Leveraging citizen science to classify and track benthic habitat states: An unsupervised UMAP-HDBSCAN pipeline applied to the global reef life survey dataset by Clément Violet, Aurélien Boyé, Stanislas Dubois, Graham J. Edgar, Elizabeth S. Oh, Rick D. Stuart-Smith, Martin P. Marzloff

“…We applied an innovative clustering pipeline that combines three algorithms — Uniform Manifold Approximation and Projection (UMAP) for dimension reduction; Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) — to identify benthic habitat states and Shapley values to interpret the clusters identified. …”
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Exploring Feature Selection with Deep Learning for Kidney Tissue Microarray Classification Using Infrared Spectral Imaging by Zachary Caterer, Jordan Langlois, Connor McKeown, Mikayla Hady, Samuel Stumo, Suman Setty, Michael Walsh, Rahul Gomes

“…In this study, we propose a deep-learning-based framework for automating classification in kidney tumor tissue microarrays (TMAs) using an IR dataset. Feature selection algorithms reduce data dimensionality, followed by a deep learning classification approach. …”
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Architected Design and Fabrication of Soft Mechanical Metamaterials by Thileepan Stalin, Aby Raj Plamootil Mathai, Naresh Kumar Thanigaivel, Elgar Kanhere, Saikrishna Dontu, Gumawang Hiramandala, Aaron Chooi, Arturo Castillo Ugalde, Pablo Valdivia Y Alvarado

“…Planar printing tool paths, utilized in layer‐by‐layer printing such as fused deposition modeling (FDM) and prevalently used in most slicing algorithms, severely limit the realization of complex topologies required in metamaterials. …”
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Physics-informed deep learning model for line-integral diagnostics across fusion devices by Cong Wang, Weizhe Yang, Haiping Wang, Renjie Yang, Jing Li, Zhijun Wang, Yixiong Wei, Xianli Huang, Chenshu Hu, Zhaoyang Liu, Xinyao Yu, Changqing Zou, Zhifeng Zhao

“…The incorporation of the PILF has been shown to correct the model’s predictions, bringing the back-projections closer to the actual inputs and reducing the errors associated with inversion algorithms. Besides, we have developed a synthetic data model to generate customized line-integral diagnostic datasets and have also collected soft x-ray diagnostic datasets from EAST and HL-2A. …”
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Research on Reservoir Identification of Gas Hydrates with Well Logging Data Based on Machine Learning in Marine Areas: A Case Study from IODP Expedition 311 by Xudong Hu, Wangfeng Leng, Kun Xiao, Guo Song, Yiming Wei, Changchun Zou

“…The internal relationship between logging data and hydrate reservoir is analyzed through six ML algorithms. The results show that the constructed ML model performs well in gas hydrate reservoir identification. …”
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What patients and caregivers want to know when consenting to the use of digital behavioral markers by Anika Sonig, Christine Deeney, Meghan E. Hurley, Eric A. Storch, John Herrington, Gabriel Lázaro-Muñoz, Casey J. Zampella, Birkan Tunc, Julia Parish-Morris, Jenny Blumenthal-Barby, Kristin Kostick-Quenet

“…Abstract Artificial intelligence (AI)-based computational tools for deriving digital behavioral markers are increasingly able to automatically detect clinically relevant patterns in mood and behavior through algorithmic analysis of continuously and passively collected data. …”
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A Systematic Integration of Artificial Intelligence Models in Appendicitis Management: A Comprehensive Review by Ivan Maleš, Marko Kumrić, Andrea Huić Maleš, Ivan Cvitković, Roko Šantić, Zenon Pogorelić, Joško Božić

“…These tools also predict disease severity, aiding decisions between conservative management and surgery. …”
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Comparative Study on EDM Wire Cutting and CO2 Laser Cutting for High‐Precision Stainless Steel Sheet Processing by A. Parthiban, K. Ananthakumar, S. Ajith Arul Daniel, S. Sivaganesan, T. Sathish, Jayant Giri, A. Johnson Santhosh, Ahmad O. Hourani

“…Using a multi‐objective optimization approach based on Genetic Algorithms, the study demonstrates a significant enhancement in surface roughness. …”
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Literature Review of Prognostic Factors in Secondary Generalized Peritonitis by Valerii Luțenco, Adrian Beznea, Raul Mihailov, George Țocu, Verginia Luțenco, Oana Mariana Mihailov, Mihaela Patriciu, Grigore Pascaru, Liliana Baroiu

“…Emerging evidence suggests that machine learning algorithms may improve early risk stratification and individualized outcome prediction when integrated with conventional scoring systems. …”
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MANAGEMENT INFORMATION SYSTEM IN THE INTERACTION BETWEEN UNIVERSITIES AND BUSINESS IN THE AGE OF ARTIFICIAL INTELLIGENCE by Rustam E. Asizbaev, Aziza B. Karbekova, Zhumagul K. Umarbekova, Venera Akylbek kyzy, Siuita М. Kasymova

“…General scientific methods of analysis, synthesis, observation, comparison, and generalisation are used. Also, the method of retrospective analysis is utilised to study the evolution of information systems. …”
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Intelligent Photolithography Corrections Using Dimensionality Reductions by Parag Parashar, Chandni Akbar, Tejender S. Rawat, Sparsh Pratik, Rajat Butola, Shih H. Chen, Yung-Sung Chang, Sirapop Nuannimnoi, Albert S. Lin

“…Also, we implement a pure machine learning approach where the input masks are directly mapped to the output etched patterns. …”
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Enhanced Feature Selection via Hierarchical Concept Modeling by Jarunee Saelee, Patsita Wetchapram, Apirat Wanichsombat, Arthit Intarasit, Jirapond Muangprathub, Laor Boongasame, Boonyarit Choopradit

“…The objectives of feature selection include simplifying modeling and making the results more understandable, improving data mining efficiency, and providing clean and understandable data preparation. With big data, it also allows us to reduce computational time, improve prediction performance, and better understand the data in machine learning or pattern recognition applications. …”
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An annotated high-content fluorescence microscopy dataset with EGFP-Galectin-3-stained cells and manually labelled outlineszenodo by Salma Kazemi Rashed, Malou Arvidsson, Rafsan Ahmed, Sonja Aits

“…In order to develop and benchmark algorithms and neural networks that can perform this task, high-quality datasets with annotated cell outlines are needed.We have created a dataset, named Aitslab_bioimaging2, consisting of 60 fluorescence microscopy images with EGFP-Galectin-3 labelled cells and their hand-labelled outlines. …”
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Predicting the Botanical Origin of Honeys with Chemometric Analysis According to Their Antioxidant and Physicochemical Properties by Anna Maria Kaczmarek, Małgorzata Muzolf-Panek, Jolanta Tomaszewska-Gras, Piotr Konieczny

“…The models based on ANN and C&RT algorithms were characterized by 100% accuracy. Study results demonstrate that the chemometric approach enables high-accuracy classification of honeys according to their botanical origin.…”
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Integrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma by Feiyu Qi, Guiming Zha, Yanfang Zhang, Sihua Liu, Yuhang Yang, Wanliang Sun, Dongdong Wang, Zhong Liu, Zheng Lu, Dengyong Zhang

“…Immune-related analysis was conducted based on seven algorithms for immune cell infiltration prediction. …”
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Evaluation and Early Detection of Downy Mildew of Lettuce Using Hyperspectral Imagery by Songtao Ban, Minglu Tian, Dong Hu, Mengyuan Xu, Tao Yuan, Xiuguo Zheng, Linyi Li, Shiwei Wei

“…Moreover, regression models developed using Partial Least Squares (PLS), Random Forest (RF), and Convolutional Neural Network (CNN) algorithms demonstrated high accuracy and reliability in predicting DI, flavonoids, and anthocyanins, with the highest R² of 0.857, 0.910, and 0.963, respectively. …”
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A novel brain tumor magnetic resonance imaging dataset (Gazi Brains 2020): initial benchmark results and comprehensive analysis by Seref Sagiroglu, Ramazan Terzi, Emrah Celtikci, Alp Özgün Börcek, Yilmaz Atay, Bilgehan Arslan, Mustafa Caglar Sahin, Kerem Nernekli, Umut Demirezen, Okan Bilge Ozdemir, Kevser Özdem Karaca, Nuh Azgınoğlu

“…ROI and whole tumor segmentations were successfully performed and compared with seven algorithms with accuracies of 87.61% and 97.18%. The Grad-CAM model also demonstrated satisfactory accuracy across the tests that were conducted. …”
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Harnessing Artificial Intelligence for the Diagnosis, Treatment and Research of Multiple Sclerosis by Manisha S. Patil, Linda Y. Lin, Rachel K. Ford, Elizaveta J. James, Stella Morton, Felix Marsh-Wakefield, Simon Hawke, Georges E. Grau

“…AI is revolutionising the diagnosis and treatment of MS by enhancing the accuracy and efficiency of both processes. AI algorithms, particularly those based on machine learning, are being used to analyse medical imaging data, such as MRI scans, to detect early signs of MS, monitor disease progression and assess patient treatment response with greater precision. …”
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Deep Learning-Enabled Dynamic Model for Nutrient Status Detection of Aquaponically Grown Plants by Mohamed Farag Taha, Hanping Mao, Samar Mousa, Lei Zhou, Yafei Wang, Gamal Elmasry, Salim Al-Rejaie, Abdallah Elshawadfy Elwakeel, Yazhou Wei, Zhengjun Qiu

“…The results demonstrated that the LSTM outperformed the convolutional neural network (CNN) and multi-class support vector machine (MCSVM) approaches. Also, features selected by the DAE showed better performance compared to features extracted using both genetic algorithms (GAs) and sequential forward selection (SFS). …”
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A Spiking Neural Network With Adaptive Graph Convolution and LSTM for EEG-Based Brain-Computer Interfaces by Peiliang Gong, Pengpai Wang, Yueying Zhou, Daoqiang Zhang

“…Recently, energy-efficient spiking neural networks (SNNs) have shown great potential in EEG analysis due to their ability to capture the complex dynamic properties of biological neurons while also processing stimulus information through precisely timed spike trains. …”
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