False data injection attack dataset for classification, identification, and detection for IIoT in Industry 5.0Zenodo by A K M Ahasan Habib, Mohammad Kamrul Hasan, Rosilah Hassan, Shayla Islam, Huda Saleh Abbas

“…The new Industry 5.0 era of interaction and accessibility has been brought about by the widespread use of Industrial Internet of Things (IIoT) devices, however, it also has uncovered several security issues, with False Data Injection (FDI) attacks being a significant concern. …”
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A novel deep learning approach for investigating liquid fuel injection in combustion system by Syed Azeem Inam, Abdullah Ayub Khan, Noor Ahmed, Tehseen Mazhar, Tariq Shahzad, Sunawar Khan, Mamoon M. Saeed, Habib Hamam

“…The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. …”
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300,000 quitters and counting; A systematic approach to tobacco cessation by Renee Fogelberg, Kelly C. Young-Wolff, Jaya Nadella, Mehreen Khan, Yi-Fen Irene Chen, Jamal S. Rana

“…Methods: We outline seven key components of KPNC's tobacco screening and intervention program that contributed to a significant decline in smoking prevalence. We also report changes in the prevalence of current smokers within KPNC from 2014 to 2023 using linear regression analyses. …”
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A statistical study on factors influencing piezoelectric properties of upside-down composites towards machine learning-driven development for recycling by Sivagnana Sundaram Anandakrishnan, Suhas Yadav, Mohadeseh Tabeshfar, Mikko Nelo, Jani Peräntie, Yang Bai

“…This multi-variable approach is also extended to different fabrication techniques. …”
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The cadenza woodwind dataset: Synthesised quartets for music information retrieval and machine learningZenodo by Gerardo Roa Dabike, Trevor J. Cox, Alex J. Miller, Bruno M. Fazenda, Simone Graetzer, Rebecca R. Vos, Michael A. Akeroyd, Jennifer Firth, William M. Whitmer, Scott Bannister, Alinka Greasley, Jon P. Barker

“…The data was created to be used as training data within Cadenza's second open machine learning challenge (CAD2) for the task on rebalancing classical music ensembles. The dataset is also intended for developing other music information retrieval (MIR) algorithms using machine learning. …”
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Quantum-Inspired Real-Time Optimization for 6G Networks: Opportunities, Challenges, and the Road Ahead by Trung Q. Duong, Long D. Nguyen, Bhaskara Narottama, James Adu Ansere, Dang Van Huynh, Hyundong Shin

“…Not only do we review the fundamental principles; we also explore the challenges and opportunities of this exciting research direction.…”
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Explaining basketball game performance with SHAP: insights from Chinese Basketball Association by Yan Ou-Yang, Wei Hong, Liming Peng, Cheng-Xi Mao, Wen-Jia Zhou, Wei-Tao Zheng, Quan Wang, Feng Qi, Xue-Wei Li, Shi-Huan Chen, Ce Xu, Yu-Fan Wang

“…Utilizing data from 4100 games across 10 CBA seasons (2013–2023), this study constructs CBA game outcome prediction models using seven machine learning algorithms, including XGBoost, LightGBM, Decision Tree, Random Forest, Support Vector Machines, Logistic Regression, and K-Nearest Neighbors. …”
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An ultrasonic-AI hybrid approach for predicting void defects in concrete-filled steel tubes via enhanced XGBoost with Bayesian optimization by Shuai Wan, Shipan Li, Zheng Chen, Yunchao Tang

“…This hybrid approach offers high efficiency and accuracy for void defect detection in CFST, providing a novel solution that leverages the strengths of both traditional ultrasonic methods and artificial intelligence algorithms. The method not only detects the presence of void defects but also quantifies their extent, advancing CFST inspection from qualitative analysis to quantitative assessment.…”
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Applied Artificial Intelligence in Materials Science and Material Design by Emigdio Chávez‐Angel, Martin Børstad Eriksen, Alejandro Castro‐Alvarez, Jose H. Garcia, Marc Botifoll, Oscar Avalos‐Ovando, Jordi Arbiol, Aitor Mugarza

“…By leveraging large datasets, AI algorithms can identify patterns, reduce noise, and predict material behavior with unprecedented accuracy. …”
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Prognostic and Diagnostic Value of Spontaneous Tumor-Related Antibodies by Sebastian Kobold, Tim Luetkens, Yanran Cao, Carsten Bokemeyer, Djordje Atanackovic

“…Larger prospective trials should be initiated to identify sets of tumor-associated autoantibodies suited for the use in diagnostic algorithms for cancer detection and followup.…”
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A review of the search for AGB stars by Hai-Ling Lu, Hai-Ling Lu, Yin-Bi Li, A-Li Luo, A-Li Luo, A-Li Luo, Zhi-Qiang Zou, Zhi-Qiang Zou, Zhi-Qiang Zou, Xiao-Ming Kong, Zhen-Ping Yi, Hugh R. A. Jones, Jun-Chao Liang, Jun-Chao Liang, Shuo Li, Shuo Li

“…By integrating multi-wavelength data such as optical and infrared spectra, along with stellar evolution models, we analyze the existing methods and potential directions for improvement. We also explore the possibility of using interpretable machine learning algorithms to discover new features and applying deep learning algorithms to enhance search efficiency. …”
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Improving resectable gastric cancer prognosis prediction: A machine learning analysis combining clinical features and body composition radiomics by Gianni S.S. Liveraro, Maria E.S. Takahashi, Fabiana Lascala, Luiz R. Lopes, Nelson A. Andreollo, Maria C.S. Mendes, Jun Takahashi, José B.C. Carvalheira

“…Body composition radiomics were integrated with clinicopathological factors using machine learning (ML) algorithms trained for patient outcome prediction. We compared results using Random Forest, Logistic Regression and Boosted Decision Tree algorithms. …”
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GeoFactory: an LLM performance enhancement framework for geoscience factual and inferential tasks by Zhou Chen, Xiao Wang, Xinan Zhang, Ming Lin, Yuanhong Liao, Juanzi Li, Yuqi Bai

“…This framework enables Mistral-7b to outperform GPT-4 and 14 baseline algorithms in terms of the average performance on geoscience tasks. …”
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Predictive modeling for rework detection in sustainable building projects by AbdulLateef Olanrewaju, Kafayat Shobowale

“…The results revealed that Random Forest and AdaBoost outperformed the other algorithms. The Adaboost achieved an accuracy of 90%, with a precision of 0.80, a recall of 0.76, and an F1-score of 0.77. …”
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Evaluating accountability, transparency, and bias in AI-assisted healthcare decision- making: a qualitative study of healthcare professionals’ perspectives in the UK by Saoudi CE Nouis, Victoria Uren, Srushti Jariwala

“…These challenges become especially critical in domains such as pathology and blood sciences, where opaque AI algorithms and non-representative datasets can impact clinical outcomes. …”
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Facial emotion based smartphone addiction detection and prevention using deep learning and video based learning by C. Joseph, P. Uma Maheswari

“…Based on detected emotions such as happiness, sadness, or anger, the system dynamically shuffles motivational videos using advanced algorithms like Fisher-Yates and Durstenfeld shuffling techniques to promote behavioral change. …”
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Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection by Kuan-Yu Chen, Yen-Chun Huang, Chih-Kuang Liu, Shao-Jung Li, Mingchih Chen

“…We found that the patient’s medication use in the previous year is also crucial in determining subsequent surgical costs. …”
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Dual-stream interactive mechanism with multi-modal hierarchical aggregation transformer for gait recognition by Jinghang Liu, Xiangyuan Xu, Yan Qiu, Chunzhi Wang

“…These issues prevent existing algorithms from fully leveraging the complementary advantages of multimodal data. …”
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Computational linguistics and sentiment analysis of misconceptions in pharmacotherapy of osteoarthritis pain by I. Yu. Torshin, A. N. Gromov, O. A. Gromova

“…Moreover, texts promoting this misconceptions are easily recognized as manipulative not only by experts in the analysis of medical literature, but also by artificial intelligence algorithms.Conclusion. …”
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Deep learning methods for clinical workflow phase-based prediction of procedure duration: a benchmark study by Emanuele Frassini, Teddy S. Vijfvinkel, Rick M. Butler, Maarten van der Elst, Benno H. W. Hendriks, John J. van den Dobbelsteen

“…We employed only the clinical phases derived from video analysis as input to the algorithms. Our results show that InceptionTime and LSTM-FCN yielded the most accurate predictions. …”
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