Theoretical investigations on analysis and optimization of freeze drying of pharmaceutical powder using machine learning modeling of temperature distribution by Turki Al Hagbani, Jawaher Abdullah Alamoudi, Majed A. Bajaber, Huda Ibrahim Alsayed, Halah Jawad Al-fanhrawi

“…The ML models explored include the Single-Layer Perceptron (SLP), Multi-Layer Perceptron (MLP), Fully Connected Neural Network (FCNN), and Deep Neural Network (DNN). Model optimization is achieved through the Fireworks Algorithm (FWA). …”
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A Bi‐level stacked LSTM‐DNN‐based decoder network for AGC dispatch under regulation market framework in presence of VPP and EV aggregators by Kingshuk Roy, Sanjoy Debbarma, Siddhartha Deb Roy, Liza Debbarma

“…In this context, a bi‐level AGC dispatch approach based on a stacked long short‐term memory (LSTM)‐deep neural network (DNN)‐based decoder framework is proposed for a power system comprising diverse CIGs forming a virtual power plant and electric vehicle aggregators. …”
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Robust adaptive optimization for sustainable water demand prediction in water distribution systems by Ke Wang, Jiayang Meng, Zhangquan Wang, Kehua Zhao, Banteng Liu

“…The predictive power of the proposed model is harnessed through the construction of deep neural networks that utilize the decomposed data to forecast minutely water demand. …”
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Balanced coarse-to-fine federated learning for noisy heterogeneous clients by Longfei Han, Ying Zhai, Yanan Jia, Qiang Cai, Haisheng Li, Xiankai Huang

“…However, heterogeneous clients have different deep neural network structures, and these models have different sensitivity to various noise types, the fixed noise-detection based methods may not be effective for each client. …”
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Deep learning-assisted arrhythmia classification using 2-D ECG spectrograms by Pinjala N Malleswari, Venkata krishna Odugu, T. J. V. Subrahmanyeswara Rao, T. V. N. L. Aswini

“…We next employ supervised learning to train the neural network on the ECG labeled data using CNN features. To train a Deep Neural Network, three sets of PhysioNet databases are used: MIT-BIH (ARR) Arrhythmia, NSR (Normal Sinus Rhythm), and BIDMC CHF (Congestive Heart Failure). …”
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A Target Domain-Specific Classifier Weight Partial Transfer Adversarial Network for Bearing Fault Diagnosis by Yin Bai, Xiangdong Hu, Kai Zheng, Yunnong Chen, Yi Tang

“…Finally, five mainstream deep neural network methods are taken for comparison using the data from Western Reserve University and the motor-magnetic brake test designed by us. …”
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Predicting the heat capacity of strontium-praseodymium oxysilicate SrPr4(SiO4)3O using machine learning, deep learning, and hybrid models by Amir Hossein Sheikhshoaei, Ali Khoshsima, Davood Zabihzadeh

“…In this study, the capability of five advanced machine learning models, including Random Forest (RF), Gradient Boosting (GBoost), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Decision Tree (DT) models, and three deep learning models, TabNet, Deep Belief Network (DBN), and Deep Neural Network (DNN) was investigated. Our analysis indicates that the Random Forest and Deep Belief Network models outperform all other competing models. …”
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Application of IoT-Based Drones in Precision Agriculture for Pest Control by Mohamad Reda. A. Refaai, Vinjamuri SNCH Dattu, N. Gireesh, Ekta Dixit, CH. Sandeep, David Christopher

“…These deep neural networks are adapted to the immediate situation using transfer learning and deep extraction of features approaches. …”
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Historical Blurry Video-Based Face Recognition by Lujun Zhai, Suxia Cui, Yonghui Wang, Song Wang, Jun Zhou, Greg Wilsbacher

“…Next, we build a deep neural network-integrated object-tracking algorithm to compensate for failed recognition over one or more video frames. …”
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Research on Mental Health Monitoring Scheme of Migrant Children Based on Convolutional Neural Network Based on Deep Learning by Guangyan Yang

“…This paper studies children’s smiling face recognition based on deep neural network. In order to obtain a better identification effect of mental health problems of children, this paper attempts to use multisource data, including consumption data, access control data, network logs, and grade data, and proposes a multisource data-based mental health problem identification algorithm. …”
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Artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitor by Daphne E. Schlesinger, Ridwan Alam, Roey Ringel, Eugene Pomerantsev, Srikanth Devireddy, Pinak Shah, Joseph Garasic, Collin M. Stultz

“…Methods We developed a deep neural network using single-lead electrocardiogram data to determine when the left atrial pressure is elevated. …”
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Raw Camera Data Object Detectors: An Optimisation for Automotive Video Processing and Transmission by Pak Hung Chan, Chuheng Wei, Anthony Huggett, Valentina Donzella

“…Whilst Deep Neural Networks (DNNs) have been developing swiftly, most of the research has been focused on videos based on RGB frames. …”
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Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network by Xiaoling Wei, Jimin Li, Chenghao Zhang, Ming Liu, Peng Xiong, Xin Yuan, Yifei Li, Feng Lin, Xiuling Liu

“…In this paper, R wave peak interval independent atrial fibrillation detection algorithm is proposed based on the analysis of the synchronization feature of the electrocardiogram signal by a deep neural network. Firstly, the synchronization feature of each heartbeat of the electrocardiogram signal is constructed by a Recurrence Complex Network. …”
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NeuralACT: Accounting Analytics Using Neural Network for Real-Time Decision Making From Big Data by Leonidas Theodorakopoulos, Alexandra Theodoropoulou, Georgios Kampiotis, Ioanna Kalliampakou

“…This paper presents a deep neural network (DNN)-based accounting analytics, NeuralACT, to support the decision-making process using real-time predictions made from big data. …”
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Speech Enhancement Using Joint DNN-NMF Model Learned with Multi-Objective Frequency Differential Spectrum Loss Function by Matin Pashaian, Sanaz Seyedin

“…We propose a multi-objective joint model of non-negative matrix factorization (NMF) and deep neural network (DNN) with a new loss function for speech enhancement. …”
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Instance-level semantic segmentation of nuclei based on multimodal structure encoding by Bo Guan, Guangdi Chu, Ziying Wang, Jianmin Li, Bo Yi

“…However, existing deep neural network-based methods often struggle to capture complex morphological features and global spatial distributions of cell nuclei due to their reliance on local receptive fields. …”
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Variational Methods in Optical Quantum Machine Learning by Marco Simonetti, Damiano Perri, Osvaldo Gervasi

“…The goal was to create a quantum deep neural network that could recognise and categorise points accurately with the fewest trainable parameters possible.…”
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A Deep Q-Learning Algorithm With Guaranteed Convergence for Distributed and Uncoordinated Operation of Cognitive Radios by Ankita Tondwalkar, Andres Kwasinski

“…To address this challenge, this work presents the uncoordinated and distributed multi-agent DQL (UDMA-DQL) technique that combines a deep neural network with learning in exploration phases, and with the use of a Best Reply Process with Inertia for the gradual learning of the best policy. …”
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Predictive modelling of hexagonal boron nitride nanosheets yield through machine and deep learning: An ultrasonic exfoliation parametric evaluation by Jerrin Joy Varughese, Sreekanth M․S․

“…A suite of machine learning regression models including Adaptive Boosting (AdaBoost) Regressor, Random Forest (RF) Regressor, Linear Regressor (LR), and Classification and Regression Tree (CART) Regressor, was employed alongside a deep neural network (DNN) architecture optimized using various algorithms such as Adaptive Moment Estimation (Adam), Root Mean Square Propagation (RMS Prop), Stochastic Gradient Descent (SGD), and Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS). …”
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A Real-Time Timetable Rescheduling Method for Metro System Energy Optimization under Dwell-Time Disturbances by Guang Yang, Junjie Wang, Feng Zhang, Shiwen Zhang, Cheng Gong

“…The real-time feature and self-adaptability of the method are attributed to the combinational use of Genetic Algorithm (GA) and Deep Neural Network (DNN). The decision system for proposing solutions, which contains multiple DNN cells with same structures, is trained by GA results. …”
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