App-DDoS detection method using partial binary tree based SVM algorithm by Bin ZHANG, Zihao LIU, Shuqin DONG, Lixun LI

“…As it ignored the detection of ramp-up and pulsing type of application layer DDoS (App-DDoS) attacks in existing flow-based App-DDoS detection methods,an effective detection method for multi-type App-DDoS was proposed.Firstly,in order to fast count the number of HTTP GET for users and further support the calculation of feature parameters applied in detection method,the indexes of source IP address in multiple time windows were constructed by the approach of Hash function.Then the feature parameters by combining SVM classifiers with the structure of partial binary tree were trained hierarchically,and the App-DDoS detection method was proposed with the idea of traversing binary tree and feedback learning to distinguish non-burst normal flow,burst normal flow and multi-type App-DDoS flows.The experimental results show that compared with the conventional SVM-based and naïve-Bayes-based detection methods,the proposed method has more excellent detection performance and can distinguish specific App-DDoS types through subdividing attack types and training detection model layer by layer.…”
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Transfer Learning Based on Multi-Branch Architecture Feature Extractor for Airborne LiDAR Point Cloud Semantic Segmentation with Few Samples by Jialin Yuan, Hongchao Ma, Liang Zhang, Jiwei Deng, Wenjun Luo, Ke Liu, Zhan Cai

“…The existing deep learning-based Airborne Laser Scanning (ALS) point cloud semantic segmentation methods require a large amount of labeled data for training, which is not always feasible in practice. …”
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A Novel Long Short-Term Memory-Based Approach for Microgrid Fault Detection and Classification Using the Wavelet Scattering Transform by Naema M. Mansour, Abdelazeem A. Abdelsalam, Ibrahim A. Awaad

“…In this study, WST was used for the first time to process fault current signals in microgrids, resulting in significant feature matrices for training and testing. WST is especially excellent for capturing hidden properties in current waveforms, making it ideal for classification applications. …”
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Hybrid model for wind power estimation based on BIGRU network and error discrimination‐correction by Yalong Li, Ye Jin, Yangqing Dan, Wenting Zha

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A Semi-Supervised Abbreviation Disambiguation Method Based on ACNN and Bi-LSTM by ZHANG Chun-xiang, PANG Shu-yang, GAO Xue-yao

“…Morphology information， part of speech and semantic information from four adjacent lexical units are extracted as disambiguation features. Training corpus is extended by using Xgboost algorithm and LightGBM algorithm， and then expanded training corpus is input into this model. …”
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AI-Based Performance Classification of Multi-Level System-of-Systems via Hypergraph Modeling by Jun Jiang, Abdeslem Smahi, Yiwen Chen, Othman Lakhal, Rochdi Merzouki

“…Using the proposed feature construction and AI-based classifiers, the SoS performance is classified into five status types. …”
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Milovanova Developing High School Students’ Listening Skills in The Context of Multimodal English Communication by Means of a MoodleBased Training Program by O. V. Gryzunova, V. V. Gryzunov

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A User-Friendly Wearable Telerehabilitation System Based on Neuromotor Training for Mild Post-Stroke Patients: The DoMoMEA System by A. Zedda, M. Caruso, S. Bertuletti, G. Baldazzi, G. Sedda, A. Spanu, D. Riboni, A. Pibiri, M. Monticone, A. Cereatti, D. Pani

“…Clinicians can also personalize the training settings to ensure a high level of engagement and challenge for patients, based on their residual motor abilities, needs and progress. …”
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Recursive feature elimination for summer wheat leaf area index using ensemble algorithm-based modeling: The case of central Highland of Ethiopia by Dereje Biru, Berhan Gessesse, Gebeyehu Abebe

“…This study explored the use of ensemble algorithm-based recursive feature elimination (RFE) for summer wheat LAI estimation using the Google Earth Engine (GEE) cloud computing platform. …”
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Feature-based ensemble modeling for addressing diabetes data imbalance using the SMOTE, RUS, and random forest methods: a prediction study by Younseo Jang

“…A feature-based ensemble model was constructed by training random forest classifiers on 10 two-feature subsets, selected based on feature importance, and combining their outputs using soft voting. …”
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Image-Based Malicious Network Traffic Detection Framework: Data-Centric Approach by Doo-Seop Choi, Taeguen Kim, Boojoong Kang, Eul Gyu Im

“…With effective feature engineering, we can reduce system resource consumption in the training period while maintaining high detection accuracy. …”
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A CT-based machine learning model for using clinical-radiomics to predict malignant cerebral edema after stroke: a two-center study by Lingfeng Zhang, Gang Xie, Yue Zhang, Yue Zhang, Junlin Li, Junlin Li, Wuli Tang, Wuli Tang, Ling Yang, Ling Yang, Kang Li

“…The radiomics features linked to MCE were pinpointed through a consistency test, Student’s t test and the least absolute shrinkage and selection operator (LASSO) method for selecting features. …”
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Prediction model for spontaneous combustion temperature of coal based on PSO-XGBoost algorithm by Hui Zhuo, Tongren Li, Wei Lu, Qingsong Zhang, Lingyun Ji, Jinliang Li

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An Accurate Book Spine Detection Network Based on Improved Oriented R-CNN by Haibo Ma, Chaobo Wang, Ang Li, Aide Xu, Dong Han

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An Evaluation of Mine Water Inrush Based on Data Expansion and Machine Learning by Ye Zhang, Shoufeng Tang

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CNN-Based Classification of Optically Critical Cutting Tools with Complex Geometry: New Insights for CNN-Based Classification Tasks by Mühenad Bilal, Ranadheer Podishetti, Tangirala Sri Girish, Daniel Grossmann, Markus Bregulla

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Semiconductor Wafer Defect Recognition Based on Improved Coordinate Attention Mechanism by Hao He, Yuanjie Wei, Xionghao Lin, Minmin Zhu, Haizhong Zhang

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Smartphone-Based SPAD Value Estimation for Jujube Leaves Using Machine Learning: A Study on RGB Feature Extraction and Hybrid Modeling by Qi Wang, Ziyan Shi, Kaiyao Hou, Ning Yan, Cuiyun Wu, Xu Li

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A novel univariate feature selection with ANOVA F-test-based machine learning model for Intrusion Detection Framework of Robotics system by Narinder Verma, Neerendra Kumar, Kuljeet Singh, Abeer Aljohani, Anurag Sinha, Syed Abid Hussain

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TSF-MDD: A Deep Learning Approach for Electroencephalography-Based Diagnosis of Major Depressive Disorder with Temporal–Spatial–Frequency Feature Fusion by Wei Gan, Ruochen Zhao, Yujie Ma, Xiaolin Ning

“…These data are then processed by a model based on 3D-CNN and CapsNet, enabling comprehensive feature extraction across domains. …”
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