MRI quantified enlarged perivascular space volumes as imaging biomarkers correlating with severity of anxiety depression in young adults with long-time mobile phone use by Li Li, Yalan Wu, Jiaojiao Wu, Bin Li, Rui Hua, Feng Shi, Lizhou Chen, Yeke Wu

“…In the current study, we aim to develop a predictive model utilizing MRI-quantified EPVS metrics and machine learning algorithms to assess the severity of anxiety and depression symptoms in patients with LTMPU.MethodsEighty-two participants with LTMPU were included, with 37 suffering from anxiety and 44 suffering from depression. …”
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From Classic to Cutting-Edge: A Near-Perfect Global Thresholding Approach with Machine Learning by Nicolae Tarbă, Costin-Anton Boiangiu, Mihai-Lucian Voncilă

“…We also compared our results with state-of-the-art binarization algorithms and outperformed them on certain datasets. …”
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Detection of breast cancer using machine learning and explainable artificial intelligence by Tharunya Arravalli, Krishnaraj Chadaga, H Muralikrishna, Niranjana Sampathila, D. Cenitta, Rajagopala Chadaga, K. S. Swathi

“…The research emphasized the results obtained by explainers such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), ELI5 (Explain Like I’m Five), Anchor and QLattice (Quantum Lattice) to decipher the findings. Interpretable algorithms can be applied in the medical sector to assist practitioners in predicting breast cancer, reducing diagnostic errors, and improving clinical decision-making.…”
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Performance of artificial neural networks and traditional methods in determining selected growth parameters of Alburnus sellal Heckel, 1843 by Ozcan Ebru Ifakat

“…In this study, predictions were made on the growth performance of Alburnus sellal Heckel, 1843 from the Munzur River using back propagation artificial neural networks and ANN algorithms. …”
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Exploring entropy measures with topological indices on colorectal cancer drugs using curvilinear regression analysis and machine learning approaches. by Maria Fazal, Salma Kanwal, Muhammad Taskeen Raza, Asima Razzaque

“…Additionally, we propose the integration of machine learning (ML) techniques to further enhance the predictive accuracy and robustness of our models. By leveraging advanced ML algorithms, we aim to uncover more complex, non-linear relationships between topological indices and drug efficacy, potentially leading to more accurate predictions and better-informed drug design strategies.…”
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Improving Bimonthly Landscape Monitoring in Morocco, North Africa, by Integrating Machine Learning with GRASS GIS by Polina Lemenkova

“…The methodology includes ML modules of GRASS GIS ‘r.learn.train’, ‘r.learn.predict’, and ‘r.random’ with algorithms of supervised classification implemented from the Scikit-Learn libraries of Python. …”
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Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study by Katarzyna Mróz, Kamil Jonak

“…<b>Methods</b>: The paper presents the application of ML algorithms, with a focus on convolutional neural networks (CNN) and recurrent neural networks (RNN), in identifying biomarkers of anxiety disorders and predicting therapy responses. …”
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Identification and analysis of neutrophil extracellular trap-related genes in periodontitis via bioinformatics and experimental verification by Miao Yu, Zhenqi Ye, Zixin Ye, Yaping Wu, Xiang Wu

“…Then, machine learning algorithms were exploited to screen hub NRGs, and a predictive model was constructed based on these hub NRGs. …”
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Enhancing Mobile App Recommendations With Crowdsourced Educational Data Using Machine Learning and Deep Learning by Naadiya Mirbahar, Kamlesh Kumar, Asif Ali Laghari

“…Although the CF techniques suffer from temporal dynamics and data sparsity, even the KNNBasic stands out among all CF algorithms with the lowest MAE of 0.548 and RMSE of 0.754, demonstrating the highest predictive accuracy.…”
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Integrating Model‐Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation by Karthik Raman, Rukmini Kumar, Cynthia J. Musante, Subha Madhavan

“…Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug–target interactions from big data, enabling more accurate predictions and novel hypothesis generation. …”
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Evaluation of the elastic modulus of pavement layers using different types of neural networks models by M. M.M. Elshamy, A. N. Tiraturyan, E. V. Uglova

“…This paper studies the capability of different types of artificial neural networks (ANN) to predict the modulus of elasticity of pavement layers for flexible asphalt pavement under operating conditions. …”
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Machine learning provides reconnaissance-type estimates of carbon dioxide storage resources in oil and gas reservoirs by Emil Attanasi, Philip Freeman, Timothy Coburn

“…We demonstrate the application of four different ML algorithms using data from onshore and offshore oil and gas reservoirs in Europe, and show they perform well when predictions are compared to engineering estimates. …”
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Constructing a machine learning model for systemic infection after kidney stone surgery based on CT values by Jiaxin Li, Yao Du, Gaoming Huang, Yawei Huang, Xiaoqing Xi, Zhenfeng Ye

“…Five machine learning algorithms and ten preoperative or intraoperative variables were used to develop a predictive model for SIRS. …”
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Congestion forecast framework based on probabilistic power flow and machine learning for smart distribution grids by Alejandro Hernandez-Matheus, Kjersti Berg, Vinicius Gadelha, Mònica Aragüés-Peñalba, Eduard Bullich-Massagué, Samuel Galceran-Arellano

“…This work proposes a framework to predict grid asset congestions on a daily basis. A congestion forecast framework is proposed by combining probabilistic power flows and machine learning algorithms to support DSOs in their daily decision-making. …”
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Machine Learning-Assisted Hartree–Fock Approach for Energy Level Calculations in the Neutral Ytterbium Atom by Kaichen Ma, Chen Yang, Junyao Zhang, Yunfei Li, Gang Jiang, Junjie Chai

“…The workflow incorporates enhanced ElasticNet and XGBoost algorithms, refined using entropy weight methodology to optimize performance. …”
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An Efficient Method for Diagnosing Brain Tumors Based on MRI Images Using Deep Convolutional Neural Networks by Thanh Han-Trong, Hinh Nguyen Van, Huong Nguyen Thi Thanh, Vu Tran Anh, Dung Nguyen Tuan, Luu Vu Dang

“…Those results of the evaluated algorithms through the coefficient F1-score are greater than 94% and the highest value is 97.65%.…”
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Machine learning insights on activities of daily living disorders in Chinese older adults by Huanting Zhang, Wenhao Zhou, Jianan He, Xingyou Liu, Jie Shen

“…Nine machine learning algorithms, including neural networks and an ensemble model, were employed with a 2/3 training and 1/3 testing split. …”
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Analysis of multiple faults in induction motor using machine learning techniques by Puja Pohakar, Ravi Gandhi, Surender Hans, Gulshan Sharma, Pitshou N. Bokoro

“…Due to their limits, machine learning algorithms outperform traditional methods in real-time fault diagnosis, predictive maintenance, and multi-fault categorization. …”
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Consumer Happiness in the Purchase of Electric Vehicles: a Fuzzy Logic Model by Fernando Lámbarry-Vilchis, Aboud Barsekh Onji, Leticia Refugio Chavarría López, Paola Judith Maldonado Colín

“…This research was conducted using a fuzzy Delphi method survey targeting a specific consumer group and two fuzzy inference systems: a multi-input single-output FIS model and an FIS Tree employing a hierarchical fuzzy inference structure, which leverages the survey's training data to optimize the models using different machine learning algorithms. The FIS tree model demonstrated superior efficacy in predicting the consumer satisfaction index, achieving an average forecast error of 0.65%. …”
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Comprehensive protein datasets and benchmarking for liquid–liquid phase separation studies by Carlos Pintado-Grima, Oriol Bárcenas, Eva Arribas-Ruiz, Valentín Iglesias, Michał Burdukiewicz, Salvador Ventura

“…Moreover, we describe limitations in classical and state-of-the-art predictive algorithms by providing the most comprehensive benchmark to date. …”
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