A Process Monitoring Framework for Imbalanced Big Data: A Wastewater Treatment Plant Case Study by Morteza Zadkarami, Ali Akbar Safavi, Krist V. Gernaey, Pedram Ramin

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Ensemble Learning-Based Wine Quality Prediction Using Optimized Feature Selection and XGBoost by Sonam Tyagi, Ishwari Singh Rajput, Bhawnesh Kumar, Harendra Singh Negi

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Survey on explainable knowledge graph reasoning methods by Yi XIA, Mingjng LAN, Xiaohui CHEN, Junyong LUO, Gang ZHOU, Peng HE

“…In recent years, deep learning models have achieved remarkable progress in the prediction and classification tasks of artificial intelligence systems.However, most of the current deep learning models are black box, which means it is not conducive to human cognitive reasoning process.Meanwhile, with the continuous breakthroughs of artificial intelligence in the researches and applications, high-performance complex algorithms, models and systems generally lack the transparency and interpretability of decision making.This makes it difficult to apply the technologies in a wide range of fields requiring strict interpretability, such as national defense, medical care and cyber security.Therefore, the interpretability of artificial intelligence should be integrated into these algorithms and systems in the process of knowledge reasoning.By means of carrying out explicit explainable intelligence reasoning based on discrete symbolic representation and combining technologies in different fields, a behavior explanation mechanism can be formed which is an important way for artificial intelligence to realize data perception to intelligence perception.A comprehensive review of explainable knowledge graph reasoning was given.The concepts of explainable artificial intelligence and knowledge reasoning were introduced briefly.The latest research progress of explainable knowledge graph reasoning methods based on the three paradigms of artificial intelligence was introduced.Specifically, the ideas and improvement process of the algorithms in different scenarios of explainable knowledge graph reasoning were explained in detail.Moreover, the future research direction and the prospect of explainable knowledge graph reasoning were discussed.…”
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Inflammation Biomarker-Driven Vertical Visualization Model for Predicting Long-Term Prognosis in Unstable Angina Pectoris Patients with Angiographically Intermediate Coronary Lesio... by Zhou B, Tan W, Duan S, Wang Y, Bian F, Zhao P, Wang J, Yao Z, Li H, Hu X, Wang J, Liu J

“…The assessed outcome was the occurrence of major adverse cardiac and cerebrovascular events (MACCEs). The Boruta algorithm was applied to identify potential risk factors and develop a prognostic multimodal model.Results: A total of 773 patients were enrolled and divided into a training cohort (n=463) and validation cohort (n=310). …”
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Active location and recovery of unbalance problems in smart distribution networks by LI Yihong, PAN Yicheng, MA Meng, WANG Ping

“…Under the rapid development of new power systems and the high penetration of distributed energy resources, three-phase unbalance issues in distribution networks have become increasingly prominent. …”
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Development and Validation of Machine Learning Models for Outcome Prediction in Patients with Poor-Grade Aneurysmal Subarachnoid Hemorrhage Following Endovascular Treatment by Du S, Wu Y, Tao J, Shu L, Yan T, Xiao B, Lv S, Ye M, Gong Y, Zhu X, Hu P, Wu M

“…Then, the prediction models developed have revealed that LightGBM algorithm has a superior performance with an AUC-ROC value of 0.842 in the validation cohort, while the SHAP results showed that age is the most important risk factor affecting functional outcomes.Conclusion: The LightGBM model holds immense potential in facilitating risk stratification for poor-grade aSAH patients undergoing endovascular treatment who are at risk of adverse outcomes, thereby enhancing clinical decision-making processes.Trial Registration: PROSAH-MPC. …”
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A Portable and Affordable Four-Channel EEG System for Emotion Recognition with Self-Supervised Feature Learning by Hao Luo, Haobo Li, Wei Tao, Yi Yang, Chio-In Ieong, Feng Wan

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Forecasting Chlorophyll-a in the Murray–Darling Basin Using Remote Sensing by Ming Li, Klaus Joehnk, Peter Toscas, Luis Riera Garcia, Huidong Jin, Tapas K. Biswas

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A Cooperative Online Learning-Based Load Balancing Scheme for Maximizing QoS Satisfaction in Dense HetNets by Hyungwoo Choi, Taehwa Kim, Hong-Shik Park, Jun Kyun Choi

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Rules-Based Energy Management System for an EV Charging Station Nanogrid: A Stochastic Analysis by Gabriel Henrique Danielsson, Leonardo Nogueira Fontoura da Silva, Joelson Lopes da Paixão, Alzenira da Rosa Abaide, Nelson Knak Neto

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Machine Learning-Based Classification and Statistical Analysis of Liver Cancer: A Comprehensive Study of Model Performance and Clinical Significance by Pratyush Kumar MAHARANA, Tapan Kumar BEHERA, Pradeep Kumar NAIK

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Multi-Stage Operational Resilience Metric-Driven Optimal Service Restoration in DER-Rich Power Distribution Systems by Samridhi Sajwan, Bijaya Ketan Panigrahi, Anurag K. Srivastava

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Automated Defect Detection through Flaw Grading in Non-Destructive Testing Digital X-ray Radiography by Bata Hena, Gabriel Ramos, Clemente Ibarra-Castanedo, Xavier Maldague

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Dynamic asymmetric assignment problem in open multi-agent systems by M. P. Revotjuk, N. V. Khajynova, A. P. Kuznetsov, L. Y. Shilin

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Verifying the relationships among the variabilities of summer rainfall extremes over Japan in the d4PDF climate ensemble, Pacific sea surface temperature, and monsoon activity by S.-Y. Lee, S. He, T. Takemi

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Smart Electric Vehicle Charging Management Using Reinforcement Learning on FPGA Platforms by Udhaya Mugil Damodarin, Gian Carlo Cardarilli, Luca Di Nunzio, Marco Re, Sergio Spanò

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Integrating Machine Learning and Multi-Objective Optimization in Biofuel Systems: A Review by Ivan P. Malashin, Dmitry A. Martysyuk, Vadim S. Tynchenko, Andrei P. Gantimurov, Vladimir A. Nelyub, Aleksei S. Borodulin

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An interpretable disruption predictor on EAST using improved XGBoost and SHAP by D.M. Liu, X.L. Zhu, Y.S. Jiang, S. Wang, S.B. Shu, B. Shen, B.H. Guo, L.C. Liu

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Adaptable Reduced-Complexity Approach Based on State Vector Machine for Identification of Criminal Activists on Social Media by Imran Shafi, Sadia Din, Zahid Hussain, Imran Ashraf, Gyu Sang Choi

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Research on data completion and generation of downhole drilling tool attitude based on Long Short-Term Memory neural networks by Qiwei Liu, Fanmin Kong, Xiaolong Chen, Kang Li

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