Cladistics with geometric morphometric data: The variability of the calvarium in the genus Homo by Margaux Simon-Maciejewski, Giorgio Manzi, Valéry Zeitoun, Aurélien Mounier

“…This study tests two different protocols to analyse 23 operational taxonomic units (OTUs) representing the genera Pongo, Gorilla, Pan, Australopithecus and Homo, in order to evaluate the phylogenetic information derived from geometric morphometric data. …”
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Ecological niche models as a tool for estimating the distribution of plant communities by Mayra Flores-Tolentino, Enrique Ortiz, José Luis Villaseñor

“…The use of widely distributed species in the definition of communities results in models with greater overestimation. It is important to adapt the available information and knowledge about the object of study, to properly integrate them into the different algorithms that allow us to obtain an approximation of what happens with the species or communities. …”
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In-Process Monitoring of Inhomogeneous Material Characteristics Based on Machine Learning for Future Application in Additive Manufacturing by André Jaquemod, Marijana Palalić, Kamil Güzel, Hans-Christian Möhring

“…The algorithms are trained to recognize patterns, anomalies, or deviations from expected behavior, which can aid in evaluating the effect of detected defects on the machining process and the resultant component quality. …”
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Metaheuristics in automated machine learning: Strategies for optimization by Francesco Zito, El-Ghazali Talbi, Claudia Cavallaro, Vincenzo Cutello, Mario Pavone

“…This work aims to fill this gap by systematically evaluating the performance of metaheuristic algorithms in optimizing hyperparameters and discussing advanced techniques such as parallel computing to adapt metaheuristic algorithms for use in hyperparameter optimization with high-dimensional hyperparameter search space.…”
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Efficient structure learning of gene regulatory networks with Bayesian active learning by Dániel Sándor, Péter Antal

“…Results We introduce novel acquisition functions for experiment design in gene expression data, leveraging active learning in both Essential Graph and Graphical Model spaces. We evaluate scalable structure learning algorithms within an active learning framework to optimize intervention selection. …”
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Enhancing e-learning through AI: advanced techniques for optimizing student performance by Rund Mahafdah, Seifeddine Bouallegue, Ridha Bouallegue

“…AI algorithms, known for their cognitive ability and capacity to learn, adapt, and make decisions, are employed to analyze and forecast student performance, thereby improving educational quality and outcomes. …”
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A Data-Driven Model for the Energy and Economic Assessment of Building Renovations by Giuseppe Piras, Francesco Muzi, Zahra Ziran

“…The proposed methodology aims to provide a digital approach that is replicable and adaptable to different territorial realities and useful for strategic planning of energy transformation in the building sector.…”
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An Autonomic Nervous System Model Applied to the Analysis of Orthostatic Tests by Virginie Le Rolle, Alfredo I. Hernández, Pierre-Yves Richard, Guy Carrault

“…One of the clinical examinations performed to evaluate the autonomic nervous system (ANS) activity is the tilt test, which consists in studying the cardiovascular response to the change of a patient's position from a supine to a head-up position. …”
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A deep multiple self-supervised clustering model based on autoencoder networks by Ling Zhu, Zijin Liu, Guangyu Liu

“…The experimental results show that the model performs significantly better than various common clustering algorithms on datasets of different types in multiple fields. …”
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A Hybrid Proactive Caching System in Vehicular Networks Based on Contextual Multi-Armed Bandit Learning by Qiao Wang, David Grace

“…Specifically, the paper proposes a distributed Hybrid cMAB Proactive Caching System where RSUs act as independent learners that implement two parallel online reinforcement learning-based mobility prediction algorithms between which they can adaptively finalize their predictions for the next RSU. …”
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Identification of reference genes for RT-qPCR in the Antarctic moss Sanionia uncinata under abiotic stress conditions. by Mira Park, Soon Gyu Hong, Hyun Park, Byeong-Ha Lee, Hyoungseok Lee

“…In this report, 11 traditionally used and 6 novel candidate reference genes were selected from transcriptome data of S. uncinata and the expression stability of these genes was evaluated under various abiotic stress conditions using three statistical algorithms; geNorm, NormFinder, and BestKeeper. …”
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A Framework for Assessment of Perception Systems in Autonomous Vehicles by Hussein Hennaoui, Daniel Paluszczyszyn, Lipika Deka, Serhan Cosar

“…The results demonstrate that the sensors with the respective algorithms in the simulation are adapted to different lighting conditions, ensuring consistent sensory performance. …”
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Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques by Sonia Val, María Pilar Lambán, Javier Lucia, Jesús Royo

“…This study analyzes the flank wear of cutting tools in milling machines, with an emphasis on evaluating different approaches to predict their lifespan. …”
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Charting new territories: fuzzy systems in English language teaching and learning by Xiaomei Wen, Deng Pan

“…Expert systems based on fuzzy logic, suggested in this study, can handle teacher and student evaluations even when faced with imprecise information and uncertainty; this is necessary since academic performance is being indexed in multiple international databases using impact indices at different scales. …”
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Contrastive Disentangled Variational Autoencoder for Collaborative Filtering by Woo-Seong Yun, Seong-Min Kang, Yoon-Sik Cho

“…The proposed contrastive disentanglement framework is generic and thus adaptable to VAE-based recommender algorithms. We comprehensively evaluate our proposed method on popular datasets such as MovieLens20M and Netflix and show that it consistently outperforms corresponding VAE models, achieving superior performance across all metrics. …”
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Human-in-the-loop control strategy for IoT-based smart thermostats with Deep Reinforcement Learning by Payam Fatehi Karjou, Fabian Stupperich, Phillip Stoffel, Drk Müller

“…The study explores the challenges of transferring control behaviors from simulation environments to real-world settings, examining the performance across different thermal zones and evaluating the integration flexibility of the control strategy within building systems. …”
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Transfer and deep learning models for daily reference evapotranspiration estimation and forecasting in Spain from local to national scale by Yu Ye, Aurora González-Vidal, Miguel A. Zamora-Izquierdo, Antonio F. Skarmeta

“…This study compares standard ML and Deep Learning (DL) algorithms for estimating and forecasting daily ET0 at different spatial scales in Spain. …”
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Developing interpretable machine learning models to predict length of stay and disposition decision for adult patients in emergency departments by Abhishek Sharma, Timothy N Fazio, Long Song, Samantha Plumb, Uwe Aickelin, Mojgan Kouhounestani, Mark John Putland

“…Eight ML algorithms were evaluated, and ultimately, twelve lasso models built from 21 features were trained to predict four outcomes of LOS and DD at three time points post-triage. …”
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A Hierarchical Machine Learning-Based Strategy for Mapping Grassland in Manitoba’s Diverse Ecoregions by Mirmajid Mousavi, James Kobina Mensah Biney, Barbara Kishchuk, Ali Youssef, Marcos R. C. Cordeiro, Glenn Friesen, Douglas Cattani, Mustapha Namous, Nasem Badreldin

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Comparative Analysis of Deep Learning Models for Intrusion Detection in IoT Networks by Abdullah Waqas, Sultan Daud Khan, Zaib Ullah, Mohib Ullah, Habib Ullah

“…This study addresses the problem of detecting intrusions in IoT environments by evaluating the performance of deep learning (DL) models under different data and algorithmic conditions. …”
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