A privacy-enhanced framework with deep learning for botnet detection by Guangli Wu, Xingyue Wang

“…And most methods are combined with machine learning and deep learning technologies, which require a large amount of training data to obtain high-precision detection models. …”
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Circulating microRNA panels for multi-cancer detection and gastric cancer screening: leveraging a network biology approach by Leila Kamkar, Samaneh Saberi, Mehdi Totonchi, Kaveh Kavousi

“…Subsequently, for specific cancer screening, gastric cancer was focused on, using a similar strategy and a further step of preservation analysis. Machine learning techniques were then applied to evaluate two distinct miRNA panels: one for multi-cancer screening and another for gastric cancer classification. …”
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A data ensemble-based approach for detecting vocal disorders using replicated acoustic biomarkers from electroglottography by Lizbeth Naranjo, Carlos J. Pérez, Daniel F. Merino

“…Future research should focus on collecting comprehensive EGG databases and further exploring multi-class classification methods to solidify EGG and machine learning as a valuable tool for non-invasive assessment of laryngeal function.…”
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Hybrid Method for Point Cloud Classification by Abdurrahman Hazer, Remzi Yildirim

“…This hybrid approach effectively combines the Residual Machine Learning Perceptron (ResMLP) with the POL mechanism to capture both local and global features of 3D point clouds. …”
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Flexible and cost-effective deep learning for accelerated multi-parametric relaxometry using phase-cycled bSSFP by Florian Birk, Lucas Mahler, Julius Steiglechner, Qi Wang, Klaus Scheffler, Rahel Heule

“…This work emphasizes the advantages of in silico DNN MP-qMRI pipelines for rapid data generation and DNN training without extensive dictionary generation, long parameter inference times, or prolonged data acquisition, highlighting the flexible and rapid nature of lightweight machine learning applications for MP-qMRI.…”
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Analog Computing for Nonlinear Shock Tube PDE Models: Test and Measurement of CMOS Chip by Hasantha Malavipathirana, Soumyajit Mandal, Nilan Udayanga, Yingying Wang, S. I. Hariharan, Arjuna Madanayake

“…Analog CMOS has power efficiency advantages over digital CMOS for low-precision applications in edge computing, scientific computing, and artificial intelligence/machine learning (AI/ML) verticals. Driven by observed non-trivial improvements in performance over digital processors while solving linear partial differential equations (PDEs), this paper presents experimental results and analysis from a single-chip CMOS analog computer for solving nonlinear PDEs. …”
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Optimizing Hydrogen Production in the Co-Gasification Process: Comparison of Explainable Regression Models Using Shapley Additive Explanations by Thavavel Vaiyapuri

“…However, optimizing this complex process to maximize hydrogen yield remains challenging, particularly when balancing diverse feedstocks and improving process efficiency. While machine learning (ML) has shown significant potential in simulating and optimizing such processes, there is no clear consensus on the most effective regression models for co-gasification, especially with limited experimental data. …”
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Integrated Spatiotemporal Hybrid Solar PV Generation Forecast Between Countries on Different Continents Using Transfer Learning Method by Bowoo Kim, Kaouther Belkilani, Gerd Heilscher, Marc-Oliver Otto, Jeung-Soo Huh, Dongjun Suh

“…The proposed CL-Transformer model outperformed established machine learning models such as LSTM, CNN-LSTM, and Transformer, consistently demonstrating superior predictive capabilities. …”
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Application of a spatial dataset for monitoring invasive woody plant species in the forests of Transcarpathia, Ukraine by Andriy Mihaly, Vasyl Roman

“…The spatial dataset can also be utilised as a source of training samples for machine learning, which is involved in the processing of satellite images to identify new habitats of invasive woody plant species.…”
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Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues by Robel Alemu, Nigussie T. Sharew, Yodit Y. Arsano, Muktar Ahmed, Fasil Tekola-Ayele, Tesfaye B. Mersha, Azmeraw T. Amare

“…We highlight the need for standardized protocols, harmonized data-sharing policies, and advanced approaches such as artificial intelligence/machine learning to integrate multi-omics data and study gene-environment interactions. …”
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Privacy-Preserving Continual Federated Clustering via Adaptive Resonance Theory by Naoki Masuyama, Yusuke Nojima, Yuichiro Toda, Chu Kiong Loo, Hisao Ishibuchi, Naoyuki Kubota

“…With the increasing importance of data privacy protection, various privacy-preserving machine learning methods have been proposed. In the clustering domain, various algorithms with a federated learning framework (i.e., federated clustering) have been actively studied and showed high clustering performance while preserving data privacy. …”
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Computational antidiabetic assessment of Salvia splendens L. polyphenols: SMOTE, ADME, ProTox, docking, and molecular dynamic studies by Hatun A. Alomar, Wafaa M. El Kady, Asmaa A. Mandour, Amany A. Naim, Neveen I. Ghali, Taghreed A. Ibrahim, Noha Fathallah

“…This study utilizes artificial intelligence and machine learning to enhance drug discovery, focusing on the antidiabetic effects of Salvia splendens leaf extract among the global epidemic of diabetes mellitus. …”
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Application of geographic information system in ecotourism: a global bibliometric analysis by Amadua, Festus O., Nhamob, Luxon, Benzougagh, Brahim, Turyasingura, Benson

“…For example, GIS application is increasingly important in tourism through novel technologies like machine learning and remote sensing. Such applications can enhance sustainable tourism. …”
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TAME 2.0: expanding and improving online data science training for environmental health research by Alexis Payton, Alexis Payton, Alexis Payton, Elise Hickman, Elise Hickman, Elise Hickman, Jessie Chappel, Jessie Chappel, Jessie Chappel, Kyle Roell, Kyle Roell, Lauren E. Koval, Lauren E. Koval, Lauren A. Eaves, Lauren A. Eaves, Chloe K. Chou, Chloe K. Chou, Chloe K. Chou, Allison Spring, Allison Spring, Sarah L. Miller, Sarah L. Miller, Sarah L. Miller, Oyemwenosa N. Avenbuan, Oyemwenosa N. Avenbuan, Oyemwenosa N. Avenbuan, Rebecca Boyles, Rebecca Boyles, Paul Kruse, Cynthia V. Rider, Grace Patlewicz, Caroline Ring, Cavin Ward-Caviness, David M. Reif, Ilona Jaspers, Ilona Jaspers, Ilona Jaspers, Ilona Jaspers, Ilona Jaspers, Rebecca C. Fry, Rebecca C. Fry, Rebecca C. Fry, Julia E. Rager, Julia E. Rager, Julia E. Rager, Julia E. Rager

“…Training topics were prioritized based upon ongoing engagement with trainees, professional colleague feedback, and emerging topics in the field of environmental health research (e.g., artificial intelligence and machine learning). TAME 2.0 is a significant expansion upon the original TAME training resource pilot. …”
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AI-assisted neurocognitive assessment protocol for older adults with psychiatric disorders by Diego D. Díaz-Guerra, Marena de la C. Hernández-Lugo, Yunier Broche-Pérez, Carlos Ramos-Galarza, Ernesto Iglesias-Serrano, Zoylen Fernández-Fleites

“…The protocol utilizes AI to enhance diagnostic accuracy by analyzing data from these tests and supplementing observations made by researchers.Anticipated resultsThe AI-assisted protocol offers several advantages, including a thorough and customized evaluation of neurocognitive functions. It employs machine learning algorithms to analyze test results, generating an individualized neurocognitive profile that highlights patterns and trends useful for clinical decision-making. …”
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Cadmium and selenium blood levels in association with congestive heart failure in diabetic and prediabetic patients: a cross-sectional study from the national health and nutrition... by Renyue Ji, Haisheng Wu, Hongli Lin, Yang Li, Yumeng Shi

“…Logistic regression, weighted quantile sum (WQS), and Bayesian kernel machine learning (BKMR) models were employed to investigate the association between exposure to mixtures of five heavy metals and the odds of having CHF in individuals with diabetes and prediabetes. …”
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Biological characteristics, immune infiltration and drug prediction of PANoptosis related genes and possible regulatory mechanisms in inflammatory bowel disease by Minglin Zhang, Tong Liu, Lijun Luo, Yuxin Xie, Fen Wang

“…The enrichment analysis suggested that these genes were related to TNF signalling, NF-κB, pyroptosis and necroptosis. Machine learning identified three model genes: OGT, GZMB and CASP5. …”
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Detecting tropical freshly-opened swidden fields using a combined algorithm of continuous change detection and support vector machine by Ningsang Jiang, Peng Li, Zhiming Feng

“…The integration CCDC with SVM represents a novelty in combining time series analysis and machine learning techniques and helps monitor annual swidden agriculture in the tropics.…”
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A Two-Point Association Tracking System Incorporated With YOLOv11 for Real-Time Visual Tracking of Laparoscopic Surgical Instruments by Nyi Nyi Myo, Apiwat Boonkong, Kovit Khampitak, Daranee Hormdee

“…This work contributes to the advancement of intelligent surgical systems, providing a foundation for further integration of machine learning techniques in the operating room.…”
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CODE-ACCORD: A Corpus of building regulatory data for rule generation towards automatic compliance checking by Hansi Hettiarachchi, Amna Dridi, Mohamed Medhat Gaber, Pouyan Parsafard, Nicoleta Bocaneala, Katja Breitenfelder, Gonçal Costa, Maria Hedblom, Mihaela Juganaru-Mathieu, Thamer Mecharnia, Sumee Park, He Tan, Abdel-Rahman H. Tawil, Edlira Vakaj

“…Converting textual rules into machine-readable formats is challenging due to the complexities of natural language and the scarcity of resources for advanced Machine Learning (ML). Addressing these challenges, we introduce CODE-ACCORD, a dataset of 862 sentences from the building regulations of England and Finland. …”
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