Harnessing self-supervised learning to boost malicious traffic detection with enhanced attention by SUN Jianwen, ZHANG Bin, LI Hongyu, CHANG Heyu

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Method on intrusion detection for industrial internet based on light gradient boosting machine by Xiangdong HU, Lingling TANG

“…Intrusion detection is a critical security protection technology in the industrial internet, and it plays a vital role in ensuring the security of the system.In order to meet the requirements of high accuracy and high real-time intrusion detection in industrial internet, an industrial internet intrusion detection method based on light gradient boosting machine optimization was proposed.To address the problem of low detection accuracy caused by difficult-to-classify samples in industrial internet business data, the original loss function of the light gradient boosting machine as a focal loss function was improved.This function can dynamically adjust the loss value and weight of different types of data samples during the training process, reducing the weight of easy-to-classify samples to improve detection accuracy for difficult-to-classify samples.Then a fruit fly optimization algorithm was used to select the optimal parameter combination of the model for the problem that the light gradient boosting machine has many parameters and has great influence on the detection accuracy, detection time and fitting degree of the model.Finally, the optimal parameter combination of the model was obtained and verified on the gas pipeline dataset provided by Mississippi State University, then the effectiveness of the proposed mode was further verified on the water dataset.The experimental results show that the proposed method achieves higher detection accuracy and lower detection time than the comparison model.The detection accuracy of the proposed method on the gas pipeline dataset is at least 3.14% higher than that of the comparison model.The detection time is 0.35s and 19.53s lower than that of the random forest and support vector machine in the comparison model, and 0.06s and 0.02s higher than that of the decision tree and extreme gradient boosting machine, respectively.The proposed method also achieved good detection results on the water dataset.Therefore, the proposed method can effectively identify attack data samples in industrial internet business data and improve the practicality and efficiency of intrusion detection in the industrial internet.…”
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Integrating explainable artificial intelligence and light gradient boosting machine for glioma grading by Teuku Rizky Noviandy, Ghalieb Mutig Idroes, Irsan Hardi

“…Methods: This study employs the Light Gradient Boosting Machine (LightGBM), an advanced ML algorithm, in combination with Explainable Artificial Intelligence (XAI) methodology to grade gliomas more effectively. …”
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Overcoming immunosuppression in cancer: how ketogenic diets boost immune checkpoint blockade by Victoria E. Stefan, Daniela D. Weber, Roland Lang, Barbara Kofler

“…The observed effects are ascribed to the ability of KDs to improve immune cell infiltration and induce downregulation of immune-inhibitory processes, thus creating a more immunogenic tumor microenvironment. …”
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AIR-SAND BLOWING PROCESS – A RETROSPECTIVE AND CURRENT STATE by G. I. Pasyuk, A. P. Melnikov, A. V. Pashkevich, A. V. Cherapovich, V. V. Fonov

Subjects: “…air-sand blowing process, system of the air-sand blowing devices, air-sand blowing installations, boost, an exhaust, compressed air, core box, the air-sand blowing tank, case, caseless option, venta, air-sand blowing nozzles, researches, development, designs, core mix, consolidation…”
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Development and Simulation of PID-Controller Fuel Cell System with DC-DC Boost Converter by Marghani Assia, Rerhrhaye Fathallah, Ennaciri Yassine, Bouguanssa Issam, El Ayachi Chater, El Akkary Ahmed, Sefiani Nacer

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Gradient Boosting Trees and Large Language Models for Tabular Data Few-Shot Learning by Carlos Huertas

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Beating Gradient Boosting: Target-Guided Binning for Massively Scalable Classification in Real-Time by Dymitr Ruta, Ming Liu, Ling Cen

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BoostSole: Design and Realization of a Smart Insole for Automatic Human Gait Classification by Badis Djamaa, Med Messaoud Bessa, Badreddine Diaf, Abdenebi Rouigueb, Ali Yachir

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Boosting grapevine phenological stages prediction based on climatic data by pseudo-labeling approach by Mehdi Fasihi, Mirko Sodini, Alex Falcon, Francesco Degano, Paolo Sivilotti, Giuseppe Serra

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Boosting oxygen evolution of LiCoO2 electrocatalysts via lithium defect by Huamei Li, Mengyuan Li, Lingling Liao, Han Yang, Kun Xiang, Guoqiang Luo, Mingjiang Xie

“…Density functional theory (DFT) calculations reveal that Li defects can influence the d-band center of active Co sites, enhancing the adsorption capabilities of Co sites towards *OOH intermediates and increasing the conductivity of the electrocatalyst during the OER process. These alterations improve the velocity of the crucial step in the reaction, ultimately boosting the catalyst's overall performance and efficiency.…”
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Research on Spread Spectrum Codes Optimized by Sparrow Search Algorithm and Extreme Gradient Boosting by LIANG Zhiru, BIAN Dongming, ZHANG Gengxin

“…【Methods】This paper proposes a method of using Sparrow Search Algorithm (SSA) to optimize Extreme Gradient Boosting (XGBOOST) for third-order correlation peak classification of direct spread signals to improve the accuracy of <italic>m</italic>-sequence classification and identification.…”
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Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm by Dai Hou, Zhiheng Yao, Bo Jin, Xingwei Cai, Huan Xu, Jiaxiang Xu, Tianping Deng

“…To solve this, this study offers an adaptive solution for dynamic, complex-weather scenarios within the IoT framework. A dynamic task-processing model was developed first, using real-time IoT sensor data for better decisions. …”
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Boosting GWAS using biological networks: A study on susceptibility to familial breast cancer. by Héctor Climente-González, Christine Lonjou, Fabienne Lesueur, GENESIS study group, Dominique Stoppa-Lyonnet, Nadine Andrieu, Chloé-Agathe Azencott

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Gradient Boosting Feature Selection for Integrated Fault Diagnosis in Series-Compensated Transmission Lines by Rab Nawaz, Abdul Wadood, Khawaja Khalid Mehmood, Syed Basit Ali Bukhari, Hani Albalawi, Aadel Mohammed Alatwi, Muhammad Sajid

“…The methodology is implemented using four ensemble classifiers: Adaptive Boosting (AB), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Extreme Gradient Boosting (XGB), and is rigorously evaluated on various standard models of differing complexity and system configurations under dynamic conditions. …”
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The relative data hungriness of unpenalized and penalized logistic regression and ensemble-based machine learning methods: the case of calibration by Peter C. Austin, Douglas S. Lee, Bo Wang

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Machine Learning Prediction of CO₂ Diffusion in Brine: Model Development and Salinity Influence Under Reservoir Conditions by Qaiser Khan, Peyman Pourafshary, Fahimeh Hadavimoghaddam, Reza Khoramian

“…This study employs three machine learning (ML) models—Random Forest (RF), Gradient Boost Regressor (GBR), and Extreme Gradient Boosting (XGBoost)—to predict DC based on pressure, temperature, and salinity. …”
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A New Proposal of Cellulosic Ethanol to Boost Sugarcane Biorefineries: Techno-Economic Evaluation by Juliana Q. Albarelli, Adriano V. Ensinas, Maria A. Silva

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Modulation and control of transformerless boosting inverters for three-phase photovoltaic systems: comprehensive analysis by Mostafa Wageh Lotfy, Ramadan Mahmoud Mostafa, Haitham S. Ramadan, Mahmoud F. Elmorshedy, Sherif M. Dabour

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Predicting the mechanical behavior in FDM printing of biopolymers through boosting artificial neural networks by M. Laurenti, I. Bavasso, E. Palazzi, J. Tirillò, F. Sarasini, F. Berto

“…The proposed approach leverages Artificial Neural Networks (ANNs), primarily utilizing a Gaussian/Gumbel-Gaussian Multi-Layer Perceptron (MLP) architecture enhanced by an encoder module derived from a Variational Autoencoder (VAE) trained on the input dataset to effectively compress and represent high-dimensional process data. To further bolster predictive performance, a boosting neural network scheme based on this architecture and a kernel based truncated Taylor expansion are integrated. …”
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