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Introduction to machine learning

Rafael A. Irizarry • Chapman & Hall/CRC eBooks (2019)

The machine learning approach is to train an algorithm using a dataset for which the people do know the outcome, and then apply this algorithm in the future to make a prediction when they don't know the outcome. In this chapter, the authors...

OPENALEX DOI: https://doi.org/10.1201/9780429341830-27

A machine learning based scheme for enhancing the detection of position falsification attacks in vehicular ad hoc networks

Eslam Abdelkreem, Sherif Hussein, Ashraf Tammam • Scientific Reports (2026)

Abstract Vehicular Ad Hoc Networks (VANETs) are wireless networks established between vehicles and their surrounding infrastructure, enabling the exchange of information. Consequently, many applications that can enhance passengers’ safety a...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-026-39867-9

Data on Vulnerability Detection in Android

Shivi Garg, Niyati Baliyan • Data in Brief (2019)

The data in this article have been collaborated from mainly four sources- Google Playstore,11 Google Playstore https://play.google.com/store/apps. Wandoujia22 Wandoujia apps http://www.wandoujia.com/apps. (third party app store market), AMD...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.dib.2018.12.038

Classification of bioactive peptides: A systematic benchmark of models and encodings

Edoardo Bizzotto, Guido Zampieri, Laura Treu, Pasquale Filannino et al. • Computational and Structural Biotechnology Journal (2024)

Bioactive peptides are short amino acid chains possessing biological activity and exerting physiological effects relevant to human health. Despite their therapeutic value, their identification remains a major problem, as it mainly relies on...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.csbj.2024.05.040

Advancing Biosensors with Machine Learning

Feiyun Cui, Yun Yue, Yi Zhang, Ziming Zhang et al. • ACS Sensors (2020)

Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved impressive advances. However, novel advanced ML methods, es...

OPENALEX DOI: https://doi.org/10.1021/acssensors.0c01424

Machine learning applications in epilepsy

Bardia Abbasi, Daniel M. Goldenholz • Epilepsia (2019)

Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recogn...

OPENALEX Open Access DOI: https://doi.org/10.1111/epi.16333 PMC: 9897263

Machine Learning Model Coupled with Graphical User Interface for Predicting Mechanical Properties of Flax Fiber

T. Nageshkumar, Prateek Shrivastava, L. Ammayapan, Manisha Jagadale et al. • Journal of Natural Fibers (2025)

Machine learning model coupled with graphical user interface was developed to predict mechanical properties of flax fiber. The experiment was conducted using test setup which applies constant rate of loading (CRL). Flax fiber was tested und...

DOAJ Open Access (DOAJ) DOI: 10.1080/15440478.2025.2502662

Machine learning for neuroimaging with scikit-learn

Alexandre Abraham, Fabián Pedregosa, Michael Eickenberg, Philippe Gervais et al. • Frontiers in Neuroinformatics (2014)

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series....

OPENALEX cc-by DOI: https://doi.org/10.3389/fninf.2014.00014 PMC: 3930868

Understanding Machine Learning

Shai Shalev‐Shwartz, Shai Ben-David • Cambridge University Press eBooks (2014)

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book prov...

OPENALEX DOI: https://doi.org/10.1017/cbo9781107298019

Machine Learning: An Applied Econometric Approach

Sendhil Mullainathan, Jann Spiess • The Journal of Economic Perspectives (2017)

Machines are increasingly doing “intelligent” things. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the presence y of a face from pixels x. This similarity to ...

OPENALEX DOI: https://doi.org/10.1257/jep.31.2.87

Machine learning methods in chemoinformatics

John B. O. Mitchell • Wiley Interdisciplinary Reviews Computational Molecular Science (2014)

Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics ...

OPENALEX cc-by DOI: https://doi.org/10.1002/wcms.1183 PMC: 4180928

Machine Learning

Mohssen M. Z. E. Mohammed, Muhammad Badruddin Khan, Eihab B. M. Bashier • CRC Press eBooks (2016)

Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This...

OPENALEX DOI: https://doi.org/10.1201/9781315371658

Machine Learning and Law

Harry Surden • Colorado Law Scholarly Commons (University of Colorado Colorado Springs) (2014)

This Article explores the application of machine learning techniques within the practice of law. Broadly speaking “machine learning” refers to computer algorithms that have the ability to “learn” or improve in performance over time on some ...

OPENALEX Open Access

Hyperparameter Search in Machine Learning

Marc Claesen, Bart De Moor • arXiv (Cornell University) (2015)

We introduce the hyperparameter search problem in the field of machine learning and discuss its main challenges from an optimization perspective. Machine learning methods attempt to build models that capture some element of interest based o...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1502.02127

Development of a Data-Based Machine Learning Model for Classifying and Predicting Property Damage Caused by Fire

Jongho Lee, Jiuk Shin, Jaewook Lee, Chorong Park et al. • Applied Sciences (2023)

Large fires in factories cause severe human casualties and property damage. Thus, preparing more economical and efficient management strategies for fire prevention can significantly improve fire safety. This study deals with property damage...

DOAJ Open Access (DOAJ) DOI: 10.3390/app132111866

From Causes to Forecasts: Granger Causality and Machine Learning Predictions of Housing Sales in Türkiye

Musa Gün, Ahmet Akusta, Haydar Karadağ • İstanbul İktisat Dergisi (2025)

This study aims to examine the impact of macro-financial indicators on the housing sales volume in Türkiye and evaluate the forecasting performance of traditional time-series models versus modern machine-learning algorithms. Using monthly d...

DOAJ Open Access (DOAJ) DOI: 10.26650/ISTJECON2025-1707729

A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices

Sun-Feel Yang, So-Won Choi, Eul-Bum Lee • Energies (2023)

The ongoing Russia–Ukraine conflict has exacerbated the global crisis of natural gas supply, particularly in Europe. During the winter season, major importers of liquefied natural gas (LNG), such as South Korea and Japan, were directly affe...

DOAJ Open Access (DOAJ) DOI: 10.3390/en16114271

Machine learning approach for the estimation of missing precipitation data: a case study of South Korea

Heechan Han, Boran Kim, Kyunghun Kim, Donghyun Kim et al. • Water Science and Technology (2023)

Precipitation is one of the driving forces in water cycles, and it is vital for understanding the water cycle, such as surface runoff, soil moisture, and evapotranspiration. However, missing precipitation data at the observatory becomes an ...

DOAJ Open Access (DOAJ) DOI: 10.2166/wst.2023.237