A multi-filter deep transfer learning framework for image-based autism spectrum disorder detection by Rodrigo Colnago Contreras, Monique Simplicio Viana, Victor José Souza Bernardino, Francisco Lledo dos Santos, Önsen Toygar, Rodrigo Capobianco Guido

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Design of an Improved Model for Anomaly Detection in CCTV Systems Using Multimodal Fusion and Attention-Based Networks by V. Srilakshmi, Sai Babu Veesam, Mallu Shiva Rama Krishna, Ravi Kumar Munaganuri, Dulam Devee Sivaprasad

“…The utilized techniques in this paper comprise the Multimodal Deep Boltzmann Machine (MDBM), Multimodal Variational Autoencoder (MVAE) and Attention-based Fusion Networks, all of which fully utilize the learned representations. …”
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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. …”
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Revolutionizing Battery Longevity by Optimising Magnesium Alloy Anodes Performance by Bankole I. Oladapo, Mattew A. Olawumi, Francis T. Omigbodun

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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

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Lithology Recognition Research Based on Wavelet Transform and Artificial Intelligence by FANG Dazhi, MA Weijun, YAN Xu, MAO Zheng, GAO Yang

“…After noise reduction, the XGBoost model performed the best, with test set accuracy, recall, and F1 score all reaching 0.998. …”
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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. …”
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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. …”
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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…”
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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

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

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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. …”
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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. …”
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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. …”
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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. …”
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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. …”
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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

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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. …”
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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

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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. …”
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