检索结果-内蒙古大学图书馆

2nd International Conference on Multidisciplinary engineering and Applied science, ICMEAS 2023

作者： Imam-Fulani, Yusuf Olayinka Faruk, Nasir Usman, Aliyu D. Abdulkarim, Abubakar Tekanyi, Abdoulie M.S Yaro, Abdulmalik S. Alozie, Emmanuel Garba, Salisu Adewole, Kayode S. Oloyede, Abdulkarim A. Sowande, Olugbenga A. Abdullahi Baba, Bashir Musa, Abdulwaheed Imoize, Agbotiname Lucky Adediran, Yinusa A. Olagunju, Hawau I. Taura, Lawan S. University of Ilorin Department of Telecommunication Science Ilorin Nigeria Ahmadu Bello University Department of Electronics and Telecommunication Engineering Zaria Nigeria Sule Lamido University Department of Information Technology Kafin Hausa Nigeria Sule Lamido University Directorate of Information and Communication Technology Kafin Hausa Nigeria Ahmadu Bello University Department of Electrical Engineering Zaria Nigeria Kampala International University Department of Electrical and Telecommunication Engineering Kampala Uganda University of Hradec Králové Faculty of Informatics and Management Department of Informatics and Qualitative Methods Hradec Králové Czech Republic Sule Lamido University Department of Software Engineering Kafin Hausa Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria Kwara State University Department of Electrical and Computer Engineering Malete Nigeria University of Johannesburg Institute for Intelligent Systems Johannesburg South Africa University of Lagos Department of Electrical and Electronics Engineering Lagos Nigeria Ruhr University Institute of Digital Communication Department of Electrical Engineering and Information Technology Bochum Germany University of Ilorin Department of Electrical and Electronics Engineering Ilorin Nigeria Sule Lamido University Department of Physics Kafin Hausa Nigeria

ISBN: (纸本)9798350358834

5G communication systems provide an end-to-end wireless connection to billions of users and devices across the globe. The quality of signals received during radio communication is notably influenced by the behavior of the radio propagation channel. A major parameter used in characterizing this channel is the Path Loss Exponent (PLE). Several works that have estimated and analyzed the PLE mostly considered the effect of distance and carrier frequency. However, the effect of base station antenna height and channel bandwidth on the PLE for 5G networks have not been adequately considered. To address this, the impact of antenna height of base station and channel bandwidth on the PLE within the 5G Frequency Range 1 (FR1) frequencies was investigated in this study, specifically at 800, 3500, and 5900 MHz. The licensed EDX Signal Pro software® with Cirrus high resolution global terrain and clutter data base was utilized to model, simulate and analyze the PLE for Kano City, Nigeria. Results showed that for the tested frequencies, an increase in either base station height or channel bandwidth leads to a significant reduction in the PLE. This study can be utilized by network planning engineers and the wireless research community to further improve network implementation and optimization toward understanding the behavior of signal propagation in 5G networks and beyond. © 2023 IEEE.

关键词： 5G mobile communication systems

来源：评论

学校读者我要写书评

暂无评论

EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic Optimization Algorithms

arXiv

引用

arXiv 2023年

作者： Peng, Mai Yazdani, Delaram She, Zeneng Yazdani, Danial Luo, Wenjian Li, Changhe Branke, Juergen Nguyen, Trung Thanh Gandomi, Amir H. Yang, Shengxiang Jin, Yaochu Yao, Xin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Research Institute Faculty of Engineering and Technology Liverpool John Moores University United Kingdom School of Computer Science and Technology Harbin Institute of Technology China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory China School of Artificial Intelligence Anhui University of Sciences & Technology China Information Systems Management and Analytics in Warwick Business School University of Warwick United Kingdom Faculty of Engineering & Information Technology University of Technology Sydney Australia Obuda University Hungary School of Computer Science and Informatics De Montfort University United Kingdom Department of Artificial Intelligence School of Engineering Westlake University China School of Computer Science University of Birmingham United Kingdom

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic Optimization LABoratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. © 2023, CC BY-NC-SA.

关键词： Optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Magnetoencephalography Brain Spectral Biomarkers of Alzheimer's Disease: A Promising Approach Demonstrated with the BioFIND dataset

Magnetoencephalography Brain Spectral Biomarkers of Alzheime...

引用

IEEE EMBS Conference on Biomedical engineering and sciences (IECBES)

作者： Alwani Liyana Ahmad Zamzuri Idris Ibrahima Faye Jose Sanchez-Bornot Roberto C. Sotero Damien Coyle Department of Fundamental and Applied Sciences Faculty of Science and Information Technology Universiti Teknologi PETRONAS Perak Malaysia Department of Neurosciences Hospital Universiti Sains Malaysia Kelantan Malaysia Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Kelantan Malaysia Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Kelantan Malaysia Centre for Intelligent Signal & Imaging Research (CISIR) Universiti Teknologi PETRONAS Perak Malaysia Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Derry~Londonderry UK Department of Radiology and Hotchkiss Brain Institute University of Calgary Calgary AB Canada The Bath Institute for the Augmented Human University of Bath Bath UK

ISBN: (数字)9798350383409

ISBN: (纸本)9798350383416

Introduction: Magnetoencephalography (MEG) is widely used to study neurodegenerative diseases, particularly Alzheimer's disease (AD). MEG is valuable for examining functional changes in the AD brain due to its non-invasive nature and excellent spatial and temporal resolution. AD is characterized by amyloid-beta and tau protein formation, which disrupts brain networks and leads to memory and cognitive impairments. Objective: This study aimed to evaluate power spectra analysis of brain activity using various source localization methods and machine learning (ML) classification analysis based on GLMNET and correlation thresholds to identify MEG-based features that improve differentiation between healthy controls (HC) and mild cognitive impairment (MCI). Methods: We used the BioFIND dataset, including MEG data from 324 participants (158 MCI, 166 HC). MEG data were analyzed using custom MATLAB scripts with SPM12 and OSL toolboxes. Source localization methods included Bayesian LORETA (COH), Multiple Sparse Priors (MSP), Empirical Bayesian Beamformer (EBB), exact Low-Resolution Tomography Analysis (eLORETA), and Linearly Constrained Minimum Variance (LCMV). Sensor-based analyses were also conducted. Logistic Regression with L1 penalty (GLMNET) was applied in both sensor and source spaces, with different correlation thresholds for magnetometer (MAG) and gradiometer (GRAD) signals separately. We used 20 Monte Carlo iterations with 10-fold nested cross-validation to assess classifier performance. Results: Optimal performance was achieved using the LCMV method on GRAD sensors with a 0.1 correlation threshold, resulting in 74.57% accuracy, 71.55% sensitivity, and a 73.29% F1 score. Conclusions: This study demonstrates the potential of MEG as a robust tool for distinguishing between individuals with MCI and HC. By evaluating various source localization methods and correlation thresholds, we identified that the LCMV beamformer applied to GRAD signals with a 0.1 correlation thresho

关键词： Location awareness Brain Correlation Monte Carlo methods Magnetoencephalography Tomography Bayes methods Alzheimer's disease Spatial resolution Spectral analysis

来源：评论

学校读者我要写书评

暂无评论

Sleep Position Detection for Closed-Loop Treatment of Sleep-Related Breathing Disorders

Sleep Position Detection for Closed-Loop Treatment of Sleep-...

引用

IEEE International Conference on Rehabilitation Robotics (ICORR)

作者： A. Breuss N. Vonau C. Ungricht E. Schwarz M. Irion M. Bradicich F. A. Grewe S. Liechti S. Thiel M. Kohler R. Riener E. Wilhelm Department of Health Science and Technology Sensory-Motor Systems Lab Institute of Robotics and Intelligent Systems ETH Zurich Zurich Switzerland Sleep Disorders Centre of the Department of Pulmonology of the University Hospital Zurich University Hospital of Zurich (USZ) Zurich Switzerland Spinal Cord Injury Center University Hospital Balgrist Zurich Switzerland Engineering and Technology Institute Faculty of Science and Engineering University of Groningen The Netherlands

ISBN: (数字)9781665488297

ISBN: (纸本)9781665488303

Reliable detection of sleep positions is essential for the development of technical aids for patients with position-dependent sleep-related breathing disorders. We compare personalized and generalizable sleeping position classifiers using unobtrusive eight-channel pressure-sensing mats. data of six male patients with confirmed position-dependent sleep apnea was recorded during three subsequent nights. Personalized position classifiers trained using leave-one-night-out cross-validation on average reached an F1-score of 61.3% for supine/non-supine and an F1-score of 46.2% for supine/lateral-left/lateral-right classification. The generalizable classifiers reached average F1-scores of 62.1% and 49.1% for supine/non-supine and supine/lateral-left/lateral-right classification, respectively. In-bed presence (“bed occupancy”) could be detected with an average F1-score of 98.1%. This work shows that personalized sleep-position classifiers trained with data from two nights have comparable performance to classifiers trained with large interpatient datasets. Simple eight-channel sensor mattresses can be used to accurately detect in-bed presence required for closed-loop systems but their use to classify sleep-positions is limited.

关键词： Pressure sensors Medical treatment Robot sensing systems Assistive robots Sleep apnea Robustness Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Atomistic Behavior of Deformed Au Thin Film during the Indentation Process

Atomistic Behavior of Deformed Au Thin Film during the Inden...

引用

Innovative Research in Applied science, engineering and Technology (IRASET),Conference on

作者： Abdelkarim Ou-khouya Hassane Mes-adi Meryem Taoufiki Imane Ait Ibrahim Amin El Harfouf Rachid Herbazi Mohamed Tahiri Process Engineering Computer Science and Mathematics Laboratory Khouribga National School of Applied Sciences Sultan Moulay Slimane University Khouribga Morocco Faculty of Sciences and Techniques Sustainable Development Laboratory Sultan Moulay Slimane University Beni-Mellal Morocco Departement of Physics Laboratory of Condensed Matter and interdisciplinary Sciences CNRST Labeled Research Unit Faculty of Sciences Mohamed V University Rabat Morocco Department of Physics Polydisciplinary Faculty of Khouribga University of Sultan Moulay Slimane Khouribga Morocco Intelligent Systems and Applications Laboratory (LSIA) EMSI Tangier Morocco

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

In this study, we investigate the mechanical properties of gold (Au) materials using molecular dynamics simulations. In this simulation, the nanoindentation process was performed to analyze the deformation defects and hardness properties of Au under different indentation velocities. The results suggest that the deformation behavior of the Au specimen was affected by different indentation velocities. These findings were confirmed by examining the load-displacement curve, which indicated elastic deformation through the appearance of a linear load-displacement relationship at low indentation velocities. In contrast, plastic deformation was identified by the presence of intensified serrations in the load-displacement curve at higher indentation velocities. Under such conditions, the hardness exhibited minimal variation when the indenter velocity increased from 5 mls to 10 mls. However, a significant increase in hardness was noted when the indentation velocity was further raised to 15 mls.

关键词： Deformable models Gold Analytical models Deformation Dynamics Plastics Load modeling

来源：评论

学校读者我要写书评

暂无评论

A CRISPR/Cas12a-empowered surface plasmon resonance platform for rapid and specific diagnosis of the Omicron variant of SARS-CoV-2

引用

National science Review 2022年第8期9卷 100-109页

作者： Zhi Chen Jingfeng Li Tianzhong Li Taojian Fan Changle Meng Chaozhou Li Jianlong Kang Luxiao Chai Yabin Hao Yuxuan Tang Omar A.Al-Hartomy Swelm Wageh Abdullah G.Al-Sehemi Zhiguang Luo Jiangtian Yu Yonghong Shao Defa Li Shuai Feng William J.Liu Yaqing He Xiaopeng Ma Zhongjian Xie Han Zhang Shenzhen Engineering Laboratory of Phosphorene and Optoelectronics International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology of Ministry of Education Shenzhen Institute of Translational Medicine Department of Otolaryngology Shenzhen Second People’s Hospital the First Affiliated Hospital Institute of Microscale Optoelectronics Shenzhen University Shenzhen International Institute for Biomedical Research Shenzhen Han’s Tech Limited Company Shenzhen Metasensing Tech Limited Company Department of Physics Faculty of Science King Abdulaziz University Research Center for Advanced Materials Science (RCAMS) King Khalid University Department of Chemistry College of Science King Khalid University Zhongmin (Shenzhen) Intelligent Ecology Co.Ltd Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University Department of Laboratory Medicine Shenzhen Children’s Hospital Optoelectronics Research Center School of Science Minzu University of China NHC Key Laboratory of Biosafety National Institute for Viral Disease Control and Prevention Chinese Center for Disease Control and Prevention Research Unit of Adaptive Evolution and Control of Emerging Viruses Chinese Academy of Medical Sciences Institute of Pathogenic Organism Shenzhen Center for Disease Control and Prevention Respiratory Department Shenzhen Children’s Hospital Institute of Pediatrics Shenzhen Children’s Hospital

The outbreak of the COVID-19 pandemic was partially due to the challenge of identifying asymptomatic and presymptomatic carriers of the virus, and thus highlights a strong motivation for diagnostics with high sensitivity that can be rapidly deployed. On the other hand, several concerning SARS-Co V-2 variants,including Omicron, are required to be identified as soon as the samples are identified as ‘positive'.Unfortunately, a traditional PCR test does not allow their specific identification. Herein, for the first time,we have developed MOPCS(Methodologies of Photonic CRISPR Sensing), which combines an optical sensing technology-surface plasmon resonance(SPR) with the ‘gene scissors' clustered regularly interspaced short palindromic repeat(CRISPR) technique to achieve both high sensitivity and specificity when it comes to measurement of viral variants. MOPCS is a low-cost, CRISPR/Cas12a-systemempowered SPR gene-detecting platform that can analyze viral RNA, without the need for amplification,within 38 min from sample input to results output, and achieve a limit of detection of 15 fM. MOPCS achieves a highly sensitive analysis of SARS-Co V-2, and mutations appear in variants B.1.617.2(Delta),B.1.1.529(Omicron) and BA.1(a subtype of Omicron). This platform was also used to analyze some recently collected patient samples from a local outbreak in China, identified by the Centers for Disease Control and Prevention. This innovative CRISPR-empowered SPR platform will further contribute to the fast, sensitive and accurate detection of target nucleic acid sequences with single-base mutations.

关键词： surface plasmon resonance CRISPR SARS-CoV-2 Omicron variant

来源：评论

学校读者我要写书评

暂无评论

Enhanced Photocatalytic Degradation of Methylene Blue Using Bimetallic C,N-Doped NiO/ZnO Derived from Metal-Organic Frameworks

SSRN

引用

SSRN 2024年

作者： Zulfa, Liyana Labiba Hidayat, Alvin Romadhoni Putra Ali, Badrut Tamam Ibnu Utomo, Wahyu Prasetyo Kusumawati, Etty Nurlia Hartanto, Djoko Ediati, Ratna Widyastuti Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Jl. Arif Rahman Hakim Kampus ITS Keputih-Sukolilo Surabaya60111 Indonesia Research Center for Process and Manufacturing Industry Technology National Research and Innovation Agency Technology Energy Cluster PUSPIPTEK South Tangerang625 Building Indonesia School of Energy and Environment City University of Hong Kong 999077 Hong Kong Chemistry Course Faculty of Science and Engineering Iwate University 4-3-5 Ueda Iwate Morioka020-8551 Japan Department of Materials and Metallurgical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Jl. Arif Rahman Hakim Kampus ITS Keputih-Sukolilo Surabaya60111 Indonesia

Organic waste pollution is a severe environmental and human health issue that needs to be addressed urgently. Photocatalysis has emerged as an effective method for removing contaminants from organic wastes. Porous metal oxides derived from metal-organic frameworks (MOFs) have been widely developed and applied to organic waste degradation processes. This study successfully synthesized p-n heterojunction NiO-ZnO doped with carbon and nitrogen using Ni-ZIF-8 as a template through a two-step calcination process. The inert atmosphere preserved the carbon and nitrogen contents present in Ni-ZIF-8, resulting in carbon- and nitrogen-doped NiO-ZnO. The presence of the p-n heterojunction system and carbon and nitrogen doping was confirmed by X-ray Diffraction (XRD), X-ray Photoelectron Spectroscopy (XPS), and optical characterization. Carbon and nitrogen-doped NiO-ZnO exhibit broad light absorption, low recombination rates, and low charge resistance, which are beneficial for methylene blue (MB) degradation. Specifically, C, N-NiO(20)-ZnO demonstrated the best MB degradation performance, achieving ~100% degradation within 150 min and maintaining high stability over four cycles. Scavenger tests indicated that •OH played a crucial role in MB degradation. This research provides new insights into the synergistic effect of the p-n heterojunction system and carbon and nitrogen doping to enhance the degradation performance of organic waste. © 2024, The Authors. All rights reserved.

关键词： Semiconductor doping

来源：评论

学校读者我要写书评

暂无评论

Recent advances of CDs-based ternary heterojunctions for enhancing photocatalytic performance in the degradation of organic pollutants: A review

引用

Nano-Structures and Nano-Objects 2024年 37卷

作者： Zulfa, Liyana Labiba Oktavianti, Nanik Ismi Hidayat, Alvin Romadhoni Putra Utomo, Wahyu Prasetyo Putri, Desindy Ramadhani Hartanto, Djoko Widyastuti Ediati, Ratna Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Jl. Arif Rahman Hakim Kampus ITS Keputih-Sukolilo Surabaya 60111 Indonesia School of Energy and Environment City University of Hong Kong Hong Kong 999077 China Department of Materials and Metallurgical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Jl. Arif Rahman Hakim Kampus ITS Keputih-Sukolilo Surabaya 60111 Indonesia

Wastewater containing organic pollutants, such as dyes, antibiotics or pesticides, is a major environmental problem. Photocatalysis is an effective, environmentally friendly and economically viable method for degrading hazardous organic pollutants in wastewater. Among the heterojunction systems, the ternary heterojunction system is the most effective system due to its higher electron transfer, excellent redox capacity and light-harvesting ability, which are advantageous in photocatalytic degradation of organic pollutants. Carbon dots (CDs) are often used as a component in ternary heterojunctions due to their strong absorption within the various regions of the solar spectrum, tunable band structure and up-conversion photoluminescence (UCPL) properties. The optical properties of CDs-based ternary heterojunctions enable faster migration of multiple carriers, effective utilization of sunlight and a shorter migration pathway which enhances the organic pollutant photodegradation efficiency. A profound understanding of the synergistic effects between CDs and semiconductors in ternary heterojunction systems as photocatalysts for organic pollutants degradation is essential for future development. In this review, we comprehensively discuss recent advances of CDs derived from various precursors and their utilization in ternary heterojunction photocatalysts. Furthermore, we conduct a critical analysis on the synergistic effect between CDs and semiconductors for fabricating photocatalysts with optimal organic pollutant degradation performance. In addition, we elaborate on the charge transfer reaction mechanisms and several factors that influence the performance of CDs-based ternary heterojunctions. We also present research gaps in the use of CDs-based heterojunctions for organic pollutant degradation and state future perspectives and research areas. This review provides an insightful discussion on the development of CDs-based ternary heterojunctions for organic pollutant degrada

关键词： Carbon dots Carbon dots-based ternary heterojunction Organic pollutants Photocatalytic Water treatment

来源：评论

学校读者我要写书评

暂无评论

Deep CNN-LSTM Autoencoder for Solar Irradiation Prediction

Deep CNN-LSTM Autoencoder for Solar Irradiation Prediction

引用

Artificial Intelligence & Green Energy (ICAIGE), IEEE International Conference on

作者： Ghizlene Cheikh Naourez Ben Hadj Boudour Ammar Kamil Amine Bentounes Hadj Adda Bentounes Arezki Larbi Energy Renewable Laboratory Science and Technology Faculty University of Abdel Hamid Ibn Badis Mostaganem Algeria LETI:Laboratory of Electronics and Information Technologies University of Sfax Engineering Schoolof Sfax (ENIS) Sfax Tunisia REGIM-Lab: REsearch Groups in Intelligent Machine University of Sfax National Engineering School of Sfax (ENIS) Sfax Tunisia Tuito-CNRS LIS Laboratory University of Sorbronne TUITO La Ciotat France CDSEE Laboratory Science and Technology Faculty University of Abdel Hamid Ibn Badis Mostaganem Algeria Signal and systems laboatory Science and Technology Faculty University of Abdel Hamid Ibn Badis Mostaganem Algeria

The hourly forecast of solar radiation is crucial for energy management, planning in solar systems and maximizing the efficiency of solar energy utilization. This paper presents a comparative study of deep learning models for solar irradiation prediction in the Sfax and Mostaganem regions. Four models were evaluated: Simple LSTM, LSTM-autoencoder (AE-LSTM), and CNN-LSTM autoencoder (AE-CNN-LSTM). Historical solar irradiation data was used as input variables for the models. The results demonstrated that the CNN-LSTM autoencoder model achieved the best performance with low RMSE and high accuracy of approximately 93%. This study showcases the effectiveness of deep learning models in solar irradiation prediction, which can contribute to improved energy planning and integration of solar power into the electric grid and provides insights into energy management and planning.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards Lightweight and Efficient Distributed Intrusion Detection Framework

Towards Lightweight and Efficient Distributed Intrusion Dete...

引用

2021 IEEE Global Communications Conference (GLOBECOM)

作者： Shuai Yuan Hongwei Li Rui Zhang Meng Hao Yiran Li Rongxing Lu School of Computer Science and Engineering University of Electronic Science and Technology of China China Chinese Academy of Sciences Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chongqing China Faculty of Computer Science University of New Brunswick Canada

ISBN: (纸本)9781728181059

Federated learning (FL), as a promising distributed learning paradigm, has put many efforts into distributed intrusion detection systems (IDS), for defending against various malicious attacks, such as SQL injection and DDoS attacks. Compared with traditional IDS based on centralized deep learning (DL), FL-based solutions require not to share users' raw data while yielding better detection performance. However, state-of-the-art FL-based methods still suffer from two key limitations: 1) insufficient detection performance on non-independent and identically distributed (non-IID) data, and 2) high communication and computational overheads due to the utilization of large-scale neural network models. In this paper, we propose a lightweight collaborative intrusion detection framework, called CoLGBM, the first of its kind in the regime of decentralized IDS, where decision tree and light gradient boosting machine (LGBM) are combined for constructing the detection scheme. The main insight is that through combining user-trained decision trees (each user's decision tree is derived from its own data with unique distribution), our framework can perform effectively on non-IID data while working efficiently for handling enormous samples. Compared with the current FL-based methods, our CoLGBM achieves higher accuracy and lower overhead on both IID and non-IID data. Extensive experiment results demonstrate our scheme with high-level performance.

关键词： Deep learning Distance learning Neural networks Intrusion detection Distributed databases SQL injection Boosting

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：