Unveiling the power of high-dimensional cytometry data with cyCONDOR by Charlotte Kröger, Sophie Müller, Jacqueline Leidner, Theresa Kröber, Stefanie Warnat-Herresthal, Jannis Bastian Spintge, Timo Zajac, Anna Neubauer, Aleksej Frolov, Caterina Carraro, DELCODE Study Group, Frank Jessen, Simone Puccio, Anna C. Aschenbrenner, Joachim L. Schultze, Tal Pecht, Marc D. Beyer, Lorenzo Bonaguro

“…The comprehensive suite of features of cyCONDOR, including guided pre-processing, clustering, dimensionality reduction, and machine learning algorithms, facilitates the seamless integration of cyCONDOR into clinically relevant settings, where scalability and disease classification are paramount for the widespread adoption of HDC in clinical practice. …”
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Integrating transcriptomics and hybrid machine learning enables high-accuracy diagnostic modeling for nasopharyngeal carcinoma by Hehe Wang, Junge Zhang, Peng Cheng, Lujie Yu, Chunlin Li, Yaowen Wang

“…Differential expression analysis identified 293 differentially expressed genes (DEGs). Twelve ML algorithms (including Stepglm, glmBoost, and RF) were systematically combined into 113 distinct models to classify NPC versus normal tissues. …”
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Deep learning and hyperspectral features for seedling stage identification of barnyard grass in paddy field by Siqiao Tan, Qiang Xie, Wenshuai Zhu, Yangjun Deng, Lei Zhu, Xiaoqiao Yu, Zheming Yuan, Zheming Yuan, Yuan Chen, Yuan Chen

“…Notably, this surpasses the capabilities of other models that rely on amalgamations of machine learning algorithms and feature dimensionality reduction methods. …”
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A Hybrid Speech Enhancement Technique Based on Discrete Wavelet Transform and Spectral Subtraction by Yasir Iqbal, Tao Zhang, Teddy Surya Gunawan, Agus Pratondo, Xin Zhao, Yanzhang Geng, Mira Kartiwi, Nasir Saleem, Sami Bourouis

“…In low Signal-to-Noise Ratio (SNR) conditions, various approaches and algorithms are applied to improve speech quality and intelligibility. …”
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Xpert Ultra stool testing to diagnose tuberculosis in children in Ethiopia and Indonesia: a model-based cost-effectiveness analysis by Peter J Dodd, Nyashadzaishe Mafirakureva, Rina Triasih, Debebe Shaweno, Jens Levy, Eveline Klinkenberg, Petra de Haas, Ineke Spruijt, Nastiti Kaswandani, Ahmed Bedru, Melaku Gebremichael, Edine W Tiemersma

“…The intervention remained cost-effective under sensitivity analyses.Conclusions The addition of the SOS stool method to national algorithms for diagnosing TB in children is likely to be cost-effective in both Ethiopia and Indonesia.…”
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Equitable and effective clinical guidance development and dissemination: trauma aims to lead the way by Deborah M Stein, Elliott R Haut, Eileen M Bulger, Vanessa P Ho, Andres M Rubiano, Ashley Farley, Lacey N LaGrone, Danielle J Wilson, Maria Michaels, Meghan B Lane-Fall, Michael A Person, Linda Reinhart

“…The Institute of Medicine and the National Academies of Science, Engineering and Medicine have called for learning health systems, with emphasis on clinical practice guidelines (CPGs) as a means of limiting preventable deaths. Reduction in mortality has been demonstrated when evidence-based trauma CPGs are adhered to; however, guidelines are variably updated, redundant, absent, inaccessible, or perceived as irrelevant. …”
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Multi-Camera Machine Learning for Salt Marsh Species Classification and Mapping by Marco Moreno, Sagar Dalai, Grace Cott, Ben Bartlett, Matheus Santos, Tom Dorian, James Riordan, Chris McGonigle, Fabio Sacchetti, Gerard Dooly

“…This demonstrates that data fusion and band reduction techniques improve species differentiation, particularly for vegetation with overlapping spectral signatures. …”
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Digital Academic Leadership in Higher Education Institutions: A Bibliometric Review Based on CiteSpace by Olaniyi Joshua Olabiyi, Carl Jansen van Vuuren, Marieta Du Plessis, Yujie Xue, Chang Zhu

“…R1-64 bit), we analyzed 5837 documents, identifying 24 key publications that formed a network of 90 nodes and 256 links. The reduction to 24 publications occurred as part of a structured bibliometric analysis using CiteSpace, which employs algorithmic thresholds to identify the most influential and structurally significant publications within a large corpus. …”
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MINIMIZATION OF RISK OF THE ERRONEOUS DECISION IN THE ASSESSMENT OF THE IMPORTANCE OF STATISTICAL RELATIONS OF TECHNICAL AND ECONOMIC INDICATORS OF THE OBJECTS OF ELECTRIC POWER SY... by E. M. Farhadzadeh, A. Z. Muradaliyev, Yu. Z. Farzaliyev, T. K. Rafiyeva, S. A. Abdullayeva

“…Traditionally, increase of reliability of the decision is reached by reduction of a Type I error. Usually it is accepted to be equal to 5%, occasionally – to 1%, and at researches – even to 0.5 %. …”
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Helicobacter pylori eradication treatment for gastric carcinoma prevention in asymptomatic or dyspeptic adults: systematic review and Bayesian meta-analysis of randomised controlle... by Teruhiko Terasawa, Isao Miyashiro, Katsuaki Kato, Takaki Yoshikawa, Reo Takaku, Hiroshi Nishida

“…Objectives Recent meta-analyses of eradication therapy in Helicobacter pylori-infected adults reported significant reductions in gastric carcinoma risk. However, concerns about supporting unfocused screening and eradication programme in healthy, asymptomatic populations have arisen. …”
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MRI-based brain tumor ensemble classification using two stage score level fusion and CNN models by Oussama Bouguerra, Bilal Attallah, Youcef Brik

“…In the second stage, ensemble learning algorithms like weighted sum, fuzzy rank, and majority vote are used to combine the scores from the trained models, enhancing prediction results. …”
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Simulation analysis of path planning for workpiece clamping robots based on digital twin technology by Xin PAN, Min LIANG, Yanchao YIN, Zhong CHEN

“…Comparative experiments with two baseline algorithms, RRT*-Connect and an improved RRT*-FN algorithm, highlight the superior performance of the proposed method. …”
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Preface to Special Issue on AI-Based Future Intelligent Networks and Communication Security by Sunil Kumar, Glenford Mapp, Abhay Bansal, Korhan Cengiz

“… Recent advancements in science focus on the study and development of algorithms that can learn from and make predictions and decisions based on data collected through intelligent devices. …”
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Identification of biomarkers associated with inflammatory response in Parkinson's disease by bioinformatics and machine learning. by Yatan Li, Wei Jia, Chen Chen, Cheng Chen, Jinchao Chen, Xinling Yang, Pei Liu

“…LASSO, SVM-RFE and Random Forest algorithms were used to screen biomarker genes. Then, ROC curves were drawn and PD risk predicting models were constructed on the basis of the biomarker genes. …”
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Transforming tabular data into images via enhanced spatial relationships for CNN processing by Hameedah A. Alenizy, Jawad Berri

“…The results showed that NCTD consistently surpassed the majority of competing algorithms in nine out of ten datasets, indicating its potential as a robust tool for extending CNN applicability beyond conventional image-based domains, particularly in complex classification and prediction.…”
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Machine Learning Ensemble Classifiers for Feature Selection in Rice Cultivars by Chandrakumar Thangavel, D Sakthipriya

“…Machine Learning is crucial for seed prediction, germination, crop production, soil moisture, and land suitability evaluation. …”
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Efficient topology control for time-varying spacecraft networks with unreliable links by Wei Zhang, Hong Ma, Tao Wu, Xueshu Shi, Yiwen Jiao

“…Simulation results demonstrate the efficiency of our model and topology control algorithms.…”
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A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems by Guillaume Canat, Antonin Duval, Nina Gidel-Dissler, Alexandra Boussommier-Calleja

“…Today, most of the literature has characterized algorithms that predict pregnancy, ploidy or blastocyst quality, leaving to the side the task of identifying key morphokinetic events. …”
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Deep reinforcement learning for inverse inorganic materials design by Christopher Karpovich, Elton Pan, Elsa A. Olivetti

“…We apply template-based crystal structure prediction to suggest feasible crystal structure matches for target inorganic compositions identified by our machine learning (ML) algorithms to highlight the plausibility of the identified target compositions. …”
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A machine learning‐based approach for wait‐time estimation in healthcare facilities with multi‐stage queues by Amjed Al‐Mousa, Hamza Al‐Zubaidi, Mohammad Al‐Dweik

“…The work employs feature engineering and compares several machine learning‐based algorithms to predict patients' waiting times for single‐stage and multi‐stage services. …”
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