Decoding dynamic landslide hazard processes for a massive refugee camp in Bangladesh by Dewan Mohammad Enamul Haque, Ritu Roy, Sumya Tasnim, Shamima Ferdousi Sifa, Suniti Karunatillake, A.S.M. Maksud Kamal, Juan M. Lorenzo

“…Our GAM approach performs better than standard machine learning (ML) techniques (e.g., Random Forest, Support Vector Machine, Neural Networks), achieving an overall ROC-AUC of 0.84 and a mean cross-validated AUC of 0.81, compared to AUC (0.64-0.74) for ML models. …”
Get full text

Physical Property Prediction and Simulation Analysis of Hydrogen‐Doped Natural Gas Pipeline by Lianghui Guo, He Zhang, Ran Liu, Keke Zhi, Xinzhe Li

“…Physical properties and operational dynamics of hydrogen‐doped natural gas pipelines are investigated to combine machine learning techniques and simulation models, which promote the development of zero carbon emission energy, hydrogen energy, thereby contributing to the reduction of global carbon emissions. …”
Get full text

Development and Validation of a Radiomics Nomogram Based on Magnetic Resonance Imaging and Clinicoradiological Factors to Predict HCC TACE Refractoriness by Dong Y, Hu J, Meng X, Yang B, Peng C, Zhao W

“…The integrated clinical-radiomics model demonstrated robust predictive performance, achieving a training cohort AUC of 0.955 (95% CI: 0.918– 0.984) with 0.885 accuracy, 0.921 sensitivity, and 0.864 specificity, and maintained strong validation performance (AUC=0.941, 95% CI: 0.880– 0.991).Conclusion: Multisequence clinical-radiomics model accurately predicts TACE refractoriness in hepatocellular carcinoma.Keywords: hepatocellular carcinoma, machine learning, transarterial chemoembolization, radiomics…”
Get full text

Prediction of the Impact of Bank Failure Risk on Micro-Credit in Iran: An Artificial Intelligence Approach by Reza Taheri Haftasiabi, Yusef Mohammadzadeh, Ameneh Naderi

“…This study was conducted with a quantitative research approach and the data of all 28 Iranian banks in the period from 2017 to 2022 were analyzed. Machine learning tools, including artificial neural networks (ANN) and support vector machine (SVM), were used to analyze macroeconomic indicators such as GDP, inflation, exchange rate, interest rate, and financial variables of banks such as investment volume, amount of loans granted, total deposits, and bankruptcy risk indicators. …”
Get full text

Application of Radiomics for Differentiating Lung Neuroendocrine Neoplasms by Aleksandr Borisov, David Karelidze, Mikhail Ivannikov, Elina Shakhvalieva, Peri Sultanova, Kirill Arzamasov, Nikolai Nudnov, Yuriy Vasilev

“…<b>Conclusions:</b> Radiomics-based machine learning models demonstrated high diagnostic accuracy in differentiating lung NENs from NSCLC and in subclassifying NENs. …”
Get full text

An Adaptive CNN-Based Approach for Improving SWOT-Derived Sea-Level Observations Using Drifter Velocities by Sarah Asdar, Bruno Buongiorno Nardelli

“…We compare our method to existing filtering techniques, including a U-Net-based model and a variational noise-reduction filter. Our adaptive-filtering CNN produces accurate velocity estimates while preserving small-scale features and achieving a substantial noise reduction in the spectral domain. …”
Get full text

Precision‐Optimised Post‐Stroke Prognoses by Thomas M. H. Hope, Howard Bowman, Rachel M. Bruce, Alex P. Leff, Cathy J. Price

“…Researchers have sought to bridge this gap by treating the post‐stroke prognostic problem as a machine learning problem, reporting prediction error metrics across samples of patients whose outcomes are known. …”
Get full text

Frequency Selection Based Separation of Speech Signals with Reduced Computational Time Using Sparse NMF by Yash Vardhan VARSHNEY, Zia Ahmad ABBASI, Musiur Raza ABIDI, Omar FAROOQ

Get full text

Prediction of Sorghum bicolor (L.) distribution ranges provides insights on potential sorghum cultivation across tropical ecoregions of Indonesia by Suyud Warno Utomo, Fatma Lestari, Andrio Adiwibowo, Micah R. Fisher

Get full text

Hyperspectral Data Driven Solid Waste Classification by Gleb Mazanov, Anna Iliushina, Sergey Nesteruk, Andrey Pimenov, Anton Stepanov, Nadezhda Mikhaylova, Andrey Somov

Get full text

TCR and BCR repertoire analysis reveals distinct signatures between benign and malignant ovarian tumors by Zhonghuang Wang, Zhonghuang Wang, Zhe Zhang, Dongli Zhao, Dongli Zhao, Zhenglin Du, Bixia Tang, Enhui Jin, Hailong Kang, Wenming Zhao, Yuanguang Meng

“…The analysis elucidates the differences between the two immune repertoires in various aspects and constructs an early screening machine learning model for ovarian tumors based on the characteristics of the immune repertoire.ResultThe finding revealed that patients with malignant ovarian tumors exhibited a reduction in balance, richness, and diversity in their immune repertoires compared to those with benign tumors. …”
Get full text

Reducing Defense Vulnerabilities in Federated Learning: A Neuron-Centric Approach by Eda Sena Erdol, Hakan Erdol, Beste Ustubioglu, Guzin Ulutas, Iraklis Symeonidis

“…Federated learning is a distributed machine learning approach where end users train local models with their own data and combine model updates on a reliable server to create a global model. …”
Get full text

Hybrid feature fusion in cervical cancer cytology: a novel dual-module approach framework for lesion detection and classification using radiomics, deep learning, and reproducibilit... by Shurong Niu, Lili Zhang, Lina Wang, Xue Zhang, Erniao Liu

Get full text

Improved Interpretability Without Performance Reduction in a Sepsis Prediction Risk Score by Adam Kotter, Samir Abdelrahman, Yi-Ki Jacob Wan, Karl Madaras-Kelly, Keaton L. Morgan, Chin Fung Kelvin Kan, Guilherme Del Fiol

Get full text

Incremental attribute reduction algorithm for dominance-based neighborhood relative decision entropy by CHEN Baoguo, CHEN Lei, DENG Ming, LI Xiaoyan, CHEN Jinlin

“…ObjectiveIn the big data environment, data is constantly being dynamically updated, which poses certain limitations and challenges to traditional machine learning algorithms. Incremental learning is a process of learning only on changing data based on the learning results of existing models, which can significantly improve the learning performance of the data update process. …”
Get full text

Comparative Analysis of Facial Expression Recognition Methods by Denys - Florin COT

“… This paper aimed to investigate human emotion recognition through the analysis of facial expressions, using both classical machine learning methods and advanced techniques based on deep neural networks. …”
Get full text

LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection by Jyoti Parashar, Rituraj Jain, Mahesh K. Singh, Ashwani Kumar, Premananda Sahu, Kamal Upreti

“…Very rigorous evaluations were performed on the model against both conventional machine learning and state of the art deep learning architectures. …”
Get full text

Assessing data and sample complexity in unmanned aerial vehicle imagery for agricultural pattern classification by Linara Arslanova, Sören Hese, Marcel Fölsch, Friedemann Scheibler, Christiane Schmullius

“…The study investigates the data and sample complexity required to develop an effective machine/deep learning (ML/DL) model, using techniques such as the Jeffries-Matusita Distance for assessment of class separability and feature importance ranking for feature and layer selection, semivariogram analysis for determining minimum sample patch sizes.The results demonstrate distinct classification capabilities based on spectral information in differentiating between sub-classes such as weed infestation, bare soil, disturbed canopy areas, and undisturbed canopy areas. …”
Get full text

Advanced sentiment analysis in online shopping: Implementing LSTM models analyzing E-commerce user sentiments by Lu Liyuan

“…This article elaborately contrasts long short-term memory (LSTM)-based models with traditional machine learning models, like support vector machines (SVM), random forest, and Naive Bayes classifiers. …”
Get full text

Advanced Classifiers and Feature Reduction for Accurate Insomnia Detection Using Multimodal Dataset by Ameya Chatur, Mostafa Haghi, Nagarajan Ganapathy, Nima TaheriNejad, Ralf Seepold, Natividad Martinez Madrid

“…Our findings emphasize the importance of tailoring feature sets and employing appropriate reduction techniques for optimal predictive modeling in sleep-related studies. …”
Get full text