Deep Learning for Opportunistic Rain Estimation via Satellite Microwave Links by Giovanni Scognamiglio, Andrea Rucci, Attilio Vaccaro, Elisa Adirosi, Fabiola Sapienza, Filippo Giannetti, Giacomo Bacci, Sabina Angeloni, Luca Baldini, Giacomo Roversi, Alberto Ortolani, Andrea Antonini, Samantha Melani

“…This study investigates a range of machine learning (ML) approaches, including deep learning (DL) models and traditional methods like gradient boosting machine (GBM), for estimating rainfall rates from SNR data collected by interactive satellite receivers. …”
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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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Vehicle detection and recognition approach in smart surveillance system: A comparative analysis by Stephanie Ness

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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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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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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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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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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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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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Improving the trial efficiency of criminal cases with the assistance of artificial intelligence by Qingxia Chen

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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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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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Bankruptcy Risk Factors of Russian Companies by A. A. Zhukov, E. D. Nikulin, D. A. Shchuchkin

“…For the study, one of the machine learning methods was used – the random forest method. …”
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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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Software Defect Prediction Using Deep Q-Learning Network-Based Feature Extraction by Qinhe Zhang, Jiachen Zhang, Tie Feng, Jialang Xue, Xinxin Zhu, Ningyang Zhu, Zhiheng Li

“…Moreover, without proper feature reduction, the interpretability and generalization ability of machine learning models in SDP may be compromised, hindering their practical utility in diverse software development environments. …”
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Smart Jamming Attack and Mitigation on Deep Transfer Reinforcement Learning Enabled Resource Allocation for Network Slicing by Shavbo Salehi, Hao Zhou, Medhat Elsayed, Majid Bavand, Raimundas Gaigalas, Yigit Ozcan, Melike Erol-Kantarci

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Semantic segmentation of optical satellite images for the illegal construction detection using transfer learning by Yashasvi Mehta, Abdullah Baz, Shobhit K. Patel

“…It employs a diverse range of machine learning models, including the U-Shaped Network and Visual Geometry Group, and incorporates customized evaluation metrics that are not typically found in earlier research. …”
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A Hubness Information-Based k-Nearest Neighbor Approach for Multi-Label Learning by Zeyu Teng, Shanshan Tang, Min Huang, Xingwei Wang

“…Hubness, a phenomenon in which a few points appear in the k-nearest neighbor (kNN) lists of many points in high-dimensional spaces, may significantly impact machine learning applications and has recently attracted extensive attention. …”
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Towards Synthetic Augmentation of Training Datasets Generated by Mobility-on-Demand Service Using Deep Variational Autoencoders by Martin Gregurić, Filip Vrbanić, Edouard Ivanjko

“…This augmentation by synthetic samples can potentially enable larger, balanced, and more consistent datasets for machine learning analysis of MoD-based data. The proposed VAE approaches are compared with common dimensionality reduction techniques and standard autoencoders concerning their efficiency in 2-dimensional clustering based on collected MoD-based data. …”
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Federated Learning-Driven IoT Request Scheduling for Fault Tolerance in Cloud Data Centers by Sheeja Rani S, Raafat Aburukba

“…At first, radial kernelized support vector regression is applied in the local training model to identify resource-efficient virtual machines. …”
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