Battery State of Charge and State of Health Estimation Using a New Hybrid Deep Neural Network Approach by Saeid Jorkesh, Ryan Ahmed, Saeid Habibi, Reza Hosseininejad, Siyuan Xu

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Monte Carlo Noise Reduction Algorithm Based on Deep Neural Network in Efficient Indoor Scene Rendering System by Xiwen Chen, Jianfei Shen

“…For this problem, we propose a Monte Carlo noise reduction algorithm based on deep neural networks and apply it to the efficient rendering of an indoor scene. …”
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Novel deep neural network architecture fusion to simultaneously predict short-term and long-term energy consumption by Abrar Ahmed, Safdar Ali, Ali Raza, Ibrar Hussain, Ahmad Bilal, Norma Latif Fitriyani, Yeonghyeon Gu, Muhammad Syafrudin

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A Review on Inverse Kinematics, Control and Planning for Robotic Manipulators With and Without Obstacles via Deep Neural Networks by Ana Calzada-Garcia, Juan G. Victores, Francisco J. Naranjo-Campos, Carlos Balaguer

“…This article presents a literature review of the advances made in the past five years in the use of Deep Neural Networks (DNN) for IK with regard to control and planning with and without obstacles for rigid robotic manipulators. …”
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Evaluation Method of Music Teaching Effect Based on Fusion of Deep Neural Network under the Background of Big Data by Yifan Fan

“…Because there is a strong subjectivity in the evaluation of the teaching effect of music subjects, this study combines deep neural network technology to study the feasibility and accuracy of the convolutional neural network (CNN) and long short-term memory neural network (LSTM) method in the evaluation of music teaching effects. …”
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Adaptive temporal-difference learning via deep neural network function approximation: a non-asymptotic analysis by Guoyong Wang, Tiange Fu, Ruijuan Zheng, Xuhui Zhao, Junlong Zhu, Mingchuan Zhang

“…In order to mitigate this issue, we propose an adaptive neural TD algorithm (AdaBNTD) inspired by the superior performance of adaptive gradient techniques in training deep neural networks. Simultaneously, we derive non-asymptotic bounds for AdaBNTD within the Markovian observation framework. …”
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Optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networks by Aboulfazl Ebrahimi, Mahboubeh Shamsi, Morteza Mohajjel

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AI-driven prediction of drug activity against Toxoplasma gondii: Data augmentation and deep neural networks for limited datasets by Natalia V. Karimova, Ravithree D. Senanayake

“…This Artificial Intelligence (AI)-driven Quantitative Structure-Activity Relationship (QSAR) study applies deep neural networks (DNNs) to predict pIC50 values for potential inhibitors, using 2D and 3D molecular descriptors and fingerprints. …”
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Analysis of Protein-Ligand Interactions of SARS-CoV-2 Against Selective Drug Using Deep Neural Networks by Natarajan Yuvaraj, Kannan Srihari, Selvaraj Chandragandhi, Rajan Arshath Raja, Gaurav Dhiman, Amandeep Kaur

Subjects: “…deep neural network (dnn)…”
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Comparative analysis of deep neural network architectures for renewable energy forecasting: enhancing accuracy with meteorological and time-based features by Sunawar Khan, Tehseen Mazhar, Muhammad Amir Khan, Tariq Shahzad, Wasim Ahmad, Afsha Bibi, Mamoon M. Saeed, Habib Hamam

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Mapping fruit tree dynamics using phenological metrics from optimal Sentinel-2 data and Deep Neural Network by Yingisani Chabalala, Elhadi Adam, Mahlatse Kganyago

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Fully automatic fossa ovalis segmentation from computed tomography images using deep neural network with atlas-based localization by Gakuto Aoyama, Toru Tanaka, Yukiteru Masuda, Naoki Matsuki, Ryo Ishikawa, Masahiko Asami, Kiyohide Satoh, Takuya Sakaguchi

“…Methods: Our proposed method roughly crops CT images based on atlas information of the FO and heart chambers, and inputs the cropped CT images to a U-Net-based deep neural network (DNN) to segment the FO region. This method was evaluated by five-fold cross validation using 215 CT images with manually annotated FO regions, and its segmentation accuracy was compared to two previously reported methods based on thinness of the IAS wall and on simple DNN. …”
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Optimal Multi-Physics Synthesis of a Dual-Frequency Power Inductor Using Deep Neural Networks and Gaussian Process Regression by Paolo Di Barba, Arash Ghafoorinejad, Maria Evelina Mognaschi, Fabrizio Dughiero, Michele Forzan, Elisabetta Sieni

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EdgeNet: An End-to-End Deep Neural Network Pretrained with Synthetic Data for a Real-World Autonomous Driving Application by Leanne Miller, Pedro J. Navarro, Francisca Rosique

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MRM-BERT: a novel deep neural network predictor of multiple RNA modifications by fusing BERT representation and sequence features by Linshu Wang, Yuan Zhou

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Paying attention to the SARS-CoV-2 dialect : a deep neural network approach to predicting novel protein mutations by Magdalyn E. Elkin, Xingquan Zhu

“…In this paper, we propose a Deep Novel Mutation Search (DNMS) method, using deep neural networks, to model protein sequence for mutation prediction. …”
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A Study of Deep Neural Network Controller-Based Power Quality Improvement of Hybrid PV/Wind Systems by Using Smart Inverter by Adel Ab-BelKhair, Javad Rahebi, Abdulbaset Abdulhamed Mohamed Nureddin

“…The main objective of this paper is to propose a new algorithm that is based on deep neural network (DNN) and maximum power point tracking (MPPT), which was simulated in a MATLAB environment for photovoltaic (PV) and wind-based power generation systems. …”
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New bitongling regulates gut microbiota to predict angiogenesis in rheumatoid arthritis via the gut-joint axis: a deep neural network approach by Yin Guan, Xiaoqian Zhao, Yun Lu, Yue Zhang, Yan Lu, Yue Wang

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A Deep Learning Approach for Mental Fatigue State Assessment by Jiaxing Fan, Lin Dong, Gang Sun, Zhize Zhou

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Re-Calibrating Network by Refining Initial Features Through Generative Gradient Regularization by Naim Reza, Ho Yub Jung

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