DeepContainer: A Deep Learning-based Framework for Real-time Anomaly Detection in Cloud-Native Container Environments by Ke Xiong, Zhonghao Wu,  Xuzhong Jia

“…DeepContainer implements a multi-layered detection approach, combining feature engineering techniques with optimized deep learning models to identify security anomalies across diverse container workloads. …”
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Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation by Yue Wan, Jialu Wu, Tingjun Hou, Chang-Yu Hsieh, Xiaowei Jia

“…However, the data scarcity, combined with the highly non-linear causal relationships between physicochemical and biological properties and conventional molecular featurization schemes, complicates the development of robust molecular machine learning models. …”
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Unsupervised Binary Classifier-Based Object Detection Algorithm with Integrated Background Subtraction Suitable for Use with Aerial Imagery by Gabija Veličkaitė, Ignas Daugėla, Ivan Suzdalev

“…Unlike conventional supervised models, SARGAS is trained in a partially unsupervised manner, learning to recognize feature patterns without requiring labeled data. …”
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Enhancing Long-Term Flood Forecasting with SageFormer: A Cascaded Dimensionality Reduction Approach Based on Satellite-Derived Data by Fatemeh Ghobadi, Amir Saman Tayerani Charmchi, Doosun Kang

“…Floods, increasingly exacerbated by climate change, are among the most destructive natural disasters globally, necessitating advancements in long-term forecasting to improve risk management. Traditional models struggle with the complex dependencies of hydroclimatic variables and environmental conditions, thus limiting their reliability. …”
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An Intelligent Ensemble-Based Detection of In-Vehicle Network Intrusion by Easa Alalwany, Imad Mahgoub, Bader Alsharif, Abdullah Alfahaid

“…For this purpose, we utilize supervised machine learning in combination with ensemble methods. We first perform data balancing and feature selection. We build and fine-tune random forest, Xtreme gradient boosting, and decision tree supervised learning models. …”
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Early-life and concurrent predictors of the healthy adolescent microbiome in a cohort study by Hannah E. Laue, Amy D. Willis, Fang Wang, Melinda C. MacDougall, Yingying Xu, Margaret R. Karagas, Juliette C. Madan, Abby F. Fleisch, Bruce P. Lanphear, Kim M. Cecil, Kimberly Yolton, Aimin Chen, Jessie P. Buckley, Joseph M. Braun

“…We described common features of the adolescent gut microbiome and applied self-organizing maps (SOMs)—a machine-learning approach—to identify distinct microbial profiles (n = 4). …”
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Semantic Attention Guided Hierarchical Decision Network for Age Estimation by Xincheng Qiao, Chunlong Hu, Bin Qin

“…As the greatest dilemma of age estimation is the similar appearance of adjacent age labels, this paper presents a two-stage age estimation model based on the idea of coarse-to-fine decision strategy and fine-grained feature learning mechanism. …”
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ResM-FusionNet for efficient landslide detection algorithm with a hybrid architecture by Xuqing Ren, Xu Wu, Donghao Zhai, Xiangpeng Wang, Ningbo He, Mehreen Tarif

“…Experimental results on remote sensing datasets demonstrate that ResM-FusionNet significantly outperforms existing models. Specifically, ResM-FusionNet achieves 94.33% accuracy, 85.73% F1-score, and a Kappa coefficient of 70.12%, surpassing other models (e.g., SegFormer, DeepLabv3, and UNet) by 4.4%, 7.7%, and 17.6% in accuracy, respectively. …”
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Efficient Building Roof Type Classification: A Domain-Specific Self-Supervised Approach by G. Mutreja, K. Bittner

“…Employing Simple Framework for Contrastive Learning of Visual Representations (SimCLR) with EfficientNet-B3 and CBAM achieves a 95.5% accuracy on our validation set, matching the performance of state-of-the-art transformer-based models while utilizing significantly fewer parameters. …”
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Hyperparameter tuned deep learning-driven medical image analysis for intracranial hemorrhage detection. by Naif Almakayeel, E Laxmi Lydia, Oleg Razzhivin, S Rama Sree, Mohammed Altaf Ahmed, Bibhuti Bhusan Dash, S P Siddique Ibrahim

“…A comprehensive simulation was conducted on the benchmark CT image dataset to demonstrate the effectiveness of the HPDL-MIAIHD approach in detecting ICH. The performance validation of the HPDL-MIAIHD approach portrayed a superior accuracy value of 99.02% over other existing models.…”
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Temporal Logical Attention Network for Log-Based Anomaly Detection in Distributed Systems by Yang Liu, Shaochen Ren, Xuran Wang, Mengjie Zhou

“…Our approach makes three key contributions: (1) a temporal logical attention mechanism that explicitly models both time-series patterns and logical dependencies between log events across distributed components, (2) a multi-scale feature extraction module that captures system behaviors at different temporal granularities while preserving causal relationships, and (3) an adaptive threshold strategy that dynamically adjusts detection sensitivity based on system load and component interactions. …”
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DASD- diagnosing autism spectrum disorder based on stereotypical hand-flapping movements using multi-stream neural networks and attention mechanisms by Theyazn H. H. Aldhyani, Theyazn H. H. Aldhyani, Abdullah H. Al-Nefaie, Abdullah H. Al-Nefaie

“…The spatial attention mechanisms method was used to enhance the proposed models by focusing on the movement characteristics of the patients in the video. …”
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Multi-fidelity graph neural networks for predicting toluene/water partition coefficients by Thomas Nevolianis, Jan G. Rittig, Alexander Mitsos, Kai Leonhard

“…Our results show that multi-target learning significantly improves predictive accuracy, achieving a root-mean-square error of 0.44 $$\log {P}$$ log P units for the EXT-Zamora, compared to a root-mean-square error of 0.63 $$\log {P}$$ log P units for single-task models. For the EXT-SAMPL9 dataset, multi-target learning achieves a root-mean-square error of 1.02 $$\log {P}$$ log P units, indicating reasonable performance even for more complex molecular structures. …”
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Revisiting the Open-End Reflection Coefficient and Turbulent Losses in an Organ Pipe with Low Mach Number Flowipe with low Mach number flow by Viktor HRUŠKA, Pavel DLASK

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Appendicular Skeletal Muscle Index Is Positively Related to Mediterranean Diet Adherence in University Students by Paraskevi Detopoulou, Olga Magni, Ioanna Pylarinou, Despoina Levidi, Vassilios Dedes, Milia Tzoutzou, Konstantina Argyri, Evangelia Fappa, Aristea Gioxari, George Panoutsopoulos

“…Multivariate linear regression models with log-aSMI as a dependent variable were applied. …”
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Strong Photospheric Heating Indicated by Fe i 6173 Å Line Emission during White-light Solar Flares by Samuel Granovsky, Alexander G. Kosovichev, Viacheslav M. Sadykov, Graham S. Kerr, Joel C. Allred

“…HMI spectropolarimetric observations of certain WLFs, in particular the X9.3 flare of 2017 September 6, reveal one or more locations within the umbra or along the umbra/penumbra boundary of the flaring active region where the Fe I 6173 Å line briefly goes into full emission, indicating significant heating of the photosphere and lower chromosphere. For five flares featuring Fe I 6173 Å line-core emission, we perform spectropolarimetric analysis using HMI 90 s cadence Stokes data. …”
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Obesity induces phenotypic switching of gastric smooth muscle cells through the activation of the PPARD/PDK4/ANGPTL4 pathway by Sanaa Dekkar, Kamilia Mahloul, Amandine Falco, Karidia Konate, Romane Pisteur, Sarah Maurel, Laurent Maïmoun, Norbert Chauvet, Prisca Boisguérin, David Nocca, Ariane Sultan, Florian Pallot, Guillaume Walther, Nicolas Cenac, Cyril Breuker, Sandrine Faure, Pascal de Santa Barbara

“…Methods An obese mouse model was established using a high-fat diet (HFD). …”
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Multiclass skin lesion classification and localziation from dermoscopic images using a novel network-level fused deep architecture and explainable artificial intelligence by Mehak Arshad, Muhammad Attique Khan, Nouf Abdullah Almujally, Areej Alasiry, Mehrez Marzougui, Yunyoung Nam

“…Traditional machine learning models require extensive feature engineering, which is time-consuming and less effective in handling complex data like skin lesions. …”
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Differentiating atypical parkinsonian syndromes with hyperbolic few-shot contrastive learning by Won June Choi, Jin HwangBo, Quan Anh Duong, Jae-Hyeok Lee, Jin Kyu Gahm

“…Deep learning methods have shown great potential in automatically detecting these differences. However, the models typically require extensively labeled training datasets, which are costly and pose patient privacy risks. …”
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Advanced machine learning artificial neural network classifier for lithology identification using Bayesian optimization by Saâd Soulaimani, Saâd Soulaimani, Ayoub Soulaimani, Kamal Abdelrahman, Abdelhalim Miftah, Mohammed S. Fnais, Biraj Kanti Mondal

“…This technique utilizes the capability of Bayesian optimization to utilize past classification outcomes to enhance the lithology models performance based on physical parameters calculated from well log data. …”
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