Data-Driven Optimized Load Forecasting: An LSTM-Based RNN Approach for Smart Grids by Muhammad Asghar Majeed, Sotdhipong Phichaisawat, Furqan Asghar, Umair Hussan

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Suppression strategies for pyrolysis gas parameter fluctuations in small pyrolysis-combustion coupled reactors for MSW by Shuhui Wang, Jianxiong Zhang, Jun Fu, Lin Wang, Zhifeng He, Zheng Yan

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High-Density Surface EMG Decomposition: Achievements, Challenges, and Concerns by Maoqi Chen, Ping Zhou

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Kidney Disease Segmentation and Classification Using Firefly Sigma Seeker and MagWeight Rank Techniques by Dilovan Asaad Zebari

“…By leveraging these techniques, the parallel convolutional layers are specifically tailored for kidney disease segmentation tasks. …”
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Autonomous detection of nail disorders using a hybrid capsule CNN: a novel deep learning approach for early diagnosis by Gunjan Shandilya, Sheifali Gupta, Salil Bharany, Ateeq Ur Rehman, Upinder Kaur, Hafizan Mat Som, Seada Hussen

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A Deep Learning Method for Improving Community Multiscale Air Quality Forecast: Bias Correction, Event Detection, and Temporal Pattern Alignment by Ioannis Stergiou, Nektaria Traka, Dimitrios Melas, Efthimios Tagaris, Rafaella-Eleni P. Sotiropoulou

“…Addressing these limitations, this study introduces a hybrid deep learning model that integrates convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) for ozone forecast bias correction. …”
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OPTIMAL CONTROL OF INVESTMENTS AROUND COURNOT POINT by Y. Aganin

“…A quasi-optimal Pareto maximization strategy for the vector prot criterion, using a linear convolution of the criteria along with the linearization of the dierential dynamics equations in the vicinity of the stationary points, is proposed.…”
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Improved deep learning method and high-resolution reanalysis model-based intelligent marine navigation by Zeguo Zhang, Zeguo Zhang, Zeguo Zhang, Liang Cao, Liang Cao, Liang Cao, Jianchuan Yin, Jianchuan Yin, Jianchuan Yin

“…This study addresses these challenges to enhance dynamic voyage planning and intelligent ship navigation. …”
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Dark-YOLO: A Low-Light Object Detection Algorithm Integrating Multiple Attention Mechanisms by Ye Liu, Shixin Li, Liming Zhou, Haichen Liu, Zhiyu Li

“…Second, the spatial feature pyramid module is improved by incorporating cross-overlapping average pooling and max pooling to extract salient features while retaining global and local information. Then, a dynamic feature extraction module is designed, which combines partial convolution with a parameter-free attention mechanism, allowing the model to flexibly capture critical and effective information from the image. …”
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Improving healthy food recommender systems through heterogeneous hypergraph learning by Jing Wang, Jincheng Zhou, Muammer Aksoy, Nidhi Sharma, Md Arafatur Rahman, Jasni Mohamad Zain, Mohammed J.F. Alenazi, Aliyeh Aminzadeh

“…Our study introduces a novel approach for recommending healthy foods by leveraging user–food and food-ingredient hyperedges, integrating both convolution and attention-based hypergraph mechanisms to dynamically adjust weights based on user similarities. …”
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RegCDNet: A RegNet-Based Framework for Remote Sensing Image Change Detection Combining Feature Enhancement and Gating Mechanism by Chuanlu Li, Xiaorong Xue, Caijia Zeng, Yifan Xu, Xingbiao Xu, Siyue Zhao

“…We designed an ac-dramit feature enhancement module (AFEM) that combines atrous convolution and a transformer containing a dual attention mechanism, which can efficiently capture long-range dependencies between pixels while focusing on local information. …”
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EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selection by Alanoud Al Mazroa, Majdy M. Eltahir, Shouki A. Ebad, Faiz Abdullah Alotaibi, Venkatachalam K, Jaehyuk Cho

“…This paper proposes a new deep-learning method called Cascaded Atrous Convolutional Network with Adaptive Weight Fusion (CA-AWFM) for classifying schizophrenia from electroencephalogram (EEG) data that combines cascaded networks with atrous convolutions and an adaptive weight fusion module (AWFM). …”
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A practical temporal transfer learning model for multi-step water quality index forecasting using A CNN-coupled dual-path LSTM network by Kok Poh Wai, Chai Hoon Koo, Yuk Feng Huang, Woon Chan Chong, Ahmed El-Shafie, Mohsen Sherif, Ali Najah Ahmed

“…A hybrid deep learning architecture was developed by combining a 1d-Convolutional Neural Network (CNN) with a dual-path Long Short-Term Memory (LSTM) network to capture long-term hydrological memory and site-specific temporal variability. …”
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Integrating fast iterative filtering and ensemble neural network structure with attention mechanism for carbon price forecasting by Wang Zhong, Wang Yue, Wang Haoran, Tang Nan, Wang Shuyue

“…Abstract Accurate carbon price forecasts are crucial for policymakers and enterprises to understand the dynamics of carbon price fluctuations, enabling them to formulate informed policies and investment strategies. …”
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Biologically inspired hybrid model for Alzheimer’s disease classification using structural MRI in the ADNI dataset by Houmem Slimi, Imen Cherif, Sabeur Abid, Mounir Sayadi

“…The model synergizes CNNs for hierarchical spatial feature extraction and SNNs for biologically inspired temporal dynamics processing. The CNN component processes image slices through convolutional layers, batch normalization, and dropout, while the SNN employs leaky integrate-and-fire (LIF) neurons across 25 time steps to simulate temporal progression of neurodegeneration—even with static sMRI inputs. …”
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Machine learning to identify suitable boundaries for band-pass spectral analysis of dynamic [ $$^{11}$$ 11 C]Ro15-4513 PET scan and voxel-wise parametric map generation by Zeyu Chang, Colm J. McGinnity, Rainer Hinz, Manlin Wang, Joel Dunn, Ruoyang Liu, Mubaraq Yakubu, Paul Marsden, Alexander Hammers

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A Study of Tool Wear Prediction Based on Digital Twins by LIU Minghao, MAO Xinhui, XIA Wei, YUE Caixu, LIU Xianli

“…By integrating the physical perception layer, virtual modeling layer, data interconnection layer, and intelligent service layer, a bidirectional communication mechanism between the physical machine tool and the virtual model was established, achieving full-factor mapping and dynamic optimization of the machining process. With tool wear prediction as the application scenario, a deep learning model based on the fusion of multi-scale convolutional neural network, residual network, bidirectional long short-term memory network, and gated recurrent unit (MSCNN-ResNet-BiLSTM-GRU) was proposed. …”
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Optimised Neural Network Model for Wind Turbine DFIG Converter Fault Diagnosis by Ramesh Kumar Behara, Akshay Kumar Saha

“…The proposed methodology integrates VMD with a hybrid convolutional neural network–long short-term memory (CNN-LSTM) architecture to efficiently extract and learn distinctive temporal and spectral properties from three-phase current sources. …”
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HIDS-IoMT: A Deep Learning-Based Intelligent Intrusion Detection System for the Internet of Medical Things by Abdelwahed Berguiga, Ahlem Harchay, Ayman Massaoudi

“…Furthermore, due to the dynamic nature of IoMT traffic, IDS has considerable difficulty preserving its current threat detection capabilities. …”
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Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy by Xuan Zhao, Weiyun Tang, Qiuyan Liu, Hongtao Cao, Fei Chen

“…Compared with traditional methods—such as deep neural networks, support vector machines, and linear regression—the proposed model effectively integrates static and dynamic agricultural data. Static features, including farmland distribution and soil types, are extracted using Convolutional Neural Networks, while temporal trends in variables such as weather patterns and policy changes are captured by the Long Short-Term Memory network. …”
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