Machine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia. by Dyah Aryani Perwitasari, Imaniar Noor Faridah, Haafizah Dania, Didik Setiawan, Triantoro Safaria

“…There were significant differences in ALT and AST between good and poor adherence groups, especially in the female patients. The Neural Network and Random Forests were the most suitable models to predict tuberculosis patients' adherence with good Area Under The Curve (AUC).…”
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OBC-YOLOv8: an improved road damage detection model based on YOLOv8 by Shizheng Zhang, Zhihao Liu, Kunpeng Wang, Wanwei Huang, Pu Li

“…Secondly, to extract the global and local feature information simultaneously to better improve the feature extraction ability of the model, BoTNet is added to the end of the backbone, which can combine the advantages of convolutional neural network (CNN) and Transformer. Finally, the coordinate attention mechanism (CA) is incorporated into the Neck section to make more accurate speculations and enhance detection accuracy further which can effectively mitigate irrelevant feature interference. …”
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A comparative analysis for crack identification in structural health monitoring: a focus on experimental crack length prediction with YUKI and POD-RBF by Zenzen, Roumaissa, Ayadi, Ayoub, Benaissa, Brahim, Belaidi, Idir, Sukic, Enes, Khatir, Tawfiq

“…Comparative evaluations with conventional optimisation algorithms, namely Cuckoo, Bat, and Particle Swarm Optimisation, reveal similar Mean Percentage Error values but with increased result variability, whereas Deep Artificial Neural Network models with varied hidden layer sizes.…”
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Prediction Model of Cutting Parameters for Turning High Strength Steel Grade-H: Comparative Study of Regression Model versus ANFIS by Adel T. Abbas, Mohanad Alata, Adham E. Ragab, Magdy M. El Rayes, Ehab A. El Danaf

“…In this paper the artificial neural network was used for predicting the surface roughness for different cutting parameters in CNC turning operations. …”
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Selection of Machine Learning Models for Oil Price Forecasting: Based on the Dual Attributes of Oil by Lei Yan, Yuting Zhu, Haiyan Wang

“…Then, based on the recurrent neural network (RNN) and long-term and short-term memory (LSTM) models, we build eight models for predicting the future and spot prices of international crude oil. …”
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Discrimination of Melanoma Using Laser-Induced Breakdown Spectroscopy Conducted on Human Tissue Samples by Muhammad Nouman Khan, Qianqian Wang, Bushra Sana Idrees, Geer Teng, Xutai Cui, Kai Wei

“…Chemometric methods, artificial neural network (ANN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and partial least square discriminant analysis (PLS-DA) are used to build the classification models. …”
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Synergetic monitoring of pressure and temperature stimulations in multisensory electronic skin based on time decoupling effect by Zhiyi Gao, Ye Zhang, Zhenyu Hu, Dongdong Zhang, Shengbin Li, Huiyun Xiao, Ziyin Xiang, Dan Xu, Haifeng Zhang, Yuanzhao Wu, Yiwei Liu, Jie Shang, Runwei Li

“…More importantly, by equipping with a multilayer neural network, the evolution from tactile perception to advanced intelligent tactile cognition is demonstrated.…”
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Motor Bearing Failure Identification Using Multiple Long Short-Term Memory Training Strategies by Youcef ATMANI, Ammar Mesloub, Said Rechak

“…Among a variety of models, the type of architecture known as Long-Short-Term Memory (LSTM) of Recurrent Neural Network (RNN) has both the ability to capture long-term dependencies and to adapt to sequential data modeling. …”
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COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings by Mohd Zulfaezal Che Azemin, Radhiana Hassan, Mohd Izzuddin Mohd Tamrin, Mohd Adli Md Ali

“…We used a deep learning model based on the ResNet-101 convolutional neural network architecture, which was pretrained to recognize objects from a million of images and then retrained to detect abnormality in chest X-ray images. …”
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Exploiting a Spatial Attention Mechanism for Improved Depth Completion and Feature Fusion in Novel View Synthesis by Anh Minh Truong, Wilfried Philips, Peter Veelaert

“…Furthermore, we combine a sequential deep neural network with a spatial attention mechanism to effectively fuse the projected features from multiple source viewpoints. …”
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AtOMICS: a deep learning-based automated optomechanical intelligent coupling system for testing and characterization of silicon photonics chiplets by Jaime Gonzalo Flor Flores, Jim Solomon, Connor Nasseraddin, Talha Yerebakan, Andrey B Matsko, Chee Wei Wong

“…This paper presents a neural network-based automated system designed for in-plane fiber-chip-fiber testing, characterization, and active alignment of silicon photonic devices that use process-design-kit library edge couplers. …”
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Fault Diagnosis of Batch Reactor Using Machine Learning Methods by Sujatha Subramanian, Fathima Ghouse, Pappa Natarajan

“…Appropriate statistical and geometric features are extracted from the residual signature and the total numbers of features are reduced using SVM attribute selection filter and principle component analysis (PCA) techniques. artificial neural network (ANN) classifiers like multilayer perceptron (MLP), radial basis function (RBF), and Bayes net are used to classify the different types of faults from the reduced features. …”
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Study on the Detection of Dairy Cows’ Self-Protective Behaviors Based on Vision Analysis by Jia Li, Pei Wu, Feilong Kang, Lina Zhang, Chuanzhong Xuan

“…The detection algorithm is used to calculate the number of tail, leg, and head movements by using an artificial neural network. The accuracy range of the tail and head reached [0.88, 1] and the recall rate was [0.87, 1]. …”
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ANN Model-Based Simulation of the Runoff Variation in Response to Climate Change on the Qinghai-Tibet Plateau, China by Chang Juan, Wang Genxu, Mao Tianxu, Sun Xiangyang

“…To identify the impacts of climate change in the runoff process in the Three-River Headwater Region (TRHR) on the Qinghai-Tibet Plateau, two artificial neural network (ANN) models, one with three input variables (previous runoff, air temperature, and precipitation) and another with two input variables (air temperature and precipitation only), were developed to simulate and predict the runoff variation in the TRHR. …”
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Identification of cardiac wall motion abnormalities in diverse populations by deep learning of the electrocardiogram by Albert J. Rogers, Neal K. Bhatia, Sabyasachi Bandyopadhyay, James Tooley, Rayan Ansari, Vyom Thakkar, Justin Xu, Jessica Torres Soto, Jagteshwar S. Tung, Mahmood I. Alhusseini, Paul Clopton, Reza Sameni, Gari D. Clifford, J. Weston Hughes, Euan A. Ashley, Marco V. Perez, Matei Zaharia, Sanjiv M. Narayan

“…We collected ECG and echocardiogram data from 35,210 patients in California and labeled WMA using unstructured language parsing of echocardiographic reports. A deep neural network (ECG-WMA-Net) was trained and outperformed both expert ECG interpretation and Q-wave indices, achieving an AUROC of 0.781 (CI: 0.762–0.799). …”
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Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review by Mohammad Faiz, Bakkanarappa Gari Mounika, Mohd Akbar, Swapnita Srivastava

“…This analysis covers both machine learning models (ML), such as support vector machine (SVM) & random forest (RF), as well as deep learning algorithms (DL), including convolution neural network (CNN), AlexNet, ResNet50, ShuffleNet, MobileNet, RNN. …”
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CoLR: Classification-Oriented Local Representation for Image Recognition by Tan Guo, Lei Zhang, Xiaoheng Tan, Liu Yang, Zhiwei Guo, Fupeng Wei

“…Specifically, the deep features of the object dataset are obtained by a well-trained convolutional neural network (CNN) with five convolutional layers and three fully connected layers on the challenging ImageNet. …”
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Fault Diagnosis Approach of Gear based on Two Features and Least Squares Support Vector Machine by Qin Bo, Yang Yunzhong, Chen Min, Guo Wei, Liu Yongliang, Wang Jianguo

“…It has higher efficiency of fault identification compared with the BP neural network and SVM model and a new way for the gear fault diagnosis is provided.…”
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A Car-Following Driver Model Capable of Retaining Naturalistic Driving Styles by Jie Hu, Sheng Luo

“…This is accomplished by using a neural network-based learning control paradigm and car-following data. …”
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Development of a Neuroevolution Machine Learning Potential of Al-Cu-Li Alloys by Fei Chen, Han Wang, Yanan Jiang, Lihua Zhan, Youliang Yang

“…To address this issue, we apply a neural network-based neuroevolutionary machine learning potential (NEP) and use evolutionary strategies to train it for large-scale molecular dynamics (MD) simulations. …”
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