Adverse childhood experiences: terms, concepts, and study methods by Diana S. Shumskaia, Anna V. Trusova, Alexander O. Kibitov

“…However, to date, there is no unique and universally accepted approach to assessment of ACE, which may lead to controversy and low reproducibility of results.The purpose of this review is to examine and compare various methods of assessing and interpreting of ACE in order to identify optimal research algorithms within the current interpretation of the biopsychosocial model in psychiatry.This article reviews current terms, concepts, and methods of studying ACE. …”
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Advances in the Automated Identification of Individual Tree Species: A Systematic Review of Drone- and AI-Based Methods in Forest Environments by Ricardo Abreu-Dias, Juan M. Santos-Gago, Fernando Martín-Rodríguez, Luis M. Álvarez-Sabucedo

“…., RGB, multispectral, hyperspectral, LiDAR), preprocessing techniques, segmentation approaches, and machine learning (ML) algorithms used for classification. Findings of this study reveal that deep learning (DL) models, particularly convolutional neural networks (CNN), are increasingly replacing traditional ML methods such as random forest (RF) or support vector machines (SVMs) because there is no need for a feature extraction phase, as this is implicit in the DL models. …”
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Prediction of Soil Field Capacity and Permanent Wilting Point Using Accessible Parameters by Machine Learning by Liwei Liu, Xingmao Ma

“…In this study, global-scale accessible soil data were retrieved from the world soil database called the World Soil Information Service (WoSIS), and artificial neural network (ANN) and gene-expression programming (GEP) algorithms were used to predict soil FC and PWP based on easily obtainable parameters from the database. …”
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Optimizing integration techniques for UAS and satellite image data in precision agriculture — a review by Aliasghar Bazrafkan, C. Igathinathane, Nonoy Bandillo, Paulo Flores

“…Future works should delve into advanced fusion methodologies, incorporating machine learning algorithms, and conduct cross-crop application studies to broaden applicability and tailor insights for specific crops.…”
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Intelligent Priority-Aware Spectrum Access in 5G Vehicular IoT: A Reinforcement Learning Approach by Adeel Iqbal, Tahir Khurshaid, Yazdan Ahmad Qadri

“…This work compares four RL algorithms: Q-Learning, Double Q-Learning, Deep Q-Network (DQN), and Actor-Critic (AC) methods. …”
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Comprehensive Analysis of the Influence of Technical and Biological Variations on De Novo Assembly of RNA-Seq Datasets by Gonzalez Sergio Alberto, Rivarola Maximo, Ribone Andres, Lew Sergio, Paniego Norma

“…De novo assembly of transcriptomes from species without reference genome remains a common problem in functional genomics. While methods and algorithms for transcriptome assembly are continually being developed and published, the quality of de novo assemblies using short reads depends on the complexity of the transcriptome and is limited by several types of errors. …”
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Exploring the binding potential of natural compounds to carbonic anhydrase of cyanobacteria through computer-based simulations by Archana Padhiary, Showkat Ahmad Mir, Aiswarya Pati, Binata Nayak

“…Next, the In-silico methodologies such as molecular docking and molecular dynamic simulations, free energy landscape analysis, hydrogen bond analysis, and binding free energy calculations were performed using various algorithms under virtual physiological conditions to identify potential SAR molecules against carbonic anhydrase. …”
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Cryptanalysis of hyperchaotic S-box generation and image encryption by Mohammad Mazyad Hazzazi, Gulraiz, Rashad Ali, Muhammad Kamran Jamil, Sameer Abdullah Nooh, Fahad Alblehai

“…Because they feature substitution boxes, substitution-permutation networks (SPNs) are crucial for cryptographic algorithms such as the popular Advanced Encryption Standard (AES). …”
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Study on debris flow vulnerability of ensemble learning model based on spy technology A case study of upper Minjiang river basin by Yutao Chen, Ning Li, Fucheng Xing, Han Xiang, Zilong Chen

“…Since it is challenging to predict debris flows with precision using traditional methods, machine learning algorithms have been used more and more in this field in recent years. …”
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Survival analysis using machine learning in transplantation: a practical introduction by Andrea Garcia-Lopez, Maritza Jiménez-Gómez, Andrea Gomez-Montero, Juan Camilo Gonzalez-Sierra, Santiago Cabas, Fernando Giron-Luque

“…This study aims to provide an introduction to the application of the RSF model in survival analysis in kidney transplantation alongside a practical guide to develop and evaluate predictive algorithms. Methods We employed a RSF model to analyze a simulated dataset of kidney transplant recipients. …”
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The effective landscape design parameters with high reflective hardscapes: guidelines for optimizing human thermal comfort in outdoor spaces by design -a case on hot arid climate w... by Reham A. Abdelwahab, Ahmed A. Fekry, Reham El-Dessuky Hamed

“…Through generative design algorithms, an optimized framework was developed to identify effective strategies for urban cooling. …”
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Integration of single‐cell and bulk RNA‐sequencing data reveals the prognostic potential of epithelial gene markers for prostate cancer by Zhuofan Mou, Lorna W. Harries

“…Subsequently, we applied 97 advanced machine‐learning algorithms across five PCa cohorts and developed an 11‐gene epithelial expression signature. …”
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Privacy-Preserving Detection of Tampered Radio-Frequency Transmissions Utilizing Federated Learning in LoRa Networks by Nurettin Selcuk Senol, Mohamed Baza, Amar Rasheed, Maazen Alsabaan

“…We evaluated the performance of multiple FL-enabled anomaly-detection algorithms, including Convolutional Autoencoder Federated Learning (CAE-FL), Isolation Forest Federated Learning (IF-FL), One-Class Support Vector Machine Federated Learning (OCSVM-FL), Local Outlier Factor Federated Learning (LOF-FL), and K-Means Federated Learning (K-Means-FL). …”
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A Promising Methodological Approach to Identifying Areas with the Greatest Potential for the Development of the City Cycling Infrastructure by O. А. Grishina, A. I. Grishin, I. A. Stroganov

“…The methodology is presented in the form of an algorithmic scheme, as well as a detailed description of its constituent stages. …”
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Constructing a machine learning model for systemic infection after kidney stone surgery based on CT values by Jiaxin Li, Yao Du, Gaoming Huang, Yawei Huang, Xiaoqing Xi, Zhenfeng Ye

“…Five machine learning algorithms and ten preoperative or intraoperative variables were used to develop a predictive model for SIRS. …”
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Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities by Sonam Dukda, Manoharan Kumar, Andrew Calcino, Ulf Schmitz, Matt A. Field

“…While interpreting genetic variation via standards such as ACMG guidelines is increasingly being recognized as a gold standard approach, the underlying datasets and algorithms recommended are often slow to incorporate additional data types and methodologies. …”
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Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. by Caroline Colijn, Aaron Brandes, Jeremy Zucker, Desmond S Lun, Brian Weiner, Maha R Farhat, Tan-Yun Cheng, D Branch Moody, Megan Murray, James E Galagan

“…E-Flux thus provides a promising new approach for algorithmically predicting metabolic state from gene expression data.…”
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Optimizing UAV performance with IoT and fuzzy linear fractional transportation models by Samaher Al-Janabi, Nawras G. Seyhood

“…Unlike conventional path-planning methods, the proposed model leverages bio-inspired artificial intelligence (AI) algorithms and mixed-integer programming to solve multi-objective optimization problems under uncertainty. …”
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Numerical modeling and neural network optimization for advanced solar panel efficiency by Udit Mamodiya, Indra Kishor, Mohammed Amin Almaiah, Monia Hamdi, Rami Shehab, Tayseer Alkhdour

“…Conventionally, such optimization techniques—MPPT (Maximum Power Point Tracking) along with heuristic algorithms—suffer significantly from slow adaptability and track sub optimality under dynamic environments. …”
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A machine learning framework for predicting cognitive impairment in aging populations using urinary metal and demographic data by Fengchun Ren, Xiao Zhao, Qin Yang, Huaqiang Liao, Yudong Zhang, Xuemei Liu

“…Six machine learning algorithms were trained and evaluated using sensitivity (SN), specificity (SP), accuracy (ACC), Matthews correlation coefficient (MCC) and AUC.ResultsThe eXtreme gradient boosting (XGBoost) model demonstrated superior performance across all metrics (SN = 0.78, SP = 0.84, ACC = 0.81, MCC = 0.62, AUC = 0.90), and was selected for subsequent interpretation. …”
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