Sustainable Selection of Machine Learning Algorithm for Gender-Bias Attenuated Prediction by Raik Orbay, Evelina Wikner

“…An investigation into the environmental burden of seven different types of ML algorithms was conducted and the popular neural network algorithm had the highest environmental burden.…”
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Investigation of ANN Architecture for Predicting Load-Carrying Capacity of Castellated Steel Beams by Thuy-Anh Nguyen, Hai-Bang Ly, Van Quan Tran

“…This paper aims to propose an artificial neural network (ANN) model with optimal architecture to predict the load-carrying capacity of CSB with a scheme of the simple beam bearing load located at the center of the beam. …”
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Investigation of an object-detection approach for estimating the rock fragmentation in the open-pit conditions by K. Reshetnikov, M. Ronkin, S. Porshnev

“…Based on the research results, YOLOv7x architecture is proposed as a baseline model. The proposed neural network architecture was trained on a dataset selected by the present authors from digital images of blasted open-pit block fragments, which consisted of 220 images. …”
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Enhanced Neural Architecture for Real-Time Deep Learning Wavefront Sensing by Jianyi Li, Qingfeng Liu, Liying Tan, Jing Ma, Nanxing Chen

“…To achieve real-time deep learning wavefront sensing (DLWFS) of dynamic random wavefront distortions induced by atmospheric turbulence, this study proposes an enhanced wavefront sensing neural network (WFSNet) based on convolutional neural networks (CNN). …”
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A novel deep learning-based 1D-CNN-optimized GRU approach for heart disease prediction by Jini Mol G., Ajith Bosco Raj T.

“…To identify the irregularities in the cardiac data pattern, a gated recurrent unit (GRU) classifier and a one-dimensional convolutional neural network (1D-CNN) are introduced. A typical genetic algorithm (GA) is used to optimize the suggested GRU network in order to pass over the local minima. …”
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Emergency Medical Resources Allocation of Periphery for Epidemic Areas: Based on Infectious Diseases Spatial-Temporal Transmission Path by Xuan Zhao, Benhong Peng, Chaoyu Zheng, Anxia Wan

“…Finally, a graph convolutional neural network (GCN) is conducted to select the supply-side areas for peripheral-epicenter supplies distribution based on information achieved from the bipartite graph. …”
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Credit Card Fraud Detection through Parenclitic Network Analysis by Massimiliano Zanin, Miguel Romance, Santiago Moral, Regino Criado

“…We show how the inclusion of features extracted from the network data representation improves the score obtained by a standard, neural network-based classification algorithm and additionally how this combined approach can outperform a commercial fraud detection system in specific operation niches. …”
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Utilizing Machine Learning-based Classification Models for Tracking Air Pollution Sources: A Case Study in Korea by Yelim Choi, Bogyeong Kang, Daekeun Kim

“…Using 972 datasets consisting of five emission sources and 27 air pollutants, different classification models were implemented and subsequently compared: Random Forest (RF), Naïve Bayes Classifier (NBC), Support Vector Machine (SVM), Artificial Neural Network (ANN), and K-Nearest Neighbors (K-NN). …”
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Comparison of Classification Algorithms with Wrapper-Based Feature Selection for Predicting Osteoporosis Outcome Based on Genetic Factors in a Taiwanese Women Population by Hsueh-Wei Chang, Yu-Hsien Chiu, Hao-Yun Kao, Cheng-Hong Yang, Wen-Hsien Ho

“…To elucidate relationships between osteoporosis and SNPs in this population, three classification algorithms were applied: multilayer feedforward neural network (MFNN), naive Bayes, and logistic regression. …”
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Early Warning and Management Method of Abnormal Performance of Tourist Scenic Spots Assisted by Image Recognition Technology by Zhaozhen Song, Jing Lu

“…This study uses the convolutional neural network (CNN) method to realize the image recognition technology of the characteristics of the scenic area’s flow of people and tourists’ preferences, and these characteristics will be displayed to the managers in the form of images. …”
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Recent advances in journal bearings: wear fault diagnostics, condition monitoring and fault diagnosis methodologies by Nazik Jebur, Wafa Soud

“…Key findings indicate that ensemble models, such as the CNN and deep neural network (CNNEPDNN) model, significantly improve convergence speed, test accuracy, and F-Score in bearing fault diagnosis by 15-20% compared to individual models. …”
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Prediction of viscosity of blast furnace slag based on NRBO-DNN model by Zhe Li, Meng Wang, Rui Xu, Juanjuan Jiang, Jie Li, Zunqian Zhang, Aimin Yang

“…Among traditional neural network models, the Deep Neural Network (DNN) demonstrated the best accuracy. …”
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A Three-Dimensional Anisotropic Diffusion Equation-Based Video Recognition Model for Classroom Concentration Evaluation in English Language Teaching by Yanghong Liu, Jintao Liu

“…DK features provide a nonlinear description of the correlation between successive face images and express face image sequences in the temporal dimension; depth features are extracted by a pretrained depth neural network model that can express the complex nonlinear mapping relationships of images and reflect the more abstract implicit information inside face images. …”
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A generic self-learning emotional framework for machines by Alberto Hernández-Marcos, Eduardo Ros

“…Applied in a case study, an artificial neural network trained on unlabeled agent’s experiences successfully learned and identified eight basic emotional patterns that are situationally coherent and reproduce natural emotional dynamics. …”
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Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19 by Guokang Zhu, Jia Li, Zi Meng, Yi Yu, Yanan Li, Xiao Tang, Yuling Dong, Guangxin Sun, Rui Zhou, Hui Wang, Kongqiao Wang, Wang Huang

“…In addition to a physiological anomaly detection algorithm defined based on data from wearable devices, an online neural network prediction modelling methodology combining both detected physiological anomaly rate and historical COVID-19 infection rate is explored. …”
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NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation by Yuki Kagaya, Zicong Zhang, Nabil Ibtehaz, Xiao Wang, Tsukasa Nakamura, Pranav Deep Punuru, Daisuke Kihara

“…NuFold is a deep neural network trained end-to-end for the output structure from the input sequence. …”
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Prediction of Dissolved Oxygen Concentration in Sewage Treatment Process Based on Data Recognition Algorithm by Lili Ma, Jiangping Liu

“…On the one hand, it avoids the excessive random or blind selection of the initial weight threshold of the neural network in the initial stage; on the other hand, in the optimization process of the weight threshold, two types of search mechanisms, FWA and COA, are used to give full play to their respective strengths and to continuously conduct information exchange and mutual cooperation between groups and individuals. …”
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Effect of Process Parameters on Short Fiber Orientation along the Melt Flow Direction in Water-Assisted Injection Molded Part by Haiying Zhou, Hesheng Liu, Qingsong Jiang, Tangqing Kuang, Zhixin Chen, Weiping Li

“…The effect of six process parameters, including filling time, melt temperature, mold temperature, delay time, water pressure, and water temperature, on the SFO along the melt flow direction were studied through orthogonal experimental design, range analysis, and variance analysis. An artificial neural network was used to establish the nonlinear agent model between the process parameters and A11 representing the fiber orientation in melt flow direction. …”
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Application of an Internet of Things Oriented Network Education Platform in English Language Teaching by Huizhen Li

“…By analyzing the current situation of the Internet of Things education platform, including its development and structure, this paper constructs the evaluation model of the combination of long-term memory neural network model and gray model and realizes the evaluation of the teaching effect of the Internet of Things education platform. …”
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Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features by Weidong Song, Guohui Jia, Hong Zhu, Di Jia, Lin Gao

“…To address these challenges, we propose the CrackSeg—an end-to-end trainable deep convolutional neural network for pavement crack detection, which is effective in achieving pixel-level, and automated detection via high-level features. …”
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