A Review of Enhancement Techniques for Cone Beam Computed Tomography Images by Hassn Mazin Al-alaaf, Mohammed Sabah Jarjees

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Deep Learning for Medical Image Analysis Applications in Disease Detection and Diagnosis by Panchadhyayee Swagata, N Shirisha, S Sureshkumar, Priya S Harthy Ruby, Ramalingam Vanaja, M Mahima

“…AI (machine learning or deep learning all belong to AI) has phenomenal potential in revolutionizing healthcare such as enhanced diagnostic precision, personalized treatment, better-quality patient outcomes along with cost reduction, etc. …”
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AI-Driven Optimization of Fly Ash-Based Geopolymer Concrete for Sustainable High Strength and CO₂ Reduction: An Application of Hybrid Taguchi–Grey–ANN Approach by Muhammad Usman Siddiq, Muhammad Kashif Anwar, Faris H. Almansour, Muhammad Ahmed Qurashi, Muhammad Adeel

“…This study addresses this challenge by developing machine learning-optimized geopolymer concrete (GPC) using industrial waste fly ash as cement replacement. …”
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Fast TILs—A pipeline for efficient TILs estimation in non-small cell lung cancer by Nikita Shvetsov, Anders Sildnes, Masoud Tafavvoghi, Lill-Tove Rasmussen Busund, Stig Manfred Dalen, Kajsa Møllersen, Lars Ailo Bongo, Thomas Karsten Kilvær

“…Such a solution in computational pathology can accelerate TIL evaluation, thereby standardizing the prognostication process and facilitating personalized treatment strategies.We develop an end-to-end automated pipeline for TIL estimation in lung cancer WSIs by integrating a patch extraction approach based on hematoxylin component filtering with a machine learning-based patch classification and cell quantification method using the HoVer-Net model architecture. …”
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Modern Deep Learning Techniques for Big Medical Data Processing in Cloud by Mohammed Y. Shakor, Mustafa Ibrahim Khaleel

“…The recent advancements in Machine Learning (ML) and Deep Learning (DL) provide a new dimension in biomedical big data analysis, while the cloud computing technologies present the breakthroughs of handling massive data from hardware, software, and storage. …”
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Reduced-Order Models and Conditional Expectation: Analysing Parametric Low-Order Approximations by Hermann G. Matthies

“…Similarly, in the field of machine learning, a function mapping the parameter set to the image space of the machine learning model is learned from a training set of samples, typically minimising the mean square error. …”
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Shallow recurrent decoder for reduced order modeling of E × B plasma dynamics by Farbod Faraji, Maryam Reza, J Nathan Kutz

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Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making by Vadim Tynchenko, Alexander Lomazov, Vadim Lomazov, Dmitry Evsyukov, Vladimir Nelyub, Aleksei Borodulin, Andrei Gantimurov, Ivan Malashin

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Vehicle detection and recognition approach in smart surveillance system: A comparative analysis by Stephanie Ness

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Automated reinforcement learning for sequential ordering problem using hyperparameter optimization and metalearning by André Luiz Carvalho Ottoni

“…Abstract AutoML systems seek to assist Artificial Intelligence users in finding the best configurations for machine learning models. Following this line, recently the area of Automated Reinforcement Learning (AutoRL) has become increasingly relevant, given the growing increase in applications for reinforcement learning algorithms. …”
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Comparing statistical learning methods for complex trait prediction from gene expression. by Noah Klimkowski Arango, Fabio Morgante

“…Genotypes have been used for trait prediction using a variety of methods such as mixed models, Bayesian methods, penalized regression methods, dimension reduction methods, and machine learning methods. …”
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Anthropogenic and natural influence on vegetation ecosystems from 1982 to 2023 by Teligeer Bao, Huaqiang Li, Yonghong Hao, Matthew Tom Harrison, Ke Liu, Gulnazar Ali

“…However, long-term changes in greening trends are rarely reported due to radiometric inconsistencies among different satellite sensors. Here, we used 12 machine learning algorithms to perform pixel correction on 42 years of moderate resolution imaging spectroradiometer normalized difference vegetation index (NDVI) and GIMMS NDVI data. …”
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Manifold and spatiotemporal learning on multispectral unoccupied aerial system imagery for phenotype prediction by Fared Farag, Trevis D. Huggins, Jeremy D. Edwards, Anna M. McClung, Ahmed A. Hashem, Jason L. Causey, Emily S. Bellis

“…We also trained deep learning models that perform automated feature extraction and compared these against a suite of other approaches. …”
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Improving the trial efficiency of criminal cases with the assistance of artificial intelligence by Qingxia Chen

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Industry 4.0 and circular economy for bolstering healthcare sector: A comprehensive view on challenges, implementation, and futuristic aspects by Mohd Javaid, Abid Haleem, Ibrahim Haleem Khan, Ravi Pratap Singh, Abid Ali Khan

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Crystal structure map for materials classification and modeling by Tamio Oguchi

“…For classifying and modeling properties of crystalline materials in terms of structure, a three-step workflow with (1) generation of structure feature vectors, (2) evaluation of distances among the feature vectors as a measure of similarity in structure, and (3) mapping of each structure in a low-dimensional space with principal components using dimension reduction is proposed. …”
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Hybrid evolutionary algorithm for maximizing medical equipment supply during pandemic✰ by C. D James, Sandeep Mondal

“…In this paper, we make use of a simulation-based model to demonstrate solution to this problem because experimental setups involve high cost and delivery risks.Firstly, we identified thirty-one factors that affect hi-tech machine efficiency. …”
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The Gust Factor Models Involving Wind Speed and Temperature Profiles for Wind Gust Estimation by Haichuan Hu, Chuanhai Qian, Shibo Gao

“…A unified upper-level gust impact model was developed through multiple regression (GF-L) and machine learning (GF-M) methods based on data from these stations to improve gust estimation accuracy. …”
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Cosmology with One Galaxy: Autoencoding the Galaxy Properties Manifold by Amanda Lue, Shy Genel, Marc Huertas-Company, Francisco Villaescusa-Navarro, Matthew Ho

“…Previous studies have demonstrated that machine learning can be used to infer the cosmological parameter Ω _m from the internal properties of even a single randomly selected simulated galaxy. …”
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Modeling residential property prices in emerging climate-responsive urban markets: a hybrid modeling framework for Baidoa City-Somalia by Mohamed Ibrahim Nor, Shuaib Nour Hussein

“…This research uniquely combines traditional econometric methods with advanced machine learning techniques, yielding a hybrid model that outperforms conventional approaches. …”
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