Artificial intelligence in degenerative cervical disease: A systematic review of MRI-based diagnostic models by Qian Du, Xinxin Shao, Minbo Zhang, Guangru Cao

“…Accuracy ranged from 81.58% to 98%, sensitivities from 84% to 98%, specificities from 90% to 100%, and AUC values reached up to 0.97. Convolutional neural networks (CNN) were the most frequently used models (four studies), followed by support vector machines (three studies). …”
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Machine learning-assisted prediction of durability behavior in pultruded fiber-reinforced polymeric (PFRP) composites by Ammar A. Alshannaq, Mohammad F. Tamimi, Muath I. Abu Qamar

“…The results reveal that Decision Trees, Artificial Neural Networks, and Random Forests are the best models to predict behavior of pultruded composites under environmental exposure, which achieved R2 values of 0.9647, 0.9537, and 0.8970, respectively for the case of flexural strength. …”
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Pore Space Reconstruction of Shale Using Improved Variational Autoencoders by Yi Du, Hongyan Tu, Ting Zhang

“…Therefore, this paper proposes an improved VAE to reconstruct shale based on VAE and Fisher information, using a real 3D shale image as a TI, and saves the parameters of neural networks to describe the probability distribution. …”
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A CNN-Based Approach for Classical Music Recognition and Style Emotion Classification by Yawen Shi

“…In this study, The model based on convolutional neural networks (CNNs) is proposed, allowing people to recognize the classical music title, style, and emotions contained in a piece of music. …”
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A Comparative Study Evaluated the Performance of Two-class Classification Algorithms in Machine Learning by Shilan Abdullah Hassan, Maha Sabah Saeed

“…A comparative study evaluated the performance of five well-known two-class classification algorithms: two-class boosted decision trees, two-class decision forests, two-class locally deep SVMs, two-class neural networks, and two-class logistic regression. Among these algorithms, the Two-Class Boosted Decision Tree method demonstrated outstanding prediction ability, achieving a 100% accuracy rating. …”
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Deeply Learned Classifiers for Age and Gender Predictions of Unfiltered Faces by Olatunbosun Agbo-Ajala, Serestina Viriri

“…More recently, Convolutional Neural Networks (CNNs) based methods have been extensively used for the classification task due to their excellent performance in facial analysis. …”
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Ensemble Deep Learning Technique for Detecting MRI Brain Tumor by Rasool Fakhir Jader, Shahab Wahhab Kareem, Hoshang Qasim Awla

“…In recent years, a variety of computational algorithms for segmentation and classification have been developed with improved results to get around the issue. Artificial neural networks (ANNs) have the capability and promise to classify in this regard. …”
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Defect modeling in semiconductors: the role of first principles simulations and machine learning by Md Habibur Rahman, Arun Mannodi-Kanakkithodi

“…ML techniques, particularly neural networks, have emerged as powerful tools for enabling rapid prediction of defect properties at DFT-accuracy in order to overcome the expense of using large supercells and advanced functionals. …”
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Attention-based interactive multi-level feature fusion for named entity recognition by Yiwu Xu, Yun Chen

“…Abstract Named Entity Recognition (NER) is an essential component of numerous Natural Language Processing (NLP) systems, with the aim of identifying and classifying entities that have specific meanings in raw text, such as person (PER), location (LOC), and organization (ORG). Recently, Deep Neural Networks (DNNs) have been extensively applied to NER tasks owing to the rapid development of deep learning technology. …”
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Optimization and loss estimation in energy-deficient polygeneration systems: A case study of Pakistan's utilities with integrated renewable energy by Muhammad Shoaib Saleem, Naeem Abas

“…Techniques used for electricity demand forecasting encompass artificial intelligence, artificial neural networks, trend line extrapolations, fuzzy logic, vector support machines, genetic algorithms and expert systems. …”
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Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements by Brady Metherall, Anna K. Berryman, Georgia S. Brennan

“…We employ artificial neural networks (ANNs) and random forests (RFs) on a dataset of 400 patients with 25 input features, which we divide into three feature sets. …”
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Machine Learning in Acute Stroke Neuroimaging. A Systematic Literature Review by D. Matuliauskas, I. Stražnickaitė, A. Samuilis, D. Jatužis

“…Most popular software used in the studies were Brainomix (n=12, 20% of studies) and RAPID (n=12, 20%), 6 studies (10%) used convolutional neural networks, and 6 studies did not iden- tify the model or name of software used. …”
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Pattems and models of flowering of some Gampanulaceae Juss. species by E. S. Fomin, T. I. Fomina

“…In combination with visualization tools, they can be used for augmenting plant phenotyping datasets with rendered images of synthetic plants for the purpose of training neural networks in this field.…”
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Application of deep learning algorithm for judicious use of anti-VEGF in diabetic macular edema by Anwesa Mondal, Apurba Nandi, Subhasish Pramanik, Lakshmi Kanta Mondal

“…The architecture combines convolutional neural networks (CNNs) for image data with multi-layer perceptron (MLP) for tabular clinical data, allowing for a comprehensive analysis of both data types. …”
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Deep Transfer Learning for Lip Reading Based on NASNetMobile Pretrained Model in Wild Dataset by Ashwaq Waleed Abdul Ameer, Pedram Salehpour, Mohammad Asadpour

“…The proposed framework involves a process that extracts features from video frames in a time sequence, employing methods such as Convolutional Neural Networks (CNN), CNN-Gated Recurrent Units (CNN-GRU), Temporal CNN, and Temporal PoinWise. …”
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How to Handle Data Imbalance and Feature Selection Problems in CNN-Based Stock Price Forecasting by Zinnet Duygu Aksehir, Erdal Kilic

“…In literature, the convolutional neural networks (CNN) models were used for stock market forecasting and gave successful results. …”
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EMG-Based Continuous Estimation of Index Finger Movements With Varying Interjoint Coordination Patterns by Modeling Musculoskeletal Dynamics by Trenton A. Gilstrap, Mada M. Alghamdi, Thanh Phan, Sang Wook Lee

“…Generally, ‘black-box’ models with high complexity such as neural networks (NN) were used to improve prediction accuracy, which may not reproduce other important movement characteristics such as smoothness or kinematic similarity. …”
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CLARITY and Light-Sheet microscopy sample preparation in application to human cerebral organoids by T. A. Shnaider, I. E. Pristyazhnyuk

“…The most commonly used approach to studying the morphological parameters of organoids is immunohistochemical analysis; therefore, the three-dimensional cytoarchitecture of organoids, such as neural networks or asymmetric internal organization, is difficult to reconstruct using routine approaches. …”
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Interval combined prediction of mine tunnel's air volume considering multiple influencing factors. by Zhen Wang, Erkan Topal, Liangshan Shao, Chen Yang

“…The tunnel air volume and influencing factors are then input into different neural networks for air volume prediction. To further improve prediction accuracy, the predicted values of wind volume intervals from the single prediction method are transformed into triangular fuzzy numbers, and the generalized induced ordered weighted average operator is introduced for the combination of prediction results. …”
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Daily reference evapotranspiration prediction using empirical and data-driven approaches: A case study of Adana plain by Semin Topaloğlu Paksoy, Deniz Levent Koç

“…The objective of this research was to examine the effectiveness of five different data-driven techniques, including artificial neural networks "multilayer perceptron" (ANN), gene expression programming (GEP), random forest (RF), support vector machine "radial basis function" (SVM), and multiple linear regression (MLR) to model the daily ET0. …”
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