Neural Networks and Long Short-Term Memory networks) architecture to capture nonlinear dependencies and temporal dynamics in multivariate treatment effect estimation. …”
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Effectiveness of three machine learning models for prediction of daily streamflow and uncertainty assessment by Luka Vinokić, Milan Dotlić, Veljko Prodanović, Slobodan Kolaković, Slobodan P. Simonovic, Milan Stojković

“…This study evaluates three Machine Learning (ML) models—Temporal Kolmogorov-Arnold Networks (TKAN), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN)—focusing on their capabilities to improve prediction accuracy and efficiency in streamflow forecasting. …”
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A prototype-based rockburst types and risk prediction algorithm considering intra-class variance and inter-class distance of microseismic data by Xiufeng Zhang, Guoying Li, Yang Chen, Hao Wang, Haikuan Zhang, Haitao Li, Weisheng Du, Xiao Li, Xuewei Xu, Yuze He

“…Therefore, based on the quantitative study of the relationship between the performance of a deep learning prediction algorithm and a rockburst prediction vector, a rockburst risk and type prediction algorithm based on a convolutional neural network (CNN)-gated recurrent unit (GRU) model with prototype-based prediction is proposed. …”
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Altitude aware trajectory prediction methods for non towered terminal airspace by Haipeng Zhu, Qiang Tong, Jinqing Hu, Xiulei Liu, Shoulu Hou

“…The model independently extracts altitude features using temporal convolutional networks(TCN), it then incorporates a channel attention fusion mechanism to dynamically fuse altitude features into the trajectory representation across different channels. …”
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Design of an intelligent optimization system for high-altitude photovoltaic power plant output power prediction and enhancement using GVSAO-CNN-BiGRU-Attention by Shuai Yuan, Hui Zhou, Kai Zhao

“…The system integrates convolutional neural networks (CNN), bi-directional gated recurrent units (BiGRU), attention mechanisms, and genetic algorithm optimization (GVSAO). …”
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Leveraging explainable artificial intelligence with ensemble of deep learning model for dementia prediction to enhance clinical decision support systems by Mohamed Medani, Ghada Moh. Samir Elhessewi, Mohammed Alqahtani, Somia A. Asklany, Sulaiman Alamro, Da’ad Albalawneh, Menwa Alshammeri, Mohammed Assiri

“…For the classification process, the proposed LXAIOA-ADPCM model implements ensemble classifiers such as the bidirectional long short-term memory (BiLSTM), sparse autoencoder (SAE), and temporal convolutional network (TCN) techniques. Finally, the hyperparameter selection of ensemble models is accomplished by utilizing the gazelle optimization algorithm (GOA) technique. …”
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Short-term and long-term inertia forecasting with low-inertia event prediction in IBR-integrated power systems using a deep learning approach by Santosh Diggikar, Arunkumar Patil, Katkar Siddhant Satyapal, Kunal Samad

“…The model is benchmarked against baseline architectures, including Bi-LSTM, Bi-GRU, and convolutional neural networks (CNNs). The proposed hybrid model achieves superior predictive performance, with a mean absolute percentage error (MAPE) of 2.74%, mean absolute error (MAE) of 4.55 GVAs, root mean square error (RMSE) of 6.65 GVAs, mean squared error (MSE) of 44.22 GVAs2, and combined accuracy (CA) of 3.70 GVAs. …”
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Expression Dynamics and Genetic Compensation of Cell Cycle Paralogues in <i>Saccharomyces cerevisiae</i> by Gabriele Schreiber, Facundo Rueda, Florian Renner, Asya Fatima Polat, Philipp Lorenz, Edda Klipp

“…In order to classify cells into specific cell cycle phases, we developed a convolutional neural network (CNN). We find that the expression levels of some cell-cycle related paralogues differ in their correlation, with <i>CLN1</i> and <i>CLN2</i> showing strong correlation and <i>CLB3</i> and <i>CLB4</i> showing weakest correlation. …”
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A Hybrid MLP and CNN Architecture for Sequential Recommendation by Li Yuan, Xue-Yi Zhao

“…Specifically, we combine Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN) to model users&#x2019; global long-term interests and recent dynamic behaviors, respectively. …”
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Leaf disease detection and classification in food crops with efficient feature dimensionality reduction. by Khasim Syed, Shaik Salma Asiya Begum, Anitha Rani Palakayala, G V Vidya Lakshmi, Sateesh Gorikapudi

“…Dimensionality reduction techniques are employed to enhance computational performance by reducing the dimensionality of inner layers. Convolutional Neural Networks (CNNs), originally designed to recognize critical image components, now learn features across multiple layers. …”
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A New and Tested Ionospheric TEC Prediction Method Based on SegED-ConvLSTM by Yuanhang Liu, Yingkui Gong, Hao Zhang, Ziyue Hu, Guang Yang, Hong Yuan

“…We compared our model with traditional image-based models such as convolutional neural networks (CNNs), convolutional long short-term memory networks (ConvLSTMs), a self-attention mechanism-integrated ConvLSTM (SAM-ConvLSTM) model, and one-day predicted ionospheric products (C1PG) provided by the Center for Orbit Determination in Europe (CODE). …”
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Bio-Inspired Motion Emulation for Social Robots: A Real-Time Trajectory Generation and Control Approach by Marvin H. Cheng, Po-Lin Huang, Hao-Chuan Chu

“…This research presents a motion prediction model developed using convolutional neural networks (CNNs) to efficiently determine the type of motions at the initial state. …”
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Neuromorphic imaging cytometry on human blood cells by Ziyao Zhang, Haoxiang Yang, Jiayin Li, Shin Wei Chong, Jason K Eshraghian, Ken-Tye Yong, Daniele Vigolo, Helen M McGuire, Omid Kavehei

“…We also trained a lightweight model combining the convolutional block attention module with a spiking neural network (CBAM-SNN) to automate cell analysis and classification. …”
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Innovative Data Solutions for Inclusive Cities: The IDEAtlas User Portal by B. Tareke, P. Silva Filho, C. Persello, M. Kuffer, R. V. Maretto, J. Wang, A. Abascal, P. Pillai, B. Singh, J. M. D'Attoli, C. Kabaria, J. Pedrassoli, P. Brito, P. Elias, E. A. Villaseñor, A. Ramírez Santiago, W. Mulyana, J. Pratomo, R. Leska, J. Streitenberger, D. Mwaniki, D. R. Thomson

“…The portal provides outputs from a custom Multi-Branch Convolutional Neural Network (MB-CNN) model trained on freely available Sentinel-1 and Sentinel-2 data, enriched with ancillary open datasets such as building footprints. …”
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Development of an Efficient Grading Model for Maize Seedlings Based on Indicator Extraction in High-Latitude Cold Regions of Northeast China by Song Yu, Yuxin Lu, Yutao Zhang, Xinran Liu, Yifei Zhang, Mukai Li, Haotian Du, Shan Su, Jiawang Liu, Shiqiang Yu, Jiao Yang, Yanjie Lv, Haiou Guan, Chunyu Zhang

“…This study combines phenotypic extraction technologies with a convolutional neural network–long short-term memory (CNN–LSTM) deep learning model to develop an advanced grading system for maize seedling quality. …”
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Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities by Izabela Rojek, Dariusz Mikołajewski, Krzysztof Galas, Adrianna Piszcz

“…Reinforcement learning models optimize power distribution by learning from historical patterns and adapting to changes in energy usage in real time. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) facilitate detailed analysis of spatial and temporal data to better predict energy usage. …”
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Inequalities in Mild Cognitive Impairment Risk Among Chinese Middle-Aged and Older Adults: Insights from an Integrated Learning Model by Bi S, Guo D, Tan H, Chen Y, Li G

“…Shengxian Bi,1 Dandan Guo,2 Huawei Tan,1 Yingchun Chen,1 Gang Li3 1School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People&amp;rsquo;s Republic of China; 2School of Public Health and Health Sciences, Hubei University of Medicine, Shiyan, Hubei, 442000, People&amp;rsquo;s Republic of China; 3School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People&amp;rsquo;s Republic of ChinaCorrespondence: Yingchun Chen, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People&amp;rsquo;s Republic of China, Email chenyingchunhust@163.com Gang Li, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People&amp;rsquo;s Republic of China, Email ligang2024@sjtu.edu.cnObjective: This study aims to address inequalities in mild cognitive impairment (MCI) risk among Chinese middle-aged and older adults by developing an integrated learning framework to predict MCI risk and identify key contributing factors.Methods: Using CHARLS data of 4626 participants, we developed a convolutional neural network-bidirectional long short-term memory-attention (CNN-BiLSTM-Attention) model to capture the temporal and spatial features of MCI progression. …”
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A hybrid model for detecting motion artifacts in ballistocardiogram signals by Yuelong Jiang, Han Zhang, Qizheng Zeng

“…The first channel uses a deep learning model, specifically a temporal Bidirectional Gated Recurrent Unit combined with a Fully Convolutional Network (BiGRU–FCN), to identify motion artifacts. …”
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Transfer of Periodic Phenomena in Multiphase Capillary Flows to a Quasi-Stationary Observation Using U-Net by Bastian Oldach, Philipp Wintermeyer, Norbert Kockmann

“…The original U-Net was modified to process input images of a size of 688 × 432 pixels while the structure of the encoder and decoder path feature 23 convolutional layers. The U-Net consists of four max pooling layers and four upsampling layers. …”
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