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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Examining the relationships between patients’ multimorbidity trajectories and prognostic outcomes after the initial hip fracture by Jessica Qiuhua Sheng, Da Xu, Paul Jen-Hwa Hu, Liang Li, Ting-Shuo Huang

“…We then leverage the discovered relationships to develop a cross-attention neural network method for estimating patients’ post-fracture prognoses and demonstrate its predictive utilities relative to several prevalent machine leaning methods. …”
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Molecular Modeling of Antimalarial Agents by 3D-QSAR Study and Molecular Docking of Two Hybrids 4-Aminoquinoline-1,3,5-triazine and 4-Aminoquinoline-oxalamide Derivatives with the... by Hanine Hadni, Mohamed Mazigh, El’mbarki Charif, Asmae Bouayad, Menana Elhallaoui

“…The QSAR model tested with artificial neural network (ANN) method shows high performance towards its predictability. …”
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Delayered IC image analysis with template‐based Tanimoto Convolution and Morphological Decision by Deruo Cheng, Yiqiong Shi, Tong Lin, Bah‐Hwee Gwee, Kar‐Ann Toh

“…The proposed TCMD‐PL model utilises the output of TCMD model as the pseudo labels for training a deep convolutional neural network in supervised manner, and it is able to achieve further performance improvement of ∼4% in comparison to TCMD model without extra data labelling.…”
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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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Model-Oriented Training of Coordinators of the Decentralized Control System of Technological Facilities With Resource Interaction by Volodymyr M. Dubovoi, Maria S. Yukhimchuk, Viacheslav V. Kovtun, Krzysztof R. Grochla

“…Conducted experimental studies of the proposed method of training neural network coordinators, implemented on Python TensorFlow, showed greater effectiveness of Collaborative Federated Learning compared to independent training of coordinators or direct transfer of learning outcomes between coordinators.…”
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A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data by Mussadiq Abdul Rahim, Sultan Daud Khan, Salabat Khan, Muhammad Rashid, Rafi Ullah, Hanan Tariq, Stanislaw Czapp

“…In this research we propose a GPS-only data trajectory analysis and a novel scheme to convert GPS trajectory data to image-based data to train a custom Convolutional Neural Network (CNN) model. The empirical results with an extensive 5-fold cross-validation show that the proposed scheme identifies turn and not turn with more than 94% recall. …”
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A Calibration Approach for Elasticity Estimation with Medical Tools by Grube S., Neidhardt M., Hermann A.-K., Sprenger J., Abdolazizi K., Latus S., Cyron C. J., Schlaefer A.

“…Second, we propose to use a more complex material model, e.g., a neural network, that can be trained with the determined elasticities. …”
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Traditional Chinese Medicine Prescription Generation Model Based on Search Enhancement by ZHAO Zijuan, REN Xueting, SONG Kai, QIANG Yan, ZHAO Juanjuan, ZHANG Junlong

“…In order to solve these problems, a hybrid neural network architecture for TCM prescription generation—PreGenerator is proposed. …”
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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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PaleAle 6.0: Prediction of Protein Relative Solvent Accessibility by Leveraging Pre-Trained Language Models (PLMs) by Wafa Alanazi, Di Meng, Gianluca Pollastri

“…We present a deep neural network architecture based on a combination of bidirectional recurrent neural networks and convolutional layers that can analyze long-range interactions within protein sequences and predict protein RSA using ESM-2 encoding. …”
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Adapting to evolving MRI data: A transfer learning approach for Alzheimer’s disease prediction by Rosanna Turrisi, Sarthak Pati, Giovanni Pioggia, Gennaro Tartarisco

“…This study explores Transfer Learning (TL) approaches to enhance AD diagnosis using a Baseline model consisting of a 3D-Convolutional Neural Network trained on 80 3T MRI scans.Two scenarios are explored: (A) utilizing historical data to address changes in MRI acquisitions (from 1.5T to 3T MRI), and (B) adapting 2D models pre-trained on ImageNet (ResNet18, ResNet50, ResNet101) for 3D image processing when historical data is unavailable. …”
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Characterization of Low Visibility and Forecasting Model in Chongqing Central Area by Yu Han, Yi Liu, Yaping Zhang, Jun He, Yan Zhang, Qu Guo, Huan Wang

“…The visibility prediction model was established by using the neural network method, and the effect of introducing the PM2.5 concentration factor on low visibility prediction was analyzed and compared. …”
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A prediction approach to COVID-19 time series with LSTM integrated attention mechanism and transfer learning by Bin Hu, Yaohui Han, Wenhui Zhang, Qingyang Zhang, Wen Gu, Jun Bi, Bi Chen, Lishun Xiao

“…Classical deep learning models including recurrent neural network (RNN), long and short-term memory (LSTM), gated recurrent unit (GRU) and temporal convolutional network (TCN) are initially trained, then RNN, LSTM and GRU are integrated with a new attention mechanism and transfer learning to improve the performance. …”
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Enhancing pediatric congenital heart disease detection using customized 1D CNN algorithm and phonocardiogram signals by Ihtisham Ul Haq, Ghassan Husnain, Yazeed Yasin Ghadi, Nisreen Innab, Masoud Alajmi, Hanan Aljuaid

“…This research presents a tailored one-dimensional convolutional neural network (1D-CNN) for the classification of phonocardiogram (PCG) signals into normal or abnormal categories, providing an automated and efficient solution for congenital heart disease (CHD) diagnosis. …”
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Machine Learning Does Not Improve Humeral Torsion Prediction Compared to Regression in Baseball Pitchers by Garrett S Bullock, Charles A Thigpen, Gary S Collins, Nigel K Arden, Thomas K Noonan, Michael J Kissenberth, Ellen Shanley

“…Support vector machine RMSE was 10° and calibration was 1.13 (95% CI: 1.08, 1.18). Artificial neural network RMSE was 15° and calibration was 1.03 (95% CI: 0.97, 1.09)…”
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Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms by Fude Duan, Mahdiyeh Eslami, Mohammad Khajehzadeh, Ali Basem, Dheyaa J. Jasim, Sivaprakasam Palani

“…In this study, a machine learning approach using a multilayer perceptron artificial neural network (MLP-ANN) has been used to forecast solar radiation, wind speed, temperature, and load data. …”
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Deep learning in gonarthrosis classification: a comparative study of model architectures and single vs. multi-model methods by Sahika Betul Yayli, Kutay Kılıç, Salih Beyaz

“…(2) How do seven convolutional neural network (CNN) architectures perform across four distinct deep learning tasks? …”
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Real-Time Multi-Task Deep Learning Model for Polyp Detection, Characterization, and Size Estimation by Phanukorn Sunthornwetchapong, Kasichon Hombubpha, Kasenee Tiankanon, Satimai Aniwan, Pasit Jakkrawankul, Natawut Nupairoj, Peerapon Vateekul, Rungsun Rerknimitr

“…In this work, we present a modified convolutional neural network (CNN) based deep learning (DL) model to perform these tasks in real-time, utilizing existing object detection models: YOLOv5 and YOLOv8. …”
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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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