检索结果-内蒙古大学图书馆

A new hybrid approach for intrusion detection using machine learning methods

APPLIED INTELLIGENCE 2019年第7期49卷 2735-2761页

作者： Cavusoglu, Unal Sakarya Univ Dept Comp Engn TR-54187 Serdivan Sakarya Turkey

In this study, a hybrid and layered Intrusion Detection System (IDS) is proposed that uses a combination of different machine learning and feature selection techniques to provide high performance intrusion detection in different attack types. In the developed system, firstly data preprocessing is performed on the NSL-KDD dataset, then by using different feature selection algorithms, the size of the dataset is reduced. Two new approaches have been proposed for feature selection operation. The layered architecture is created by determining appropriate machine learning algorithms according to attack type. Performance tests such as accuracy, DR, TP Rate, FP Rate, F-Measure, MCC and time of the proposed system are performed on the NSL-KDD dataset. In order to demonstrate the performance of the proposed system, it is compared with the studies in the literature and performance evaluation is done. It has been shown that the proposed system has high accuracy and a low false positive rates in all attack types.

关键词： Intrusion detection system machine learning algorithm Hybrid system Feature selection NSL-KDD

来源：评论

学校读者我要写书评

暂无评论

Detailed analysis of mass transfer in solar food dryer with different methods

引用

INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2021年 128卷 105600-105600页

作者： Das, Mehmet Alic, Erdem Akpinar, Ebru Kavak Tokat Gaziosmanpasa Univ Mech Engn Dept TR-60250 Tokat Turkey Kahramanmaras Sutcu Imam Univ Andirin Vocat High Sch TR-46410 Andirin Kahramanmaras Turkey Firat Univ Mech Engn Dept TR-23279 Elazig Turkey

In this study, the mass transfer, effective diffusion coefficient (Deff), activation energy (Ea), and drying efficiency values of the product dried in the solar food drying system were experimentally investigated. It was determined that Deff values, Ea values, and drying efficiency values calculated with experimental data varied between 0.15 x 10(-10) -0.1 x 10(-10) m(2)/s, between 21.6 and 15.9 kJ/mol, and between 51.8% and 15.9%, respectively. Mass transfer values in the drying system are modeled in detail with four different methods such as numerical analysis method (CFD), machine learning algorithms (ANN, SVM) and drying kinetics mathematical models (Newton, Page, Midilli and Kucuk, Thompson). The root means square error (RMSE) error analysis method was used to determine the validation of the models. For mass transfer studies, the moisture content (MR) values of the 10 mm thick product were taken into account. The novelty of this study is that mass transfer, which is an important parameter for drying system design and product selection, is expressed by mathematical empirical equations, artificial intelligence methods, and numerical analysis methods. The mass transfer of the product is shown by numerical analysis simulation with moisture contours. The method that models the MR values with the least error (0.01 RMSE) is the SVM algorithm.

关键词： Mass transfer Solar energy CFD machine learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Big data simulation for financial risk assessment of real estate bubble based on embedded system and artificial intelligence algorithm

引用

MICROPROCESSORS AND MICROSYSTEMS 2021年 82卷 103941-103941页

作者： Zhang, Nannan BoHai Univ Sch Management Jinzhou 121000 Liaoning Peoples R China

The land is accepted as having oscillation examples and slacks from business change. Driving pointers and a few slacks of each wheel somewhat increase the risk of backing acceptance and silly desire inland giving companies. The board should consider the risks from diversity and desire and the risks from business tasks and administrations. Different types and risk levels for land board companies may differ from single financial experts. The administration of these business activities' risk is exceptional between the sizes of the companies, the scope of administration, and the geographical area and board of less land, working on and adjusting to private property. Therefore, options regarding land venture valuation should not simply be considered. Benefits, yet additionally accurately assess risk. The advantages and disadvantages are simultaneous-the more important, the more noticeable the comparable risk. Many domestic and unknown developing and economists are concerned about this problem. Develop a hypothesis and strategy for land venture risk - this will give land speculators some significant help in their venture options.

关键词： Financial risk assessment Real estate bubble Embedded system machine learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Rapid elemental composition measurement of commercial pellets using line-scan hyperspectral imaging analysis

引用

ENERGY 2021年 220卷 119698-119698页

作者： Pitak, Lakkana Sirisomboon, Panmanas Saengprachatanarug, Khwantri Wongpichet, Seree Posom, Jetsada Khon Kaen Univ Dept Agr Engn Fac Engn Khon Kaen 40002 Thailand King Mongkuts Inst Technol Ladkrabang Dept Agr Engn Fac Engn Bangkok 10520 Thailand Khon Kaen Univ Dept Agr Engn North East Res Grp Appl Engn Important CropsFac Engn Khon Kaen 40002 Thailand Khon Kaen Univ Dept Agr Engn Biosensing & Field Robot Lab KKU Fac Engn Khon Kaen 40002 Thailand

The use of biomass pellets as a renewable energy source is increasing, leading to the need for rapid assessment of biofuel pellet quality for production monitoring. The purpose of this work was to use line scan near-infrared (NIR) hyperspectral image technology coupled with chemometric tools to assess the elemental components of biomass pellets. The parameters influencing model performance were investigated, i.e. wavelength and spectral pretreatment technique. Either full wavelength or partial wavelength selected using interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA) were investigated. Either raw spectra or pretreated spectra were used for model development. The models were developed using partial least squares regression (PLSR). The most effective model for the prediction of carbon (C), hydrogen (H), and nitrogen (N) content was developed using iGA wavelength selection and standard normal variate (SNV) spectral pretreatment and provided the highest accuracy with a coefficient of determination of prediction set (r2p) and standard error of prediction (SEP) of 0.83 and 1.33%;0.84 and 0.17%;and 0.90 and 0.098%, respectively. The model could be used for quality assurance. The S content model was poor and not recommended. The relationship between pellet chemical parameters and reflectance characteristics could be used for predicting C, H, and N of biomass pellets. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Elemental composition Biofuel pellets Line-scan hyperspectral imaging machine learning algorithm Wavelength selection method Partial least squares regression

来源：评论

学校读者我要写书评

暂无评论

AI@ntiPhish - machine learning Mechanisms for Cyber-Phishing Attack

引用

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2019年第5期E102D卷 878-887页

作者： Chen, Yu-Hung Chen, Jiann-Liang Natl Taiwan Univ Sci & Technol Taipei Taiwan Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei Taiwan TREND MICRO Inc Tokyo Japan

This study proposes a novel machine learning architecture and various learning algorithms to build-in anti-phishing services for avoiding cyber-phishing attack. For the rapid develop of information technology, hackers engage in cyber-phishing attack to steal important personal information, which draws information security concerns. The prevention of phishing website involves in various aspect, for example, user training, public awareness, fraudulent phishing, etc. However, recent phishing research has mainly focused on preventing fraudulent phishing and relied on manual identification that is inefficient for real-time detection systems. In this study, we used methods such as ANOVA, X-2, and information gain to evaluate features. Then, we filtered out the unrelated features and obtained the top 28 most related features as the features to use for the training and evaluation of traditional machine learning algorithms, such as Support Vector machine (SVM) with linear or rbf kernels, Logistic Regression (LR), Decision tree, and K-Nearest Neighbor (KNN). This research also evaluated the above algorithms with the ensemble learning concept by combining multiple classifiers, such as Adaboost, bagging, and voting. Finally, the eXtreme Gradient Boosting (XGBoost) model exhibited the best performance of 99.2%, among the algorithms considered in this study.

关键词： anti-phishing machine learning algorithm ensemble learning mechanism cyber attack

来源：评论

学校读者我要写书评

暂无评论

machine learning Prediction Models for Diagnosing Hepatocellular Carcinoma with HCV-related Chronic Liver Disease

引用

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020年 196卷 105551-105551页

作者： Hashem, Somaya ElHefnawi, Mahmoud Habashy, Shahira El-Adawy, Mohamed Esmat, Gamal Elakel, Wafaa Abdelazziz, Ashraf Omar Nabeel, Mohamed Mahmoud Abdelmaksoud, Ahmed Hosni Elbaz, Tamer Mahmoud Shousha, Hend Ibrahim Natl Res Ctr Syst & Informat Dept Engn Res Div Giza Egypt Helwan Univ Fac Engn Commun Elect & Comp Dept Cairo Egypt Cairo Univ Diagnost & Intervent Radiol Dept Cairo Egypt Cairo Univ Fac Med Endem Hepatogastroenterol Dept Cairo Egypt Natl Res Ctr Ctr Excellence Med Res Biomed Informat & Chemoinformat Grp Cairo Egypt

Background and Objective: Considered as one of the most recurrent types of liver malignancy, Hepatocellular Carcinoma (HCC) needs to be assessed in a non-invasive way. The objective of the current study is to develop prediction models for Chronic Hepatitis C (CHC)-related HCC using machine learning techniques. Methods: A dataset, for 4423 CHC patients, was investigated to identify the significant parameters for predicting HCC presence. In this study, several machine learning techniques (Classification and regression tree, alternating decision tree, reduce pruning error tree and linear regression algorithm) were used to build HCC classification models for prediction of HCC presence. Results: Age, alpha-fetoprotein (AFP), alkaline phosphate (ALP), albumin, and total bilirubin attributes were statistically found to be associated with HCC presence. Several HCC classification models were constructed using several machine learning algorithms. The proposed HCC classification models provide adequate area under the receiver operating characteristic curve (AUROC) and high accuracy of HCC diagnosis. AUROC ranges between 95.5% and 99%, plus overall accuracy between 93.2% and 95.6%. Conclusion: Models with simplistic factors have the power to predict the existence of HCC with outstanding performance. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Hepatocellular Carcinoma prediction machine learning algorithm Decision learning Tree Serum marker Hepatitis C virus

来源：评论

学校读者我要写书评

暂无评论

Soil salinity inversion based on novel spectral index

引用

ENVIRONMENTAL EARTH SCIENCES 2021年第16期80卷 501-501页

作者： Zhou, Xiaohong Zhang, Fei Liu, Changjiang Kung, Hsiang-te Johnson, Verner Carl Xinjiang Univ Coll Resources & Environm Sci Key Lab Smart City & Environm Modeling Higher Edu Urumqi 830046 Peoples R China Xinjiang Univ Key Lab Oasis Ecol Urumqi 830046 Peoples R China Natl Adm Surveying Mapping & Geoinformat Engn Res Ctr Cent Asia Geoinformat Dev & Utilizat Urumqi 830002 Peoples R China Univ Memphis Dept Earth Sci Memphis TN 38152 USA Colorado Mesa Univ Dept Phys & Environm Sci Grand Junction CO 81501 USA

Soil salinization is one of the most important causes for land degradation and desertification and is an important threat to land management, farming activities, water quality, and sustainable development, with the management of saline soil crucial in arid and semi-arid areas. The salt-affected soil is predominant in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in the Northwestern China. It influences the development of agricultural economy. Rapid and accurate measurement of the soil salt content (SSC) is significant for the soil salinization control. However, the traditional method of obtaining soil salt is time-consuming and laborious. Nowadays, it is an unprecedented perspective to monitor soil salinity through Sentinel-2A multispectral remote sensing image to construct three-dimensional spectral index. In this study, through soil salt data of 97 ground-truth measurements, Sentinel-2A data-derived spectral indices, based on particle swarm optimization support vector machine (PSO-SVM), gray wolf optimization support vector machine (GWO-SVM) and differential evolution support vector machine (DE-SVM) algorithm to construct a best soil salt inversion models. The results show that three-band (3D) spectral index has better correlation with soil salinity than single band and two-band (2D) spectral index, among TBI5 and TBI7 has a high correlation with the salinity of the soil, and the points are concentrated on the 1:1 line. Therefore, this approach could be applied to estimate the soil salinity in the Ebinur Lake region. The established models were validated using the machine learning algorithm. The DE-SVM model performed the best by the three Model of accuracy with R-2, Bias, and SEPC2 of 0.56, - 2.03, and 8.62, respectively. Therefore through the soil salinity value predicted by the modeling constructs a linear relationship with the indexes TBI5 and TBI7, and draw the soil salt inversion map, soil salinity around the lake is relatively high, and decreases

关键词： machine learning algorithm Sentinel-2A Spectral indices

来源：评论

学校读者我要写书评

暂无评论

Validation of a Sensor-Based Gait Analysis System with a Gold-Standard Motion Capture System in Patients with Parkinson's Disease

引用

SENSORS 2021年第22期21卷 7680-7680页

作者： Jakob, Verena Kuederle, Arne Kluge, Felix Klucken, Jochen Eskofier, Bjoern M. Winkler, Juergen Winterholler, Martin Gassner, Heiko Sana Krankenhaus Rummelsberg Movement & Gait Lab D-90592 Schwarzenbruck Germany Friedrich Alexander Univ Erlangen Nurnberg FAU Dept Artificial Intelligence Biomed Engn AIBE Machine Learning & Data Analyt Lab D-91052 Erlangen Germany Univ Luxembourg Digital Med L-4365 Luxembourg Luxembourg Luxembourg Inst Hlth Digital Med 1445 Str Luxembourg Luxembourg Friedrich Alexander Univ Erlangen Nurnberg FAU Univ Hosp Erlangen Dept Mol Neurol D-91054 Erlangen Germany Sana Krankenhaus Rummelsberg Dept Neurol D-90592 Schwarzenbruck Germany Fraunhofer Inst Integrated Circuits IIS Fraunhofer IIS D-91058 Erlangen Germany

Digital technologies provide the opportunity to analyze gait patterns in patients with Parkinson's Disease using wearable sensors in clinical settings and a home environment. Confirming the technical validity of inertial sensors with a 3D motion capture system is a necessary step for the clinical application of sensor-based gait analysis. Therefore, the objective of this study was to compare gait parameters measured by a mobile sensor-based gait analysis system and a motion capture system as the gold standard. Gait parameters of 37 patients were compared between both systems after performing a standardized 5 x 10 m walking test by reliability analysis using intra-class correlation and Bland-Altman plots. Additionally, gait parameters of an age-matched healthy control group (n = 14) were compared to the Parkinson cohort. Gait parameters representing bradykinesia and short steps showed excellent reliability (ICC > 0.96). Shuffling gait parameters reached ICC > 0.82. In a stridewise synchronization, no differences were observed for gait speed, stride length, stride time, relative stance and swing time (p > 0.05). In contrast, heel strike, toe off and toe clearance significantly differed between both systems (p < 0.01). Both gait analysis systems distinguish Parkinson patients from controls. Our results indicate that wearable sensors generate valid gait parameters compared to the motion capture system and can consequently be used for clinically relevant gait recordings in flexible environments.

关键词： Parkinson's disease wearables inertial sensors three-dimensional gait analysis machine learning algorithm spatiotemporal gait parameters

来源：评论

学校读者我要写书评

暂无评论

Modified Maximum Curvature Method (MMCM) and Logistic Regression: A Hybrid Architecture for Finger Vein Biometric Recognition System 23

Modified Maximum Curvature Method (MMCM) and Logistic Regres...

引用

23rd International Conference on Computer and Information Technology (ICCIT)

作者： Naquib, Faizah Binte Tabassom, Sharika Elahee, Fariha Mim, Farhana Hossain, Tonmoy Kalpoma, Kazi A. Ahsanullah Univ Sci & Technol Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781665422444

The finger vein authentication system is a prominent field in biometric-based research that prevents identity theft by forgery or spoofing. However, as the finger images are affected by many environmental factors such as illumination or shifting during imaging, they are often noisy and have irregularity in thickness or brightness which can cause a decline in the verification accuracy. Therefore, a meticulous finger vein pattern extraction method along with an accurate classification is necessary. Though the Maximum Curvature Method (MCM) gives promising verification accuracy, it fails to tackle the stated limitations. For this purpose, we proposed a Modified Maximum Curvature Method (MMCM) for vein extraction. In this paper, a hybrid architecture for finger vein biometric recognition system is stated with the combination of proposed MMCM and Logistic Regression (LR) machine learning classifier. Proposed MMCM incorporates Finger Region Extraction, Image Enhancement using Contrast Limited Adaptive Histogram Equalization (CLAHE), and Affine transform Normalization. The authentication is then carried out by fusing the proposed feature extraction with a set of machine learning Classifiers and evaluated based on their Equal Error Rate (EER) on the public database SDUMLA-HMT. The combination of MMCM vein extraction and LR classifier gives a satisfactory low EER of 0.043.

关键词： Maximum Curvature Method machine learning algorithm Logistic Regression Finger Vein Extraction CLAHE Biometric Recognition System

来源：评论

学校读者我要写书评

暂无评论

Mapping Forest Height and Aboveground Biomass by Integrating ICESat-2, Sentinel-1 and Sentinel-2 Data Using Random Forest algorithm in Northwest Himalayan Foothills of India

引用

GEOPHYSICAL RESEARCH LETTERS 2021年第14期48卷 e2021GL093799-e2021GL093799页

作者： Nandy, Subrata Srinet, Ritika Padalia, Hitendra Govt India Indian Inst Remote Sensing Indian Space Res Org Dept Space Dehra Dun Uttarakhand India

The present study aims to map forest canopy height by integrating ICESat-2 and Sentinel-1 data and investigate the effect of integrating forest canopy height information with Sentinel-2 data-derived spectral variables on the prediction of spatial distribution of forest aboveground biomass (AGB). Random forest (RF) algorithm was used to develop forest canopy height and AGB models. It was observed that ICESat-2 and Sentinel-1 based model was able to predict forest canopy height with R-2 = 0.84 and %RMSE = 4.48%. Two forest AGB models were developed, with only spectral variables and by incorporating forest height information with spectral variables. The results reflected that incorporation of forest canopy height in the forest AGB model improved the accuracy of the AGB predictions (R-2 = 0.83, %RMSE = 4.64%). The study presents a comprehensive methodology for mapping forest canopy height and AGB.

关键词： Spaceborne LiDAR C-band SAR multi-spectral optical data spectral variables machine learning algorithm

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：