Security decision method for the edge of multi-layer satellite network based on reinforcement learning by Peiliang ZUO, Shaolong HOU, Chao GUO, Hua JIANG, Wenbo WANG

“…Specifically, the edge center node obtains the environmental state of the satellite network through the perception system, and on this basis, uses the ability of deep reinforcement learning algorithm to learn independently, and obtains the optimal data offloading strategy in the scene by fitting, and obtains the optimal link planning., so that the onboard resources can be fully utilized, so as to achieve the goal of minimizing the average return delay of many observation tasks.First,the edge center node observes the environment and obtains state elements such as the data volume, channel conditions, and edge node processing capability of the observation satellite mission in the environment. …”
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A deep machine learning model development for the biomarkers of the anatomical and functional anti-VEGF therapy outcome detection on retinal OCT images by B.E. Malyugin, S.N. Sakhnov, L.E. Axenova, K.D. Axenov, E.V. Kozina, V.V. Vronskaya, V.V. Myasnikova

“…To automatize the processes of identifying morphological structures in OCT images deep learning methods are used. Purpose. The aim of this work was to create an algorithm for the automated detection of the anti-VEGF therapy outcome biomarkers in patients with n-AMD and PED on OCT images. …”
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Comprehensive Evaluation of Techniques for Intelligent Chatter Detection in Micro-Milling Processes by Guilherme Serpa Sestito, Wesley Angelino De Souza, Alessandro Roger Rodrigues, Maira Martins Da Silva

“…The performance of several ML classifiers is compared in each feature reduction stage with the Deep Learning algorithm. The results discuss the need for applying complex algorithms since the accuracy and F1-score indices presented similar values regardless of the number of features. …”
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Classification Prediction of Jujube Variety Based on Hyperspectral Imaging: A Comparative Study of Intelligent Optimization Algorithms by Quancheng Liu, Jun Zhou, Zhaoyi Wu, Didi Ma, Yuxuan Ma, Shuxiang Fan, Lei Yan

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Machine Learning Creates a Simple Endoscopic Classification System that Improves Dysplasia Detection in Barrett’s Oesophagus amongst Non-expert Endoscopists by Vinay Sehgal, Avi Rosenfeld, David G. Graham, Gideon Lipman, Raf Bisschops, Krish Ragunath, Manuel Rodriguez-Justo, Marco Novelli, Matthew R. Banks, Rehan J. Haidry, Laurence B. Lovat

“…These generate a simple algorithm to accurately predict dysplasia. Once taught to non-experts, the algorithm significantly improves their rate of dysplasia detection. …”
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Volcanic Activity Classification Through Semi-Supervised Learning Applied to Satellite Radiance Time Series by Francesco Spina, Giuseppe Bilotta, Annalisa Cappello, Marco Spina, Francesco Zuccarello, Gaetana Ganci

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Fast Monocular Measurement via Deep Learning-Based Object Detection for Real-Time Gas-Insulated Transmission Line Deformation Monitoring by Guiyun Yang, Wengang Yang, Entuo Li, Qinglong Wang, Huilong Han, Jie Sun, Meng Wang

“…Within these ROIs, grayscale data is used to dynamically set thresholds for FAST corner detection, while the Shi–Tomasi algorithm filters redundant corners to extract unique feature points for precise tracking. …”
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A Hybrid Deep Learning and Improved SVM Framework for Real-Time Railroad Construction Personnel Detection with Multi-Scale Feature Optimization by Jianqiu Chen, Huan Xiong, Shixuan Zhou, Xiang Wang, Benxiao Lou, Longtang Ning, Qingwei Hu, Yang Tang, Guobin Gu

“…Finally, an SVM classification algorithm is employed for personnel detection. To process small sample categories, data enhancement techniques (e.g., random flip and rotation) and K-fold cross-validation are applied to optimize the model parameters. …”
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Deep Learning Model for CS-Based Signal Recovery for IRS-Assisted Near-Field THz MIMO System by Vaishali Sharma, Prakhar Keshari, Sanjeev Sharma, Kuntal Deka, Ondrej Krejcar, Vimal Bhatia

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Blood pressure abnormality detection and interpretation utilizing explainable artificial intelligence by Hedayetul Islam, Md. Sadiq Iqbal, Muhammad Minoar Hossain

“…The results of the study show that the algorithm has an AUC of 0.95, indicating good discriminatory power in detecting abnormal blood pressure. …”
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Tea Disease Detection Method Based on Improved YOLOv8 in Complex Background by Junchen Ai, Yadong Li, Shengxiang Gao, Rongsheng Hu, Wengang Che

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Manifold embeddings achieve comparable performance with multispectral imagery for time-series based land disturbance detection by Mengyao Li, Jianbo Qi, Su Ye, Qiao Wang

“…The resulting manifold embeddings are applied to the Continuous Change Detection and Classification (CCDC) algorithm for land disturbance detection. …”
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A Hybrid Approach to Reliable Jamming Identification in UAV Communications Using Combined DNNs and ML Algorithms by Hamed Farkhari, Joseanne Viana, Sarang Kahvazadeh, Pedro Sebastiao, Victor P. Gil Jimenez, Rui Dinis

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A hybrid ensemble framework with particle swarm optimization for network anomaly detection by Narinder Verma, Neerendra Kumar, Gourav Kumar, Kuljeet Singh

“…Traditional IDS approaches often struggle to detect sophisticated attacks due to their reliance on predefined patterns. …”
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Multimodal image fusion for ich detection and classification using parallel Dl models by Sri Sangepu Nagaraju, S. Prince Mary, V. Pavani Chandra, Nandam Gayatri

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Optimizing RetinaNet anchors using differential evolution for improved object detection by Asaad Mohammed, Hosny M. Ibrahim, Nagwa M. Omar

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Automating areas of interest analysis in mobile eye tracking experiments based on machine learning by Julian Wolf, Stephan Hess, David Bachmann, Quentin Lohmeyer, Mirko Meboldt

“…We introduce a new machine learning-based algorithm, the computational Gaze-Object Mapping (cGOM), that automatically maps gaze data onto respective AOIs. cGOM extends state-of-the-art object detection and segmentation by mask R-CNN with a gaze mapping feature. …”
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Vibration, current, torque, RPM dataset for multiple fault conditions in industrial-scale electric motors under randomized speed and load variationsMendeley DataMendeley DataMendel... by Wonho Jung, Junho Kim, Kangmin Jang, Sung-Hyun Yun, Daeguen Lim, Minje Jin, Yong-Hwa Park

“…Unlike existing public datasets that often assume constant speed or isolated fault types, this dataset uniquely incorporates multi-fault, multi-severity conditions under randomized speed/load variations, filling critical gaps in real-world applicability for robust fault diagnosis algorithms. The dataset enables robust evaluation of machine learning models and signal processing algorithms for fault detection, condition monitoring, and predictive maintenance in rotating machinery. …”
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Automated Detection of Recent Mud Extrusions Using UAV Imagery and Deep Learning: A Comparative Analysis of Traditional and CNN-Based Approaches by M. Guastella, M. Guastella, A. Pisciotta, R. Martorana, A. D’Alessandro

“…Traditional algorithms rely on handcrafted features, while CNNs learn hierarchical representations directly from raw data. …”
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Enhancing Hazard Detection and Risk Severity Assessment in Construction through Multinomial Naive Bayes and Regression by Akaninyene Michael Akwaisua, Anietie Ekong, Godwin Ansa

“…In the first phase of the study, the system is designed to detect hazards present in construction sites. Leveraging these data, the machine learning models are trained to predict potential hazards based on the information provided. …”
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