Predicting Hit Songs Using Audio and Visual Features by Cheng-Yuan Lee, Yi-Ning Tu

“…However, we combined audio and visual data to make predictions on 1000 YouTube songs. In total, 1000 songs were grouped into two categories based on YouTube view counts: popular and non-popular. …”
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Prediction of Chemical Gas Emissions Based on Ecological Environment by Guobin Chen, Shijin Li

“…Relevant intelligent chemical algorithms control the emission of chemical gases, which can effectively reduce emissions and predict emissions more accurately. …”
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Statistical Prediction of Summer Rainfall and Vegetation in the Ethiopian Highlands by Mark R. Jury

“…After step-wise multivariate regression, the leading predictors are: surface temperature across Europe (cold-favourable), 850 mb zonal winds over the tropical Atlantic (easterly-favourable), and surface temperature in the tropical Indian Ocean (cold-favourable). Predictive algorithms for early and late rainfall exhibit a consistent r2 fit of ~0.50, while those for vegetation reach ~0.65 in late summer, indicating that fluctuations in food resources could be forewarned.…”
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Prediction Models for Diabetes in Children and Adolescents: A Review by Livija Cveticanin, Marko Arsenovic

“…Newly identified factors for differentiating between types of diabetes are discussed, alongside an overview of various machine learning and deep learning algorithms specifically adapted for diabetes prediction in children and adolescents. …”
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Towards Predicting Basketball Player Positions with Transformative Insights by Angelos Tsiannis, Christodoulos Efstathiades

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Model predictive control for energy efficient AC motor drives: An overview by Muhammad Bilal Shahid, Weidong Jin, Muhammad Abbas Abbasi, Abdul Rashid Bin Husain, Hafiz Mudassir Munir, Mannan Hassan, Aymen Flah, Ahmed Saad Eddine Souissi, Thamer A. H. Alghamdi

“…Abstract State‐of‐the‐art model‐based predictive control techniques for AC motor drives are reviewed in this paper. …”
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RAFFLE: active learning accelerated interface structure prediction by Ned Thaddeus Taylor, Joe Pitfield, Francis Huw Davies, Steven Paul Hepplestone

“…Abstract Interfaces between materials are critical to the performance of many devices, yet predicting their structure is computationally demanding due to the vast configuration space. …”
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Machine learning for predicting earthquake magnitudes in the Central Himalaya by Ram Krishna Tiwari, Rudra Prasad Poudel, Harihar Paudyal

“…The findings illustrate that RFR is achieving better performance than the other two algorithms, as the predicted magnitudes are close to the actual magnitudes. …”
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Score-Predictive Scale for Assessing the Risk of Inguinal and Femoral Hernias Incarceration by B. V. Sigua, V. P. Zemlyanoy, A. A. Kozobin, D. S. Semin

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Proactive Edge Caching With Popularity Prediction and Content Replication by Ilhan Demirci, Omer Korcak

“…The algorithm incorporates both global and local content popularity predictions—obtained via exponential moving average (EMA) and long short-term memory (LSTM) models, and dynamically scales content placement decisions based on delay-aware replication benefits. …”
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Wineinformatics: Wine Score Prediction with Wine Price and Reviews by Yuka Nagayoshi, Bernard Chen

“…The goal of this paper is to determine whether incorporating wine price can improve the accuracy of score prediction. To explore the relationship between wine price and wine score, naive Bayes classifier and support vector machine (SVM) classifier are employed to predict the scores as either equal to or above 90 or below 90. …”
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Aberrant gene expression prediction across human tissues by Florian R. Hölzlwimmer, Jonas Lindner, Georgios Tsitsiridis, Nils Wagner, Francesco Paolo Casale, Vicente A. Yépez, Julien Gagneur

“…Abstract Despite the frequent implication of aberrant gene expression in diseases, algorithms predicting aberrantly expressed genes of an individual are lacking. …”
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Machine Learning‐Assisted Simulations and Predictions for Battery Interfaces by Zhaojun Sun, Xin Li, Yiming Wu, Qilin Gu, Shiyou Zheng

“…This review highlights recent progress in ML‐assisted simulations and predictions at battery interfaces, illustrating how ML accelerates the research and development trajectory. …”
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Predicting the performance of ORB-SLAM3 on embedded platforms by Jacques Matthee, Kenneth Uren, George van Schoor, Corne van Daalen

“…Therefore, a need exists to  evaluate the performance of SLAM algorithms in practical embedded environments – this paper addresses this need by creating  prediction models to estimate the performance that ORB-SLAM3 can achieve on embedded platforms. …”
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Explainable Supervised Learning Models for Aviation Predictions in Australia by Aziida Nanyonga, Hassan Wasswa, Keith Joiner, Ugur Turhan, Graham Wild

“…Given the safety-critical nature of aviation, the lack of transparency in AI-generated predictions poses significant challenges for industry stakeholders. …”
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Machine Learning-Driven Transcriptome Analysis of Keratoconus for Predictive Biomarker Identification by Shao-Hsuan Chang, Lung-Kun Yeh, Kuo-Hsuan Hung, Yen-Jung Chiu, Chia-Hsun Hsieh, Chung-Pei Ma

“…<b>Methods:</b> We analyzed the GSE77938 (PRJNA312169) dataset for differential gene expression (DGE) and performed gene set enrichment analysis (GSEA) using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways to identify enriched pathways in keratoconus (KTCN) versus controls. Machine learning algorithms were then used to analyze the gene sets, with SHapley Additive exPlanations (SHAP) applied to assess the contribution of key feature genes in the model’s predictions. …”
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Predicting Employee Turnover Using Machine Learning Techniques by Adil Benabou, Fatima Touhami, My Abdelouahed Sabri

“…This study aims to identify the most effective machine learning model for predicting employee attrition, thereby providing organizations with a reliable tool to anticipate turnover and implement proactive retention strategies.Objective: This study aims to address the challenge of employee attrition by applying machine learning techniques to provide predictive insights that can improve retention strategies.Methods: Nine machine learning algorithms are applied to a dataset of 1,470 employee records. …”
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Energy prediction and optimization for robotic stereoscopic statue processing by Xu-Hui Cheng, Fang-Chen Yin, Cong-Wei Wen, Ye Wang, Yi-Hao Li, Ji-Xiang Huang, Shen-Gui Huang

“…Firstly, a prediction model for the robot’s body power is established by analyzing the energy consumption characteristics of the robot system. …”
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QSAR Models for Predicting the Antioxidant Potential of Chemical Substances by Sofia Ghironi, Edoardo Luca Viganò, Gianluca Selvestrel, Emilio Benfenati

“…To enable the rapid screening of large libraries of substances for antioxidant activity and to provide a useful tool for the initial evaluation of substances of interest with unknown activity, we developed Quantitative Structure–Activity Relationship (QSAR) models to predict the antioxidant potential of chemical substances. …”
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Machine Learning‐Enabled Drug‐Induced Toxicity Prediction by Changsen Bai, Lianlian Wu, Ruijiang Li, Yang Cao, Song He, Xiaochen Bo

“…In this review, 10 categories of drug‐induced toxicity is examined, summarizing the characteristics and applicable ML models, including both predictive and interpretable algorithms, striking a balance between breadth and depth. …”
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