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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Feature Selection Using Pearson Correlation for Ultra-Wideband Ranging Classification by Gita Indah Hapsari, Rendy Munadi, Bayu Erfianto, Indrarini Dyah Irawati

“…The selected features are used to train multiple machine-learning classifiers, including Random Forest, Ridge Classifier, Gradient Boosting, K-Nearest Neighbor, and Logistic Regression. …”
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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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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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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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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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Methodics and tools of cough sound processing on basic of neural net by U. A. Vishniakou, Bahaa Shaya

“…To recognize COVID-19 cough, a classifier was analyzed using CNN as a machine learning model. The proposed CNN system is designed to classify and detect cough sounds based on ESC-50. …”
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Advancing Smart City Sustainability Through Artificial Intelligence, Digital Twin and Blockchain Solutions by Ivica Lukić, Mirko Köhler, Zdravko Krpić, Miljenko Švarcmajer

“…This paper presents an integrated Smart City platform that combines digital twin technology, advanced machine learning, and a private blockchain network to enhance data-driven decision making and operational efficiency in both public enterprises and small and medium-sized enterprises (SMEs). …”
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Comparative Analysis of Non-Negative Matrix Factorization in Fire Susceptibility Mapping: A Case Study of Semi-Mediterranean and Semi-Arid Regions by Iraj Rahimi, Lia Duarte, Wafa Barkhoda, Ana Cláudia Teodoro

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Exploring Task-Related EEG for Cross-Subject Early Alzheimer’s Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral Analysis by Ziyang Li, Hong Wang, Jianing Song, Jiale Gong

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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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Advancing Scholarship Management: A Blockchain-Enhanced Platform With Privacy-Secure Identities and AI-Driven Recommendations by Tu-Anh Nguyen-Hoang, Ngoc Cu Hoang, Phu Thien Hua, Mong-Thy Nguyen Thi, Thu-Thuy Ta, Thu Nguyen, Khoa Tan-Vo, Ngoc-Thanh Dinh, Hong-Tri Nguyen

“…Besides, the machine learning model achieved a good performance rating, achieving a balanced accuracy of 86.75% and a mean average precision of 91.68% on a realistically imbalanced test set, reflecting real-world conditions.…”
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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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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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Landslide Susceptibility Prediction Based on a CNN–LSTM–SAM–Attention Hybrid Model by Honggang Wu, Jiabi Niu, Yongqiang Li, Yinsheng Wang, Daohong Qiu

“…Experimental results demonstrate that the CNN–LSTM–SAM–Attention model significantly outperforms traditional machine learning approaches in terms of accuracy, precision, recall, F1 score, ROC–AUC, and PR–AUC. …”
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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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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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Internet of Things and Artificial Intelligence for Secure and Sustainable Green Mobility: A Multimodal Data Fusion Approach to Enhance Efficiency and Security by Manuel J. C. S. Reis

“…This paper proposes an IoT and AI-driven framework for secure and sustainable green mobility, leveraging multimodal data fusion to enhance traffic management, energy efficiency, and emissions reduction. Using publicly available datasets, including METR-LA for traffic flow and OpenWeatherMap for environmental context, the framework integrates machine learning models for congestion prediction and reinforcement learning for dynamic route optimization. …”
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