Comparative Analysis of Automated Machine Learning for Hyperparameter Optimization and Explainable Artificial Intelligence Models by Muhammad Salman Khan, Tianbo Peng, Hanzlah Akhlaq, Muhammad Adeel Khan

“…One of the most significant challenges in AI lies in selecting and fine-tuning the optimal algorithm for a given task. Automated Machine Learning (AutoML) models have emerged as a promising solution to address this challenge by systematically exploring hyperparameter spaces to identify optimal configurations efficiently. …”
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Optimal Markowitz portfolio using returns forecasted with time series and machine learning models by Damian Ślusarczyk, Robert Ślepaczuk

“…Abstract We aim to answer whether using forecasted stock returns based on machine learning and time series models in a mean-variance portfolio framework yields better results than relying on historical returns. …”
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Sex Estimation Through Orbital Measurements: A Machine Learning Approach for Forensic Science by George Triantafyllou, George G. Botis, Maria Piagkou, Konstantinos Papanastasiou, George Tsakotos, Ioannis Paschopoulos, George K. Matsopoulos, Stavroula Papadodima

“…Methods: In this study, orbital measurements from 92 dried skulls, comprising 35 males and 57 females, were utilized to develop a machine-learning-based classifier for sex estimation with potential applications in forensic science. …”
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Is 2 h of Hypothermic Machine Perfusion for Pancreas Preservation Effective in Improving Graft Reperfusion? by Benoit Mesnard, MD, Christophe Masset, MD, PhD, Etohan Ogbemudia, MD, PhD, Sarah Bruneau, PhD, Mohamed Elzawahry, MD, Stéphanie Le Bas-Bernardet, PhD, David Minault, Jeremy Hervouet, Diego Cantarovich, MD, PhD, Jérôme Rigaud, MD, PhD, Lionel Badet, MD, PhD, Peter Friend, MD, PhD, Rutger Ploeg, MD, PhD, Gilles Blancho, MD, PhD, James Hunter, MD, PhD, Thomas Prudhomme, MD, Julien Branchereau, MD, PhD

“…After these 2 h of preservation, the pancreases were reperfused using a normothermic machine perfusion (NMP) for 2 h. During NMP, oxygenation, perfusion parameters, biochemical analyses, a glucose stimulation insulin secretion test, and an evaluation of ischemia/reperfusion injury by photoacoustic tomography were assessed. …”
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Simplifying Field Traversing Efficiency Estimation Using Machine Learning and Geometric Field Indices by Gavriela Asiminari, Lefteris Benos, Dimitrios Kateris, Patrizia Busato, Charisios Achillas, Claus Grøn Sørensen, Simon Pearson, Dionysis Bochtis

“…This study aimed to simplify field efficiency estimation by training machine learning regression algorithms on data generated from a farm management information system covering a combination of different field areas and shapes, working patterns, and machine-related parameters. …”
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Enhancing and Optimizing User-Machine Closed-Loop Co-Adaptation in Dynamic Myoelectric Interface by Wei Li, Ping Shi, Sujiao Li, Hongliu Yu

“…These findings provide a novel approach to designing myoelectric interfaces with cross-scene recognition through user-machine closed-loop co-adaptation.…”
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Feature importance analysis of solar flares and prediction research with ensemble machine learning models by Yun Yang, Yun Yang

“…Besides, analyzing the feature parameters of four different active regions, we find that the geographical information of the flare occurrence is an important factor. …”
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Identifying drivers of surface ozone bias in global chemical reanalysis with explainable machine learning by K. Miyazaki, Y. Marchetti, J. Montgomery, S. Lu, K. Bowman

“…<p>This study employs an explainable machine learning (ML) framework to examine the regional dependencies of surface ozone biases and their underlying drivers in global chemical reanalysis. …”
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ST40 electromagnetic predictive studies supported by machine learning applied to experimental database by M. Scarpari, S. Minucci, G. Sias, R. Lombroni, P. F. Buxton, M. Romanelli, G. Calabrò

“…The database contains both disrupted and non-disrupted pulses. Using Machine Learning, common features within disruptions are automatically classified and identified, mapping the controllable operational space in terms of plasma displacement and variation of specific plasma internal parameters. …”
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Optimizing Reservoir Separability in Liquid State Machines for Spatio-Temporal Classification in Neuromorphic Hardware by Oscar I. Alvarez-Canchila, Andres Espinal, Alberto Patiño-Saucedo, Horacio Rostro-Gonzalez

“…In this paper, we propose an optimization approach using Particle Swarm Optimization (PSO) to enhance reservoir separability in Liquid State Machines (LSMs) for spatio-temporal classification in neuromorphic systems. …”
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Alertness assessment by optical stimulation-induced brainwave entrainment through machine learning classification by Yong Zhou, Yizhou Tan, Shasha Wang, Hanshu Cai, Ying Gu

“…To further improve classification efficacy, these three features were used as input parameters in machine learning models. We found that Naive Bayes model showed the best classification efficacy in 30 Hz optical stimulation, with AUC reaching 0.90, an average accuracy of 0.90, an average sensitivity of 0.89, and an average specificity of 0.90. …”
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Assessing acoustic receiver detection efficiency using autocorrelation adjusted machine learning models by Devon A. Smith, James A. Crossman, Eduardo G. Martins

“…To predict detection efficiency, we applied regression-based machine learning models in two distinct river systems: a small mountainous and a large regulated river. …”
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A Guide to the Implementation and Design of Ex Vivo Perfusion Machines for Vascularized Composite Allotransplantation by Tessa E. Muss, MD, Amanda H. Loftin, DVM, Zachary H. Zamore, BA, Eleni M. Drivas, MD, Yi-Nan Guo, MD, Yichuan Zhang, MS, John Brassil, MS, Byoung Chol Oh, DVM, PhD, Gerald Brandacher, MD, FAST

“…Background:. Ex vivo machine perfusion (EVMP) is a versatile platform utilized in vascularized composite allotransplantation (VCA) to prolong preservation, salvage tissue, and evaluate graft viability. …”
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Machine learning-based analyses of contributing factors for the development of hypertension: a comparative study by Marenao Tanaka, Yukinori Akiyama, Kazuma Mori, Itaru Hosaka, Keisuke Endo, Toshifumi Ogawa, Tatsuya Sato, Toru Suzuki, Toshiyuki Yano, Hirofumi Ohnishi, Nagisa Hanawa, Masato Furuhashi

“…Objectives Sufficient attention has not been given to machine learning (ML) models using longitudinal data for investigating important predictors of new onset of hypertension. …”
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Blind Parameter Identification of MAR Model and Mutation Hybrid GWO-SCA Optimized SVM for Fault Diagnosis of Rotating Machinery by Wenlong Fu, Jiawen Tan, Xiaoyuan Zhang, Tie Chen, Kai Wang

“…Afterwards, a hybrid optimization algorithm combining mutation operator, grey wolf optimizer (GWO), and sine cosine algorithm (SCA), termed mutation hybrid GWO-SCA (MHGWOSCA), was proposed for parameter selection of support vector machine (SVM). …”
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Compressive strength of nano concrete materials under elevated temperatures using machine learning by Abdullah M. Zeyad, Alaa A. Mahmoud, Alaa A. El-Sayed, Ayman M. Aboraya, Islam N. Fathy, Nikos Zygouris, Panagiotis G. Asteris, Ibrahim Saad Agwa

“…Abstract In this study, four Artificial intelligence (AI) - based machine learning models were developed to estimate the Residual compressive strength (RCS) value of concrete supported with nano additives of Nanocarbon tubes (NCTs) and Nano alumina (NAl), after exposure to elevated temperatures ranging from 200 to 800 degrees. …”
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Glaucoma detection and staging from visual field images using machine learning techniques. by Nahida Akter, Jack Gordon, Sherry Li, Mikki Poon, Stuart Perry, John Fletcher, Thomas Chan, Andrew White, Maitreyee Roy

“…The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters.…”
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Design of an Iterative Method for Malware Detection Using Autoencoders and Hybrid Machine Learning Models by Rijvan Beg, R. K. Pateriya, Deepak Singh Tomar

“…In this context, we propose a comprehensive framework that applies machine learning methods to enhance evidence collection and malware activity analysis. …”
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Research on machine vision online monitoring system for egg production and quality in cage environment by Zhenlong Wu, Hengyuan Zhang, Cheng Fang

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Construction and interpretation of tobacco leaf position discrimination model based on interpretable machine learning by Ranran Kou, Cong Wang, Jinxia Liu, Ran Wan, Zhe Jin, Le Zhao, Youjie Liu, Junwei Guo, Feng Li, Hongbo Wang, Song Yang, Cong Nie

“…The study proposes a novel approach that integrates machine learning with advanced interpretability techniques for both tobacco leaf position discrimination and analysis. …”
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