Mathematical Modeling of Cyberattack Defense Mechanism Using Hybrid Transfer Learning With Snow Ablation Optimization Algorithm in Critical Infrastructures by Mohamad Khairi Ishak

“…For the cybersecurity classification process, the presented CDMHTL-SAOA technique applies the hybrid of convolutional neural network and bi-directional long short-term memory (CNN-BiLSTM) method. …”
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Person Detection for an Orthogonally Placed Monocular Camera by Pavel Skrabanek, Petr Dolezel, Zdenek Nemec, Dominik Stursa

“…The first approach is the utilization of an appropriate convolutional neural network (ConvNet), which is currently the prevailing approach in computer vision. …”
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Multiobjective Neuro-Fuzzy Controller Design and Selection of Filter Parameters of UPQC Using Predator Prey Firefly and Enhanced Harmony Search Optimization by Koganti Srilakshmi, Gummadi Srinivasa Rao, Katragadda Swarnasri, Sai Ram Inkollu, Krishnaveni Kondreddi, Praveen Kumar Balachandran, C. Dhanamjayulu, Baseem Khan

“…The reference signals for voltage source converters of UPQC are produced by the Levenberg–Marquardt back propagation (LMBP) trained artificial neural network control (ANNC). This method removes the necessity for conventional dq0, abc complex shifting. …”
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Multiobjective Optimization of Surface Roughness and Tool Wear in High-Speed Milling of AA6061 by Machine Learning and NSGA-II by Anh-Tu Nguyen, Van-Hai Nguyen, Tien-Thinh Le, Nhu-Tung Nguyen

“…Four ML models were used to predict Ra and Vbmax: linear regression (LIN), support vector machine regression (SVR), a gradient boosting tree (GBR), and an artificial neural network (ANN). The input variables were the significant factors that affect the surface quality and tool wear: the feed rate, depth of cut, cutting speed, and cutting time. …”
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PreMevE Update: Forecasting Ultra‐Relativistic Electrons Inside Earth's Outer Radiation Belt by Saurabh Sinha, Yue Chen, Youzuo Lin, Rafael Pires de Lima

“…We evaluated 32 supervised machine learning models that fall into four different classes of linear and neural network architectures, and successfully tested ensemble forecasting by using groups of top‐performing models. …”
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Intelligent Ensemble Deep Learning System for Blood Glucose Prediction Using Genetic Algorithms by Dae-Yeon Kim, Dong-Sik Choi, Ah Reum Kang, Jiyoung Woo, Yechan Han, Sung Wan Chun, Jaeyun Kim

“…Although there are numerous deep learning algorithms available, this study applied five algorithms, namely, recurrent neural network (RNN), which is optimized for sequence data (e.g., time-series), and RNN-based algorithms (e.g., long short-term memory (LSTM), stacked LSTM, bidirectional LSTM, and gated recurrent unit). …”
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Cheating Detection in Online Exams Using Deep Learning and Machine Learning by Bahaddin Erdem, Murat Karabatak

“…For regression and classification, deep neural network (DNN) from deep learning algorithms and support vector machine (SVM), decision trees (DTs), k-nearest neighbor (KNN), random forest (RF), logistic regression (LR), and extreme gradient boosting (XGBoost) algorithms from machine learning algorithms were used. …”
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Network Intrusion Detection and Prevention System Using Hybrid Machine Learning with Supervised Ensemble Stacking Model by Godfrey A. Mills, Daniel K. Acquah, Robert A. Sowah

“…Three machine learning algorithms comprising a multilayer perceptron neural network, a modified self-organizing map, and a decision tree were used for the detection framework. …”
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Unveiling unexpected adverse events: post-marketing safety surveillance of gilteritinib and midostaurin from the FDA Adverse Event Reporting database by Tingting Jiang, Yanping Li, Ni Zhang, Lanlan Gan, Hui Su, Guiyuan Xiang, Yuanlin Wu, Yao Liu

“…Methods: We conducted disproportionality analyses to identify drug-AE associations, including the reporting odds ratio and the Bayesian confidence propagation neural network. A signal was detected if both methods achieved statistical significance. …”
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Prediction of Current and Future Distributions of Chalcophora detrita (Coleoptera: Buprestidae) Under Climate Change Scenarios by Arif Duyar, Muhammed Arif Demir, Mahmut Kabalak

“…An ensemble model was created by using 11 different algorithms (Artificial Neural Network, Classification Tree Analysis, eXtreme Gradient Boosting, Flexible Discriminant Analysis, Generalised Additive Model, Generalised Boosting Model, Generalised Linear Model, Multivariate Adaptive Regression Splines, Maximum Entropy, Random Forest, Surface Range Envelope) to predict the potential suitable habitats of C. detrita. …”
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Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network by Shengwei Qin, Yao Jin, Hailong Hu

“…With the rapid development of deep learning technology, many neural network-based methods have been proposed for point cloud upsampling. …”
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Development and evaluation of a deep learning segmentation model for assessing non-surgical endodontic treatment outcomes on periapical radiographs: A retrospective study. by Dennis Dennis, Siriwan Suebnukarn, Sothana Vicharueang, Wasit Limprasert

“…Mask Region-based Convolutional Neural Network (Mask R-CNN) was used to pixel-wise segment the root from other structures in the image and trained to predict class label into healed, healing and disease. …”
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RETRACTED ARTICLE: An intelligent dynamic cyber physical system threat detection system for ensuring secured communication in 6G autonomous vehicle networks by Shanthalakshmi M, Ponmagal R S

“…So we present a novel approach to mitigating these security risks by leveraging pre-trained Convolutional Neural Network (CNN) models for dynamic cyber-attack detection within the cyber-physical systems (CPS) framework of AVs. …”
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Machine Learning Models for Predicting the Compressive Strength of Concrete with Shredded PET Bottles and M-Sand as Fine Aggregate by Altamashuddinkhan Nadimalla, Siti Aliyyah Masjuki, Abdullah Gubbi, Anjum Khan, Imran Mokashi

“…The study employs Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Decision Tree (DT) models, using the experimental data for predictive analysis. …”
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CNN-Based Object Recognition and Tracking System to Assist Visually Impaired People by Fahad Ashiq, Muhammad Asif, Maaz Bin Ahmad, Sadia Zafar, Khalid Masood, Toqeer Mahmood, Muhammad Tariq Mahmood, Ik Hyun Lee

“…For object detection and recognition, a deep Convolution Neural Network (CNN) model is employed with an accuracy of 83.3%, whereas the dataset contains more than 1000 categories. …”
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Global surface eddy mixing ellipses: spatio-temporal variability and machine learning prediction by Tian Jing, Ru Chen, Chuanyu Liu, Chunhua Qiu, Chunhua Qiu, Cuicui Zhang, Mei Hong

“…We also assessed the predictability of global mixing ellipses using machine learning algorithms, including Spatial Transformer Networks (STN), Convolutional Neural Network (CNN) and Random Forest (RF), with mean-flow and eddy- properties as features. …”
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Investigating heterogeneity across autism, ADHD, and typical development using measures of cortical thickness, surface area, cortical/subcortical volume, and structural covariance by Younes Sadat-Nejad, Younes Sadat-Nejad, Marlee M. Vandewouw, Marlee M. Vandewouw, R. Cardy, J. Lerch, J. Lerch, J. Lerch, M. J. Taylor, M. J. Taylor, A. Iaboni, C. Hammill, B. Syed, J. A. Brian, J. A. Brian, E. Kelley, E. Kelley, E. Kelley, M. Ayub, J. Crosbie, J. Crosbie, R. Schachar, R. Schachar, S. Georgiades, R. Nicolson, E. Anagnostou, E. Anagnostou, A. Kushki, A. Kushki

“…We integrated cortical thickness, surface area, and cortical/subcortical volume, with a measure of single-participant structural covariance using a graph neural network approach.ResultsOur findings suggest two large clusters, which differed in measures of adaptive functioning (χ2 = 7.8, P = 0.004), inattention (χ2 = 11.169, P < 0.001), hyperactivity (χ2 = 18.44, P < 0.001), IQ (χ2 = 9.24, P = 0.002), age (χ2 = 70.87, P < 0.001), and sex (χ2 = 105.6, P < 0.001).DiscussionThese clusters did not align with existing diagnostic labels, suggesting that brain structure is more likely to be associated with differences in adaptive functioning, IQ, and ADHD features.…”
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Characterizing Perception Deep Learning Algorithms and Applications for Vehicular Edge Computing by Wang Feng, Sihai Tang, Shengze Wang, Ying He, Donger Chen, Qing Yang, Song Fu

“…Additionally, our investigation of Deep Neural Network (DNN) layers revealed that certain convolutional layers experienced computation time increases exceeding <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2849</mn><mo>%</mo></mrow></semantics></math></inline-formula>, while activation layers showed a rise of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1173.34</mn><mo>%</mo></mrow></semantics></math></inline-formula>. …”
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Research on optimal selection of runoff prediction models based on coupled machine learning methods by Xing Wei, Mengen Chen, Yulin Zhou, Jianhua Zou, Libo Ran, Ruibo Shi

“…The study first selects artificial neural network (ANN) and support vector machine (SVM) as the base models. …”
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A Fusion Method Incorporating Dual-Attention Mechanism and Transfer Learning Into UNet++ for Remote Sensing Image Coastline Extraction by Yanru Song, Bai Xue, Yueyue Meng, Xiang Qin, Yixiao Li, Qi Liu

“…This paper applies a deep convolutional neural network to the problem of sea-land segmentation in high-spatial resolution remote sensing images and innovates upon the classic encoder-decoder architecture. …”
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