IAR 2.0: An Algorithm for Refining Inconsistent Annotations for Time-Series Data Using Discriminative Classifiers by Einari Vaaras, Manu Airaksinen, Okko Rasanen

“…The performance of discriminative machine-learning classifiers, such as neural networks, is limited by training label inconsistencies. …”
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Toll-like receptors in atopic dermatitis: pathogenesis and therapeutic implications by Ahmad Vafaeian, Fateme Rajabi, Nima Rezaei

“…Future directions include CRISPR-based gene editing to understand TLR functions, development of specific TLR modulators for targeted therapy, and machine learning applications to predict drug responses and identify novel ligands. …”
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Short‐Term Memory and Regional Climate Drive City‐Scale Water Demand in the Contiguous US by Wenjin Hao, Andrea Cominola, Andrea Castelletti

“…We develop a causal discovery framework based on a Neural Granger Causal (NGC) model, a machine learning approach that identifies nonlinear causal relationships between determinants and water demand, enabling comprehensive water demand determinants discovery and water demand forecasting across the CONUS. …”
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Multi-dimensional performance-based intelligent optimization of atrium building prototype with informed approach by Yiquan Zou, Yanyan Li, Han Chen, Benlin Xiao, Zhou Li

“…Furthermore, the ranges for optimal solutions when pursuing different design objectives were analyzed, series of global optimal solutions were identified, and the optimization process was accelerated using machine learning technology. Ultimately, architects were provided with prototype forms of the atrium space in an "Informed" manner, which expedited the architectural design process. …”
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Accelerating editorial processes in scientific journals: Leveraging AI for rapid manuscript review by Luca Fiorillo, Vini Mehta

“…Materials and methods: The study explores AI's role in chatbot development, examining how AI algorithms and machine learning facilitate human-like conversations in diverse industries. …”
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Determination of the area index of lettuce leaves with a monocular camera by Laimonas Kairiūkštis, Başak Yalçıner, Emre Özkul

“…This study showcases the potential of computer vision and machine learning algorithms in transforming traditional agricultural practices into more efficient and data-driven processes. …”
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System-Level Digital Twin Modeling for Underwater Wireless IoT Networks by Lei Wang, Lei Yan, Xinbin Li, Song Han

“…Federated Learning (FL) presents a distributed machine learning paradigm that enables devices within underwater wireless IoT networks to collaboratively refine a DT model. …”
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A systematic review of passive data for remote monitoring in psychosis and schizophrenia by Siân Bladon, Emily Eisner, Sandra Bucci, Anuoluwapo Oluwatayo, Glen P. Martin, Matthew Sperrin, John Ainsworth, Sophie Faulkner

“…Mixed effects models were used in 21 studies and time-series and machine-learning methods were used in 18 studies. Reporting of methods to process and analyse data was inconsistent, highlighting a need to improve the standardisation of methods and reporting in this area of research.…”
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Improving Health Through Indoor Environmental Quality Monitoring: A Review of Data-Driven Models and Smart Sensor Innovations by Kidari Rachid, Tilioua Amine

“…The present study reviews recent scientific work on monitoring and improving indoor environmental quality (IEQ), focusing on the use of statistical learning models and smart sensor technology. Machine learning has been shown to effectively detect office occupancy using environmental measurements, improving energy efficiency and occupant comfort. …”
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Fake News Detection and Classification: A Comparative Study of Convolutional Neural Networks, Large Language Models, and Natural Language Processing Models by Konstantinos I. Roumeliotis, Nikolaos D. Tselikas, Dimitrios K. Nasiopoulos

“…In this study, the effectiveness is investigated of advanced machine learning models—convolutional neural networks (CNNs), bidirectional encoder representations from transformers (BERT), and generative pre-trained transformers (GPTs)—for robust fake news classification. …”
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Visual Gradation of Biological Soil Crust Development: A Simple and Effective Recording Method by Xinyu Zhang, Ping He, Jie Xu

“…Future research should focus on expanding the grading method to include lichen crust, refining it across diverse ecosystems, and exploring the integration of advanced technologies such as hyperspectral imaging and machine learning to automate and improve the classification process. …”
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Single-channel attention classification algorithm based on robust Kalman filtering and norm-constrained ELM by Jing He, Zijun Huang, Yunde Li, Jiangfeng Shi, Yehang Chen, Chengliang Jiang, Jin Feng

“…The method outperformed traditional Kalman filtering, LMS adaptive filtering, and TGAM’s eSense algorithm in both noise reduction and attention classification accuracy.DiscussionThe study highlights the effectiveness of combining advanced signal processing and machine learning techniques to improve the robustness and generalization of EEG-based attention classification. …”
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Advances in Biomedical Missing Data Imputation: A Survey by Miriam Barrabes, Maria Perera, Victor Novelle Moriano, Xavier Giro-I-Nieto, Daniel Mas Montserrat, Alexander G. Ioannidis

“…These methods range from traditional statistical analysis to more modern approaches such as discriminative machine learning models and deep generative networks. This survey paper provides a comprehensive overview of the extensive literature on missing data imputation techniques, with a specific focus on applications in genomics, single-cell RNA sequencing, health records, and medical imaging. …”
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Canopy Height Integration for Precise Forest Aboveground Biomass Estimation in Natural Secondary Forests of Northeast China Using Gaofen-7 Stereo Satellite Data by Caixia Liu, Huabing Huang, Zhiyu Zhang, Wenyi Fan, Di Wu

“…The aboveground biomass model was constructed using the random forest machine learning technique, which was accomplished by combining the use of in situ field measurements, pairs of GF-7 stereo images, and the corresponding generated canopy height model (CHM). …”
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SViG: A Similarity-Thresholded Approach for Vision Graph Neural Networks by Ismael Elsharkawi, Hossam Sharara, Ahmed Rafea

“…Image representation in computer vision is a long-standing problem that has a significant impact on any machine learning model performance. There have been multiple attempts to tackle this problem that were introduced in the literature, starting from traditional Convolutional Neural Networks (CNNs) to Vision Transformers and MLP-Mixers that were more recently introduced to represent images as sequences. …”
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TDP-43 as a potential retinal biomarker for neurodegenerative diseases by Margit Glashutter, Margit Glashutter, Printha Wijesinghe, Joanne A. Matsubara, Joanne A. Matsubara

“…Future directions for the TDP-43 as a retinal biomarker for NDDs include retinal tracers, hyperspectral imaging, oculomics, and machine learning development.…”
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A generative deep neural network for pan-digestive tract cancer survival analysis by Lekai Xu, Tianjun Lan, Yiqian Huang, Liansheng Wang, Junqi Lin, Xinpeng Song, Hui Tang, Haotian Cao, Hua Chai

“…With the rapid advancement of artificial intelligence, various machine learning algorithms have been successfully applied in this field. …”
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Serum and Cerebrospinal Fluid Cytokine Biomarkers for Diagnosis of Multiple Sclerosis by Ekaterina Martynova, Mehendi Goyal, Shikhar Johri, Vinay Kumar, Timur Khaibullin, Albert A. Rizvanov, Subhash Verma, Svetlana F. Khaiboullina, Manoj Baranwal

“…ANOVA-based feature and Pearson correlation coefficient scores were calculated to select the features which were used as input by machine learning models, to predict and classify MS. Results. …”
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Comparative Analysis of Re-Identification Methods of Multi-Criteria Decision Analysis Models by Bartlomiej Kizielewicz, Jakub Wieckowski, Bogdan Franczyk, Jaroslaw WaTrobski, Wojciech Salabun

“…In this paper, we present innovative methods for re-identifying MCDA models utilizing optimization and machine learning techniques. Firstly, we introduce Support Vector Regression - Characteristic Objects Method (SVR-COMET), which combines the SVR and COMET methods. …”
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A Novel Two-Stage Deep Learning Model for Network Intrusion Detection: LSTM-AE by Vanlalruata Hnamte, Hong Nhung-Nguyen, Jamal Hussain, Yong Hwa-Kim

“…Machine learning and deep learning techniques are widely used to evaluate intrusion detection systems (IDS) capable of rapidly and automatically recognizing and classifying cyber-attacks on networks and hosts. …”
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