Balancing Explainability and Privacy in Bank Failure Prediction: A Differentially Private Glass-Box Approach by Junyoung Byun, Jaewook Lee, Hyeongyeong Lee, Bumho Son

“…Predicting bank failures is a critical task requiring balancing the need for model explainability with the necessity of preserving data privacy. Traditional machine learning models often lack transparency, which poses challenges for stakeholders who need to understand the factors leading to predictions. …”
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Heuristic Forest Fire Detection Using the Deep Learning Model with Optimized Cluster Head Selection Technique by Sengottaiyan N., Ananthi J., Rajesh Sharma R., Hamsanandhini S., Sungheetha Akey, Chinnaiyan R., Ketema Adare Gemeda

“…The major goal is to augment the accuracy and efficiency of forest fire prediction, leveraging the capabilities of advanced machine learning algorithms and optimized sensor network management. …”
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Multimodal heterogeneous graph fusion for automated obstructive sleep apnea-hypopnea syndrome diagnosis by Haoyu Wang, Xihe Qiu, Bin Li, Xiaoyu Tan, Jingjing Huang

“…It demonstrated superior diagnostic performance compared to conventional machine learning models and existing deep learning approaches, effectively integrating domain knowledge with data-driven learning to produce explainable representations and robust generalization capabilities, which can potentially be utilized for clinical use. …”
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Monitoring of ball bearings via vibration analysis and envelope technique for predictive maintenance purposes by Adiel Pessoa, Paulo Cezar Büchner

“…The next step we will intend to explore the new technologies like machine learning and artificial intelligence, to also analyze all variants of defects in a bearing. …”
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Toward improving precision and complexity of transformer-based cost-sensitive learning models for plant disease detection by Manh-Tuan Do, Manh-Hung Ha, Duc-Chinh Nguyen, Oscal Tzyh-Chiang Chen, Oscal Tzyh-Chiang Chen

“…This study introduces an automated system for early disease detection in plants that enhanced a lightweight model based on the robust machine learning algorithm. In particular, we introduced a transformer module, a fusion of the SPP and C3TR modules, to synthesize features in various sizes and handle uneven input image sizes. …”
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Application of Extreme Gradient Boosting Based on Grey Relation Analysis for Prediction of Compressive Strength of Concrete by Liyun Cui, Peiyuan Chen, Liang Wang, Jin Li, Hao Ling

“…The prediction of concrete strength is an interesting point of investigation and could be realized well, especially for the concrete with the complex system, with the development of machine learning and artificial intelligence. Therefore, an excellent algorithm should put emphasis to receiving increased attention from researchers. …”
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A Study on the Differences in Optimized Inputs of Various Data-Driven Methods for Battery Capacity Prediction by Kuo Xin, Fu Jia, Byoungik Choi, Geesoo Lee

“…This paper extracts 11 types of lithium battery-related health features, and experiments are conducted on two traditional machine learning networks and three advanced deep learning networks in three aspects of input differences. …”
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Smart Contracts and Shared Platforms in Sustainable Health Care: Systematic Review by Carlos Antonio Marino, Claudia Diaz Paz

“…A quantitative assessment of the studies based on machine learning and data reduction methodologies was complemented with a qualitative, in-depth, detailed review of the frameworks propounded in the literature. …”
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Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model Analysis by Sunil L. Bangare, Deepali Virmani, Girija Rani Karetla, Pankaj Chaudhary, Harveen Kaur, Syed Nisar Hussain Bukhari, Shahajan Miah

“…The application of new technologies like machine learning, deep learning, and other models support doctors, nurses, and radiologists to predict heart disease effectively. …”
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Artificial intelligence artificial muscle of dielectric elastomers by Dongyang Huang, Jiaxuan Ma, Yubing Han, Chang Xue, Mengying Zhang, Weijia Wen, Sheng Sun, Jinbo Wu

“…Establishing an AM material database is highly valuable, as seemingly minor material data can be correlated with descriptors and target values via machine learning. Through material data mining integrating materials science and data science, we can predict potential breakthroughs in AM materials. …”
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A feasibility study on using soft insoles for estimating 3D ground reaction forces with incorporated 3D-printed foam-like sensors by Nick Willemstein, Saivimal Sridar, Herman van der Kooij, Ali Sadeghi

“…These results were comparable to or outperformed other works that used commercial force-sensing resistors with machine learning. Four participants participated in three trials over a week, which showed a decrease in estimation performance over time but stayed on average 11.35% RMS and 8.6% MAE after a week with the performance seeming consistent between days two and seven. …”
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Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework by Milos Antonijevic, Miodrag Zivkovic, Milica Djuric Jovicic, Bosko Nikolic, Jasmina Perisic, Marina Milovanovic, Luka Jovanovic, Mahmoud Abdel-Salam, Nebojsa Bacanin

“…This study revolves around hybrid framework that combines convolutional neural networks (CNN) and machine learning (ML) classifying models, like categorical boosting (CatBoost) and light gradient-boosting machine (LightGBM), further optimized through metaheuristics optimizers for leveraged performance. …”
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Exploration of transfer learning techniques for the prediction of PM10 by Michael Poelzl, Roman Kern, Simonas Kecorius, Mario Lovrić

“…Abstract Modelling of pollutants provides valuable insights into air quality dynamics, aiding exposure assessment where direct measurements are not viable. Machine learning (ML) models can be employed to explore such dynamics, including the prediction of air pollution concentrations, yet demanding extensive training data. …”
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Contemporary digital sociology in search of meaning-making models: the proper and the actual. Review on articles from the research handbook of digital sociology edited by J. Skopek... by E. V. Andrianova, V. A. Davydenko, A. I. Rozmanov

“…The first part of this review presents the key ideas of 15 chapters, focusing on the foundational components of social  theory and their connections to the internet in daily life, digital  technologies for surveys and data processing, the significance  of mobile devices, big data, machine learning, agent-based  models of social phenomena, inclusive digital focus groups, social networking sites in professional contexts, online dating, partner selection based on digital information, the use of digital approaches in online markets, the application of YouTube in social sciences, and automated image analysis. …”
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Exploring the potential of aerial drone imagery to distinguish breeding Adélie (Pygoscelis adeliae), chinstrap (Pygoscelis antarcticus) and gentoo (Pygoscelis papua) penguins in An... by Christian Pfeifer, Stefan Knetsch, Jakob Maercker, Osama Mustafa, Marie-Charlott Rümmler, Alexander Brenning

“…Consequently, these results enable specific flight planning for optimal species discrimination under the given conditions and serve as the basis for future automated mapping of penguin species using machine learning, facilitating early detection of changes.…”
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Multi-Relational Graph Representation Learning for Financial Statement Fraud Detection by Chenxu Wang, Mengqin Wang, Xiaoguang Wang, Luyue Zhang, Yi Long

“…Financial statement fraud refers to malicious manipulations of financial data in listed companies’ annual statements. Traditional machine learning approaches focus on individual companies, overlooking the interactive relationships among companies that are crucial for identifying fraud patterns. …”
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Astronomaly Protege: Discovery through Human-machine Collaboration by Michelle Lochner, Lawrence Rudnick

“…Here we introduce ASTRONOMALY: PROTEGE, an extension of the general-purpose machine-learning-based active anomaly detection framework ASTRONOMALY. …”
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Time-resolved spectroscopy uncovers deprotonation-induced reconstruction in oxygen-evolution NiFe-based (oxy)hydroxides by Dan Wu, Longfei Hu, Xiaokang Liu, Tong Liu, Xiangyu Zhu, Qiquan Luo, Huijuan Zhang, Linlin Cao, Jinlong Yang, Zheng Jiang, Tao Yao

“…In this study, utilizing in situ energy-dispersive X-ray absorption spectroscopy and machine learning analysis, we reveal the occurrence of deprotonation and elucidate the role of incorporated iron in facilitating the transition from nickel-iron layered double hydroxide (NiFe LDH) into its active oxyhydroxide. …”
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A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), naïve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). …”
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Uncertainty-aware deep learning in healthcare: A scoping review. by Tyler J Loftus, Benjamin Shickel, Matthew M Ruppert, Jeremy A Balch, Tezcan Ozrazgat-Baslanti, Patrick J Tighe, Philip A Efron, William R Hogan, Parisa Rashidi, Gilbert R Upchurch, Azra Bihorac

“…Conformal prediction offered similar strong performance in estimating uncertainty, along with ease of interpretation and application not only to deep learning but also to other machine learning approaches. Among the six articles describing non-imaging applications, model architectures and uncertainty estimation methods were heterogeneous, but predictive performance was generally strong, and uncertainty estimation was effective in comparing modeling methods. …”
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