A Combined Deep Learning Method with Attention-Based LSTM Model for Short-Term Traffic Speed Forecasting by Pan Wu, Zilin Huang, Yuzhuang Pian, Lunhui Xu, Jinlong Li, Kaixun Chen

“…Short-term traffic speed prediction is a promising research topic in intelligent transportation systems (ITSs), which also plays an important role in the real-time decision-making of traffic control and guidance systems. …”
Get full text

Urban Greening Analysis: A Multimodal Large Language Model for Pinpointing Vegetation Areas in Adverse Weather Conditions by Hanzhang Liu, Shijie Yang, Chengwu Long, Jiateng Yuan, Qirui Yang, Jiahua Fan, Bingnan Meng, Zhibo Chen, Fu Xu, Chao Mou

“…Additionally, these algorithms also have performance limitations such as inaccurate boundary area positioning. …”
Get full text

Western disturbances and climate variability: a review of recent developments by K. M. R. Hunt, K. M. R. Hunt, J.-P. Baudouin, A. G. Turner, A. G. Turner, A. P. Dimri, A. P. Dimri, G. Jeelani, Pooja, R. Chattopadhyay, R. Chattopadhyay, F. Cannon, T. Arulalan, T. Arulalan, M. S. Shekhar, T. P. Sabin, E. Palazzi

“…Use of new modelling frameworks and tracking algorithms has significantly improved knowledge of WD structure and variability, and a more frequentist approach can now be taken. …”
Get full text

Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection by Hongyan Zhu, Hongyan Zhu, Chengzhi Lin, Chengzhi Lin, Gengqi Liu, Gengqi Liu, Dani Wang, Dani Wang, Shuai Qin, Shuai Qin, Anjie Li, Anjie Li, Jun-Li Xu, Yong He

“…Additionally, some widely used traditional machine learning (ML) algorithms were presented and the performance results were tabulated to form a comparison. …”
Get full text

Revolutionizing climbing perch disease management: AI-Driven solutions for sustainable aquaculture by Kosit Sriputhorn, Rapeepan Pitakaso, Surasak Matitopanum, Peerawat Luesak, Surajet Khonjun, Rungwasun Kraiklang, Chakat Chueadee, Sarayut Gonwirat

“…This system uniquely combines non-population based differential evolution algorithms for optimizing the mix of image augmentation techniques, with population-based algorithms for integrating image segmentation and convolutional neural network (CNN) architectures. …”
Get full text

Machine learning-assisted quantitative metabolomics of West African patients with advanced breast cancer by Aboubacar Dit Tietie Bissan, Mathieu Michel, Xavier Dieu, Cinzia Bocca, Awo Emmanuela Hilda Amegonou, Fatoumata Matokoma Sidibe, Madani Ly, Bocary Sidi Koné, Nènè Oumou Kesso Barry, Kletigui Casimir Dembélé, Bakary Cissé, Bourèma Kouriba, Gilles Simard, Delphine Mirebeau-Prunier, Juan Manuel Chao de la Barca, Zahra Ouzzif, Pascal Reynier

“…The predictive models obtained with the machine learning algorithms were comparable, with the best AUC of 0.878 obtained with ridge logistic regression using Boruta feature selection. …”
Get full text

Dilated cardiomyopathy evaluation with Imagenomics: combining multimodal cardiovascular imaging and genetics by Kristian Galanti, Ghaith Sharaf Dabbagh, Fabrizio Ricci, Sabina Gallina, Roberta Giansante, Ron Jacob, Edmond Obeng‐Gyimah, Leslie T. Cooper Jr, Sanjay K. Prasad, David H. Birnie, Andrew P. Landstrom, Selma F. Mohammed, Saidi Mohiddin, Mohammed Y. Khanji, Anwar A. Chahal

“…The aim of this review is to propose a systematic approach to the diagnosis of DCM, emphasizing the importance of genetics and clinical findings for a precise and practical clinical approach. Also, we strive to qualify the role of cardiac imaging in the diagnosis of DCM, particularly on the relevance of novel techniques and clinical utility of actionable parameters to improve current diagnostic schemes and risk stratification algorithms. …”
Get full text

Automated Recognition of Abnormalities in Gastrointestinal Endoscopic Images – Evaluation of an AI Tool for Identifying Polyps and Other Irregularities by Weronika Jarych, Elżbieta Tokarczyk, Patryk Iglewski, Daria Ziemińska, Karina Motolko, Rafał Burczyk, Konrad Duszyński, Michał Kociński, Jan Reinald Wendt

“…In conclusion, the study highlights the promising role of AI in gastrointestinal endoscopy while underscoring the importance of continued research, algorithmic refinement, and the establishment of regulatory frameworks to fully harness its clinical potential. …”
Get full text

Application of supervised machine learning and unsupervised data compression models for pore pressure prediction employing drilling, petrophysical, and well log data by Abu Bakker Siddique, Tanveer Alam Munshi, Nazmul Islam Rakin, Mahamudul Hashan, Sushmita Sarker Chnapa, Labiba Nusrat Jahan

“…This study proposes an effective data-driven approach that utilizes machine learning algorithms to forecast reservoir pore pressure. A total of five machine learning algorithms, namely multivariable regression (MVR), polynomial regression (PR), random forest (RF), CatBoost regression, and multilayer perception (MLP), are applied in this research. …”
Get full text

Using explainable machine learning and eye-tracking for diagnosing autism spectrum and developmental language disorders in social attention tasks by Adoración Antolí, Adoración Antolí, Francisco Javier Rodriguez-Lozano, José Juan Cañas, Julia Vacas, Julia Vacas, Fátima Cuadrado, Fátima Cuadrado, Araceli Sánchez-Raya, Araceli Sánchez-Raya, Carolina Pérez-Dueñas, Carolina Pérez-Dueñas, Juan Carlos Gámez-Granados

“…They also underscore the clinical relevance of such approaches for identifying the variables and parameters that differentiate between disorders.…”
Get full text

Composite Mapping for Peptide‐Based Data Storage with Higher Coding Density and Fewer Synthesis Cycles by Anxun Zhang, Longjie Wang, Xiaowei Zhai, Yao Xiao, Yanchan Wu, Yongxi Zhao, Kai Liu, Ji‐Shen Zheng, Dong Chen

“…The whole process of encoding data into composite letter sequences, synthesizing composite letter sequences via solid‐phase peptide synthesis, sequencing composite letter sequences by mass spectrometry, and decoding data from composite letter sequences is successfully demonstrated for the first time. Composite mapping also demonstrates several distinct advantages, including high coding density, few synthesis cycles, high reliability against errors, low probability of homopolymers, and good compatibility with other encoding algorithms. …”
Get full text

Opportunities and challenges of quantum computing for climate modeling by Mierk Schwabe, Lorenzo Pastori, Inés de Vega, Pierre Gentine, Luigi Iapichino, Valtteri Lahtinen, Martin Leib, Jeanette Miriam Lorenz, Veronika Eyring

“…We also discuss hurdles and obstacles facing current quantum computing paradigms. …”
Get full text

A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions by Raman Kumar, Raman Kumar, Sarvesh Garg, Rupinder Kaur, M. G. M. Johar, Sehijpal Singh, Sehijpal Singh, Soumya V. Menon, Pulkit Kumar, Pulkit Kumar, Ali Mohammed Hadi, Shams Abbass Hasson, Jasmina Lozanović

“…This review showcases the progression of ML approaches from traditional classifiers to hybrid DL structures and federated learning (FL) frameworks. It also discusses ethical issues, dataset limitations, and model transparency. …”
Get full text

Integrated Workflow for Drug Repurposing in Glioblastoma: Computational Prediction and Preclinical Validation of Therapeutic Candidates by Nazareno Gonzalez, Melanie Pérez Küper, Matías Garcia Fallit, Jorge A. Peña Agudelo, Alejandro Nicola Candia, Maicol Suarez Velandia, Ana Clara Romero, Guillermo Videla Richardson, Marianela Candolfi

“…Research Design and Methods: We analyzed drug sensitivity data for 272 compounds from CancerRxTissue and employed in silico algorithms to assess blood-brain barrier permeability. …”
Get full text

Critical review of patient outcome study in head and neck cancer radiotherapy by Jingyuan Chen, Yunze Yang, Chenbin Liu, Hongying Feng, Jason M. Holmes, Lian Zhang, Steven J. Frank, Charles B. Simone, II, Daniel J. Ma, Samir H. Patel, Wei Liu

“…Three transformative methodological advances are reviewed: radiomics, AI-based algorithms, and causal inference frameworks. The integration of linear energy transfer in patient outcomes study, which has uncovered critical mechanisms behind unexpected toxicity, was also introduced for proton therapy. …”
Get full text

Development and validation of machine learning-based risk prediction models for ICU-acquired weakness: a prospective cohort study by Yimei Zhang, Yu Wang, Jingran Yang, Qinglan Li, Min Zhou, Jiafei Lu, Qiulan Hu, Fang Ma

“…Therefore, this study aims to develop and validate risk prediction models for ICUAW based on multiple machine learning algorithms. Methods Four machine learning algorithms were employed. …”
Get full text

Construction of a Prediction Model for Sleep Quality in Embryo Repeated Implantation Failure Patients Undergoing Assisted Reproductive Technology Based on Machine Learning: A Singl... by Zhao Y, Xu C, Qin N, Bai L, Wang X, Wang K

“…Use Lasso regression to screen variables and construct a risk prediction model using six machine learning algorithms. Evaluate the validity of the model using the area under the curve (AUC), and comprehensively evaluate the performance of the model based on F1 score, accuracy, sensitivity, and specificity. …”
Get full text

Non-invasive detection of Parkinson’s disease based on speech analysis and interpretable machine learning by Huanqing Xu, Wei Xie, Mingzhen Pang, Ya Li, Luhua Jin, Fangliang Huang, Xian Shao

“…Non-linear metrics, including Recurrence Plot Dimension Entropy (RPDE) and Pitch Period Entropy (PPE), also made significant contributions to the model’s predictive power. …”
Get full text

Annotation-free prediction of microbial dioxygen utilization by Avi I. Flamholz, Joshua E. Goldford, Philippa A. Richter, Elin M. Larsson, Adrian Jinich, Woodward W. Fischer, Dianne K. Newman

“…Annotation-free algorithms enable rapid characterization of natural samples, highlighting quantitative correspondence between sequences and local O2 levels in a data set from the Black Sea. …”
Get full text

Machine learning-based ultrasound radiomics for predicting risk of recurrence in breast cancer by Wei Fan, Hao Cui, Xiaoxue Liu, Xudong Zhang, Xinran Fang, Junjia Wang, Zihao Qin, Xiuhua Yang, Jiawei Tian, Lei Zhang

“…The informative radiomics features were screened using the minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithms. Subsequently, radiomics models were constructed with eight machine learning algorithms. …”
Get full text