Interplanetary Influence on Thermospheric Mass Density: Insights From Deep Learning Analyses by Wenbo Li, Libo Liu, Yiding Chen, Yi‐Jia Zhou, Huijun Le, Ruilong Zhang

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Reducing Data Volume in News Topic Classification: Deep Learning Framework and Dataset by Luigi Serreli, Claudio Marche, Michele Nitti

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Machine and Deep Learning Models for Stress Detection Using Multimodal Physiological Data by Eman Abdelfattah, Shreehar Joshi, Shreekar Tiwari

“…This study employs machine learning and deep learning techniques on multimodal dataset from wearable sensors, focusing on processed metrics for the three-axis acceleration (ACC), electrocardiogram (ECG), blood volume pulse (BVP), body temperature (TEMP), respiration (RESP), electromyogram (EMG), and electrodermal activity (EDA) from the 15 subjects in the WESAD dataset to effectively classify four different states – baseline, stress, amusement, and meditation. …”
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A Deep-Learning Prediction Model for Imbalanced Time Series Data Forecasting by Chenyu Hou, Jiawei Wu, Bin Cao, Jing Fan

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A hybrid model for smart grid theft detection based on deep learning by Yinling LIAO, Jincan LI, Bing WANG, Jun ZHANG, Yaoyuan LIANG

“…A hybrid deep learning model was proposed to effectively detect electricity theft in smart grids.The hybrid model employed a deep learning convolutional neural network (AlexNet) to tackle the curse of dimensionality, significantly enhancing data processing accuracy and efficiency.It further improved classification accuracy by differentiating between normal and abnormal electricity usage using adaptive boosting (AdaBoost).To resolve the issue of class imbalance, undersampling techniques were utilized, ensuring balanced performance across various data classes.Additionally, the artificial bee colony algorithm was used to optimize hyperparameters for both AdaBoost and AlexNet, effectively boosting overall model performance.The effectiveness of this hybrid model was evaluated using real smart meter datasets from an electricity company.Compared to similar models, this hybrid model achieves accuracy, precision, recall, F1-score, Matthews correlation coefficient (MCC), and area under the curve-receiver operating characteristic curve (AUC-ROC) scores of 88%, 86%, 84%, 85%, 78%, and 91%, respectively.The proposed model not only increases the accuracy of electricity usage monitoring, but also offers a new perspective for intelligent analysis in power systems.…”
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Optimized YOLOV8: An efficient underwater litter detection using deep learning by Faiza Rehman, Mariam Rehman, Maria Anjum, Afzaal Hussain

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Automated strabismus detection and classification using deep learning analysis of facial images by Mahsa Yarkheir, Motahhareh Sadeghi, Hamed Azarnoush, Mohammad Reza Akbari, Elias Khalili Pour

“…This research presents a new deep-learning-based approach for automatically identifying and classifying strabismus from facial images. …”
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Deep Learning Landscape Evaluation System Integrating Poetic Emotion and Visual Features by Chuanyuan Li

Subjects: “…Deep learning…”
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Interpretable Deep Learning for Pneumonia Detection Using Chest X-Ray Images by Jovito Colin, Nico Surantha

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Lung Nodule Detection For CT-Guided Biopsy Images Using Deep Learning by B. Prashanthi, S.P. Angelin Claret

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TEC Map Completion Through a Deep Learning Model: SNP‐GAN by Yang Pan, Mingwu Jin, Shunrong Zhang, Yue Deng

“…Compared to the conventional image inpainting methods, the deep learning methods using generative adversarial networks (GANs) offer an effective image inpainting tool. …”
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Prenatal depression level prediction using ensemble based deep learning model by Abinaya Gopalakrishnan, Xujuan Zhou, Revathi Venkataraman, Raj Gururajan, Ka Ching Chan, Guohun Zhu, Niall Higgins

“…Results:: We subsequently applied the ensemble based deep learning model on a testing dataset and our method proved to be 93.87 percent accurate, proving its superiority over the standard supervised classification models. …”
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Music Generation Using Deep Learning and Generative AI: A Systematic Review by Rohan Mitra, Imran Zualkernan

“…This paper presents a systematic review of recent advances in music generation using deep learning techniques, categorizing the latest research in the field and identifying key contributions from various approaches. …”
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Predictive modeling of air quality in the Tehran megacity via deep learning techniques by Abdullah Kaviani Rad, Mohammad Javad Nematollahi, Abbas Pak, Mohammadreza Mahmoudi

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Real-Time Prediction of the Trend of Ground Motion Intensity Based on Deep Learning by Tao Liu, Zhijun Dai

“…In order to predict the intensity of earthquake damage in advance and improve the effectiveness of earthquake emergency measures, this paper proposes a deep learning model for real-time prediction of the trend of ground motion intensity. …”
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USING ARTIFICIAL INTELLIGENCE (AI) AND DEEP LEARNING TECHNIQUES IN FINANCIAL RISK MANAGEMENT by Joseph Olorunfemi AKANDE

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Deep Learning Implementation Using CNN to Classify Bali God Sculpture Pictures by Ni Luh Gede Pivin Suwirmayanti, I Wayan Budi Sentana, I Ketut Gede Darma Putra, Made Sudarma, I Made Sukarsa, Komang Budiarta

“…The technique used in Deep Learning is Convolutional Neural Network (CNN). …”
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Identification of cardiac wall motion abnormalities in diverse populations by deep learning of the electrocardiogram by Albert J. Rogers, Neal K. Bhatia, Sabyasachi Bandyopadhyay, James Tooley, Rayan Ansari, Vyom Thakkar, Justin Xu, Jessica Torres Soto, Jagteshwar S. Tung, Mahmood I. Alhusseini, Paul Clopton, Reza Sameni, Gari D. Clifford, J. Weston Hughes, Euan A. Ashley, Marco V. Perez, Matei Zaharia, Sanjiv M. Narayan

“…This study aimed to identify novel ECG features using deep learning to enhance WMA detection, referencing echocardiography as the gold standard. …”
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An Unsupervised Deep Learning Framework for Retrospective Gating of Catheter-Based Cardiac Imaging by Zheng Sun, Yue Yao, Ru Wang

“…The network was trained on clinical data sets in an unsupervised manner, addressing the difficulty of obtaining the gold standard in deep learning-based motion suppression techniques. Experimental results of in vivo intravascular ultrasound and optical coherence tomography sequences show that the proposed method has better performance in terms of motion artifact suppression and processing efficiency compared with the state-of-the-art nonlearning signal-based and IBG methods.…”
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Fake Detect: A Deep Learning Ensemble Model for Fake News Detection by Nida Aslam, Irfan Ullah Khan, Farah Salem Alotaibi, Lama Abdulaziz Aldaej, Asma Khaled Aldubaikil

“…Due to the nature of the dataset attributes, two deep learning models were used. For the textual attribute “statement,” Bi-LSTM-GRU-dense deep learning model was used, while for the remaining attributes, dense deep learning model was used. …”
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