Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health: Explainable Artificial Intelligence Approach by Guanjin Wang, Hachem Bennamoun, Wai Hang Kwok, Jenny Paola Ortega Quimbayo, Bridgette Kelly, Trish Ratajczak, Rhonda Marriott, Roz Walker, Jayne Kotz

“…Several machine learning models, including random forest (RF), CatBoost (CB), light gradient-boosting machine (LightGBM), extreme gradient boosting (XGBoost), k-nearest neighbor (KNN), support vector machine (SVM), and explainable boosting machine (EBM), were developed and compared for predictive performance. …”
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TTG-Text: A Graph-Based Text Representation Framework Enhanced by Typical Testors for Improved Classification by Carlos Sánchez-Antonio, José E. Valdez-Rodríguez, Hiram Calvo

“…Unlike traditional TF-IDF weighting, TTG-Text leverages typical testors to enhance feature relevance within text graphs, resulting in improved model interpretability and performance, particularly for smaller datasets. …”
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Shared entanglement for three-party causal order guessing game by Ryszard Kukulski, Paulina Lewandowska, Karol Życzkowski

“…Our research provides a basis for examining computational model featuring a specific gate set while examining the diverse operations achievable through permutations of its elements.…”
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Small-Sample Authenticity Identification and Variety Classification of <i>Anoectochilus roxburghii</i> (Wall.) Lindl. Using Hyperspectral Imaging and Machine Learning by Yiqing Xu, Haoyuan Ding, Tingsong Zhang, Zhangting Wang, Hongzhen Wang, Lu Zhou, Yujia Dai, Ziyuan Liu

“…In contrast, traditional machine learning models showed varied performance, with SVM proving superior due to its ability to handle high-dimensional feature spaces. …”
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Multimodal Ensemble Fusion Deep Learning Using Histopathological Images and Clinical Data for Glioma Subtype Classification by Satoshi Shirae, Shyam Sundar Debsarkar, Hiroharu Kawanaka, Bruce Aronow, V. B. Surya Prasath

“…Based on the performances of the deep learning models, we ensemble feature sets from top three models and perform further classifications. …”
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Integrated Machine Learning Algorithms-Enhanced Predication for Cervical Cancer from Mass Spectrometry-Based Proteomics Data by Da Zhang, Lihong Zhao, Bo Guo, Aihong Guo, Jiangbo Ding, Dongdong Tong, Bingju Wang, Zhangjian Zhou

“…Furthermore, by integrating feature importance values, Shapley values, and local interpretable model-agnostic explanation (LIME) values, we demonstrated that the diagnostic area under the curve (AUC) achieved by our multi-dimensional learning models approached 1, significantly outperforming the diagnostic AUC of single markers derived from the PRIDE database. …”
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Explainable Artificial Intelligence for predicting the compressive strength of soil and ground granulated blast furnace slag mixtures by Ahmed Mohammed Awad Mohammed, Omayma Husain, Muyideen Abdulkareem, Nor Zurairahetty Mohd Yunus, Nadiah Jamaludin, Elamin Mutaz, Hashim Elshafie, Mosab Hamdan

“…The study highlights the performance of these models and employs SHAP and LIME analysis to evaluate feature importance. …”
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Multi-Stage Neural Network-Based Ensemble Learning Approach for Wheat Leaf Disease Classification by Samia Nawaz Yousafzai, Inzamam Mashood Nasir, Sara Tehsin, Dania Saleem Malik, Ismail Keshta, Norma Latif Fitriyani, Yeonghyeon Gu, Muhammad Syafrudin

“…The utilization of conventional models has major limitations in wheat disease detection, including dataset-specific performance, overfitting due to limited data, and high computational needs, making deployment in resource-constrained situations difficult. …”
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xAAD&#x2013;Post-Feedback Explainability for Active Anomaly Discovery by Damir Kopljar, Vjekoslav Drvar, Jurica Babic, Vedran Podobnik

“…This paper introduces xAAD, a novel approach that combines Active Anomaly Discovery (AAD) with the Assist-Based Weighting Scheme (AWS) explainability metric for Isolation Forest-based anomaly detection. Our method enhances model interpretability and reduces false positives by incorporating expert feedback and providing post-feedback feature importance values. …”
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Parametric Analysis of Auxetic Honeycombs by Stefan Tabacu, Ana-Gabriela Badea, Alina-Ionela Aparaschivei, Daniel-Constantin Anghel

“…The present study discusses the methodology used to examine these structures using the finite element method and how to adapt simple numerical models to capture structural behavior. Subsequently, the numerical model is used to run parametric analyses to determine the performance and provide the background for discussing the influence of the dimensional set on the response. …”
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Non-Invasive Ventilation Failure in Pediatric ICU: A Machine Learning Driven Prediction by Maria Vittoria Chiaruttini, Giulia Lorenzoni, Marco Daverio, Luca Marchetto, Francesca Izzo, Giovanna Chidini, Enzo Picconi, Claudio Nettuno, Elisa Zanonato, Raffaella Sagredini, Emanuele Rossetti, Maria Cristina Mondardini, Corrado Cecchetti, Pasquale Vitale, Nicola Alaimo, Denise Colosimo, Francesco Sacco, Giulia Genoni, Daniela Perrotta, Camilla Micalizzi, Silvia Moggia, Giosuè Chisari, Immacolata Rulli, Andrea Wolfler, Angela Amigoni, Dario Gregori

“…Additionally, an ensemble approach was implemented. Model performances were measured using sensitivity, specificity, AUROC, and predictive values. …”
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Optimizing Academic Certificate Management With Blockchain and Machine Learning: A Novel Approach Using Optimistic Rollups and Fraud Detection by Khoa Tan-Vo, Khanh Pham, Phu Huynh, Mong-Thy Nguyen Thi, Thu-Thuy Ta, Thu Nguyen, Tu-Anh Nguyen-Hoang, Ngoc-Thanh Dinh, Hong-Tri Nguyen

“…Moreover, the machine learning model displays impressive performance, achieving high accuracy in detecting fraudulent users, with an average F1-score of 99.42% and an AUC score nearing perfection. …”
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BA-ATEMNet: Bayesian Learning and Multi-Head Self-Attention for Theoretical Denoising of Airborne Transient Electromagnetic Signals by Weijie Wang, Xuben Wang, Xiaodong Yu, Debiao Luo, Xinyue Liu, Kai Yang, Wen Yang, Xiaolan Yang, Ke Hu, Wenyi Hu

“…Moreover, the combination of Bayesian learning with a weighted integration of prior knowledge and SNR enhances the model’s performance across varying noise levels, thereby increasing its adaptability to complex noise environments. …”
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VDGA-Based Resistorless Mixed-Mode Universal Filter and Dual-Mode Quadrature Oscillator by Orapin Channumsin, Jetwara Tangjit, Tattaya Pukkalanun, Worapong Tangsrirat

“…Several PSPICE simulations with the TSMC 0.18 μm CMOS model confirm the feasibility of the proposed configurations. …”
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A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm by Zhixin Xia, Zhangqi Zheng, Lexin Bai, Xiaolei Yang, Yongshan Liu

“…The algorithm can obtain hypergraphs without knowing the attribute information of hypergraph nodes and combines the graph convolutional network (GCN) framework to capture node feature information for link prediction. Experiments show that the HWA algorithm proposed in this paper, combined with the GCN framework, shows better link prediction performance than other graph-based neural network benchmark algorithms on eight real networks. …”
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Sustainable phytoprotection: a smart monitoring and recommendation framework using Puma Optimization for potato pathogen detection by Amal H. Alharbi, Faris H. Rizk, Khaled Sh. Gaber, Marwa M. Eid, Marwa M. Eid, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy, Pushan Kumar Dutta, Doaa Sami Khafaga

“…By fusing copulabased transformations with PO-driven optimization, the framework effectively models complex nonlinear dependencies among heterogeneous features, enabling high-fidelity probabilistic inference in high-dimensional ecological spaces. …”
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FraudGNN-RL: A Graph Neural Network With Reinforcement Learning for Adaptive Financial Fraud Detection by Yiwen Cui, Xu Han, Jiaying Chen, Xinguang Zhang, Jingyun Yang, Xuguang Zhang

“…The RL component, implemented as a Deep Q-Network (DQN), dynamically adjusts the fraud detection threshold and feature importance, allowing the model to adapt to evolving fraud patterns and minimize detection costs. …”
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Complex-Scene SAR Aircraft Recognition Combining Attention Mechanism and Inner Convolution Operator by Wansi Liu, Huan Wang, Jiapeng Duan, Lixiang Cao, Teng Feng, Xiaomin Tian

“…By integrating the MTCN module and involution, performance is enhanced. The Multi-TASP-Conv network (MTCN) module aims to effectively extract low-level semantic and spatial information using a shared lightweight attention gate structure to achieve cross-dimensional interaction between “channels and space” with very few parameters, capturing the dependencies among multiple dimensions and improving feature representation ability. …”
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Understanding How Short-Termism and a Dynamic Investor Network Affects Investor Returns: An Agent-Based Perspective by Matthew Oldham

“…Introducing investor heterogeneity also allows researchers to identify the characteristics of higher performing investors and the implications of investors exhibiting short-termism, a feature recognized by some as detrimental to the performance of the economy. …”
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Impact of COVID-19 vaccination on preventive behavior: The importance of confounder adjustment in observational studies. by Laura Sità, Marta Caserotti, Manuel Zamparini, Lorella Lotto, Giovanni de Girolamo, Paolo Girardi

“…This study examines the application of covariate adjustment and propensity score (PS) estimation, particularly through inverse probability treatment weighting (IPTW), to assess their performance in reducing bias in a framework featuring ordinal outcomes and cumulative logistic regression models, as commonly used in observational studies related to social sciences and psychology. …”
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