The role of the SOX2 gene in cervical cancer: focus on ferroptosis and construction of a predictive model by Shenping Liu, Zhi Wei, Huiqing Ding

“…Methods A multidimensional approach integrating advanced bioinformatics, comprehensive molecular profiling, and state-of-the-art machine learning algorithms was employed to assess SOX2 expression patterns and their correlation with ferroptosis marker expression patterns in cervical cancer tissues. …”
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Application of artificial intelligence technologies in HR management by S. V. Okladnikova, A. S. Pankrashov

“…Based on the fact that in the field of AI, methods mean algorithms by which tasks are solved, the following number of methods related to AI theory were identified: neural networks, fuzzy logic, expert systems, evolutionary modeling, Machine Learning.Result. …”
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Classification of differentially activated groups of fibroblasts using morphodynamic and motile features by Minwoo Kang, Chanhong Min, Somayadineshraj Devarasou, Jennifer H. Shin

“…We extract these features from label-free live-cell imaging data of fibroblasts co-cultured with breast cancer cell lines using deep learning and machine learning algorithms. Our findings show that morphodynamic and motile features offer robust insights into fibroblast activation states, complementing molecular markers and overcoming their limitations. …”
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A comprehensive evaluation of oversampling techniques for enhancing text classification performance by Salimkan Fatma Taskiran, Bahaeddin Turkoglu, Ersin Kaya, Tunc Asuroglu

“…Each dataset was vectorized using the MiniLMv2 transformer model to obtain semantically rich representations, and classification was performed using six machine learning algorithms. The balanced and imbalanced scenarios were compared in terms of F1-Score and Balanced Accuracy. …”
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Equivariant learning leveraging geometric invariances in 3D molecular conformers for accurate prediction of quantum chemical properties by Jianhua Sun, Ye Cao, Huijing Hu, Baoqiao Qi

“…In this study, we present a computational framework termed 3D molecular structure enhanced (3DMSE) that seamlessly integrates the rich structural information inherent in 3D molecular geometries with state-of-the-art machine learning algorithms to enable highly precise and computationally efficient prediction of crucial quantum chemical properties. …”
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Appliance-Specific Noise-Aware Hyperparameter Tuning for Enhancing Non-Intrusive Load Monitoring Systems by João Góis, Lucas Pereira

“…The results indicate that the noise metric provides valuable guidance for selecting the input sequence length, particularly for user-dependent appliances with more unpredictable usage patterns, such as washing machines and electric kettles.…”
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Editorial by Bulent Cavas

“…The eighth article, “Prediction of Middle School Students’ Recycling Behaviors with Machine Learning Algorithms,” by Mustafaoğlu and Alkan (Türkiye), applies machine learning techniques to identify factors predicting students' recycling behavior. …”
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Comprehensive Analysis Reveals the Molecular Features and Immune Infiltration of PANoptosis-Related Genes in Metabolic Dysfunction-Associated Steatotic Liver Disease by Yan Huang, Jingyu Qian, Zhengyun Luan, Junling Han, Limin Tang

“…Machine learning algorithms prioritized key PANoDEGs, while ROC curves assessed their diagnostic efficacy. …”
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Symmetry-Based Data Augmentation Method for Deep Learning-Based Structural Damage Identification by Long Li, Xiaoming Tao, Hui Song, Xiaolong Li, Zhilong Ye, Yao Jin, Qiuyu He, Shiyin Wei, Wenli Chen

“…These methods typically use ML algorithms to identify patterns within features extracted from data representing structural conditions, thereby inferring damage from changes in these patterns. …”
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Self-Supervised Neural Networks for Precoding in MIMO Rate Splitting Multiple Access Systems by Dheeraj Raja Kumar, Carles Anton-Haro, Xavier Mestre

“…The intention is to explore several alternatives to conventional iterative precoding benchmarks like Weighted Minimum Mean Square Error (WMMSE) which are computationally intensive algorithms. We evaluate the different precoding policies learnt by the neural network architectures by closely studying the respective radiation patterns. …”
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AI-Driven Belt Failure Prediction and Prescriptive Maintenance with Motor Current Signature Analysis by João Paulo Costa, José Torres Farinha, Mateus Mendes, Jorge O. Estima

“…Through the integration of motor current signature analysis (MCSA) and machine learning algorithms, particularly long short-term memory (LSTM) networks, this study aims to predict and detect belt degradation in real time. …”
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Multi-Stage Data-Driven Framework for Customer Journey Optimization and Operational Resilience by Tzu-Chien Wang, Ruey-Shan Guo, Chialin Chen, Chia-Kai Li

“…To address these limitations, this study proposes a multi-stage data-driven framework integrating latent Dirichlet allocation (LDA) for behavioral insights, deep learning for predictive modeling, and heuristic algorithms for adaptive decision-making. Empirical validation using Taiwanese financial institution data shows a 15% improvement in predictive accuracy compared to traditional machine-learning models, significantly enhancing customer lifetime value (CLV) predictions and multi-channel resource allocation. …”
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Adaptive Real-Time Transmission in Large-Scale Satellite Networks Through Software-Defined-Networking-Based Domain Clustering and Random Linear Network Coding by Shangpeng Wang, Chenyuan Zhang, Yuchen Wu, Limin Liu, Jun Long

“…Existing research primarily emphasizes traffic prediction and scheduling using spatiotemporal models and machine learning. However, these approaches often depend on extensive historical data for training, making real-time adaptation to rapidly changing network topologies and traffic patterns challenging in dynamic satellite environments. …”
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MetaStackD A robust meta learning based deep ensemble model for prediction of sensors battery life in IoE environment by D. Gayathri, S. P. Shantharajah

“…This work focuses on proposing a novel framework integrating pre-processing, standardization, encoding scheme, and predictive modeling that includes two algorithms, RFRImpute and MetaStackD, for predicting the RBL of sensors in any IoE device using a meta-learning-based deep ensemble approach blue for analyzing factors such as power consumption, environmental conditions, operational frequency, and workload patterns. …”
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Methodology for Data Integration in 3D-HBIM Digital Models. Case Study: the Holy Chalice Chapel of Valencia Cathedral by Pablo Ariel Escudero, Concepción López González, Jorge Luis García Valldecabres

“…This phase involves the use of various machine learning algorithms, including Random Sample Consensus (RANSAC) and K-Means, for data classification. …”
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How does artificial intelligence shape the productivity and quality of research in business studies? A systematic literature review and future research framework by Sugandha Agarwal, Qian Long Kweh, Dima Jamali, Walton Wider, Syed Far Abid Hossain, Muhammad Ashraf Fauzi

“…We show that AI helps reduce research time and improve data management. Methods like machine learning and natural language processing can effectively uncover patterns and trends that conventional research methods may overlook. …”
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Artificial intelligence model in the cognitive and learning activities of university subjects by N. Abishev, R. Ramazanov, M. Abaideldanova, K. Chesnokova, A. Baizhumayeva

“…The authors design this model using algorithms–sets of rules that enable programs to make decisions, recognize patterns, and generate predictions based on input data relevant to the learning and cognitive processes of university subjects. …”
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Assessing Physiological Stress Responses in Student Nurses Using Mixed Reality Training by Kamelia Sepanloo, Daniel Shevelev, Young-Jun Son, Shravan Aras, Janine E. Hinton

“…The simulation consists of six segments, during which critical events like hypotension and hypoxia occur, and the patient’s condition changes based on the nurse’s clinical decisions. Machine learning algorithms were then used to analyze the nurse’s physiological data and to classify different levels of stress. …”
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Automatic Fault Classification in Photovoltaic Modules Using Denoising Diffusion Probabilistic Model, Generative Adversarial Networks, and Convolutional Neural Networks by Carlos Roberto da Silveira Junior, Carlos Eduardo Rocha Sousa, Ricardo Henrique Fonseca Alves

“…Deep convolutional neural networks (CNNs) are machine learning algorithms that perform tasks involving images, such as image classification and object recognition. …”
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Identifying Suicidal Ideation Through Automatic Extraction of Emotional Traces in Suicide Notes by Angel Hernandez-Castaneda, Rene Arnulfo Garcia-Hernandez, Yulia Ledeneva

“…The primary objective of this study is to classify suicide notes based on their emotional content using machine and deep learning algorithms. We propose an innovative approach to automatically identify emotional changes in a suicide note’s content, leveraging these shifts as key indicators of suicidal ideation. …”
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