Improving Balanced Accuracy for Minority Plant Species under Data Imbalance by Ruben Gonzalez-Villanueva, Jose Carranza-Rojas

“…Regardless of the widely known success of deep learning in classification, such models are commonly measured by metrics that do not account for data imbalance, especially in terms of predictions per class, ignoring minority classes. This can be a problem, as minority classes are often the most difficult to predict and collect data for. …”
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Future Era of Accountants under the Impact of AI by Guan Ran

“…As AI reshapes the industry, accountants must develop expertise in data analytics, predictive modeling, and cybersecurity to remain competitive. …”
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Digital augmentation of aftercare for patients with anorexia nervosa: the TRIANGLE RCT and economic evaluation by Janet Treasure, Katie Rowlands, Valentina Cardi, Suman Ambwani, David McDaid, Jodie Lord, Danielle Clark Bryan, Pamela Macdonald, Eva Bonin, Ulrike Schmidt, Jon Arcelus, Amy Harrison, Sabine Landau

“…As there were considerable missing values in outcome variables and non-adherence with ECHOMANTRA predicted later dropout from data collection, multiple imputation (MI) was used to adjust for missing data biases. …”
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An Analysis of Semi-Supervised Machine Learning in Electrical Machines by V. Raju Arvind, S. Shyamsharan, Poorvajaa Gurunathan, Krishna Kumba, Nawin Ra

“…The research investigates important SSML algorithms such as self-training, co-training, generative models, and graph-based methods, highlighting their particular uses in fault diagnosis, condition monitoring, and predictive maintenance of electrical machines. …”
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Exploration of potential biomarkers and immune cell infiltration characteristics for peripheral atherosclerosis in sjögren’s syndrome based on comprehensive bioinformatics analysis... by Chunjiang Liu, Yuan Wang, Lina Zhou, Feifei Cai, Xiaoqi Tang, Liying Wang, Xiang Zhang

“…Subsequently, machine learning algorithms and protein-protein interaction (PPI) network analysis were employed to further investigate potential predictive genes. …”
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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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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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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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Effortless Student Attendance: A Smart Human-Computer Interactive System Using Real Time Facial Recognition by Ahmad S. Lateef, Mohammed Y. Kamil

“…Result: The system demonstrated accuracy of up to 100%, with deep learning algorithms outperforming machine learning methods. Conclusion: These promising results suggest that face recognition technology can effectively streamline and automate attendance tracking, offering a viable solution for educational institutions seeking to improve operational efficiency and accuracy…”
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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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