Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study by Xiangkui Jiang, Bingquan Wang

“…Subsequently, we constructed 6 predictive models using different algorithms: logistic regression, support vector machine, gradient boosting machine, Extreme Gradient Boosting, multilayer perception, and graph convolutional networks. …”
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Study of the Current–Voltage Characteristics of Membrane Systems Using Neural Networks by Evgenia Kirillova, Anna Kovalenko, Makhamet Urtenov

“…During this work, several different neural network architectures were developed and tested. …”
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A semantic‐based method for analysing unknown malicious behaviours via hyper‐spherical variational auto‐encoders by Yi‐feng Wang, Yuan‐bo Guo, Chen Fang

“…The authors further use a Graph Convolutional Network (GCN) to reduce the impact of different user behaviour patterns before projection. …”
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Species-independent analysis and identification of emotional animal vocalizations by Stavros Ntalampiras

“…Abstract Animal vocalizations can differ depending on the context in which they are produced and serve as an instant indicator of an animal’s emotional state. …”
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Reconstruction of human dispersal during Aurignacian on pan-European scale by Yaping Shao, Christian Wegener, Konstantin Klein, Isabell Schmidt, Gerd-Christian Weniger

“…However, human dispersal is a highly convoluted process which is until today not well understood. …”
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4D hypercomplex-valued neural network in multivariate time series forecasting by Radosław Kycia, Agnieszka Niemczynowicz

“…Abstract The goal of this paper is to test three classes of neural network (NN) architectures based on four-dimensional (4D) hypercomplex algebras for multivariate time series forecasting. We evaluate different architectures, varying the input layers to include convolutional, Long Short-Term Memory (LSTM), or dense hypercomplex layers for 4D algebras. …”
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Fusion of Deep Features of Wavelet Transform for Wildfire Detection by Akbar Asgharzadeh-Bonab, Salar Ghamati, Farid Ahmadi, Hashem Kalbkhani

“…Forests uniquely deliver different vital resources, particularly oxygen and carbon dioxide purification. …”
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Advanced investing with deep learning for risk-aligned portfolio optimization. by Minh Duc Nguyen

“…This study introduces a deep learning-based framework for portfolio optimization tailored to different investor risk preferences. We combine two prediction models, Long Short-Term Memory (LSTM) and One-Dimensional Convolutional Neural Network (1D-CNN), with three portfolio frameworks: Mean-Variance with Forecasting (MVF), Risk Parity Portfolio (RPP), and Maximum Drawdown Portfolio (MDP). …”
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Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke by Gökalp Çınarer

“…A total of 30 different object detection models, including the proposed model, were run with the extended Wildfire Smoke dataset, and the results were compared. …”
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Detection of IPv6 routing attacks using ANN and a novel IoT dataset by Murat Emec

“…Using artificial intelligence and machine-learning techniques, a performance evaluation was performed on four different artificial neural network models (convolutional neural network, deep neural network, multilayer perceptron structure, and routing attack detection-fed forward neural network [RaD-FFNN]). …”
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Detection of Welding Defects Tracked by YOLOv4 Algorithm by Yunxia Chen, Yan Wu

“…The improvements include optimizing the stacking method of residual blocks, modifying the activation functions for different convolutional layers, and eliminating the downsampling layer in the PANet (Pyramid Attention Network) to preserve edge information. …”
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An Improved CEEMDAN-FE-TCN Model for Highway Traffic Flow Prediction by Heyao Gao, Hongfei Jia, Lili Yang

“…The fuzzy entropy (FE) is then calculated to recombine subsequences, highlighting traffic flow dynamics in different frequencies and improving prediction efficiency. …”
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D2D cooperative caching strategy based on graph collaborative filtering model by Ningjiang CHEN, Linming LIAN, Pingjie OU, Xuemei YUAN

“…A D2D cooperative caching strategy based on graph collaborative filtering model was proposed for the problem of difficulty in obtaining sufficient data to predict user preferences in device-to-device (D2D) caching due to the limited signal coverage of base stations.Firstly, a graph collaborative filtering model was constructed, which captured the higher-order connectivity information in the user-content interaction graph through a multilayer graph convolutional neural network, and a multilayer perceptron was used to learn the nonlinear relationship between users and content to predict user preferences.Secondly, in order to minimize the average access delay, considering user preference and cache delay benefit, the cache content placement problem was modeled as a Markov decision process model, and a cooperative cache algorithm based on deep reinforcement learning was designed to solve it.Simulation experiments show that the proposed caching strategy achieves optimal performance compared with existing caching strategies for different content types, user densities, and D2D communication distance parameters.…”
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Series-arc-fault diagnosis using feature fusion-based deep learning model by Won-Kyu Choi, Se-Han Kim, Ji-Hoon Bae

“…Experimental results show that the proposed model achieves an accuracy of 99.99% in classifying series arc faults for five different loads. Hence, a perfor-mance improvement of approximately 1.7% in classification accuracy is reached compared with a feature fusion model that does not incorporate TL-based model transfer and the attention mechanism.…”
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A deep learning approach for accurate COVID-19 diagnosis from x-ray images using OBLMPA by Xiaohua Li, Shuai Fu

“…The method is analyzed based on some different measurement indicators, and the results are compared with some state-of-the-art methods. …”
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Towards real-world monitoring scenarios: An improved point prediction method for crowd counting based on contrastive learning. by Rundong Cao, Jiazhong Yu, Ziwei Liu, Qinghua Liang

“…Additionally, a multi-scale feature fusion module is proposed to obtain high-quality feature maps for detecting targets of different scales. Comparative experimental results on public crowd counting datasets demonstrate that the proposed method achieves state-of-the-art performance.…”
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Meta-Learning-Based Lightweight Method for Food Calorie Estimation by Jinlin Ma, Yuetong Wan, Ziping Ma

“…Additionally, an adaptive fine-tuning module is also designed to refine estimation accuracy according to different datasets. The extensive experiments demonstrate the superiority of the MeLL-cal in terms of a PMAE of 18.7% and 31.1%, respectively, with only 2.313K parameters and 1.036 ms inference time on the Menu match dataset and the Calo world dataset.…”
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Using deep learning models to decode emotional states in horses by Romane Phelipon, Lea Lansade, Misbah Razzaq

“…We perform data exploration and use different cropping methods, mainly based on Yolo and Faster R-CNN, to create two new datasets: 1) the cropped body, and 2) the cropped head dataset. …”
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Statistical Data-Generative Machine Learning-Based Credit Card Fraud Detection Systems by Xiaomei Feng, Song-Kyoo Kim

“…This study highlights the importance of robust data handling techniques in developing effective fraud detection systems, setting the stage for future research on combining different datasets and improving predictive accuracy in the financial sector.…”
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Research on information extraction methods for historical classics under the threshold of digital humanities by Lifan HAN, Zijing JI, Zirui CHEN, Xin WANG

“…Digital humanities aims to use modern computer network technology to help traditional humanities research.Classical Chinese historical books are the important basis for historical research and learning, but since their writing language is classical Chinese, it is quite different from the vernacular Chinese in grammar and meaning, so it is not easy to read and understand.In view of the above problems, the solution to extract entities and relations in historical books based on pre-trained models was proposed to obtain the rich information contained in historical texts effectively.The model usedmulti-level pre-training tasks instead of BERT's original pre-training tasks to fully capture semantic information.And the model added some structures such as convolutional layers and sentence-level aggregations on the basis of the BERT model to optimize the generated word representation further.Then, in view of the scarcity of classical Chinese annotation data, a crowdsourcing system for the task of labeling historical classics was constructed, high-quality, large-scale entity and relation data was obtained and the classical Chinese knowledge extraction dataset was constructed.So it helped to evaluate the performance of the model and fine-tune the model.Experiments on the dataset constructed in this paper and on the GulianNER dataset demonstrated the effectiveness of the model proposed in this paper.…”
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