Neural Network Models for Ionospheric Electron Density Prediction at a Fixed Altitude Using Neural Architecture Search by Yang Pan, Mingwu Jin, Shun‐Rong Zhang, Simon Wing, Yue Deng

“…In this work, we propose to use neural architecture search (NAS), an automatic machine learning method, to mitigate this problem. NAS aims to find the optimal network structure through the alternate optimization of the hyperparameters and the corresponding network parameters within a pre‐defined hyperparameter search space. …”
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A copula-based multivariate flood frequency analysis under climate change effects by Marzieh Khajehali, Hamid R. Safavi, Mohammad Reza Nikoo, Mohammad Reza Najafi, Reza Alizadeh-Sh

“…Downscaled GCM outputs are utilized as predictors of the machine learning model to simulate daily streamflow. Then, a trivariate copula-based framework assesses flood events in terms of duration, volume, and flood peak in the Kan River basin, Iran. …”
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Deciphering sepsis: transforming diagnosis and treatment through systems immunology by Robert E. W. Hancock, Andy An, Claudia C. dos Santos, Amy H. Y. Lee

“…Systems immunology methods, including multiomics (notably RNA sequencing transcriptomics), machine learning, and network biology analysis, have the potential to transform the management paradigm toward precision approaches. …”
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Classifying IoT Botnet Attacks With Kolmogorov-Arnold Networks: A Comparative Analysis of Architectural Variations by Phuc Hao do, Tran Duc Le, Truong Duy Dinh, van Dai Pham

“…This study aims to evaluate the effectiveness of Kolmogorov-Arnold Networks (KANs) and their architectural variations in classifying IoT botnet attacks, comparing their performance with traditional machine learning and deep learning models. We conducted a comparative analysis of five KAN architectures, including Original-KAN, Fast-KAN, Jacobi-KAN, Deep-KAN, and Chebyshev-KAN, against models like Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRU). …”
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Coupling Beams’ Shear Capacity Prediction by Hybrid Support Vector Regression and Particle Swarm Optimization by Emad A. Abood, Mustafa Kamal Al-Kamal, Sabih Hashim Muhodir, Nadia Moneem Al-Abdaly, Luís Filipe Almeida Bernardo, Dario De Domenico, Hamza Imran

“…SVR is a distinguished machine learning regression method that has been positively utilized in former works to forecast the performance of several structural members. …”
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Multimodal Zero-Shot Shelf Deformation Detection Based on MEMS Sensors and Images by Hong Yan, Jingjing Fan, Yajun Liu

“…To address the issue of a lack of negative samples in the dataset, the study employs oversampling techniques, including SMOTE, ADASYN, and Borderline-SMOTE, combined with machine learning models such as Random Forest and Gradient Boosting Decision Trees (GBDT). …”
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Satisfaction with Nighttime Outdoor Activity Environment Among Elderly Residents in Old Gated Communities by Fang Wen, Liang Peng, Bo Zhang, Yan Zhang, Yuyang Zhang

“…Taking “satisfaction with the environment for NOAs”, “the biggest environmental problem with NOAs”, and “locations that need improved lighting” as dependent variables, we used machine learning to calculate the contributions of various influencing factors on the dependent variables. …”
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Robust self management classification via sparse representation based discriminative model for mild cognitive impairment associated with diabetes mellitus by Yun-xian Wang, Rong Lin, Hao Liang, Yuan-jiao Yan, Ji-xing Liang, Ming-feng Chen, Hong Li

“…Specifically, an L1-minimization sparse representation model, an efficient machine learning model, is used to obtain the sparse histogram that encodes the identity of the test sample. …”
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Transfer Learning-Empowered Physical Layer Security in Aerial Reconfigurable Intelligent Surfaces-Based Mobile Networks by Yosefine Triwidyastuti, Tri Nhu Do, Ridho Hendra Yoga Perdana, Kyusung Shim, Beongku An

“…Additionally, we employ Artificial Intelligence (AI) and Machine Learning (ML) techniques, specifically Deep Neural Networks (DNN), for performance prediction of PHY security metrics. …”
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Evaluating the Accuracy of the ERA5 Model in Predicting Wind Speeds Across Coastal and Offshore Regions by Mohamad Alkhalidi, Abdullah Al-Dabbous, Shoug Al-Dabbous, Dalal Alzaid

“…Enhancements such as localized calibration using high-resolution datasets, hybrid models incorporating machine learning techniques, and long-term monitoring networks are recommended to improve accuracy. …”
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Relating satellite NO2 tropospheric columns to near-surface concentrations: implications from ground-based MAX-DOAS NO2 vertical profile observations by Bowen Chang, Haoran Liu, Chengxin Zhang, Chengzhi Xing, Wei Tan, Cheng Liu

“…Abstract Given the significant environmental and health risks associated with near-surface nitrogen dioxide (NO2), machine learning is frequently employed to estimate near-surface NO2 concentrations (SNO2) from satellite-derived tropospheric NO2 column densities (CNO2). …”
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Delayered IC image analysis with template‐based Tanimoto Convolution and Morphological Decision by Deruo Cheng, Yiqiong Shi, Tong Lin, Bah‐Hwee Gwee, Kar‐Ann Toh

“…Abstract Supervised machine learning techniques are being pursued for delayered Integrated Circuit (IC) image analysis. …”
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Evolution of artificial intelligence in healthcare: a 30-year bibliometric study by Yaojue Xie, Yuansheng Zhai, Yuansheng Zhai, Guihua Lu, Guihua Lu

“…IntroductionIn recent years, the development of artificial intelligence (AI) technologies, including machine learning, deep learning, and large language models, has significantly supported clinical work. …”
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A Disentangled Representation-Based Multimodal Fusion Framework Integrating Pathomics and Radiomics for KRAS Mutation Detection in Colorectal Cancer by Zhilong Lv, Rui Yan, Yuexiao Lin, Lin Gao, Fa Zhang, Ying Wang

“…Recently, the advancement of machine learning, especially deep learning, has greatly promoted the development of KRAS mutation detection from tumor phenotype data, such as pathology slides or radiology images. …”
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Cognitive load detection through EEG lead wise feature optimization and ensemble classification by Jammisetty Yedukondalu, Kalyani Sunkara, Vankayalapati Radhika, Sivakrishna Kondaveeti, Murali Anumothu, Yadadavalli Murali Krishna

“…Using six optimized machine learning (ML) classifiers, we conducted an exhaustive study that encompassed both lead-wise and overall feature classification. …”
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Real-Time Overshoot and Undershoot Detection in Cellular Networks by Jose Antonio Trujillo, Rasmus Lykke, Isabel de-la-Bandera, Soren Sondergaard, Troels B. Sonrensen, Raquel Barco, Preben E. Mogensen

“…To achieve this goal, KPI (Key Performance Indicators) are analyzed using machine learning techniques. Given the difficulty of detecting coverage problems in mobile networks, the results obtained suggest that the methodology provides a consistent knowledge base for optimizing the antenna tilt, thereby improving network performance.…”
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Intelligent deep federated learning model for enhancing security in internet of things enabled edge computing environment by Nasser Nammas Albogami

“…The promptly developing IoT-connected devices below an integrated Machine Learning (ML) method might threaten data confidentiality. …”
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Personalized Federated Learning for Heterogeneous Residential Load Forecasting by Xiaodong Qu, Chengcheng Guan, Gang Xie, Zhiyi Tian, Keshav Sood, Chaoli Sun, Lei Cui

“…As a novel distributed machine learning (ML) technique, it only exchanges model parameters without sharing raw data. …”
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Network Diffusion Framework to Simulate Spreading Processes in Complex Networks by Michał Czuba, Mateusz Nurek, Damian Serwata, Yu-Xuan Qiu, Mingshan Jia, Katarzyna Musial, Radosław Michalski, Piotr Bródka

“…This results in a significant proliferation of tools used by researchers and, consequently, a lack of a universally accepted technological stack that would standardise experimental methods (as seen, e.g., in machine learning). This article addresses that issue by presenting an extended version of the Network Diffusion library. …”
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MagNet—A Data‐Science Competition to Predict Disturbance Storm‐Time Index (Dst) From Solar Wind Data by Manoj Nair, Rob Redmon, Li‐Yin Young, Arnaud Chulliat, Belinda Trotta, Christine Chung, Greg Lipstein, Isaac Slavitt

“…However, while the recent Machine‐Learning (ML) models generally perform better than other approaches, many are unsuitable for operational use. …”
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