Enhanced Disc Herniation Classification Using Grey Wolf Optimization Based on Hybrid Feature Extraction and Deep Learning Methods by Yasemin Sarı, Nesrin Aydın Atasoy

“…The proposed approach begins with feature extraction using ResNet50, a deep convolutional neural network known for its robust feature representation capabilities. …”
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Forecasting Indoor Air Quality in Mexico City Using Deep Learning Architectures by Jorge Altamirano-Astorga, J. Octavio Gutierrez-Garcia, Edgar Roman-Rangel

“…However, those systems forecast outdoor air quality while people living in relatively large cities spend most of their time indoors. Hence, this work proposes an indoor air quality forecasting system, which was trained with data from Mexico City, and that is supported by deep learning architectures. …”
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A role for the thalamus in danger evoked awakening during sleep by Ida Luisa Boccalaro, Mattia Aime, Florence Marcelle Aellen, Thomas Rusterholz, Micaela Borsa, Ivan Bozic, Andrea Sattin, Tommaso Fellin, Carolina Gutierrez Herrera, Athina Tzovara, Antoine Adamantidis

“…Here, we showed that neutral auditory stimuli evoked responses across parallel auditory and non-auditory pathways, including the auditory cortex and thalamus, the hippocampus and centro-medial thalamus (CMT). Using a convolutional neural network, we identified CMT activity as the most discriminant hub for auditory-evoked sleep-to-wake transitions among all recorded structures. …”
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An interpretable wheat yield estimation model using an attention mechanism-based deep learning framework with multiple remotely sensed variables by Mingqi Li, Pengxin Wang, Kevin Tansey, Yue Zhang, Fengwei Guo, Junming Liu, Hongmei Li

“…The attention weights indicated that the most significant variable influencing wheat yield was FPAR, followed by LAI and VTCI. …”
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Wearable Artificial Intelligence for Sleep Disorders: Scoping Review by Sarah Aziz, Amal A M Ali, Hania Aslam, Alaa A Abd-alrazaq, Rawan AlSaad, Mohannad Alajlani, Reham Ahmad, Laila Khalil, Arfan Ahmed, Javaid Sheikh

“…Respiratory data were used by 25 of 46 (54%) studies as the primary data for model development, followed by heart rate (22/46, 48%) and body movement (17/46, 37%). The most popular algorithm was the convolutional neural network, adopted by 17 of 46 (37%) studies, followed by random forest (14/46, 30%) and support vector machines (12/46, 26%). …”
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Hybrid Reinforcement Learning-Based Collision Avoidance Algorithm for Autonomous Vehicle Clusters by Chubing Guo, Jianshe Wu, Panzheng Luo, Zhigang Wang, Kai Zhang, Ziyi Yang, Zengfa Dou, Kan Song

“…The experimental results show that the proposed algorithm in this study showed a high success rate of collision avoidance and a low average reaction time under most collision avoidance times.…”
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Time-Distributed Vision Transformer Stacked With Transformer for Heart Failure Detection Based on Echocardiography Video by Mgs M. Luthfi Ramadhan, Adyatma W. A. Nugraha Yudha, Muhammad Febrian Rachmadi, Kevin Moses Hanky Jr Tandayu, Lies Dina Liastuti, Wisnu Jatmiko

“…Existing studies have employed artificial intelligence to help with heart failure screening and diagnosis processes based on echocardiography videos. Specifically, most existing studies use a convolutional neural network that only captures the local context of an image hindering it from learning the global context of an image. …”
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Global surface eddy mixing ellipses: spatio-temporal variability and machine learning prediction by Tian Jing, Ru Chen, Chuanyu Liu, Chunhua Qiu, Chunhua Qiu, Cuicui Zhang, Mei Hong

“…Feature importance rankings indicate that eddy velocity magnitude and eddy size are the most significant factors in predicting the major axis and anisotropy. …”
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MWFNet: A multi-level wavelet fusion network for hippocampal subfield segmentation by Xinwei Li, Linjin Wang, Weijian Tao, Hongying Meng, Haiming Li, Jiangtao He, Yue Zhao, Jun Hu, Zhangyong Li

“…Additionally, we developed a Multi-scale Attention Residual Block (MARB) that leverages convolutional kernels of different sizes to facilitate multi-scale feature extraction. …”
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Novel Quanvolutional Neural Network-Based COVID-19 Diagnosis From Raman Spectroscopic Signals of Human Serum by Shivanya Shomir Dutta, Sahil Sandeep, S. Sridevi, Rajiv Mistry, Jonathan Ortega, Gorkem Kar

“…COVID-19 has proven to be one of the most devastating pandemics of the 21st century, causing widespread mortality and overwhelming healthcare systems globally. …”
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Multiclass leukemia cell classification using hybrid deep learning and machine learning with CNN-based feature extraction by Sazzli Kasim, Sorayya Malek, JunJie Tang, Xue Ning Kiew, Song Cheen, Bryan Liew, Norashikin Saidon, Raja Ezman, Raja Shariff

“…Abstract Leukemia is the most prevalent form of blood cancer, affecting individuals across all age groups. …”
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Enhanced MRI brain tumor detection using deep learning in conjunction with explainable AI SHAP based diverse and multi feature analysis by Asif Rahman, Maqsood Hayat, Nadeem Iqbal, Fawaz Khaled Alarfaj, Salem Alkhalaf, Fahad Alturise

“…In addition, The SHAP analysis was used to identify the most important features in classification. In a small dataset, CNN obtained 97.8% accuracy while SVC yielded 98.06% accuracy. …”
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Deep Learning Model for Precipitation Nowcasting Based on Residual and Attention Mechanisms by Zhan Zhang, Qingping Song, Minzheng Duan, Hailei Liu, Juan Huo, Congzheng Han

“…Meanwhile, depthwise separable convolutions are employed to replace conventional convolutions, significantly improving computational efficiency while preserving model performance. …”
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A capsule network framework for flood mapping integrating remote sensing fusion techniques by Pouya Ahmadi, Mohammad Javad Valadan Zoej, Mehdi Mokhtarzade, Nazila Kardan Halvaie, Ebrahim Ghaderpour

“…Flooding is one of the most serious natural hazards worldwide, causing significant and often irreversible damage to economies, infrastructure, and human health in urban areas. …”
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Investigation into the prognostic factors of early recurrence and progression in previously untreated diffuse large B-cell lymphoma and a statistical prediction model for POD12 by Ke Lian, Wenyao Zhu, Zhihui Hu, Fang Su, CaiXia Xu, Hui Wang

“…Comparatively, the CNN-LSTM and PSO-GRNN models are the most suitable to predict the risk level of the POD12 in the future.…”
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A Multi-Spatial-Scale Ocean Sound Speed Profile Prediction Model Based on a Spatio-Temporal Attention Mechanism by Shuwen Wang, Ziyin Wu, Shuaidong Jia, Dineng Zhao, Jihong Shang, Mingwei Wang, Jieqiong Zhou, Xiaoming Qin

“…Moreover, in terms of ocean sound speed, most of these models predict an ocean sound speed profile (SSP) at a single coordinate position, and only a few predict multi-spatial-scale SSPs. …”
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Multimodal Emotion Recognition Based on Facial Expressions, Speech, and EEG by Jiahui Pan, Weijie Fang, Zhihang Zhang, Bingzhi Chen, Zheng Zhang, Shuihua Wang

“…For work on the speech branch, this paper proposes a lightweight fully convolutional neural network (LFCNN) for the efficient extraction of speech emotion features. …”
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An Accelerated FPGA-Based Parallel CNN-LSTM Computing Device by Xin Zhou, Wei Xie, Han Zhou, Yongjing Cheng, Ximing Wang, Yun Ren, Shandong Yuan, Liuwen Li

“…Most of these studies connect LSTM networks behind CNNs. …”
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Application of Deep Learning Techniques in Uranium Microparticle Fission Track Detection by ZHAO Xiong, REN Fangda, SHEN Yan

“…To address the issue of long-distance dependencies in convolutional operations, a window multi-head attention mechanism (swin transformer) was integrated to design the uranium microparticle detection network. …”
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Design of an Iterative Method for Malware Detection Using Autoencoders and Hybrid Machine Learning Models by Rijvan Beg, R. K. Pateriya, Deepak Singh Tomar

“…In the evolving cyber threat landscape, one of the most visible and pernicious challenges is malware activity detection and analysis. …”
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