Introduction to Computational Creativity by Anna Longo, ChatGPT

“…By deconstructing the cognitive processes involved in human creativity, researchers can design algorithms that simulate these processes. This involves machine learning, neural networks, evolutionary algorithms, and other AI techniques that enable computers to recognize patterns, generate new ideas, and refine them through iterative processes.  …”
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Inferring free surface disturbance properties from Kelvin wakes using convolutional neural network by Xuanting Hao

“…Kelvin wakes are fluid motions generated by a moving disturbance at a free surface. We present a machine learning-based framework for inferring the properties of such moving disturbances from the Kelvin-wake patterns. …”
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A Backdoor Approach With Inverted Labels Using Dirty Label-Flipping Attacks by Orson Mengara

“…Audio-based machine learning systems frequently use public or third-party data, which might be inaccurate. …”
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Pyrolysis Kinetics of Pine Waste Based on Ensemble Learning by Alok Dhaundiyal, Laszlo Toth

“…Moreover, the multiclassification in pyrolysis kinetics through the proposed scheme was not able to capture the distribution pattern of target values of the differential method. …”
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ARTIFICIAL INTELLIGENCE IN AGRICULTURE: CURRENT TRENDS AND INNOVATIONS by Jonathan Masasi, John N. Ng’ombe, Blessing Masasi

“…This review study provides an overview of AI applications in agriculture, focusing on its applications to predict and monitor crop growth rate and yield, climate change and weather patterns, pests and diseases management, weed management, animal production, agricultural machinery, crop irrigation, and soil management, and crop fertilization. …”
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Computational intelligence investigations on evaluation of salicylic acid solubility in various solvents at different temperatures by Adel Alhowyan, Wael A. Mahdi, Ahmad J. Obaidullah

“…This study emphasizes the resilience of ensemble methods in capturing intricate data patterns and their effectiveness in regression tasks for application of pharmaceutical manufacturing.…”
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On the role of knowledge graphs in AI-based scientific discovery by Mathieu d’Aquin

“…Indeed, the ability of such models to extrapolate from data, seemingly finding unknown patterns relating implicit features of the objects under study to their properties can, at the very least, help accelerate and scale up those studies as demonstrated in fields such as molecular biology and chemistry. …”
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Potential Applications and Ethical Considerations for Artificial Intelligence in Traumatic Brain Injury Management by Kryshawna Beard, Ashley M. Pennington, Amina K. Gauff, Kelsey Mitchell, Johanna Smith, Donald W. Marion

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Electricity demand from national interconnected system of paraguay and meteorological datasetMendeley Data by Félix Morales-Mareco, Matías Verdún, Diego H. Stalder, Carlos Sauer, Sebastián Grillo

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Advancing Real-Time Remote Learning: A Novel Paradigm for Cognitive Enhancement Using EEG and Eye-Tracking Analytics by Nuraini Jamil, Abdelkader Nasreddine Belkacem

“…This study explores the convergence of biometric analytics and machine learning in online education, where the level of student participation directly impacts academic achievement. …”
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Evaluating the Accuracy of Land-Use Change Models for Predicting Vegetation Loss Across Brazilian Biomes by Macleidi Varnier, Eliseu José Weber

“…While machine-learning-based models use computational methods and spatial variables to identify patterns that explain the occurrence of changes. …”
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Interpretable Transformer Neural Network Prediction of Diverse Environmental Time Series Using Weather Forecasts by Enrique Orozco López, David Kaplan, Anna Linhoss

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Advanced explainable models for strength evaluation of self-compacting concrete modified with supplementary glass and marble powders by Khan Kaffayatullah, Khan Muhammad Ehsan Ullah, Al-Naghi Ahmed A. Alawi, Amin Muhammad Nasir, Iftikhar Bawar, Qadir Muhammad Tahir

“…This motivates the adoption of machine learning (ML) techniques, which can efficiently analyze complex datasets and identify patterns that influence concrete performance. …”
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An Autism Spectrum Disorder Identification Method Based on 3D-CNN and Segmented Temporal Decision Network by Zhiling Liu, Ye Chen, Xinrui Dong, Jing Liu

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Severity Classification of a Seismic Event based on the Magnitude-Distance Ratio Using Only One Seismological Station by Luis Hernán Ochoa Gutiérrez, Luis F Niño, Carlos A. Vargas

“…Seismological stations have recorded data during many events that have been characterized by classical methods, and these data can be used as previous "knowledge" to train such stations to recognize patterns. This knowledge can be used to make faster characterizations using only one three-component broadband station by applying bio-inspired algorithms or recently developed stochastic methods, such as kernel methods. …”
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Density matrix emulation of quantum recurrent neural networks for multivariate time series prediction by J D Viqueira, D Faílde, M M Juane, A Gómez, D Mera

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Explainability of a Deep Learning-Based Classification Model for Antineutrophil Cytoplasmic Autoantibody–Associated Glomerulonephritis by Maria A.C. Wester Trejo, Maryam Sadeghi, Shivam Singh, Naghmeh Mahmoodian, Samir Sharifli, Zdenka Hruskova, Vladimír Tesař, Xavier Puéchal, Jan Anthonie Bruijn, Georg Göbel, Ingeborg M. Bajema, Andreas Kronbichler

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TCR and BCR repertoire analysis reveals distinct signatures between benign and malignant ovarian tumors by Zhonghuang Wang, Zhonghuang Wang, Zhe Zhang, Dongli Zhao, Dongli Zhao, Zhenglin Du, Bixia Tang, Enhui Jin, Hailong Kang, Wenming Zhao, Yuanguang Meng

“…TCR demonstrated more distinct spatial distribution patterns between benign and malignant states, suggesting its potential as a more sensitive biomarker for ovarian tumor detection. …”
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Leveraging machine learning for analyzing the nexus between land use and land cover change, land surface temperature and biophysical indices in an eco-sensitive region of Brahmani-... by Bhaskar Mandal

“…This present study aims to evaluate land use and land cover changes using five machine-learning algorithms in Google Earth Engine. …”
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Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunothera... by Shenjun Huang, Tingting Tu

“…We analyzed various datasets, applying negative matrix factorization alongside machine learning algorithms to reveal gene expression patterns and construct diagnostic models. …”
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