Academic Articles & Research Papers

Application of MRI-Based Radiomics in Preoperative Prediction of NF2 Alteration in Intracranial Meningiomas

PurposeThis study aimed to investigate the feasibility of predicting NF2 mutation status based on the MR radiomic analysis in patients with intracranial meningioma.MethodsThis retrospective study included 105 patients with meningiomas, incl...

A Numerical-Based Attention Method for Stock Market Prediction With Dual Information

Stock market prediction is of great importance for financial analysis. Traditionally, many studies only use the news or numerical data for the stock market prediction. In the recent years, in order to explore their complementary, some studi...

Prediction of Drug Targets for Specific Diseases Leveraging Gene Perturbation Data: A Machine Learning Approach

Identification of the correct targets is a key element for successful drug development. However, there are limited approaches for predicting drug targets for specific diseases using omics data, and few have leveraged expression profiles fro...

A novel machine learning-based model for predicting the transition fatigue lifetime in piston aluminum alloys

The estimation of transition fatigue lifetimes for piston aluminum alloys was carried out using unsupervised machine learning (ML) with the K-means algorithm. For this purpose, an experimental dataset representing standard ISO specimens wit...

Accelerated Prediction of Terahertz Performance Metrics in GaN IMPATT Sources via Artificial Neural Networks

This work investigates the application of artificial neural network (ANN)-based regression models to predict the static and dynamic characteristics of GaN impact avalanche transit time (IMPATT) sources in the terahertz (THz) frequency regim...

Machine learning and applications in microbiology

To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence applica...

Predicting the persistence of Long COVID 36 months after hospitalisation: a prospective Ukrainian cohort study.

Synergistic multi-altimeter for estimating water level in the coastal zone of Beibu Gulf using SEL, ALES + and BFAST algorithms

Accurately monitoring and predicting the large-scale dynamic changes of water levels in coastal zones is essential for its protection, restoration and sustainable development. However, there has been a challenge for achieving this goal usin...

Applications of Machine Learning in Cancer Prediction and Prognosis

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to "learn" from past examples and to detect hard-to-discern patterns from large, ...

Predicting crude oil returns and trading position: evidence from news sentiment

We study the effectiveness of textual information in predicting the returns of crude oil futures and understanding the behavior of market participants. Using a machine learning method to extract oil market sentiment from news articles, we f...

Importance of scale in seascape habitat suitability modeling: Predicting species distributions in changing ecosystems.

Explainable machine learning for predicting clinical outcomes in HIV/TB co-infection: a comparative retrospective study

Abstract Background HIV/TB co-infection presents substantial public-health challenges, showing greater treatment-failure and mortality rates than tuberculosis alone. Recent advances in machine learning (ML) provide a robust means of identif...

Using Soft Voting Classifier of Machine Learning Algorithms for Earthquake Prediction

Earthquakes are among the most dangerous natural disasters that can cause major losses to buildings and threaten human lives. The research community is very interested in the topic of earthquakes because they occur suddenly and predicting t...

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

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...

Machine learning methods in chemoinformatics

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 ...

Machine Learning and Law

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 ...

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

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...

Towards instance-wise calibration: local amortized diagnostics and reshaping of conditional densities (LADaR)

Key science questions, such as galaxy distance estimation and weather forecasting, often require knowing the full predictive distribution of a target variable Y given complex inputs X . Despite recent advances in machine learning and physic...

Predicting Urban Quality of Life Using Machine Learning on Street Images and Personal Information with Statistical Feature Selection: A Comparative Study of Bangkok and London

This study proposes a machine learning framework for predicting perceived urban quality of life (QoL) by integrating visual features from street-level imagery with personal attributes, including demographic, socioeconomic, and travel behavi...

Machine Learning in Action

SummaryMachine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to bui...