Unveiling midcrustal seismic activity at the front of the Bolivian altiplano, Cochabamba region by Gonzalo Antonio Fernandez M, Benoit Derode, Laurent Bollinger, Bertrand Delouis, Mayra Nieto, Felipe Condori, Nathan Sarret, Jean Letort, Stephanie Godey, Mathilde Wimez, Teddy Griffiths, Walter Arce

“…This study highlights the initial 6-month seismic bulletin made by manual and automated deep-neural-network based seismic phase picking. We also test the network's ability to resolve focal mechanisms of moderate to small events with a combined inversion of waveforms and polarities. …”
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PaleAle 6.0: Prediction of Protein Relative Solvent Accessibility by Leveraging Pre-Trained Language Models (PLMs) by Wafa Alanazi, Di Meng, Gianluca Pollastri

“…Inspired by the remarkable success of NLP techniques, this study leverages pre-trained language models (PLMs) to enhance RSA prediction. We present a deep neural network architecture based on a combination of bidirectional recurrent neural networks and convolutional layers that can analyze long-range interactions within protein sequences and predict protein RSA using ESM-2 encoding. …”
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Characterizing Perception Deep Learning Algorithms and Applications for Vehicular Edge Computing by Wang Feng, Sihai Tang, Shengze Wang, Ying He, Donger Chen, Qing Yang, Song Fu

“…Additionally, our investigation of Deep Neural Network (DNN) layers revealed that certain convolutional layers experienced computation time increases exceeding <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2849</mn><mo>%</mo></mrow></semantics></math></inline-formula>, while activation layers showed a rise of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1173.34</mn><mo>%</mo></mrow></semantics></math></inline-formula>. …”
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Combining machine learning algorithms for bridging gaps in GRACE and GRACE Follow-On missions using ERA5-Land reanalysis by Jaydeo K. Dharpure, Ian M. Howat, Saurabh Kaushik, Bryan G. Mark

“…Unlike previous studies, we use a combination of Machine Learning (ML) methods—Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Deep Neural Network (DNN), and Stacked Long-Short Term Memory (SLSTM)—to identify and efficiently bridge the gap between GRACE and GFO by using the best-performing ML model to estimate TWSA at each grid cell. …”
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Single-shot super-resolved fringe projection profilometry (SSSR-FPP): 100,000 frames-per-second 3D imaging with deep learning by Bowen Wang, Wenwu Chen, Jiaming Qian, Shijie Feng, Qian Chen, Chao Zuo

“…SSSR-FPP uses only one pair of low signal-to-noise ratio (SNR), low-resolution, and pixelated fringe patterns as input, while the high-resolution unwrapped phase and fringe orders can be deciphered with a specific trained deep neural network. Our approach exploits the significant speed gain achieved by reducing the imaging window of conventional high-speed cameras, while “regenerating” the lost spatial resolution through deep learning. …”
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Intelligent model for forecasting fluctuations in the gold price by Mahdieh Tavassoli, Mahnaz Rabeei, Kiamars Fathi Hafshejani

“…It is the first Iranian research in which the fluctuations in this market are modeled using non-linear Bayesian Model Averaging (BMA) and deep neural network approaches.Methodology: It is applied research where monthly data collected from 2010 to 2022 were used. …”
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Analisis Perbandingan Algoritma SVM, KNN, dan CNN untuk Klasifikasi Citra Cuaca by Mohammad Farid Naufal

“…KNN dan SVM merupakan algoritma klasifikasi dari Machine Learning sedangkan CNN merupakan algoritma klasifikasi dari Deep Neural Network. Penelitian ini bertujuan untuk membandingkan performa dari tiga algoritma tersebut sehingga diketahui berapa gap performa diantara ketiganya. …”
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Randomized radial basis function neural network for solving multiscale elliptic equations by Yuhang Wu, Ziyuan Liu, Wenjun Sun, Xu Qian

“…Ordinary deep neural network (DNN)-based methods frequently encounter difficulties when tackling multiscale and high-frequency partial differential equations. …”
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A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering by Yu Liu, Shuai Wang, M. Shahrukh Khan, Jieyu He

“…To tackle these problems, some authors have considered the integration of a deep neural network to learn user and item features with traditional collaborative filtering. …”
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A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity by Ryan L'Abbate, Anthony D'Onofrio, Samuel Stein, Samuel Yen-Chi Chen, Ang Li, Pin-Yu Chen, Juntao Chen, Ying Mao

“…Compared to state-of-the-art approaches, co-TenQu enhances a classical deep neural network by up to 41.72&#x0025; in a fair setting. …”
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Multitask Learning-Based Pipeline-Parallel Computation Offloading Architecture for Deep Face Analysis by Faris S. Alghareb, Balqees Talal Hasan

“…Deep Neural Networks (DNNs) have been widely adopted in several advanced artificial intelligence applications due to their competitive accuracy to the human brain. …”
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Clinical feasibility of deep learning-driven magnetic resonance angiography collateral map in acute anterior circulation ischemic stroke by Ye Jin Jeon, Hong Gee Roh, Sumin Jung, Hyun Yang, Hee Jong Ki, Jeong Jin Park, Taek-Jun Lee, Na Il Shin, Ji Sung Lee, Jin Tae Kwak, Hyun Jeong Kim

“…We employed a 3D multitask regression and ordinal regression deep neural network, called as 3D-MROD-Net, to generate DL-driven MRA collateral maps. …”
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Analysis of Sparse Trajectory Features Based on Mobile Device Location for User Group Classification Using Gaussian Mixture Model by Yohei Kakimoto, Yuto Omae, Hirotaka Takahashi

“…We then construct three machine learning (ML) models—support vector classifier (SVC), random forest (RF), and deep neural network (DNN)—using the GMM-based features and compare their performance with that of the improved DNN (IDNN), which is an existing feature extraction approach. …”
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Correction of CAMS PM₁₀ Reanalysis Improves AI-Based Dust Event Forecast by Ron Sarafian, Sagi Nathan, Dori Nissenbaum, Salman Khan, Yinon Rudich

“…To evaluate the contribution, we train a deep neural network to predict city-scale dust events (0–72 h) over the Balkans using PM₁₀ fields. …”
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Enhancing feature selection for multi-pose facial expression recognition using a hybrid of quantum inspired firefly algorithm and artificial bee colony algorithm by Mu Panliang, Sanjay Madaan, Siddiq Ahmed Babikir Ali, Gowrishankar J., Ali Khatibi, Anas Ratib Alsoud, Vikas Mittal, Lalit Kumar, A. Johnson Santhosh

“…The evaluated features are utilized for classifying face expressions by utilizing the deep neural network model, ResNet-50. The presented FER system has been tested using multi-pose facial expression benchmark datasets, including RaF (Radboud Faces) and KDEF (Karolinska Directed Emotional Faces). …”
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PCMMD: A Novel Dataset of Plasma Cells to Support the Diagnosis of Multiple Myeloma by Caio L. B. Andrade, Marcos V. Ferreira, Brenno M. Alencar, Jorge L. S. B. Filho, Matheus A. Guimaraes, Iarley Porto Cruz Moraes, Tiago J. S. Lopes, Allan S. dos Santos, Mariane M. dos Santos, Maria I. C. S. e Silva, Izabela M. D. R. P. Rosa, Gilson C. de Carvalho, Herbert H. M. Santos, Márcia M. L. Santos, Roberto Meyer, Luciana M. P. B. Knop, Songeli M. Freire, Ricardo A. Rios, Tatiane N. Rios

“…We also share a Deep Neural Network model, as a benchmark, trained to identify and count plasma and non-plasma cells automatically. …”
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Comparative study on deep and machine learning approaches for predicting wind pressures on tall buildings by Mosbeh R. Kaloop, Abidhan Bardhan, Pijush Samui, Jong Wan Hu, Mohamed Elsharawy

“…Two deep learning methods viz deep belief network (DBN) and deep neural network (DNN), and five machine learning methods namely feedforward neural network, extreme learning machine, weighted extreme learning machine, random forest, and gradient boosting machine were evaluated, and compared in predicting the design wind pressures on tall buildings. …”
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Sistem Identifikasi Pembicara Berbahasa Indonesia Menggunakan X-Vector Embedding by Alim Misbullah, Muhammad Saifullah Sani, Husaini, Laina Farsiah, Zahnur, Kikye Martiwi Sukiakhy

“…Selanjutnya, dibangun empat model dengan cara mengombinasikan dua jenis konfigurasi MFCC dan dua jenis arsitektur Deep Neural Network (DNN) yang memanfaatkan Time Delay Neural Network (TDNN). …”
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Monitoring changes of forest height in California by Samuel Favrichon, Jake Lee, Yan Yang, Yan Yang, Ricardo Dalagnol, Ricardo Dalagnol, Fabien Wagner, Le Bienfaiteur Sagang, Le Bienfaiteur Sagang, Sassan Saatchi, Sassan Saatchi, Sassan Saatchi

“…Exploring the reliability of machine learning methods for temporal monitoring of forest is still a developing field. We train a deep neural network to predict forest height metrics at 10-m resolution from radar and optical satellite imagery. …”
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A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification by Cheng-Jiang Wei, Yan Tang, Yang-Bai Sun, Tie-Long Yang, Cheng Yan, Hui Liu, Jun Liu, Jing-Ning Huang, Ming-Han Wang, Zhen-Wei Yao, Ji-Long Yang, Zhi-Chao Wang, Qing-Feng Li

“…To address privacy concerns, we utilized a lightweight deep neural network suitable for hospital deployment. The final model achieved an accuracy of 85.71% for MPNST diagnosis in the validation cohort and 84.75% accuracy in the independent test set, outperforming another classic two-step model. …”
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