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Orges Koci, Richard K. Russell, M. Guftar Shaikh, Christine Edwards et al. • Microbiome
(2024)
Abstract Background Shotgun metagenomics for microbial community survey recovers enormous amount of information for microbial genomes that include their abundances, taxonomic, and phylogenetic information, as well as their genomic makeup, t...
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Open Access (DOAJ)
DOI: 10.1186/s40168-024-01834-9
Hanjie Xu, Jian Xiong, Jinyu Wu, Xianlai Zhou et al. • Scientific Reports
(2025)
Abstract The Ivy Algorithm (IVYA), a swarm intelligence algorithm inspired by plant growth, presents a novel framework for optimization. To unlock its full potential in complex, high-dimensional problems, it is crucial to address the fundam...
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Open Access (DOAJ)
DOI: 10.1038/s41598-025-32510-z
Upendra Kumar Pradhan, Nitesh Kumar Sharma, Prakash Kumar, Ashwani Kumar et al. • PLoS ONE
(2021)
Formation of mature miRNAs and their expression is a highly controlled process. It is very much dependent upon the post-transcriptional regulatory events. Recent findings suggest that several RNA binding proteins beyond Drosha/Dicer are inv...
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Open Access (DOAJ)
DOI: 10.1371/journal.pone.0258550
Felix Brockherde, Leslie Vogt-Maranto, Li Li, Mark E. Tuckerman et al. • Nature Communications
(2017)
Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy fun...
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DOI: https://doi.org/10.1038/s41467-017-00839-3
PMC: 5636838
Albert P. Bartók, Sandip De, Carl R. Poelking, Noam Bernstein et al. • Science Advances
(2017)
Determining the stability of molecules and condensed phases is the cornerstone of atomistic modeling, underpinning our understanding of chemical and materials properties and transformations. We show that a machine-learning model, based on a...
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DOI: https://doi.org/10.1126/sciadv.1701816
PMC: 5729016
Haldun Aytuğ, Siddhartha Bhattacharyya, G.J. Koehler, Jane L. Snowdon • IEEE Transactions on Engineering Management
(1994)
This paper has two primary purposes: to motivate the need for machine learning in scheduling systems and to survey work on machine learning in scheduling. In order to motivate the need for machine learning in scheduling, we briefly motivate...
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DOI: https://doi.org/10.1109/17.293383
Sarkar Siddique, James C. L. Chow • Encyclopedia
(2021)
Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelli...
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DOI: https://doi.org/10.3390/encyclopedia1010021
Gavin Hackeling (2014)
If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential...
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S. Vijayan, Chiranji Lal Chowdhary • Scientific Reports
(2025)
Abstract The agricultural industry significantly relies on autonomous systems for detecting and analyzing rice diseases to minimize financial and resource losses, reduce yield reductions, improve processing efficiency, and ensure healthy cr...
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Open Access (DOAJ)
DOI: 10.1038/s41598-025-92646-w
Kostiantyn Fuks • Green, Blue and Digital Economy Journal
(2024)
The purpose of this article is to provide a comprehensive examination of the informational and organisational capabilities of marketing activities in the market for digital products and services. It highlights the importance of data analysi...
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Open Access (DOAJ)
DOI: 10.30525/2661-5169/2024-1-4
Yiming Mao, Ting Li, Wenqiang Lu, Chunhong Li et al. • Discover Oncology
(2025)
Abstract Glioblastoma (GBM) is an extremely aggressive and diverse type of brain tumor that is associated with a poor prognosis. Immunogenic cell death (ICD) is a particular form of cellular demise that is closely linked to antineoplastic i...
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Open Access (DOAJ)
DOI: 10.1007/s12672-025-03557-1
Monika Dandotiya, Nikhil Kumar Goyal, Ajay Khunteta, Babita Tiwari • Scientific Reports
(2026)
Abstract Phishing attack continues to rank among the deadliest online threats. They create phony websites in an attempt to obtain personal data. This study offers a framework for a browser extension that uses machine learning to examine URL...
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Open Access (DOAJ)
DOI: 10.1038/s41598-026-35655-7
Simran Saggu, Hirad Daneshvar, Reza Samavi, Paulo Pires et al. • BMC Medical Informatics and Decision Making
(2024)
Abstract Background The proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent unmet menta...
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Open Access (DOAJ)
DOI: 10.1186/s12911-024-02450-1
Irfan Kareem, Syed Farooq Ali, Muhammad Bilal, Muhammad Shehzad Hanif • PLoS ONE
(2024)
Over the last decade, there have been a lot of advances in the area of human posture recognition. Among multiple approaches proposed to solve this problem, those based on deep learning have shown promising results. Taking another step in th...
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Open Access (DOAJ)
DOI: 10.1371/journal.pone.0314959
J. R. Quinlan (1992)
Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is ...
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Hongyu Liu, Bo Lang • Applied Sciences
(2019)
Networks play important roles in modern life, and cyber security has become a vital research area. An intrusion detection system (IDS) which is an important cyber security technique, monitors the state of software and hardware running in th...
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DOI: https://doi.org/10.3390/app9204396
Stephen Marsland (2009)
Traditional books on machine learning can be divided into two groups - those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is r...
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Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola • The Annals of Statistics
(2008)
We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a ke...
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Open Access
DOI: https://doi.org/10.1214/009053607000000677
Qinxia HAO, Haitao SHANG • Meikuang Anquan
(2023)
In order to accurately predict the risk level of coal mine gas explosion accidents, based on the feature vector in line with the actual situation, the particle swarm optimization probabilistic neural network (RWPSO-PNN) is improved to reali...
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Open Access (DOAJ)
DOI: 10.13347/j.cnki.mkaq.2023.10.034
Khaled Bensid, Abdelhai Lati, Azeddine Benlamoudi, Brahim Elkhalil Ghouar et al. • Diagnostyka
(2022)
The spread of the coronavirus has claimed the lives of millions around the world, which led to the emergence of an economic and health crisis at the global level, which prompted many researchers to submit proposals for early diagnosis of th...
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Open Access (DOAJ)
DOI: 10.29354/diag/156751