Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification by R. Rajesh Sharma, P. Marikkannu

“…Support vector machines, over backpropagation network, and k-nearest neighbor are used to evaluate the goodness of classifier approach. …”
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Machine learning to predict penumbra core mismatch in acute ischemic stroke using clinical note data by Shaun Kohli, Parul Agarwal, “Andy” Ho Wing Chan, Asala Erekat, Girish Nadkarni, Benjamin Kummer

“…We identified 120 patients; an extreme-gradient-boosting model classified P:C ratios as ≥ or <1.8, achieving an AUROC of 0.80 (95% CI 0.57–0.92) with optimal performance using text limited to 500 characters. …”
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Precision Measurement and Feature Selection in Medical Diagnostics using Hybrid Genetic Algorithm and Support Vector Machine by Gowri Subadra K, Sathish Babu P

“…BoM is then used to select the optimal classification model from a set of candidates, to further improve the accuracy of diagnosis. …”
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Spike-RISC: Algorithm/ISA Co-Optimization for Efficient SNNs on RISC-V by Ipek Akdeniz, Sandy A. Wasif, Paul R. Genssler, Hussam Amrouch

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Machine learning: enhanced dynamic clustering for privacy preservation and malicious node detection in industrial internet of things by Nabeela Hasan, Saima Saleem, Mudassir Khan, Abdulatif Alabdultif, Mohammad Mazhar Nezami, Mansaf Alam

“…This research introduces ML-DCPP, a Machine Learning-based Dynamic Clustering and Privacy Preservation framework tailored to safeguard IIoT ecosystems. …”
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On-demand prediction of low-frequency average sound absorption coefficient of underwater coating using machine learning by Nansha Gao, Mou Wang, Xiao Liang, Guang Pan

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Klasifikasi Sentimen Ulasan Turis Terhadap Objek Wisata di Bali Menggunakan Support Vector Machine by Raudhah Raudhah, Fajri Profesio Putra

“…Temuan ini menunjukkan bahwa model bekerja lebih optimal pada data dengan distribusi sentimen yang seimbang. …”
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Machine learning based obesity and aging related signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma by Yongheng Chen, Xiaoxia Yu, Wencan Xu, Anqi Huang, Zhengbing Li

“…Results The predictive model developed using the LASSO method achieved the highest average C-index and was identified as the optimal ORS. …”
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Research on energy optimization control strategy for parallel hybrid tractor based on AIPSO. by Xiaohui Liu, Yiwei Wu, Jingyun Zhang, Yifan Zhao, Yangming Hu, Xianghai Yan

“…The study includes the design of power system parameters and the construction of a dynamic model for the entire machine. Based on this, an energy optimization control strategy, termed adaptive immune particle swarm optimization fuzzy control strategy (AIPSOFCS), is proposed. …”
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Physical Health Portrait and Intervention Strategy of College Students Based on Multivariate Cluster Analysis and Machine Learning by Rong Guo, Rou Dong, Ni Lu, Lin Yu, Chaoxian Chen, Yonglin Che, Jiajin Zhang, Jianke Yang

“…This study employs an improved K-means algorithm integrated with machine learning models to analyze university students’ fitness data and develop personalized health intervention strategies. …”
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Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory by Sergei Manzhos, Johann Lüder, Pavlo Golub, Manabu Ihara

“…Machine learning (ML) of kinetic energy functionals (KEF) for orbital-free density functional theory (DFT) holds the promise of addressing an important bottleneck in large-scale ab initio materials modeling where sufficiently accurate analytic KEFs are lacking. …”
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Machine learning-assisted high-throughput prediction and experimental validation of high-responsivity extreme ultraviolet detectors by R. A. W. Ayyubi, Mei Xian Low, Salar Salimi, Majid Khorsandi, M. Mosarof Hossain, Hurriyat Arooj, Shoaib Masood, M. Husnain Zeb, Nasir Mahmood, Qiaoliang Bao, Sumeet Walia, Babar Shabbir

“…Machine learning models often depend on extensive datasets, which are typically lacking in specialized research domains such as extreme ultraviolet (EUV) radiation detection. …”
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Facilitating automated fact-checking: a machine learning based weighted ensemble technique for claim detection by Md. Rashadur Rahman, Rezaul Karim, Mohammad Shamsul Arefin, Pranab Kumar Dhar, Gahangir Hossain, Tetsuya Shimamura

“…An annotated text dataset comprising 5010 sentences sourced from various online platforms, including several online fact-checking sites, was developed. To determine the optimal model and feature representation for claim detection, various machine learning algorithms were evaluated using BoW, TF-IDF, Word2Vec, and FastText features. …”
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Prediction of Selenium-Enriched Crop Zones in Xiaoyan Town Using Fuzzy Logic and Machine Learning Approaches by Jiacheng Li, Shuyun Xie, Wenbing Yang, Weihang Zhou, Emmanuel John M. Carranza, Weiji Wen, Hongtao Shi

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AI-Driven Signal Processing for SF6 Circuit Breaker Performance Optimization by Philippe A. V. D. Liz, Giovani B. Vitor, Ricardo T. Lima, Aurélio L. M. Coelho, Eben P. Silveira

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Optimizing Inventory Costs in Manufacturing Systems Using Trapezoidal and Pentagonal Fuzzy Numbers by Prasantha Bharathi Dhandapani, Kalaiarasi Kalaichelvan, Soundaria Ramalingam, Mohamed Biomy, Taha Radwan

“…The nonlinear programming Lagrangian method is used to determine the best solution, which affects the average monthly cost. In the suggested model, the graded mean integration representation method is used to describe defuzzification while trapezoidal and pentagonal fuzzy numbers are used to calculate the optimal cost though there are different types of fuzzy numbers available that are used to test the optimality. …”
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Automatic priority analysis of emergency response systems using internet of things (IoT) and machine learning (ML) by Abu S.M. Mohsin, Shadab H. Choudhury, Munyem Ahammad Muyeed

“…ABSTRACT: Effective and timely resource deployment is essential during emergencies. By integrating machine learning (ML) and the Internet of Things (IoT), automatic priority analysis of emergency response systems could revolutionise this vital process, save life and minimize damages. …”
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In vitro machine learning-based CAR T immunological synapse quality measurements correlate with patient clinical outcomes. by Alireza Naghizadeh, Wei-Chung Tsao, Jong Hyun Cho, Hongye Xu, Mohab Mohamed, Dali Li, Wei Xiong, Dimitri Metaxas, Carlos A Ramos, Dongfang Liu

“…To improve their efficacy and expand their applicability to solid tumors, scientists optimize different CARs with different modifications. …”
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Dynamical patterns of EEG connectivity unveil Parkinson’s disease progression: insights from machine learning analysis by Caroline L Alves, Loriz Francisco Sallum, Francisco Aparecido Rodrigues, Thaise G L de O Toutain, Patrícia Maria de Carvalho Aguiar, Michael Moeckel

“…Functional connectivity matrices were constructed using multiple metrics, including coherence, Pearson, Spearman, canonical correlation, and Ledoit–Wolf shrinkage. Machine learning models were applied for both binary (PD vs HC) and multiclass (HC vs early vs advanced PD) classification. …”
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Predicting overall survival in glioblastoma patients using machine learning: an analysis of treatment efficacy and patient prognosis by Razvan Onciul, Razvan Onciul, Felix-Mircea Brehar, Felix-Mircea Brehar, Adrian Vasile Dumitru, Adrian Vasile Dumitru, Carla Crivoi, Razvan-Adrian Covache-Busuioc, Razvan-Adrian Covache-Busuioc, Matei Serban, Matei Serban, Petrinel Mugurel Radoi, Petrinel Mugurel Radoi, Corneliu Toader, Corneliu Toader

“…Six machine learning models—XGBoost, Random Forests, Support Vector Machines, Artificial Neural Networks, Extra Trees Regressor, and K- Nearest Neighbors—were employed to classify patients into predefined survival categories. …”
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