Deep Learning-Based Coding Strategy for Improved Cochlear Implant Speech Perception in Noisy Environments by Billel Essaid, Hamza Kheddar, Noureddine Batel, Muhammad E. H. Chowdhury

“…The proposed approach includes two strategies: the first integrates temporal convolutional networks (TCNs) and multi-head attention (MHA) layers to capture both local and global dependencies within the speech signal, enabling precise noise filtering and improved clarity. …”
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AI-based defect detection and self-healing in metal additive manufacturing by Jan Akmal, Kevin Minet-Lallemand, Jukka Kuva, Tatu Syvänen, Pilvi Ylander, Tuomas Puttonen, Roy Björkstrand, Jouni Partanen, Olli Nyrhilä, Mika Salmi

“…The outcome confirmed that Ti–6Al–4V can self-heal these defective regions by up to 7 ± 1 layers using the standard VED. A convolutional neural network was trained (n = 211) and was verified with XCT. …”
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Deep learning approaches for bias correction in WRF model outputs for enhanced solar and wind energy estimation: A case study in East and West Malaysia by Abigail Birago Adomako, Ehsan Jolous Jamshidi, Yusri Yusup, Emad Elsebakhi, Mohd Hafiidz Jaafar, Muhammad Izzuddin Syakir Ishak, Hwee San Lim, Mardiana Idayu Ahmad

“…Unlike previous studies, this research integrates a diverse array of DL models: Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Feedforward Neural Networks (FNN), to address both temporal and spatial prediction challenges. …”
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The role of machine learning in infectious disease early detection and prediction in the MENA region: A systematic review by Radwan Qasrawi, Ghada Issa, Suliman Thwib, Razan AbuGhoush, Malak Amro, Raghad Ayyad, Stephanny Vicuna, Eman Badran, Yousef Khader, Raeda Al Qutob, Faris Al Bakri, Hana Trigui, Elie Sokhn, Emmanuel Musa, Jude Dzevela Kong

“…The findings reveal a predominance of deep learning approaches, particularly Convolutional Neural Networks (CNNs), achieving mean accuracy rates of 96.3 % in pathogen detection from medical imaging. …”
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Unlocking Dynamic Subtle Stimuli Tactile Perception: A Deep Learning‐Enhanced Super‐Resolution Tactile Sensor Array with Rapid Response by Shuyao Zhou, Depeng Kong, Mengke Wang, Baocheng Wang, Yuyao Lu, Honghao Lyu, Zhangli Lu, Yong Tao, Kaichen Xu, Geng Yang

“…Here, a 130 μm‐thick flexible tactile sensor array is designed, with spatial resolution enhanced by a tailored deep learning model, multistage attention‐based adaptive spatial–temporal graph convolutional networks (MS‐AASTGCN), simultaneously achieving a dynamic response of ≈30 ms and a super‐resolution factor of 75.19. …”
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Study on the inversion and spatiotemporal variation mechanism of soil salinization at multiple depths in typical oases in arid areas: A case study of Wei-Ku Oasis by Jinming Zhang, Jianli Ding, Zihan Zhang, Jinjie Wang, Xu Zeng, Xiangyu Ge

“…Taking the Wei-Ku Oasis, a typical arid region oasis, as an example, this study uses Landsat remote sensing imagery as the data source, incorporating soil salinity field measurements over a decade, employing the Bootstrap Soft Shrinkage(BOSS) algorithm to select feature variables, and building soil salinity inversion models at various depths through a Convolutional Neural Networks and Long Short-Term Memory networks (CNN-LSTM) framework. …”
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A novel soil moisture evaluation framework incorporating brightness temperature and a high-resolution 1 km summer brightness temperature dataset by Ziyue Zhu, Runze Zhang, Bin Fang, Hyunglok Kim, Hoang Hai Nguyen, Venkataraman Lakshmi

“…The two-step evaluation method, which combines physical modeling (RTM and additional models) with machine learning techniques such as non-linear regression and convolutional neural networks (CNN), offers improvements in both temporal and spatial coverage. …”
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Energy-Efficient Secure Cell-Free Massive MIMO for Internet of Things: A Hybrid CNN–LSTM-Based Deep-Learning Approach by Ali Vaziri, Pardis Sadatian Moghaddam, Mehrdad Shoeibi, Masoud Kaveh

“…By jointly considering energy consumption and secrecy rate, our analysis provides a comprehensive assessment of security-aware energy efficiency in CF m-MIMO-based IoT networks. To enhance SEE, we introduce a hybrid deep-learning (DL) framework that integrates convolutional neural networks (CNN) and long short-term memory (LSTM) networks for joint EE and security optimization. …”
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Gear Classification in Skating Cross-Country Skiing Using Inertial Sensors and Deep Learning by Antonio Pousibet-Garrido, Aurora Polo-Rodríguez, Juan Antonio Moreno-Pérez, Isidoro Ruiz-García, Pablo Escobedo, Nuria López-Ruiz, Noel Marcen-Cinca, Javier Medina-Quero, Miguel Ángel Carvajal

“…Data were collected on the smartphone and stored on the SD memory cards included in each IMU. Convolutional neural networks combined with long short-term memory were utilized to classify and extract spatio-temporal features. …”
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The utility of combining deep learning with metabarcoding to model biodiversity dynamics at a national scale by Adrian Baggström, Robert Goodsell, Laura van Dijk, Ela Iwaszkiewicz-Eggebrecht, Andreia Miraldo, Ayco J.M. Tack, Tobias Andermann

“…Here, we present a biodiversity modeling approach that utilizes metabarcoding-derived biodiversity data, remote sensing, and convolutional neural networks (CNNs). We apply CNNs to predict the spatial pattern of seasonal arthropod richness across Sweden and compare the results with other statistical models commonly used in spatial modeling. …”
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A hybrid deep learning framework for global irradiance prediction using fuzzy C-Means, CNN-WNN, and Informer models by Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, Monia Raissi, Wissem Dimassi, Salah Hannachi

“…Artificial intelligence (AI) is revolutionizing solar energy forecasting, enabling precise irradiance prediction for electric solar vehicles (ESVs) to optimize energy efficiency and extend driving range.This study introduces a novel AI-powered hybrid deep learning framework that synergistically combines fuzzy C-means (FCM) clustering, convolutional neural networks (CNNs), wavelet neural networks (WNNs), and an Informer model to achieve superior accuracy. …”
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Deep-learning based multi-modal models for brain age, cognition and amyloid pathology prediction by Chenxi Wang, Weiwei Zhang, Ming Ni, Qiong Wang, Chang Liu, Linbin Dai, Mengguo Zhang, Yong Shen, Feng Gao

“…We designed a multi-modal deep-learning framework that employs 3D convolutional neural networks to analyze MRI and additional neural networks to evaluate demographic data. …”
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Application of a deep learning algorithm for the diagnosis of HCC by Philip Leung Ho Yu, Keith Wan-Hang Chiu, Jianliang Lu, Gilbert C.S. Lui, Jian Zhou, Ho-Ming Cheng, Xianhua Mao, Juan Wu, Xin-Ping Shen, King Ming Kwok, Wai Kuen Kan, Y.C. Ho, Hung Tat Chan, Peng Xiao, Lung-Yi Mak, Vivien W.M. Tsui, Cynthia Hui, Pui Mei Lam, Zijie Deng, Jiaqi Guo, Li Ni, Jinhua Huang, Sarah Yu, Chengzhi Peng, Wai Keung Li, Man-Fung Yuen, Wai-Kay Seto

“…Results: From 2,832 patients and 4,305 CT observations, the best-performing model was Spatio-Temporal 3D Convolution Network (ST3DCN), achieving area under receiver-operating-characteristic curves (AUCs) of 0.919 (95% CI, 0.903–0.935) and 0.901 (95% CI, 0.879–0.924) at the observation (n = 1,077) and patient (n = 685) levels, respectively during internal validation, compared with 0.839 (95% CI, 0.814–0.864) and 0.822 (95% CI, 0.790–0.853), respectively for standard of care radiological interpretation. …”
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High-gamma and beta bursts in the left supramarginal gyrus can differentiate verbal memory states and performance by Shennan Aibel Weiss, Nicolás Sawczuk, Daniel Y. Rubinstein, Michael R. Sperling, Katrina Wendel-Mitoraj, Päivi Österman, René Dumay-Roscher, Charles B. Mikell, Sima Mofakham, Kelly Coulehan, Petar M. Djuric, Diego Fernandez Slezak, Juan Esteban Kamienkowski

“…Intracranial EEG (iEEG) data, recorded solely from LSMG electrode contacts, were processed to create two-dimensional (2D) tensors of convolved high-gamma (HG), and beta (15–40 Hz) burst activity. Convolutional neural networks (CNNs) were trained and cross-validated on these 2D tensors to classify memory state (encoding versus recall) and performance (remembered versus forgotten items) within subjects.ResultsThe latter CNN, used to label subsequently recalled words based on iEEG recorded during the encoding epoch, performed at or below chance in 79 of the 141 experiments. …”
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Driver Drowsiness Detection Using Swin Transformer and Diffusion Models for Robust Image Denoising by Samy Abd El-Nabi, Ahmed F. Ibrahim, El-Sayed M. El-Rabaie, Osama F. Hassan, Naglaa F. Soliman, Khalil F. Ramadan, Walid El-Shafai

“…While conventional convolutional neural networks (CNNs) are effective in standard vision tasks, they often suffer performance degradation in real-world driving scenarios due to noise, poor lighting, motion blur, and adversarial attacks. …”
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Contactless Detection of Abnormal Breathing Using Orthogonal Frequency Division Multiplexing Signals and Deep Learning in Multi-Person Scenarios by Muneeb Ullah, Xiaodong Yang, Zhiya Zhang, Tong Wu, Nan Zhao, Lei Guan, Malik Muhammad Arslan, Akram Alomainy, Hafiza Maryum Ishfaq, Qammer H. Abbasi

“…A hybrid deep learning model, VGG16-GRU, combining convolutional neural networks (CNNs) and gated recurrent units (GRUs), was developed to capture both spatial and temporal features of continuous respiratory data. …”
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Forecasting Electric Vehicle Charging Demand in Smart Cities Using Hybrid Deep Learning of Regional Spatial Behaviours by Muhammed Cavus, Huseyin Ayan, Dilum Dissanayake, Anurag Sharma, Sanchari Deb, Margaret Bell

“…Motivated by the accelerating regional demand accompanying EV adoption, this work introduces HCB-Net: a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) for spatial feature extraction with Extreme Gradient Boosting (XGBoost) for robust regression. …”
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A Fusion Strategy for High-Accuracy Multilayer Soil Moisture Downscaling and Mapping by Xu Zhang, Xin Liu, Xiang Zhang, Aminjon Gulakhmadov, Jiefeng Wu, Xihui Gu, Won-Ho Nam, Panda Rabindra Kumar, Veber Afonso Figueiredo Costa, Mahlatse Kganyago, Berhanu Keno Terfa, Wenying Du, Chao Wang, Peng Wang, Jing Yuan, Nengcheng Chen

“…Finally, a bias correction method based on convolutional neural networks with transfer learning was employed for point-to-area fusion calibration to improve data accuracy. …”
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A data-efficient deep transfer learning framework for methane super-emitter detection in oil and gas fields using the Sentinel-2 satellite by S. Zhao, S. Zhao, Y. Zhang, Y. Zhang, S. Zhao, S. Zhao, X. Wang, X. Wang, D. J. Varon

“…Results show that DSAN (average macro <span class="inline-formula"><i>F</i>₁</span> score 0.86) outperforms four convolutional neural networks (CNNs), MethaNet (average macro <span class="inline-formula"><i>F</i>₁</span> score 0.70), ResNet-50 (average macro <span class="inline-formula"><i>F</i>₁</span> score 0.77), VGG16 (average macro <span class="inline-formula"><i>F</i>₁</span> score 0.73), and EfficientNet-V2L (average macro <span class="inline-formula"><i>F</i>₁</span> score 0.78), in transfer tasks. …”
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SS-EMERGE - self-supervised enhancement for multidimension emotion recognition using GNNs for EEG by Chirag Ahuja, Divyashikha Sethia

“…Therefore, this study introduces a hybrid SSL framework: Self-Supervised Enhancement for Multidimension Emotion Recognition using Graph Neural Networks (SS-EMERGE). This model enhances cross-subject EEG-based emotion recognition by incorporating Causal Convolutions for temporal feature extraction, Graph Attention Transformers (GAT) for spatial modelling, and Spectral Embedding for spectral domain analysis. …”
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