A hybrid prediction and multi-objective optimization framework for limestone calcined clay cement concrete mixture design by Xi Chen, Weiyi Chen, Zongao Li, Pu Zhang

“…This study proposes a hybrid framework combining machine learning (ML) and multi-objective optimization (MOO) to design cost-effective and eco-friendly LC3 mixtures. …”
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Prediction of Imbalance Prices Through Gradient Boosting Algorithms: An Application to the Greek Balancing Market by Konstantinos Plakas, Nikos Andriopoulos, Dimitrios Papadaskalopoulos, Alexios Birbas, Efthymios Housos, Ioannis Moraitis

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Explainable artificial intelligence for sustainable urban water systems engineering by Shofia Saghya Infant, Sundaram Vickram, A Saravanan, C M Mathan Muthu, Devarajan Yuarajan

“…In terms of numbers, XAI applications in water distribution systems have led to up to 20 % savings in energy consumption by optimizing pump schedules based on interpretable machine learning models. Qualitative benefits have included interpretable neural networks for monitoring water quality that detected anomalies and provided transparent contamination alerts that increased stakeholder trust. …”
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An EEG-based framework for automated discrimination of conversion to Alzheimer’s disease in patients with amnestic mild cognitive impairment: an 18-month longitudinal study by Yingfeng Ge, Jianan Yin, Caie Chen, Shuo Yang, Yuduan Han, Chonglong Ding, Jiaming Zheng, Yifan Zheng, Jinxin Zhang

“…Spectral, nonlinear, and functional connectivity features were extracted from the EEG data, subjected to feature selection and dimensionality reduction, and then fed into various machine learning classifiers for discrimination. …”
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Maximizing multi-source data integration and minimizing the parameters for greenhouse tomato crop water requirement prediction by Xinyue Lv, Youli Li, Lili Zhangzhong, Chaoyang Tong, Yibo Wei, Guangwei Li, Yingru Yang

“…We constructed average fusion, weighted fusion, and stacking fusion models based on RandomForest, LightGBM, and CatBoost machine learning algorithms to accurately predict the water requirements of greenhouse tomato crops. …”
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A Survey of Data Stream-Based Intrusion Detection Systems by Rodrigo Sanches Miani, Gustavo Di Giovanni Bernardo, Guilherme Weigert Cassales, Hermes Senger, Elaine Ribeiro de Faria

“…Advances in the field have led to the development of several algorithms that approach the problem under the view of a data stream machine learning task. This task involves a set of steps: data collection or choice of public datasets, data pre-processing, data reduction, development or application of data mining techniques, and evaluation methodology. …”
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NSA-CHG: An Intelligent Prediction Framework for Real-Time TBM Parameter Optimization in Complex Geological Conditions by Youliang Chen, Wencan Guan, Rafig Azzam, Siyu Chen

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Data Compactness Versus Prediction Performance: Achieving Both by Pruning Redundant Samples With Dominant Patterns and Hamming Distance Based Sampling Scheme by Abdul Majeed, Seong Oun Hwang

“…Machine learning (ML) practitioners are always in pursuit of refined data to develop robust and generalizable ML models to solve real-world problems. …”
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Prediction of Rice Chlorophyll Index (CHI) Using Nighttime Multi-Source Spectral Data by Cong Liu, Lin Wang, Xuetong Fu, Junzhe Zhang, Ran Wang, Xiaofeng Wang, Nan Chai, Longfeng Guan, Qingshan Chen, Zhongchen Zhang

“…This study aimed to explore the feasibility of predicting rice canopy CHI using nighttime multi-source spectral data combined with machine learning models. In this study, ground truth CHI values were obtained using a SPAD-502 chlorophyll meter. …”
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A Global Irradiance Prediction Model Using Convolutional Neural Networks, Wavelet Neural Networks, and Masked Multi-Head Attention Mechanism by Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, Monia Raissi, Salah Hannechi

“…Existing approaches, including statistical methods, conventional machine learning models, and standalone deep learning techniques like LSTM, fail to integrate local features and long-term dependencies simultaneously, creating a need for more robust solutions. …”
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ML-Based Control Strategy for PHEV Under Predictive Vehicle Usage Behaviour by Aleksandr Doikin, Aleksandr Korsunovs, Felician Campean, Oscar García-Afonso, Enrico Agostinelli

“…This paper introduces a novel strategy for an intelligent plug-in hybrid electric vehicle (PHEV) energy optimization strategy based on machine learning (ML) prediction of the upcoming journey, without recourse to navigation or other external data, which underpins many of the existing approaches. …”
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Analyzing the Efficacy of Computer-Aided Detection in Cerebral Aneurysm Diagnosis Using MRI Modality: A Review by Keerthi A. S. Pillai, Preena K. P., Madhu S. Nair

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Proximal remote sensing of dissolved organic matter in aqua-culture ponds via multi-temporal spectral correction by Wenxu Lv, Yancang Wang, Huiqiong Cao, Peng Cheng, Xiaohe Gu, Zhuoran Ma, Mengjie Li, Ruiyin Tang, Qichao Zhao, Xuqing Li, Lan Zhang, Shuaifei Liu

“…This study employed a spectral correction method to unify multi-temporal datasets. Estimation models were constructed using the 2023 dataset with Light Gradient Boosting Machine, Extreme Gradient Boosting, and Random Forest algorithms, and their cross-year performance was validated on the 2024 dataset through transfer learning. …”
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Power Assessment and Performance Comparison of Wind Turbines Driven by Multivariate Environmental Factors by Bubin Wang, Bin Zhou, Denghao Zhu, Mingheng Zou, Zhao Rao, Haoxuan Luo, Weihao Ji

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An Analytical Review of Construction and Demolition Waste Management and Quantification Methods Using a Science Mapping Approach by Weihan Sun, Quddus Tushar, Guomin Zhang, Andy Song, Lei Hou, Jingxuan Zhang, Shuxi Wang

“…This conjugate approach highlights the importance of the successful implementation of waste quantification and the imperative of machine learning for further investigation. This review offers an evidence-based framework to identify key stakeholders, guide future research, and implement sustainable waste management policies.…”
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Application of AI-based Predictive Maintenance for Industrial Processes by Marko Fabić

“…With the advancement of artificial intelligence (AI) and machine learning, data analytics can accurately predict failures, thereby reducing the need for preventive and corrective maintenance in the form that was common before the application of AI. …”
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Short-term and long-term inertia forecasting with low-inertia event prediction in IBR-integrated power systems using a deep learning approach by Santosh Diggikar, Arunkumar Patil, Katkar Siddhant Satyapal, Kunal Samad

“…However, most traditional and online estimation techniques provide reactive inertia assessments, limiting their effectiveness for proactive grid management. Moreover, existing machine learning (ML)-based models primarily focus on either short-term or long-term forecasting and are often trained on limited datasets, which undermines their robustness and generalisation capabilities. …”
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Players’ Performance Prediction for Fantasy Premier League, Using Transformer-based Sentiment Analysis on News and Statistical Data by Tamimi M., Tran T.

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Key technical indicators for stock market prediction by Seyed Mostafa Mostafavi, Ali Reza Hooman

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IonoBench: Evaluating Spatiotemporal Models for Ionospheric Forecasting Under Solar-Balanced and Storm-Aware Conditions by Mert Can Turkmen, Yee Hui Lee, Eng Leong Tan

“…While machine learning approaches have shown promise, progress is hindered by the absence of standardized benchmarking practices and narrow test periods. …”
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