A Novel LSTM Architecture for Automatic Modulation Recognition: Comparative Analysis With Conventional Machine Learning and RNN-Based Approaches by Sam Ansari, Soliman Mahmoud, Sohaib Majzoub, Eqab Almajali, Anwar Jarndal, Talal Bonny

“…Experimental results demonstrate that the model achieves a recognition accuracy of 99.87% at an SNR of -5 dB, outperforming several conventional machine learning techniques, including multi-layer perceptron (MLP), radial basis function (RBF) networks, adaptive neuro-fuzzy inference systems (ANFIS), decision trees (DT), naïve Bayes (NB), support vector machines (SVM), probabilistic neural networks (PNN), k-nearest neighbors (KNN), and ensemble learning models, as well as recurrent neural networks (RNNs). …”
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Designing Laves-phase RFe2-type alloy with excellent magnetostrictive performance by physics-informed interpretable machine learning by Pengqiang Hu, Chao Zhou, Ruisheng Zhang, Sidan Ding, Yuanjun Guo, Bo Wang, Dezhen Xue, Yizhe Ma, Zhiyong Dai, Yin Zhang, Fanghua Tian, Sen Yang

“…By comparing different models, the XGBoost (XGB) regression model is selected to predict magnetostriction of quaternary TbxDy1-xFeyV2-y alloys. …”
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A Modified Support Vector Machine Classifiers Using Stochastic Gradient Descent with Application to Leukemia Cancer Type Dataset by Ghadeer Jasim Mahdi

“…Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. …”
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Seleksi Fitur dengan Particle Swarm Optimization pada Klasifikasi Penyakit Parkinson Menggunakan XGBoost by Deni Kurnia, Muhammad Itqan Mazdadi, Dwi Kartini, Radityo Adi Nugroho, Friska Abadi

“…Selain itu model juga akan diterapkan SMOTE untuk mengatasi masalah ketidakseimbangan kelas data dan hyperparameter tuning pada XGBoost untuk mendapatkan hyperparameter yang optimal. …”
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Prediction of post-irradiation swelling rate of 316L stainless steel based on Variational Autoencoders and interpretable machine learning by Chengcheng Liu, Hang Su

“…By comparing various machine learning models, it was found that the Extreme Trees Regression (ETR) model performed best on the test set, achieving an R2 of 0.79 and a Root Mean Square Error (RMSE) of 1.65 %. …”
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Improving air quality prediction using hybrid BPSO with BWAO for feature selection and hyperparameters optimization by Mohamed S. Sawah, Hela Elmannai, Alaa A. El-Bary, Kh. Lotfy, Osama E. Sheta

“…Machine learning models, including Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), and Linear Regression (LR), were evaluated before and after feature selection. …”
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A comparative assessment of causal machine learning and traditional methods for enhancing supply chain resiliency and efficiency in the automotive industry by Ishansh Gupta, Adriana Martinez, Sergio Correa, Hendro Wicaksono

“…This study presents a comparative analysis of decision-making strategies for supplier escalation, evaluating causal machine learning (CML), traditional machine learning (ML), and current escalation practices in a leading German automotive company. …”
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Detection of Cumulative Bruising in Prunes Using Vis–NIR Spectroscopy and Machine Learning: A Nonlinear Spectral Response Approach by Lisi Lai, Hui Zhang, Jiahui Gu, Long Wen

“…Spectral data were collected from the equatorial region of each fruit and processed using a hybrid modeling framework comprising continuous wavelet transform (CWT) for spectral enhancement, uninformative variable elimination (UVE) for optimal wavelength selection, and support vector machine (SVM) for classification. …”
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Comparing the effect of pre-anesthesia clonidine and tranexamic acid on intraoperative bleeding volume in rhinoplasty: a machine learning approach by Zahra Asghari Varzaneh, Akram Hemmatipour, Hadi Kazemi-Arpanahi

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Machine-based morphologic analysis of glioblastoma using whole-slide pathology images uncovers clinically relevant molecular correlates. by Jun Kong, Lee A D Cooper, Fusheng Wang, Jingjing Gao, George Teodoro, Lisa Scarpace, Tom Mikkelsen, Matthew J Schniederjan, Carlos S Moreno, Joel H Saltz, Daniel J Brat

“…For each nucleus, a Nuclear Score (NS) was calculated based on the degree of oligodendroglioma appearance, using a regression model trained from the optimal feature set. Using the frequencies of neoplastic nuclei in low and high NS intervals, we were able to cluster patients into three well-separated disease groups that contained low, medium, or high Oligodendroglioma Component (OC). …”
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A novel two stage neighborhood search for flexible job shop scheduling problem considering reconfigurable machine tools by Yanjun Shi, Chengjia Yu, Shiduo Ning

“…Initially, a mixed-integer linear programming (MILP) model is formulated to comprehensively represent the problem. …”
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Optimizing Chlorophyll-a Concentration Inversion in Coastal Waters Using SVD and Deep Learning Approach by Lili Zhan, Yongxin Xu, Jinshan Zhu, Zhangshuo Liu

“…Other machine learning methods, such as random forest (RF) and the support vector machine (SVM) are also used to establish the inversion models for the comparison. …”
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Exploration of Key Factors in the Preparation of Highly Hydrophobic Silica Aerogel from Rice Husk Ash Assisted by Machine Learning by Yun Deng, Ziyan Sha, Xingxing Wang, Ke Duan, Weijie Xue, Ian Beadham, Xiaolan Xiao, Changbo Zhang

“…To expand the applications of hydrophobic silica aerogels derived from rice husk ash (HSA) through simple traditional methods (without adding special materials or processes), this paper employs machine learning to establish mathematical models to identify optimal conditions for extracting water glass and investigates how preparation conditions and heat treatment temperatures affect properties such as the porosity and hydrophobicity of HSA. …”
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Development of hybrid aluminum nanocomposites for automotive applications: An in-depth analysis using experimental approaches and predictive machine learning techniques by Chitti Babu Golla, R. Narasimha Rao, Syed Ismail, Mutlu Özcan, P. Syam Prasad

“…This study integrates experimental characterization and machine learning (ML) to predict wear behavior and optimize composite design. …”
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Machine Learning-Based Classification of Anterior Circulation Cerebral Infarction Using Computational Fluid Dynamics and CT Perfusion Metrics by Xulong Yin, Yusheng Zhao, Fuping Huang, Hui Wang, Qi Fang

“…The classification performance of six machine learning models was evaluated using ROC and PR curves. …”
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Advancing soil mapping and management using geostatistics and integrated machine learning and remote sensing techniques: a synoptic review by Sunshine A. De Caires, Chaney St Martin, Melissa A. Atwell, Fuat Kaya, Glorious A. Wuddivira, Mark N. Wuddivira

“…Emphasis was placed on hybrid approaches that fuse geostatistics with ML algorithms including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), along with the enrichment of spatial models using RS data. …”
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Study of Spatial and Temporal Characteristics and Influencing Factors of Net Carbon Emissions in Hubei Province Based on Interpretable Machine Learning by Junyi Zhao, Bingyao Jia, Jing Wu, Xiaolu Wu

“…This study constructed a precise 1 km resolution net carbon emissions map of Hubei Province, China (2000–2020), and compared the ten distinct machine learning models to identify the most effective model for revealing the relationship between carbon emissions and their influencing factors. …”
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Spatial Evolution and Driving Mechanisms of Vegetation Cover in China’s Greater Khingan Mountains Based on Explainable Geospatial Machine Learning by Zihao Wang, Bing Wang, Qiuliang Zhang, Changwei Lü

“…The key findings reveal that (1) from 2001 to 2022, FVC showed an increasing trend, confirming the effectiveness of ecological restoration. (2) The XGeoML model successfully revealed nonlinear relationships and threshold effects between driving factors and FVC. …”
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Deploying machine learning for long-term road pavement moisture prediction: A case study from Queensland, Australia by Ayesh Dushmantha, Ruixuan Zhang, Yilin Gui, Jinjiang Zhong, Chaminda Gallage

“…Addressing this gap, the present study employs five traditional machine learning (ML) algorithms, K-nearest neighbors (KNN), regression trees, random forest, support vector machines (SVMs), and gaussian process regression (GPR), to forecast moisture levels within pavement layers over time, with varying algorithm complexities. …”
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Hybrid LSTM–Transformer Architecture with Multi-Scale Feature Fusion for High-Accuracy Gold Futures Price Forecasting by Yali Zhao, Yingying Guo, Xuecheng Wang

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