The Discriminative Lexicon: A Unified Computational Model for the Lexicon and Lexical Processing in Comprehension and Production Grounded Not in (De)Composition but in Linear Discr... by R. Harald Baayen, Yu-Ying Chuang, Elnaz Shafaei-Bajestan, James P. Blevins

“…The discriminative lexicon also incorporates the insight from machine learning that end-to-end modeling is much more effective than working with a cascade of models targeting individual subtasks. …”
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Ecological momentary interventions for mental health: A scoping review. by Andreas Balaskas, Stephen M Schueller, Anna L Cox, Gavin Doherty

“…Recent years have seen increased exploration of the use of sensors and machine learning, but the role of humans in the delivery of EMI is also varied. …”
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Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data by Yuju Lee, Edward L. Y. Chen, Darren C. H. Chan, Anuroopa Dinesh, Somaieh Afiuni-Zadeh, Conor Klamann, Alina Selega, Miralem Mrkonjic, Hartland W. Jackson, Kieran R. Campbell

“…To address these limitations, we present STARLING, a probabilistic machine learning model designed to quantify cell populations from spatial protein expression data while accounting for segmentation errors. …”
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RASGRF2 as a potential pathogenic gene mediating the progression of alcoholic hepatitis to alcohol-related cirrhosis and hepatocellular carcinoma by Zhengyuan Chen, Danfeng Fan, Tianyi Hang, Xiaoqing Yue

“…After screening with two machine learning algorithms, five shared genes remained. …”
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Erythrocyte modified controlling nutritional status as a biomarker for predicting poor prognosis in post-surgery breast cancer patients by Jingjing Hu, Jiaming Dong, Xiang Yang, Zhiyi Ye, Guoming Hu

“…The predictive effects of nutritional and inflammatory indicators on DFS were evaluated. Machine learning was used to evaluate and rank laboratory indicators, select relatively important variables to modify nutritional or inflammatory indicators with the best predictive power, and evaluate their predictive role in patients’ postoperative recurrence and metastasis. …”
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Hierarchical Clustering of Residential Appliances Considering the Characteristics of the Appliances by Shima Simsar, Mahmood Alborzi, Ali Rajabzadeh Ghatari, Ali Yazdian

“…In this research, an unsupervised machine learning model was proposed for the clustering of home appliances to manage the bills of customers based on their inherent characteristics. …”
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Ensemble Deep Learning Technique for Detecting MRI Brain Tumor by Rasool Fakhir Jader, Shahab Wahhab Kareem, Hoshang Qasim Awla

“…As a result, this paper concentrated on the tasks of segmentation, feature extraction, classifier building, and classification into four categories using various machine learning algorithms. The authors used VGG-16, ResNet-50, and AlexNet models based on the transfer learning algorithm for three models to classify images as an ensemble model. …”
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Source Code Error Understanding Using BERT for Multi-Label Classification by Md Faizul Ibne Amin, Yutaka Watanobe, Md Mostafizer Rahman, Atsushi Shirafuji

“…Additionally, we employed several combinations of large language models (CodeT5, CodeBERT) with machine learning classifiers (Decision Tree, Random Forest, Ensemble Learning, ML-KNN), demonstrating the superiority of our proposed approach. …”
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Robust kernel extreme learning machines for postgraduate learning performance prediction by Hongxing Gao, Tianzi Xu, Nan Zhang

“…In practice, outliers often appear during the data collection stage, and they have a significant impact on the convergence speed and prediction accuracy of machine learning models. In order to mitigate the impact of outliers, we propose a novel kernel extreme learning machine model that is robust to outliers and name it a robust kernel extreme learning machine (RK-ELM). …”
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A Novel Hybrid Model for Credit Risk Assessment of Supply Chain Finance Based on Topological Data Analysis and Graph Neural Network by Kosar Farajpour Mojdehi, Babak Amiri, Amirali Haddadi

“…Results demonstrate that the proposed BallMapper- Graph Neural Network (BM-GNN) model achieves higher accuracy and F1-scores, outperforming traditional machine learning approaches. Notably, incorporating network-based features alongside financial ratios yields the most favorable results in credit risk assessment. …”
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Optimizing Artificial Neural Network Learning Using Improved Reinforcement Learning in Artificial Bee Colony Algorithm by Taninnuch Lamjiak, Booncharoen Sirinaovakul, Siriwan Kornthongnimit, Jumpol Polvichai, Aysha Sohail

“…Artificial neural networks (ANNs) are widely used machine learning techniques with applications in various fields. …”
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Seurat function argument values in scRNA-seq data analysis: potential pitfalls and refinements for biological interpretation by Mikhail Arbatsky, Ekaterina Vasilyeva, Veronika Sysoeva, Ekaterina Semina, Ekaterina Semina, Valeri Saveliev, Kseniya Rubina

“…Here we narrow our focus down to a small set of mathematical methods applied upon standard processing of scRNA-seq data: preprocessing, dimensionality reduction, integration, and clustering (using machine learning methods for clustering). Normalization and scaling are standard manipulations for the pre-processing with LogNormalize (natural-log transformation), CLR (centered log ratio transformation), and RC (relative counts) being employed as methods for data transformation. …”
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On the implications of artificial intelligence methods for feature engineering in reliability sector with computer knowledge graph by Heling Jiang, Yongping Xia, Changjie Yu, Zhao Qu, Huaiyong Li

“…To improve operational efficiency and lower long-term maintenance costs, policy ideas include standardizing data collection techniques, investing in real-time monitoring systems, and implementing machine learning-based predictive maintenance across industries.…”
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Influence of aluminium distribution on the diffusion mechanisms and pairing of [Cu(NH3)2]+ complexes in Cu-CHA by Joachim D. Bjerregaard, Martin Votsmeier, Henrik Grönbeck

“…Abstract The performance of Cu-exchanged chabazite (Cu-CHA) for the ammonia-assisted selective catalytic reduction of NO x (NH3-SCR) depends critically on the presence of paired $${[{{{\rm{Cu}}}}{({{{{\rm{NH}}}}}_{3})}_{2}]}^{+}$$ [ Cu ( NH 3 ) 2 ] + complexes. Here, a machine-learning force field augmented with long-range Coulomb interactions is developed to investigate the effect of Al-distribution and Cu-loading on the mobility and pairing of $${[{{{\rm{Cu}}}}{({{{{\rm{NH}}}}}_{3})}_{2}]}^{+}$$ [ Cu ( NH 3 ) 2 ] + complexes. …”
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Robust Model-Based Reliability Approach to Tackle Shilling Attacks in Collaborative Filtering Recommender Systems by Santiago Alonso, Jesus Bobadilla, Fernando Ortega, Ricardo Moya

“…Nowadays, there is a growing research area focused on the design of robust machine learning methods to neutralize the malicious profiles inserted into the system. …”
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Understanding the role of the gut microbiome in solid tumor responses to immune checkpoint inhibitors for personalized therapeutic strategies: a review by Mi Young Lim, Seungpyo Hong, Young-Do Nam

“…Moreover, we review studies investigating the possibility of patient outcome prediction using machine learning models based on gut microbiome data before starting ICI therapy and clinical trials exploring whether gut microbiota modulation, for example via fecal microbiota transplantation or live biotherapeutic products, can improve results of ICI therapy in patients with cancer. …”
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Instruction and demonstration-based secure service attribute generation mechanism for textual data by LI Chenhao, WANG Na, LIU Aodi

“…Traditionally, the calibration of secure service attribute for textual data has been primarily reliant on human experts and machine learning methods, yet the efficiency and few-shot ability are insufficient. …”
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A pediatric emergency prediction model using natural language process in the pediatric emergency department by Arum Choi, Chohee Kim, Jisu Ryoo, Jangyeong Jeon, Sangyeon Cho, Dongjoon Lee, Junyeong Kim, Changhee Lee, Woori Bae

“…Various NLP models, including four machine learning (ML) models with Term Frequency-Inverse Document Frequency (TF-IDF) and two DL models based on the KM-BERT framework, were trained to differentiate emergency cases using clinician transcripts. …”
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IMPLEMENTATION OF LEARNING MANAGEMENT SYSTEMS WITH GENERATIVE ARTIFICIAL INTELLIGENCE FUNCTIONS IN THE POST-PANDEMIC ENVIRONMENT by Denis-Cătălin Arghir

“…To demonstrate the system's effectiveness, a curriculum was crafted for a specialized field of study - Artificial Intelligence (AI), with a specific focus on the practical application of Machine Learning algorithms. This curriculum incorporates theoretical and practical application components, complemented by a suite of assessment tools and assignments tailored to the proposed subject. …”
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Validating Ionospheric Models Against Technologically Relevant Metrics by A. T. Chartier, J. Steele, G. Sugar, D. R. Themens, S. K. Vines, J. D. Huba

“…Autoscaled and then machine learning “cleaned” Digisonde NmF2 data indicate a 1.0 × 1011 el. m3 median positive bias in SAMI3 (equivalent to a 27% overestimation). …”
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