Academic Articles & Research Papers

Perceptual-Preference-Based Touring Routes in Xishu Gardens Using Panoramic Digital-Twin Modeling

Xishu Gardens, an exemplary narrative of classical Chinese gardens, faces challenges in preserving its commemorative spatial structures while accommodating modern visitors’ needs. While trajectory analysis is critical, existing studies stru...

Incorporating snow accumulation and melting into the Xin'anjiang model using differentiable parameter learning

The Xin'anjiang model stands as one of the most important hydrological models. This paper develops a differentiable Xin'anjiang model coupled with the CemaNeige module（dMXAJ）under the framework of differentiable parameter learning. Specific...

Interpretable machine learning for predicting compression index of clays using SHAP and gradient boosting models

Abstract This study introduces a novel, interpretable machine learning framework for predicting the compression index (Cc) of clay soils by integrating three advanced gradient boosting algorithms—XGBoost, CatBoost, and LightGBM—with SHapley...

Improving Intrusion Detection Model Prediction by Threshold Adaptation

Network traffic exhibits a high level of variability over short periods of time. This variability impacts negatively on the accuracy of anomaly-based network intrusion detection systems (IDS) that are built using predictive models in a batc...

An assessment of subject-comprehension and subject-command attained through digital learning platforms

Abstract A synthesized investigation, employing graphical and analytical approach, has been conducted to examine inadequacy of electronic education and limitations posed by transformative mode of learning from students’ perspective. Moreove...

A review of machine learning in obesity

Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine lea...

Machine Learning for Evolution Strategies

Machine Learning and the Future of Cardiovascular Care

The role of physicians has always been to synthesize the data available to them to identify diagnostic patterns that guide treatment and follow response. Today, increasingly sophisticated machine learning algorithms may grow to support clin...

Machine Learning Approaches for Clinical Psychology and Psychiatry

Machine learning approaches for clinical psychology and psychiatry explicitly focus on learning statistical functions from multidimensional data sets to make generalizable predictions about individuals. The goal of this review is to provide...

Introduction to machine learning: k-nearest neighbors

Machine learning techniques have been widely used in many scientific fields, but its use in medical literature is limited partly because of technical difficulties. k-nearest neighbors (kNN) is a simple method of machine learning. The articl...

Correction: The BCPM method: decoding breast cancer with machine learning

Development of Machine Learning Models to Predict Compressed Sward Height in Walloon Pastures Based on Sentinel-1, Sentinel-2 and Meteorological Data Using Multiple Data Transformations

Accurate information about the available standing biomass on pastures is critical for the adequate management of grazing and its promotion to farmers. In this paper, machine learning models are developed to predict available biomass express...

Multidimensional Correlates of Childhood Stunting in India: A Spatial Machine Learning and Explainable AI Approach

Childhood stunting remains a major public health challenge in India and is influenced by multiple socioeconomic and environmental factors. This ecological study examined district-level correlates of childhood stunting, including Crimes Agai...

Development and web-based implementation of a machine learning model for predicting one-year adverse outcomes in acute ischaemic stroke

BackgroundThis study aimed to develop an interpretable and clinically applicable machine learning prediction model to assess the risk of adverse outcomes within one year following acute ischaemic stroke, and to establish a corresponding web...

Credit Card Fraud Detection Using AdaBoost and Majority Voting

Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to confidentiality issues. In...

Knowledge-transfer learning for prediction of matrix metalloprotease substrate-cleavage sites

Abstract Matrix Metalloproteases (MMPs) are an important family of proteases that play crucial roles in key cellular and disease processes. Therefore, MMPs constitute important targets for drug design, development and delivery. Advanced pro...

The challenge of studying perovskite solar cells’ stability with machine learning

Perovskite solar cells are the most dynamic emerging photovoltaic technology and attracts the attention of thousands of researchers worldwide. Recently, many of them are targeting device stability issues–the key challenge for this technolog...

Machine Learning for Stock Selection

Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning techniques can be practical investment tools. Although machine learning algorithms can un...

Machine Learning in Agriculture: A Review

Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive revi...

Neural Networks for Machine Learning

In order for a digital neocortex to learn a new skill, it will still require many iterations of education, just as a biological neocortex does, but once a single neocortex somewhere and at some time learns something, it can share that knowl...