Artificial intelligence models in corporate financial and accounting processes: systematic literature review by Deivi David Fuentes Doria, Aníbal Toscano Hernández, Johana Elisa Fajardo Pereira

“…The results suggest that supervised models are the most applied in the accounting and financial field, while the algorithms that have been most used are decision trees, support vector machines, random forests, neural networks, and logistic regressions, employed in specific areas of financial fraud, stock market predictions, and cash flow. …”
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Enhanced Spring Wheat Soil Plant Analysis Development (SPAD) Estimation in Hetao Irrigation District: Integrating Leaf Area Index (LAI) Under Variable Irrigation Conditions by Qiang Wu, Dingyi Hou, Min Xie, Qi Gao, Mengyuan Li, Shuiyuan Hao, Chao Cui, Keke Fan, Yu Zhang, Yongping Zhang

“…This study evaluated three machine learning algorithms (Random Forest, Support Vector Regression, and Multi-Layer Perceptron) for SPAD estimation in spring wheat cultivated in the Hetao Irrigation District. …”
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Data Mining Techniques for Early Detection and Classification of Plant Diseases: An Optimization-Based Approach by Wagh Swapnil, Sharma Ruchi

“…The model proposed uses the state-of-art algorithms including the decision trees, support vector machines and the deep learning techniques in the feature extraction and pattern recognition as well as binary classification. …”
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CT-based radiomics deep learning signatures for non-invasive prediction of metastatic potential in pheochromocytoma and paraganglioma: a multicohort study by Yongjie Zhou, Yuan Zhan, Jinhong Zhao, Linhua Zhong, Fei Zou, Xuechao Zhu, Qiao Zeng, Jiayu Nan, Lianggeng Gong, Yongming Tan, Lan Liu

“…Optimal models were selected based on receiver operating characteristic analysis and integrated with radiological features to develop a combined model, which was evaluated on external datasets, and explored prognostic information. Results The support vector machine radiomics and 2D ResNet-50 models demonstrated good performance. …”
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Development of a robust FT-IR typing system for Salmonella enterica, enhancing performance through hierarchical classification by Diego Fredes-García, Javiera Jiménez-Rodríguez, Alejandro Piña-Iturbe, Pablo Caballero-Díaz, Tamara González-Villarroel, Fernando Dueñas, Aniela Wozniak, Aiko D. Adell, Andrea I. Moreno-Switt, Patricia García

“…The IR Biotyper was used to acquire spectra from these isolates. Machine learning algorithms, including support vector machines, were trained to classify the isolates. …”
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Streamlining Thoracic Radiotherapy Quality assurance: One-Class Classification for Automated OAR Contour Assessment by Yihao Zhao BSc, Cuiyun Yuan MSc, Ying Liang PhD, Yang Li MSc, Chunxia Li MSc, Man Zhao MSc, Jun Hu PhD, Ningze Zhong Bsc, Wei Liu PhD, Chenbin Liu PhD

“…A ResNet-152 network was used as a feature extractor, and a one-class support vector machine (OC-SVM) was employed to classify contours as ‘high’ or ‘low’ quality. …”
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Revolutionizing pharmacology: AI-powered approaches in molecular modeling and ADMET prediction by Irfan Pathan, Arif Raza, Adarsh Sahu, Mohit Joshi, Yamini Sahu, Yash Patil, Mohammad Adnan Raza, Ajazuddin

“…It outlines the evolution of computational chemistry and the transformative role of AI in interpreting complex molecular data, automating feature extraction, and improving decision-making across the drug development pipeline. Core AI algorithms support vector machines, random forests, graph neural networks, and transformers are examined for their applications in molecular representation, virtual screening, and ADMET property prediction. …”
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Artificial intelligence tools for engagement prediction in neuromotor disorder patients during rehabilitation by Simone Costantini, Anna Falivene, Mattia Chiappini, Giorgia Malerba, Carla Dei, Silvia Bellazzecca, Fabio A. Storm, Giuseppe Andreoni, Emilia Ambrosini, Emilia Biffi

“…Additionally, feature reduction did not yield any advantages, while data augmentation consistently enhanced classifiers performance. Support Vector Machine and Extreme Gradient Boosting models were found to be the most effective architectures for predicting self-perceived engagement and therapist-perceived engagement, with a macro-averaged F1 score of 95.6% and 95.4%, respectively. …”
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NDVI estimation using Sentinel-1 data over wheat fields in a semiarid Mediterranean region by Emna Ayari, Zeineb Kassouk, Zohra Lili-Chabaane, Nadia Ouaadi, Nicolas Baghdadi, Mehrez Zribi

“…To estimate the NDVI values, different methods are used: curve-fitting equations and machine learning regressors such as the random forest (RF) and the support vector regressor (SVR). …”
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AI in Medical Questionnaires: Innovations, Diagnosis, and Implications by Xuexing Luo, Yiyuan Li, Jing Xu, Zhong Zheng, Fangtian Ying, Guanghui Huang

“…Overall, 24 AI technologies were identified, covering traditional algorithms such as random forest, support vector machine, and k-nearest neighbor, as well as deep learning models such as convolutional neural networks, Bidirectional Encoder Representations From Transformers, and ChatGPT. …”
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Investigating Reddit Data on Type 2 Diabetes Management During the COVID-19 Pandemic Using Latent Dirichlet Allocation Topic Modeling and Valence Aware Dictionary for Sentiment Rea... by Meghan Nagpal, Niloofar Jalali, Diana Sherifali, Plinio Morita, Joseph A Cafazzo

“…MethodsData from Reddit forums related to T2D, from January 2018 to early March 2021, were downloaded using the Pushshift API; support vector machines were used to classify whether a post was made in the context of the pandemic. …”
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Classification of left and right-hand motor imagery in acute stroke patients using EEG microstate by Shiyang Lv, Xiangying Ran, Mengsheng Xia, Yehong Zhang, Ting Pang, Xuezhi Zhou, Zongya Zhao, Yi Yu, Zhixian Gao

“…Significant features were used to construct classification models using Linear Discriminant Analysis(LDA), Support Vector Machines(SVM), and K-Nearest Neighbors(KNN) algorithms. …”
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Prediction of the volume of shallow landslides due to rainfall using data-driven models by J. Tuganishuri, C.-Y. Yune, G. Kim, S. W. Lee, M. D. Adhikari, S.-G. Yum

“…The objectives of this research are to construct a model using advanced data-driven algorithms (i.e., ordinary least squares or linear regression (OLS), random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), generalized linear model (GLM), decision tree (DT), deep neural network (DNN), <span class="inline-formula"><i>k</i></span>-nearest-neighbor (KNN), and ridge regression (RR) algorithms) for the prediction of the volume of landslides due to rainfall, considering geological, geomorphological, and environmental conditions. …”
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Rice seed integrity evaluation: Developing a rapid onsite system to check seed fraud using a portable NIR spectroscopic device coupled with smartphone technology by Ernest Teye, Charles Lloyd Yeboah Amuah, Vida Gyimah Boadu, Kwadwo Anokye Dompreh, Maxwell Darko Asante, Francis Padi Lamptey, Stephen Narh, Daniel Dzorkpe Gamenyah, George Oduro Nkansah, Selorm Akaba

“…Support Vector Machine (SVM) demonstrated 98.15 % accuracy in distinguishing between rice seed and paddy for detecting seed fraud. …”
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A Nomogram for Predicting Recurrence in Stage I Non‐Small Cell Lung Cancer by Rongrong Bian, Feng Zhao, Bo Peng, Jin Zhang, Qixing Mao, Lin Wang, Qiang Chen

“…In the discovery phase, two algorithms, least absolute shrinkage and selector operation and support vector machine‐recursive feature elimination, were used to identify candidate genes. …”
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Bioinformatics&amp;#x2011;Based Analysis Reveals Diagnostic Biomarkers and Immune Landscape in Atopic Dermatitis by Yang M, Zhang X, Zhou C, Du Y, Zhou M, Zhang W

“…Least Absolute Shrinkage and Selection Operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to screen hub genes. …”
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The Impact Prediction of Income Tax Standards on Company Performance: A Hybrid Spatial Artificial Intelligence Approach by Sawsan Kareem Abdullah

“…In this study, various artificial intelligence methods such as artificial neural networks, support vector machines, deep learning, decision trees, random forests, and genetic algorithms were used in combination with spatial modeling. …”
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A Fast and Cost-Effective Electronic Nose Model for Methanol Detection Using Ensemble Learning by Bilge Han Tozlu

“…A Voting Classifier, an ensemble model, was used with Linear Discriminant Analysis, Support Vector Machines, and Extra Trees algorithms. …”
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Comparative Analysis of Diabetes Prediction Models Using the Pima Indian Diabetes Database by Zhao Yize

“…In comparison, the random forest model, which builds multiple decision trees (DT) to do their predictions, demonstrates superior performance over several widely used algorithms such as K-Nearest Neighbours (KNN), Logistic Regression (LR), DT, Support Vector Machines (SVM), and Gradient Boosting (GB). …”
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Shipyard Manpower Digital Recruitment: A Data-Driven Approach for Norwegian Stakeholders by Bogdan Florian Socoliuc, Andrei Alexandru Suciu, Mădălina Ecaterina Popescu, Doru Alexandru Plesea, Florin Nicolae

“…Additionally, pre-vetting candidates enhances hiring precision, achieving a CAR of 90% and reducing mismatches. The application of machine learning algorithms provides predictive insights that support real-time adjustments to job postings, optimizing recruitment strategies. …”
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