Predicting emotional responses in interactive art using Random Forests: a model grounded in enactive aesthetics by Xiaowei Chen, Xiaowei Chen, Zainuddin Ibrahim, Azlan Abdul Aziz

“…However, these emotional responses’ inherently dynamic, subjective, and often pre-reflective nature poses significant challenges to their systematic prediction and computational modeling.MethodsTo address these challenges, the present study introduces an interpretable machine learning framework grounded in the Random Forest (RF) algorithm, which provides a balanced trade-off between predictive performance and model transparency, thereby aligning with the needs of theory-driven emotion research. …”
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Prediction of the Quality of Anxi Tieguanyin Based on Hyperspectral Detection Technology by Tao Wang, Yongkuai Chen, Yuyan Huang, Chengxu Zheng, Shuilan Liao, Liangde Xiao, Jian Zhao

“…The characteristic wavelengths were extracted via principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and the successive projection algorithm (SPA). …”
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Next-generation cutting-edge hybrid AI frameworks for predicting rheological properties and CO₂ emissions in alkali-activated concrete by Lu Zhang, Kangning Liu, Ali H. AlAteah, Sadiq Alinsaif, Muhammad Sufian, Ayaz Ahmad

“…To address this challenge, this study presents a next-generation AI-based predictive framework utilizing three hybrid machine learning techniques: adaptive neuro-fuzzy inference system with genetic algorithm (ANFIS-GA), convolutional neural networks with long short-term memory (CNN-LSTM), and multi-objective optimization (MOO). …”
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Hyperbolic geometry enhanced feature filtering network for industrial anomaly detection by Yanjun Feng, Jun Liu, Yonggang Gai

“…Abstract In recent years, Cutting-edge machine learning algorithms and systems in Industry 4.0 enhance quality control and increase production efficiency. …”
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A Comparative Analysis of Black-Box and Glass-Box Models for Poplar Plantation Mapping with Remote Sensing Data by M. Y. Ozturk, I. Colkesen

“…In this study, the poplar tree mapping and feature selection performances of the glass-box Explainable Boosting Machine (EBM) algorithm were investigated using satellite images having different resolutions (i.e., Sentinel-2 and PlanetScope) and texture features. …”
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Rolling Bearing Fault Diagnosis Method Based on Improved Variational Mode Decomposition and Information Entropy by Wen FAN, Lian GE, Xiaoting XIAO, Fangji GAN, Xin LAI, Hongxia DENG, Qi HUANG

“…A new fitness function combining differential evolution (DE) algorithm with gray wolf optimization (GWO) algorithm is proposed to form a new hybrid optimization algorithm, named DEGWO. …”
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Research on Obstacle-Avoidance Trajectory Planning for Drill and Anchor Materials Handling by a Mechanical Arm on a Coal Mine Drilling and Anchoring Robot by Siya Sun, Sirui Mao, Xusheng Xue, Chuanwei Wang, Hongwei Ma, Yifeng Guo, Haining Yuan, Hao Su

“…Then, in order to adapt to the dynamic environment and avoid the global planning algorithm from falling into the local minima, on the basis of the above planning methods, an improved Bi-RRT trajectory planning algorithm incorporating the artificial potential field was proposed, which takes the planned paths as the guiding potential field of the artificial potential field to make full use of the global information and avoid falling into the local minimization. …”
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Advancements in Optimization Techniques for Active Magnetic Bearing Systems: Current Trends and Future Directions by Tasnemul Hasan Nehal, Waleed M. Hamanah, Mohammad Ali Abido

“…The emergence of hybrid optimization, adaptive fuzzy controllers, and machine learning-enhanced models is pushing the boundaries of AMB performance, offering substantial gains in stability, efficiency, fault tolerance, and vibration suppression. …”
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Multi-Stage Data-Driven Framework for Customer Journey Optimization and Operational Resilience by Tzu-Chien Wang, Ruey-Shan Guo, Chialin Chen, Chia-Kai Li

“…To address these limitations, this study proposes a multi-stage data-driven framework integrating latent Dirichlet allocation (LDA) for behavioral insights, deep learning for predictive modeling, and heuristic algorithms for adaptive decision-making. Empirical validation using Taiwanese financial institution data shows a 15% improvement in predictive accuracy compared to traditional machine-learning models, significantly enhancing customer lifetime value (CLV) predictions and multi-channel resource allocation. …”
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Multi-Source Causal Invariance for Cuffless Blood Pressure Estimation Based on Photoplethysmography Signal Features by Yiliu Xu, Zhaoming He, Hao Wang

“…The MDSFS-EMB algorithm integrated PPFS and HITON-MB, enabling adaptability to different data scales and distribution scenarios. …”
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The Use of Artificial Intelligence in Combating Financial Crimes and Money Laundering in International Trade A Data-Driven Analysis (2010–2024) by Balouz Mohamed

“…The primary objectives are to assess the development and effectiveness of AI-driven algorithms in detecting illicit transactions, analyze the role of machine learning in real-time monitoring and predictive analytics, and investigate regulatory and ethical challenges that constrain AI’s full potential in financial crime prevention. …”
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An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application by N. I. Md. Ashafuddula, Bayezid Islam, Rafiqul Islam

“…Chronic kidney disease (CKD) is a progressive condition characterized by the gradual deterioration of kidney functions, potentially leading to kidney failure if not promptly diagnosed and treated. Machine learning (ML) algorithms have shown significant promise in disease diagnosis, but in healthcare, clinical data pose challenges: missing values, noisy inputs, and redundant features, affecting early-stage CKD prediction. …”
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Review of sEMG for Exoskeleton Robots: Motion Intention Recognition Techniques and Applications by Xu Zhang, Yonggang Qu, Gang Zhang, Zhiqiang Wang, Changbing Chen, Xin Xu

“…It is proposed that the focus of current research is to find algorithms with strong adaptability and high classification accuracy. …”
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Impurity rates detection for pepper harvesting based on YOLOv8n-Seg-ASB and random forest by Lijian Lu, Jin Lei, Chenming Cheng, Shiguo Wang, Chengfu Wang, Xinyan Qin

“…To address the inaccuracies and inefficiencies of pepper impurity rates detection caused by complex material compositions and variable harvesting environments, this paper proposes a detection technique based on deep and machine learning algorithms. First, a machine vision-based image acquisition device for pepper material is designed to reliably capture high-quality real-time images. …”
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Near-field sound source localization using principal component analysis–multi-output support vector regression by Lanmei Wang, Yao Wang, Guibao Wang, Jianke Jia

“…Simulation results show that this method has high estimation accuracy and training speed, and has strong adaptability at low signal-to-noise ratio, and the performance is better than that of the back-propagation neural network algorithm and the two-step multiple signal classification algorithm.…”
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Oversampling based on generative adversarial networks to overcome imbalance data in predicting fraud insurance claim by Ranu Agastya Nugraha, Hilman Ferdinandus Pardede, Agus Subekti

“…The new balanced data are used to train 17 classification algorithms. Our experiments show that our proposed method achieves better performance on several evaluation metrics: accuracy, precision score, F1-score, and also ROC than other referenced methods to deal imbalance data random over sampling (ROS), random under sampling (RUS), Synthetic Minority Oversampling Technique (SMOTE), Borderline SMOTE (B-SMO), and adaptive synthetic (ADASYN) methods. …”
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Sparse Feature-Weighted Double Laplacian Rank Constraint Non-Negative Matrix Factorization for Image Clustering by Hu Ma, Ziping Ma, Huirong Li, Jingyu Wang

“…As an extension of non-negative matrix factorization (NMF), graph-regularized non-negative matrix factorization (GNMF) has been widely applied in data mining and machine learning, particularly for tasks such as clustering and feature selection. …”
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Small-sample-data augmentation and transfer strategies for forest cover change monitoring by Kun Feng, Shaoxiu Ma, Haiyang Xi, Linhao Liang, Weiqi Liu, Atsushi Tsunekawa

“…The large sample data were used to develop the 30-meter annual forest cover dataset (AFD_QLM) from 1986 to 2023, using a locally adaptive machine learning algorithm at 1°×1° grid cells. …”
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Developing an efficient explainable artificial intelligence approach for accurate reverse osmosis desalination plant performance prediction: application of SHAP analysis by Meysam Alizamir, Mo Wang, Rana Muhammad Adnan Ikram, Sungwon Kim, Kaywan Othman Ahmed, Salim Heddam

“…In this study, the predictive accuracy of six different machine learning models, including Natural Gradient-based Boosting (NGBoost), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Support vector regression (SVR), Gaussian Process Regression (GPR), and Extremely Randomized Tree (ERT) was evaluated for modelling the parameter of permeate flow as a key element in system efficiency, energy consumption, and water quality using six various input combinations of feed water salt concentration, condenser inlet temperature, feed flow rate, and evaporator inlet temperature. …”
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GEM-CRAP: a fusion architecture for focal seizure detection by Jianwei Shi, Yuanyuan Zhang, Ziang Song, Hang Xu, Yanfeng Yang, Lei Jin, Hengxin Dong, Zhaoying Li, Penghu Wei, Yongzhi Shan, Guoguang Zhao

“…Abstract Background Identification of seizures is essential for the treatment of epilepsy. Current machine-learning and deep-learning models often perform well on public datasets when classifying generalized seizures with prominent features. …”
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