Traditional Chinese Medicine Prescription Generation Model Based on Search Enhancement by ZHAO Zijuan, REN Xueting, SONG Kai, QIANG Yan, ZHAO Juanjuan, ZHANG Junlong

“…In order to solve these problems, a hybrid neural network architecture for TCM prescription generation—PreGenerator is proposed. …”
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Multi-Scale Feature Fusion Model for Bridge Appearance Defect Detection by Rong Pang, Yan Yang, Aiguo Huang, Yan Liu, Peng Zhang, Guangwu Tang

“…Although the Faster Region-based Convolutional Neural Network (Faster R-CNN) model has obvious advantages in defect recognition, it still cannot overcome challenging problems, such as time-consuming, small targets, irregular shapes, and strong noise interference in bridge defect detection. …”
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Adapting to evolving MRI data: A transfer learning approach for Alzheimer’s disease prediction by Rosanna Turrisi, Sarthak Pati, Giovanni Pioggia, Gennaro Tartarisco

“…This study explores Transfer Learning (TL) approaches to enhance AD diagnosis using a Baseline model consisting of a 3D-Convolutional Neural Network trained on 80 3T MRI scans.Two scenarios are explored: (A) utilizing historical data to address changes in MRI acquisitions (from 1.5T to 3T MRI), and (B) adapting 2D models pre-trained on ImageNet (ResNet18, ResNet50, ResNet101) for 3D image processing when historical data is unavailable. …”
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FTA-Net: Frequency-Temporal-Aware Network for Remote Sensing Change Detection by Taojun Zhu, Zikai Zhao, Min Xia, Junqing Huang, Liguo Weng, Kai Hu, Haifeng Lin, Wenyu Zhao

“…First, it has a two-branch Transformer-INN feature extractor using a Lite-Transformer that utilizes remote attention for low-frequency global features, and a invertible neural network that focuses on extracting high-frequency local information. …”
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Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application by Xiaolei Zhou, Xingyue Wang, Ruifeng Guo

“…To address this problem, a convolutional neural network (CNN) model combining the improved particle swarm optimization (IPSO) algorithm and SHAP analysis, called SHAP-IPSO-CNN, is developed in this study, aiming to reveal the key factors affecting ground-level ozone pollution and their interaction mechanisms. …”
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Fuzzy Comprehensive Evaluation Model of Project Investment Risk Based on Computer Vision Technology by Hongjian Wang

“…Then, this paper establishes a model of fuzzy comprehensive evaluation of project investment risk through computer vision technology, real-time embedded systems, and neural network models in big data and artificial intelligence technology to realize the analysis and prediction of project investment risk. …”
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Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab–Column Connections by Sarmed Wahab, Nasim Shakouri Mahmoudabadi, Sarmad Waqas, Nouman Herl, Muhammad Iqbal, Khurshid Alam, Afaq Ahmad

“…Compared with the design codes and other machine learning models, the particle swarm optimization-based feedforward neural network (PSOFNN) performed the best predictions. …”
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Innovative Framework for Historical Architectural Recognition in China: Integrating Swin Transformer and Global Channel–Spatial Attention Mechanism by Jiade Wu, Yang Ying, Yigao Tan, Zhuliang Liu

“…Through extensive experiments on a constructed historical building dataset, our model achieves an outstanding performance of over 97.8% in key metrics including accuracy, precision, recall, and F1 score (harmonic mean of the precision and recall), surpassing traditional CNN (convolutional neural network) architectures and contemporary deep learning models. …”
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Transformer-Based Optimization for Text-to-Gloss in Low-Resource Neural Machine Translation by Younes Ouargani, Noussaim El Khattabi

“…With a 55.18 Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score, and a 63.6 BiLingual Evaluation Understudy 1 (BLEU1) score, our proposed model not only outperforms state-of-the-art models on the Phoenix14T dataset but also outperforms some of the best alternative architectures, specifically Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU). …”
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Accelerating Multilingual Cryptocurrency Forensics: An NLP-Driven Approach for Efficient Mnemonic Identification by Hsin-Hsiung Kao

“…Our analysis reveals that the Text Convolutional Neural Network (TextCNN) model exhibits superior performance, achieving a 99.9993% accuracy rate, nearly matching the 100% accuracy of the Mnemonic Library Matching Method. …”
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Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients. by Yulin Lai, Peiyuan Huang

“…<h4>Methods</h4>Machine learning components, including ridge regression, XGBoost, k-nearest neighbor, light gradient boosting machine, logistic regression, support vector machine, neural network, and random forest, were used to construct a predictive model and identify the risk factors for SPMs with data from the Surveillance, Epidemiology and End Results. …”
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Nonintrusive Load Identification for Industrial Users Integrated with LSQR and Sequential Leader Clustering by Shuhui Yi, Yinglong Diao, Junjie Liu, Tian Fang, Xiaodong Yin

“…The results indicate that the model proposed can effectively achieve the nonintrusive industrial load identification, and least unified residue (LUR) is about 10−16, which is much better than the factorial hidden Markov model (FHMM) and the artificial neural network (ANN) model.…”
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Machine learning prediction of combat basic training injury from 3D body shape images. by Steven Morse, Kevin Talty, Patrick Kuiper, Michael Scioletti, Steven B Heymsfield, Richard L Atkinson, Diana M Thomas

“…Predictions were made using logistic regression, random forest, and artificial neural network (ANN) models. Model comparison was done using the area under the curve (AUC) of a ROC curve.…”
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Exploiting question-answer framework with multi-GRU to detect adverse drug reaction on social media by Jiao-huang Luo, Ai-hua Yang

“…To solve the problem, we regard ADR detection as a question-answer problem and introduces an innovative neural network framework with multiple GRU layers designed for extracting ADR-related information from tweets. …”
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Social Risk Early Warning of Environmental Damage of Large-Scale Construction Projects in China Based on Network Governance and LSTM Model by Junmin Fang, Dechun Huang, Jingrong Xu

“…Experiments show that the long short-term memory neural network model is effective and feasible for predicting the social risk trend of environmental damage of large-scale construction projects. …”
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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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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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A proximal policy optimization based deep reinforcement learning framework for tracking control of a flexible robotic manipulator by Joshi Kumar V, Vinodh Kumar Elumalai

“…This paper puts forward a policy feedback based deep reinforcement learning (DRL) control scheme for a partially observable system by leveraging the potentials of proximal policy optimization (PPO) algorithm and convolutional neural network (CNN). Although several DRL algorithms have been investigated for a fully observable system, there has been limited studies on devising a DRL control for a partially observable system with uncertain dynamics. …”
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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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ESTIMATED ELECTRICITY BILLING SYSTEM AND ITS EFFECTS ON CONSUMERS IN RESIDENTIAL AND BUSINESS CENTRES IN WUKARI METROPOLIS, TARABA STATE by Abubakar Ahmadu, Vyonkhen Tanko Nacho

“…It is recommended among others the adoption of Artificial Neural Network (ANN) to gauge consumers’ energy consumption pending the provision of prepaid meters and that National Electricity Regulatory Commission (NERC) should intensify efforts in the provision of free and/or subsidized prepaid meters to consumers. …”
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