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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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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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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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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A privacy-enhanced framework with deep learning for botnet detection by Guangli Wu, Xingyue Wang

“…And most methods are combined with machine learning and deep learning technologies, which require a large amount of training data to obtain high-precision detection models. …”
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Circulating microRNA panels for multi-cancer detection and gastric cancer screening: leveraging a network biology approach by Leila Kamkar, Samaneh Saberi, Mehdi Totonchi, Kaveh Kavousi

“…Subsequently, for specific cancer screening, gastric cancer was focused on, using a similar strategy and a further step of preservation analysis. Machine learning techniques were then applied to evaluate two distinct miRNA panels: one for multi-cancer screening and another for gastric cancer classification. …”
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A data ensemble-based approach for detecting vocal disorders using replicated acoustic biomarkers from electroglottography by Lizbeth Naranjo, Carlos J. Pérez, Daniel F. Merino

“…Future research should focus on collecting comprehensive EGG databases and further exploring multi-class classification methods to solidify EGG and machine learning as a valuable tool for non-invasive assessment of laryngeal function.…”
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Hybrid Method for Point Cloud Classification by Abdurrahman Hazer, Remzi Yildirim

“…This hybrid approach effectively combines the Residual Machine Learning Perceptron (ResMLP) with the POL mechanism to capture both local and global features of 3D point clouds. …”
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Flexible and cost-effective deep learning for accelerated multi-parametric relaxometry using phase-cycled bSSFP by Florian Birk, Lucas Mahler, Julius Steiglechner, Qi Wang, Klaus Scheffler, Rahel Heule

“…This work emphasizes the advantages of in silico DNN MP-qMRI pipelines for rapid data generation and DNN training without extensive dictionary generation, long parameter inference times, or prolonged data acquisition, highlighting the flexible and rapid nature of lightweight machine learning applications for MP-qMRI.…”
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Optimizing Hydrogen Production in the Co-Gasification Process: Comparison of Explainable Regression Models Using Shapley Additive Explanations by Thavavel Vaiyapuri

“…However, optimizing this complex process to maximize hydrogen yield remains challenging, particularly when balancing diverse feedstocks and improving process efficiency. While machine learning (ML) has shown significant potential in simulating and optimizing such processes, there is no clear consensus on the most effective regression models for co-gasification, especially with limited experimental data. …”
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Application of a spatial dataset for monitoring invasive woody plant species in the forests of Transcarpathia, Ukraine by Andriy Mihaly, Vasyl Roman

“…The spatial dataset can also be utilised as a source of training samples for machine learning, which is involved in the processing of satellite images to identify new habitats of invasive woody plant species.…”
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Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues by Robel Alemu, Nigussie T. Sharew, Yodit Y. Arsano, Muktar Ahmed, Fasil Tekola-Ayele, Tesfaye B. Mersha, Azmeraw T. Amare

“…We highlight the need for standardized protocols, harmonized data-sharing policies, and advanced approaches such as artificial intelligence/machine learning to integrate multi-omics data and study gene-environment interactions. …”
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Computational antidiabetic assessment of Salvia splendens L. polyphenols: SMOTE, ADME, ProTox, docking, and molecular dynamic studies by Hatun A. Alomar, Wafaa M. El Kady, Asmaa A. Mandour, Amany A. Naim, Neveen I. Ghali, Taghreed A. Ibrahim, Noha Fathallah

“…This study utilizes artificial intelligence and machine learning to enhance drug discovery, focusing on the antidiabetic effects of Salvia splendens leaf extract among the global epidemic of diabetes mellitus. …”
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