Exploring the role of ferroptosis in pemphigus: identification of diagnostic markers and regulatory mechanisms by Jing Mao, Jianping Lan, Zheyu Zhuang, Ying Chen, Ying Chen, Yushan Ou, Xinhong Su, Xueting Zeng, Fuchen Huang, Zequn Tong, Xiaoqing Lv, Xiaoqing Lv, Xiaoqing Lv, Hui Ke, Zhenlan Wu, Ying Zou, Bo Cheng, Bo Cheng, Bo Cheng, Chao Ji, Chao Ji, Chao Ji, Ting Gong

“…Weighted Gene Co-expression Network Analysis (WGCNA) was employed to identify co-expressed gene modules related to pemphigus. Machine learning algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were used to select key ferroptosis-related genes. …”
Get full text

From Words to Ratings: Machine Learning and NLP for Wine Reviews by Iliana Ilieva, Margarita Terziyska, Teofana Dimitrova

“…These findings can be applied by a wide range of stakeholders—researchers, producers, retailers, and marketing specialists.…”
Get full text

The calcitron: A simple neuron model that implements many learning rules via the calcium control hypothesis. by Toviah Moldwin, Li Shay Azran, Idan Segev

“…Theoretical neuroscientists and machine learning researchers have proposed a variety of learning rules to enable artificial neural networks to effectively perform both supervised and unsupervised learning tasks. …”
Get full text

Depression and Anxiety Screening for Pregnant Women via Free Conversational Speech in Naturalistic Condition by Rafael T. Sousa, Gustavo D. M. Silva, Paula L. L. Pinto, Juliano Backes, Amanda S. Mota, Thiago M. Paixao, Maria G. S. Teixeira, Wilian H. Hisatugu, Anilton S. Garcia, Kelly S. Prado, Rodrigo S. Dias, Marco A. Galletta, Hermano Tavares

“…By leveraging machine learning techniques to analyze speech patterns, this research seeks to offer a non-invasive, objective, and accessible method for early detection and intervention. …”
Get full text

Critical soil moisture detection and water–energy limit shift attribution using satellite-based water and carbon fluxes over China by Y. Liu, J. Xiao, X. Li, Y. Li

Get full text

An Evolutionary Deep Reinforcement Learning-Based Framework for Efficient Anomaly Detection in Smart Power Distribution Grids by Mohammad Mehdi Sharifi Nevisi, Mehrdad Shoeibi, Francisco Hernando-Gallego, Diego Martín, Sarvenaz Sadat Khatami

“…To address these challenges, this study proposes a novel deep reinforcement learning (DRL)-based framework, integrating a convolutional neural network (CNN) for hierarchical feature extraction and a recurrent neural network (RNN) for sequential pattern recognition and time-series modeling. To enhance model performance, we introduce a novel non-dominated sorting artificial bee colony (NSABC) algorithm, which fine-tunes the hyper-parameters of the CNN-RNN structure, including weights, biases, the number of layers, and neuron configurations. …”
Get full text

The Relationship Between Surface Meteorological Variables and Air Pollutants in Simulated Temperature Increase Scenarios in a Medium-Sized Industrial City by Ronan Adler Tavella, Daniele Feijó das Neves, Gustavo de Oliveira Silveira, Gabriella Mello Gomes Vieira de Azevedo, Rodrigo de Lima Brum, Alicia da Silva Bonifácio, Ricardo Arend Machado, Letícia Willrich Brum, Romina Buffarini, Diana Francisca Adamatti, Flavio Manoel Rodrigues da Silva Júnior

“…The lack of a correlation between temperature and pollutant levels, along with their relationship with other meteorological variables, explains the observed pattern in Rio Grande. This research provides important contributions to the understanding of the interactions between climate change, air pollution, and meteorological factors in similar contexts.…”
Get full text

Uncovering the impact of outliers on clusters’ evolution in temporal data-sets: an empirical analysis by Muhammad Atif, Muhammad Farooq, Muhammad Shafiq, Tmader Alballa, Somayah Abdualziz Alhabeeb, Hamiden Abd El-Wahed Khalifa

“…The findings of this study provide useful insights for practitioners and researchers in the field of stream clustering and can help guide the development of more robust and accurate stream clustering algorithms.…”
Get full text

Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep by Fernando Amarilho-Silveira, Ignacio De Barbieri, Elly A. Navajas, Jaime Araujo Cobuci, Gabriel Ciappesoni

“…The dataset was randomly partitioned into two subsets: one for training (80%) the algorithms and the other for direct validation (20%). …”
Get full text

Random Undersampled Digital Elevation Model Super-Resolution Based on Terrain Feature-Aware Deep Learning Network by Ziqiang Huo, Meng Xi, Jingyi He, Zhengjian Li, Jiabao Wen

“…In order to solve these problems, we conducted in-depth research on the terrain feature patterns of DEMs and proposed a DEM super-resolution reconstruction network that is terrain feature-aware, named D-ResDCN. …”
Get full text

Real‐Climatic Microcontroller‐in‐the‐Loop (RCMIL) Framework: A Novel, Rapid, and Cost‐Effective Approach for Verifying Photovoltaic Control Systems by Ambe Harrison, Idriss Dagal, Wulfran Fendzi Mbasso, Fritz Nguemo Kemdoum, Reagan Jean Jacques Molu, Hamid Belghiti, Pradeep Jangir

“…By pushing the frontiers of PV system research, this paper provides a vital resource that speeds up the transition from simulation to real‐world applications.…”
Get full text

“Voice is the New Blood”: a discourse analysis of voice AI health-tech start-up websites by Alden Blatter, Hortense Gallois, Emily Evangelista, Yael Bensoussan, Bridge2AI-Voice Consortium, Jean-Christophe Bélisle-Pipon, Yael E. Bensoussan, Olivier Elemento, Jean-Christophe Bélisle-Pipon, David Dorr, Satrajit Ghosh, Alistair Johnson, Phillip Payne, Maria E. Powell, Anaïs Rameau, Vardit Ravitsky, Alexandros Sigaras

“…However, descriptions of voice datasets used to train algorithms vary widely and are often absent, reflecting broader gaps in acoustic and ethical standards for voice data collection and insufficient incentives for start-ups to disclose key data details.DiscussionStart-ups play a crucial role in the research, development, and marketization of voice AI health-tech, prefacing the integration of this new technology into healthcare systems. …”
Get full text

Unraveling shared diagnostic genes and cellular microenvironmental changes in endometriosis and recurrent implantation failure through multi-omics analysis by Dongxu Qin, Yongquan Zheng, Libo Wang, Zhenyi Lin, Yao Yao, Weidong Fei, Caihong Zheng

“…Single-cell analysis was conducted to investigate the expression patterns of these diagnostic genes across various cellular subpopulations. …”
Get full text

Predictive performance of risk prediction models for lung cancer incidence in Western and Asian countries: a systematic review and meta-analysis by Yah Ru Juang, Lina Ang, Wei Jie Seow

“…Our review also highlights a significant gap in prediction models for never-smokers. Future research should focus on externally validating existing Asian models or incorporating relevant Asian risk factors into widely used Western models (PLCOM2012) to better account for unique risk profiles and lung cancer progression patterns in Asian populations.…”
Get full text

Medical Device Failure Predictions Through AI-Driven Analysis of Multimodal Maintenance Records by Noorul Husna Abd Rahman, Khairunnisa Hasikin, Nasrul Anuar Abd Razak, Ayman Khallel Al-Ani, D. Jerline Sheebha Anni, Prabu Mohandas

“…Then, four machine learning algorithms and three deep learning networks are evaluated to determine the best predictive model. …”
Get full text

Prediksi Kesiapan Sekolah Menggunakan Machine Learning Berbasis Kombinasi Adam dan Nesterov Momentum by Indah Mustika Rahayu, Ahmad Yusuf, Mujib Ridwan

“…Based on the Chi-Square correlation, variables that have a strong significant relationship to school readiness test results are pre-elementary status, number of siblings and age with p<.05. The research used Artificial Neural Network algorithms with a combination of Adam and Nesterov Momentum optimization method. …”
Get full text

Scalable Clustering of Complex ECG Health Data: Big Data Clustering Analysis with UMAP and HDBSCAN by Vladislav Kaverinskiy, Illya Chaikovsky, Anton Mnevets, Tatiana Ryzhenko, Mykhailo Bocharov, Kyrylo Malakhov

“…The study aims to apply unsupervised clustering algorithms to ECG data to detect latent risk profiles related to heart failure, based on distinctive ECG features. …”
Get full text

Development and validation of machine learning-based diagnostic models using blood transcriptomics for early childhood diabetes prediction by Xin Huang, Xin Huang, Di Ouyang, Weiming Xie, Huawei Zhuang, Siyu Gao, Pan Liu, Lizhong Guo

Get full text

Soil moisture dominates gross primary productivity variation during severe droughts in Central Asia by Tao Yu, Guli Jiapaer, Anming Bao, Ye Yuan, Jiayu Bao, Tim Van de Voorde

Get full text

Analysis of soil salinization and land use change under water conservation retrofit in the Hetao irrigation district by Yi Zhao, Shuya Yang, Haibin Shi, Haoqi Han, Yunlei Dong, Xianyue Li, Jianwen Yan, Yan Yan, Xu Dou, Feng Tian, Qingfeng Miao

“…We establish soil sampling sites in this region, employ Google Earth Engine algorithms to develop models for soil salinity inversion and land use classification, and analyze the change patterns of land salinization and land use types. …”
Get full text