Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework by Milos Antonijevic, Miodrag Zivkovic, Milica Djuric Jovicic, Bosko Nikolic, Jasmina Perisic, Marina Milovanovic, Luka Jovanovic, Mahmoud Abdel-Salam, Nebojsa Bacanin

“…This study revolves around hybrid framework that combines convolutional neural networks (CNN) and machine learning (ML) classifying models, like categorical boosting (CatBoost) and light gradient-boosting machine (LightGBM), further optimized through metaheuristics optimizers for leveraged performance. …”
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Application of Motor Imagery Brain-Computer Interface in Rehabilitation of Neurological Diseases by YANG Banghua

“…To address the poor generalization issues in traditional machine learning algorithms at the individual level of stroke patients, MI-BCI rehabilitation training system is built on the transfer learning technology. …”
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Coffee Rust Severity Analysis in Agroforestry Systems Using Deep Learning in Peruvian Tropical Ecosystems by Candy Ocaña-Zuñiga, Lenin Quiñones-Huatangari, Elgar Barboza, Naili Cieza Peña, Sherson Herrera Zamora, Jose Manuel Palomino Ojeda

“…This research demonstrates the capacity of machine learning algorithms in disease diagnosis, which could be an alternative to help experts quantify the severity of coffee rust in coffee trees and broadens the field of research for future low-cost computational tools for disease recognition and classification…”
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Exploration of transfer learning techniques for the prediction of PM10 by Michael Poelzl, Roman Kern, Simonas Kecorius, Mario Lovrić

“…Abstract Modelling of pollutants provides valuable insights into air quality dynamics, aiding exposure assessment where direct measurements are not viable. Machine learning (ML) models can be employed to explore such dynamics, including the prediction of air pollution concentrations, yet demanding extensive training data. …”
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Contemporary digital sociology in search of meaning-making models: the proper and the actual. Review on articles from the research handbook of digital sociology edited by J. Skopek... by E. V. Andrianova, V. A. Davydenko, A. I. Rozmanov

“…The first part of this review presents the key ideas of 15 chapters, focusing on the foundational components of social  theory and their connections to the internet in daily life, digital  technologies for surveys and data processing, the significance  of mobile devices, big data, machine learning, agent-based  models of social phenomena, inclusive digital focus groups, social networking sites in professional contexts, online dating, partner selection based on digital information, the use of digital approaches in online markets, the application of YouTube in social sciences, and automated image analysis. …”
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A Dynamic Discretization Algorithm for Learning BN Model: Predicting Causation Probability of Ship Collision in the Sunda Strait, Indonesia by Iis Dewi Ratih, Ketut Buda Artana, Heri Kuswanto, Dhimas Widhi Handani, Renata Zahabiya

“…The Bayesian Network (BN) machine learning method is capable of predicting ship collisions. …”
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Exploring the potential of aerial drone imagery to distinguish breeding Adélie (Pygoscelis adeliae), chinstrap (Pygoscelis antarcticus) and gentoo (Pygoscelis papua) penguins in An... by Christian Pfeifer, Stefan Knetsch, Jakob Maercker, Osama Mustafa, Marie-Charlott Rümmler, Alexander Brenning

“…Consequently, these results enable specific flight planning for optimal species discrimination under the given conditions and serve as the basis for future automated mapping of penguin species using machine learning, facilitating early detection of changes.…”
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Classification of cervical cancer using Dense CapsNet with Seg-UNet and denoising autoencoders by Hui Yang, Walid Aydi, Nisreen Innab, Mohamed E. Ghoneim, Massimiliano Ferrara

“…Cervical cancer classification using machine learning (ML) and deep learning (DL) has been extensively studied to enhance the conventional diagnostic process. …”
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Multi-Relational Graph Representation Learning for Financial Statement Fraud Detection by Chenxu Wang, Mengqin Wang, Xiaoguang Wang, Luyue Zhang, Yi Long

“…Financial statement fraud refers to malicious manipulations of financial data in listed companies’ annual statements. Traditional machine learning approaches focus on individual companies, overlooking the interactive relationships among companies that are crucial for identifying fraud patterns. …”
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Astronomaly Protege: Discovery through Human-machine Collaboration by Michelle Lochner, Lawrence Rudnick

“…Here we introduce ASTRONOMALY: PROTEGE, an extension of the general-purpose machine-learning-based active anomaly detection framework ASTRONOMALY. …”
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Time-resolved spectroscopy uncovers deprotonation-induced reconstruction in oxygen-evolution NiFe-based (oxy)hydroxides by Dan Wu, Longfei Hu, Xiaokang Liu, Tong Liu, Xiangyu Zhu, Qiquan Luo, Huijuan Zhang, Linlin Cao, Jinlong Yang, Zheng Jiang, Tao Yao

“…In this study, utilizing in situ energy-dispersive X-ray absorption spectroscopy and machine learning analysis, we reveal the occurrence of deprotonation and elucidate the role of incorporated iron in facilitating the transition from nickel-iron layered double hydroxide (NiFe LDH) into its active oxyhydroxide. …”
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Explainable artificial intelligence to identify follicles that optimize clinical outcomes during assisted conception by Simon Hanassab, Scott M. Nelson, Artur Akbarov, Arthur C. Yeung, Artsiom Hramyka, Toulin Alhamwi, Rehan Salim, Alexander N. Comninos, Geoffrey H. Trew, Tom W. Kelsey, Thomas Heinis, Waljit S. Dhillo, Ali Abbara

“…IVF generates complex data, which can challenge the utilization of the full richness of data during decision-making, leading to reliance on simple ‘rules-of-thumb’. Machine learning techniques are well-suited to analyzing complex data to provide data-driven recommendations to improve decision-making. …”
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Improving fMRI-Based Autism Severity Identification via Brain Network Distance and Adaptive Label Distribution Learning by Junling Du, Shangyu Wang, Rentong Chen, Shaoping Wang

“…Machine learning methodologies have been profoundly researched in the realm of autism spectrum disorder (ASD) diagnosis. …”
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A Novel Optimization Method Using the Box–Behnken Design Integrated with a Back Propagation Neural Network–Genetic Algorithm for Hydrogen Purification by Nannan Zhang, Sumeng Hu, Qianqian Xin

“…To explore a better optimization solution, a novel optimization method using machine learning with a back propagation neural network (BPNN) was then proposed, and a kind of heuristic algorithm–genetic algorithm (GA) was introduced to enhance the architecture of the BPNN. …”
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A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), naïve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). …”
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Topic-aware neural attention network for malicious social media spam detection by Maged Nasser, Faisal Saeed, Aminu Da’u, Abdulaziz Alblwi, Mohammed Al-Sarem

“…Recently, several machine learning methods have been introduced for social network malicious spam classification. …”
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Uncertainty-aware deep learning in healthcare: A scoping review. by Tyler J Loftus, Benjamin Shickel, Matthew M Ruppert, Jeremy A Balch, Tezcan Ozrazgat-Baslanti, Patrick J Tighe, Philip A Efron, William R Hogan, Parisa Rashidi, Gilbert R Upchurch, Azra Bihorac

“…Conformal prediction offered similar strong performance in estimating uncertainty, along with ease of interpretation and application not only to deep learning but also to other machine learning approaches. Among the six articles describing non-imaging applications, model architectures and uncertainty estimation methods were heterogeneous, but predictive performance was generally strong, and uncertainty estimation was effective in comparing modeling methods. …”
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The algebraic extended atom-type graph-based model for precise ligand–receptor binding affinity prediction by Farjana Tasnim Mukta, Md Masud Rana, Avery Meyer, Sally Ellingson, Duc D. Nguyen

“…Recent advances in machine learning (ML)–based scoring functions have improved these predictions, yet challenges remain in modeling complex molecular interactions. …”
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The future of plant lectinology: Advanced technologies and computational tools by Vinicius J.S. Osterne, Kyria S. Nascimento, Benildo S. Cavada, Els J.M. Van Damme

“…Additionally, computational methods—including molecular docking, molecular dynamics simulations, and machine learning pipelines—support predictions of lectin structures and binding properties, underpinning experimental efforts. …”
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Steel surface defect detection and segmentation using deep neural networks by Sara Ashrafi, Sobhan Teymouri, Sepideh Etaati, Javad Khoramdel, Yasamin Borhani, Esmaeil Najafi

“…While manual recognition is one of the most reliable techniques, recent advances in computer vision and machine learning have led to the development of automatic defect detection techniques. …”
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