General Framework of Reversible Watermarking Based on Asymmetric Histogram Shifting of Prediction Error by Sheng Chen, Xianyi Chen, Huijuan Fu

“…Different from the conventional algorithms using single-prediction scheme to create symmetric histogram, the proposed method employs a multi-prediction scheme, which calculates multiple prediction values for the pixels. …”
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An SMVQ compressed data hiding scheme based on multiple linear regression prediction by Hengxiao Chi, Chin-Chen Chang, Yanjun Liu

“…In this paper, we propose a side matching vector quantisation (SMVQ) data hiding scheme for image using multiple linear regression prediction. For each pixel block, the proposed scheme combines the multiple linear regression algorithm and the SMVQ algorithm, so that it can more accurately match the codeword or directly obtain the predicted value closer to the real pixel. …”
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Noise-robust linear prediction analysis of speech based on super-Gaussian excitation by Bin ZHOU, Xia ZOU, Xiong-wei ZHANG, Gai-hua ZHAO

“…To overcome the problem that the performance of the traditional linear prediction (LP) analysis of speech dete-riorates significantly in the presence of background noise,a novel algorithm for robust LP analysis of speech based on super-Gaussian excitation was proposed.The excitation noise of LP was modeled as a Student-t distribution,which was shown to be super-Gaussian.Then a novel probabilistic graphical model for robust LP analysis of speech was built by in-corporating the effect of additive noise explicitly.Furthermore,variational Bayesian inference was adopted to approxi-mate the intractable posterior distributions of the model parameters,based on which the LP coefficients of the noisy speech were estimated iteratively.The experimental results show that the developed algorithm performs well in terms of LP coefficients estimation of speech and is much more robust to ambient noise than several other algorithms.…”
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Transforming Cardiac Care: Machine Learning in Heart Condition Prediction Using Phonocardiograms by Sandra D’Souza, Niranjan Reddy S, Saikonda Krishna Tarun, Sohan P, aneesha acharya k

“…In the research, machine learning-based prediction methods work on the audio recordings of heartbeats known as phonocardiograms (PCG) to develop an algorithm that differentiates a normal healthy heart from an abnormal heart based on the heart sounds. …”
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Impact of dimensionality reduction techniques on student performance prediction using machine learning by Koushik Roy, Huu-Hoa Nguyen, Dewan Md. Farid

“… This study addresses the crucial issue of predicting student performance in educational data mining (EDM) by proposing an Adaptive Dimensionality Reduction Algorithm (ADRA). …”
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Machine learning-based ultrasound radiomics for predicting risk of recurrence in breast cancer by Wei Fan, Hao Cui, Xiaoxue Liu, Xudong Zhang, Xinran Fang, Junjia Wang, Zihao Qin, Xiuhua Yang, Jiawei Tian, Lei Zhang

“…PurposeTo develop a radiomics model based on ultrasound images for predicting risk of recurrence in breast cancer patients.MethodsIn this retrospective study, 420 patients with pathologically confirmed breast cancer were included, randomly divided into training (70%) and test (30%) sets, with an independent external validation cohort of 90 patients. …”
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Prediction of the volume of shallow landslides due to rainfall using data-driven models by J. Tuganishuri, C.-Y. Yune, G. Kim, S. W. Lee, M. D. Adhikari, S.-G. Yum

“…The objectives of this research are to construct a model using advanced data-driven algorithms (i.e., ordinary least squares or linear regression (OLS), random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), generalized linear model (GLM), decision tree (DT), deep neural network (DNN), <span class="inline-formula"><i>k</i></span>-nearest-neighbor (KNN), and ridge regression (RR) algorithms) for the prediction of the volume of landslides due to rainfall, considering geological, geomorphological, and environmental conditions. …”
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Prediction of postoperative vault after implantable collamer lens implantation with deep learning by Dong-Qing Yuan, Fu-Nan Tang, Ying Wang, Hui Zhang, Wei-Wei Zhang, Liu-Wei Gu, Qing-Huai Liu

“…AIM: To predict the post-operative vault and the suitable size of the implantable collamer lens (ICL) by comparing the performance of multiple artificial intelligence (AI) algorithms. …”
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Personalized Human Thermal Sensation Prediction Based on Bayesian-Optimized Random Forest by Hao Yang, Maoyu Ran

“…More accurate personalized thermal sensation prediction models were then constructed using various machine learning algorithms, followed by a comparative analysis of their performance. …”
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Onboard Interference Prediction for the Cognitive Medium Access in the LEO Satellite Uplink Transmission by Zhuochen Xie, Huijie Liu, Xuwen Liang

“…With MCB, we can achieve the effective long-term interference prediction to meet the special requirements of the LEO satellite. …”
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Prediction of customer engagement behaviour response to marketing posts based on machine learning by Yonghui Dai, Tao Wang

“…In order to better understand customer behaviours in the social media marketing context, we draw on the Stimulus-Organism-Response theory, and conceptualise and characterise marketing posts from six dimensions to get various features as stimuli, which induce or activate customers’ cognitive and affective states to varying levels, and ultimately lead to different behaviour responses. Machine learning algorithms are applied to the customer engagement behaviour choice prediction when facing marketing posts. …”
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Prediction of Thermal and Optical Properties of Oxyfluoride Glasses Based on Interpretable Machine Learning by Yuhao Xie, Xiangfu Wang

“…Based on the components of glasses, four algorithms, namely K-Nearest Neighbor, Random Forest, Support Vector Machine, and eXtreme Gradient Boosting, were used to construct an optimal machine learning model to predict the thermal and optical properties of oxyfluoride glass, namely glass transition temperature, density, Abbe number, liquidus temperature, thermal expansion coefficient, and refractive index. …”
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A Critical Comparison of Machine Learning Classifiers to Predict Match Outcomes in the NFL by Beal Ryan, Norman Timothy J., Ramchurn Sarvapali D.

“…In this paper, we critically evaluate the performance of nine machine learning classification techniques when applied to the match outcome prediction problem presented by American Football. Specifically, we implement and test nine techniques using real-world datasets of 1280 games over 5 seasons from the National Football League (NFL). …”
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Early Prediction of Stroke Risk Using Machine Learning Approaches and Imbalanced Data by Hassan Qassim

“… Classifying medical datasets using machine learning algorithms could help physicians to provide accurate diagnosing and suitable treatment. …”
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Prediction of HIV status based on socio-behavioural characteristics in East and Southern Africa. by Erol Orel, Rachel Esra, Janne Estill, Amaury Thiabaud, Stéphane Marchand-Maillet, Aziza Merzouki, Olivia Keiser

“…<h4>Methods</h4>We analysed the most recent Demographic and Health Survey from these 10 countries to predict individual's HIV status using four different algorithms (a penalized logistic regression, a generalized additive model, a support vector machine, and a gradient boosting trees). …”
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Support Vector Machine and Granular Computing Based Time Series Volatility Prediction by Yuan Yang, Xu Ma

“…With the development of information technology, a large amount of time-series data is generated and stored in the field of economic management, and the potential and valuable knowledge and information in the data can be mined to support management and decision-making activities by using data mining algorithms. In this paper, three different time-series information granulation methods are proposed for time-series information granulation from both time axis and theoretical domain: time-series time-axis information granulation method based on fluctuation point and time-series time-axis information granulation method based on cloud model and fuzzy time-series prediction method based on theoretical domain information granulation. …”
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Improving the Predictability of the Madden‐Julian Oscillation at Subseasonal Scales With Gaussian Process Models by Haoyuan Chen, Emil Constantinescu, Vishwas Rao, Cristiana Stan

“…In spite of the improvement in MJO predictions made by machine learning algorithms, such as neural networks, most of them cannot provide the uncertainty levels in the MJO forecasts directly. …”
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Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers by Jüri Reimand, Gary D Bader

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Comparative Analysis of Diabetes Prediction Models Using the Pima Indian Diabetes Database by Zhao Yize

“…The K-means model operates by grouping data points into separate clusters according to their characteristics, achieving an accuracy of 90.04% in diabetes prediction. In comparison, the random forest model, which builds multiple decision trees (DT) to do their predictions, demonstrates superior performance over several widely used algorithms such as K-Nearest Neighbours (KNN), Logistic Regression (LR), DT, Support Vector Machines (SVM), and Gradient Boosting (GB). …”
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Using machine learning to predict gamma shielding properties: a comparative study by T A Nahool, A M Abdelmonem, M S Ali, A M Yasser

“…This study employed machine learning (ML) algorithms to predict the linear attenuation coefficients (LACs) of materials in inorganic scintillation detectors, which are crucial for evaluating self-shielding properties. …”
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