检索结果-内蒙古大学图书馆

Enhance data availability and network consistency using artificial neural network for IoT

MULTIMEDIA TOOLS AND APPLICATIONS 2022年第1期83卷 3111-3131页

作者： Tabassum, Mujahid Perumal, Sundresan Kashem, Saad Bin Abdul Ponnan, Suresh Chakraborty, Chinmay Chowdhury, Muhammad E. H. Khandakar, Amith Univ Sains Islam Malaysia Fac Sci & Technol Nilai 71800 Negeri Sembilan Malaysia Qatar Fdn Qatar Armed Forces Acad Bridge Program Robot & Adv Comp Fac Doha Qatar Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept ECE Chennai 600062 Tamil Nadu India Birla Inst Technol Elect & Commun Engn Jharkhand India Qatar Univ Coll Engn Dept Elect Engn Doha 2713 Qatar

IoT networks have become famous and utilized in many industries such as agriculture, medical, manufacture, and others due to their efficiency and productivity. WSN is an IoT network used for smart farming and smart health monitoring. WSNs can self-manage, self-configure, self-diagnose, and self-heal, making them ideal for agricultural monitoring. A wireless sensor network collects data from numerous sensor nodes scattered across the physical world. WSN data processing is critical when a node fails for unknown reasons. Data handling is an essential aspect of WSN;once any node fails due to unknown reasons, data reliability and availability become crucial. Hence, limited battery energy, low bandwidth, limited computing capacity, and link failure affect network performance. Therefore, an effective cluster-based data aggregation with an appropriate routing must be designed in the media access control. The proposed hybrid artificial neural network and decision tree algorithm with cognitive radio is developed to select the cluster head. The higher amount of residual energy increases the number of packets received at the base station and aggregate the data from the normal sensor nodes. The on-demand routing protocol is designed to keep data in local storage for retransmission during link failure to obtain reliable data transmission. The proposed method performance is analyzed as residual energy, end to end delay, normalized overhead, packet delivery ratio, packet drop, and throughput. This proposed method is evaluated with the cluster-based data aggregation scheme to prove its efficiency. The proposed method residual energy is 8.3Joules for 50 nodes;it is high compared to the cluster-based data aggregation scheme.

关键词： Artificial Neural Network Cluster base network Wireless Sensor Network Data aggregation decision tree algorithm On-demand routing protocol

来源：评论

学校读者我要写书评

暂无评论

Development of a Cloud-Based Clinical decision Support System for Ophthalmology Triage Using decision tree Artificial Intelligence

引用

OPHTHALMOLOGY SCIENCE 2023年第1期3卷 100231-100231页

作者： Tanya, Stuti M. Nguyen, Anne X. Buchanan, Sean Jackman, Christopher S. McGill Univ Dept Ophthalmol & Visual Sci Montreal PQ Canada McGill Univ Fac Med Montreal PQ Canada Jackman Eye Inst St John NF Canada Mem Univ Newfoundland Dept Surg Div Ophthalmol St John NF Canada

Purpose: Clinical decision support systems (CDSS) are an emerging frontier in teleophthalmology, drawing on heuristic decision making to augment processes such as triage and referral. We describe the development and implementation of a novel cloud-based decision tree CDSS for on-call ophthalmology consults. The objective was to standardize the triage and referral process while providing a more accurate provisional diagnosis and ***: Prospective comparative cohort ***: On-call referrals to a Canadian community ophthalmology ***: A web-based decision tree algorithm was developed using current guidelines and expert opinion. The algorithm collected tailored information on the patient's ophthalmic concern, and outputted a provisional diagnosis and urgency before sending an electronic referral to the on-call ophthalmology clinic. Data were described using descriptive statistics. Spearman-rho correlations and Cohen's kappa coefficient were used to characterize the observed relationships. Post hoc analysis was conducted using analysis of contingency tables and adjusted *** Outcome Measures: Diagnostic category, provisional diagnosis, and urgency for the referring pro-vider, CDSS, and ophthalmologist. Results: Ninety-six referrals were processed. Referring providers included medical doctors (76.0%, n = 73), optometrists (20.8%, n = 20), and nurse practitioners (3.1%, n = 3). The CDSS (k = 0.5898;95% confidence interval [CI], 0.4868-0.6928;P < 0.0001) performed equally well with 66.7% agreement in determining category when compared with referring providers (k = 0.5880;95% CI, 0.4798-0.6961;P < 0.0001). The CDSS (agreement = 53.1%;k = 0.4999;95% CI, 0.4021-0.5978;P < 0.0001) performed better than referring providers (agreement = 43.8%;k = 0.4191;95% CI, 0.3194-0.5188;P < 0.0001) in determining a diagnosis. The CDSS (r = 0.5014;95% CI, 0.3092-0.6935;P < 0.0001) also performed better than referring providers (r = 0.4035;95% CI, 0.2406-

关键词： Clinical decision support system decision tree algorithm Triage and referral Teleophthalmology Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Developing an Integrated Model for Heart Disease Diagnosis (IM-HDD) using ensemble classification methods

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第4期43卷 4161-4171页

作者： Mohammed, Parves Begum, S. Jabeen JNTUH Sree Chaitanya Coll Engn Hyderabad Telangana India Anna Univ Velalar Coll Engn & Technol Chennai Tamil Nadu India

In present scenario, Heart Disease has become the vital cause of mortality and diagnosis of heart diseases is a great confrontation in the field of medical data analysis. Data Mining is an efficient technique for processing and analyzing larger databases for deriving hidden knowledge appropriately. Hence, it is incorporated in medical data analysis for assisting in effective decision making and disease predictions. With that concern, this paper concentrates on framing an Integrated Model for Heart Disease Diagnosis (IM-HDD) using the advanced data mining conceits. The model considers the significant features of patient data that are available in benchmark datasets. Here, the main objective of the proposed model is to enhance the classification accuracy of patient data on classes under NORMAL and ABNORMAL. For enhancing the classification accuracy, the proposed integrated model utilizes the algorithms such as decision tree algorithm, Naive Baye's Classification and Ensemble Classifiers called Random Forest and Bagging. Further, performance evaluation is performed for analyzing the proposed work. For that, images from UCI repository are utilized and the comparative analysis shows that the proposed work produces better results than the existing models compared.

关键词： Heart disease diagnosis data mining classification accuracy decision tree algorithm Naive Baye's classification random forest and bagging

来源：评论

学校读者我要写书评

暂无评论

The Relationship between Computer Graphics Graphic Element Recognition and Judgment of Visual Impact Effect 3

The Relationship between Computer Graphics Graphic Element R...

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Zhong, Fan Wuhan Huaxia Institute of Technology Hubei Wuhan430223 China

ISBN: (纸本)9798350319125

The computer technology, computer Graphic element recognition and Judgment of visual impact effect have become an indispensable part of the design industry. How to use computer technology and algorithms to improve design efficiency and design quality has always been one of the research hotspots in the field of design. Therefore, a decision tree algorithm is proposed for the evaluation and analysis of innovative design strategies. Firstly, the design strategy evaluation requirements to reduce the interference factors in the design strategy evaluation. the design strategy evaluation scheme, and comprehensively analyzes the design strategy evaluation results. Finally, the relationship between computer graphic image design and Judgment of visual impact effect is analyzed through design strategy evaluation, and a design scheme based on decision tree algorithm is proposed for experimental verification. © 2023 IEEE.

关键词： composition design theory Computer graphics Graphic element recognition decision tree algorithm Judgment of visual impact effect

来源：评论

学校读者我要写书评

暂无评论

Modelling Soil Fertilizer Levels and Crop Yields in Agriculture Using Machine Learning

Modelling Soil Fertilizer Levels and Crop Yields in Agricult...

引用

2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Suma, S. Mamatha, B. Shweta, N. Raju, K. Srujan Bhagyashree, K.B. Rani, D. Sandhya Cmr Technical Campus Department of Cse Telangana Hyderabad India

ISBN: (纸本)9798350315455

Technology is everywhere in the 21st Century. So, for optimum results in each field, various methods to minimize loss and maximize benefits. The application of Machine Learning in crop type prediction for modern farming is crucial. Also, suggesting the type of fertilizer and amount can increase the application's usability. Various aspects of rain amount and other real-world parameters are taken into account when predicting crop yields and suggesting fertilizer applications. To predict output, the application uses the Flask web framework to store a large number of datasets. The application then creates a link between Random Forest algorithms, decision tree algorithms, and XGBoost algorithms. This work focuses on crops that are significant in the area chosen. Based on soil fertility and weather data, the proposed model predicts crop type. The type of fertilizer to use for the chosen crop should be determined. Predict crop yields using a fertilizer calculator and estimate the economic impact of cultivation by calculating the amount of fertilizer needed. © 2023 IEEE.

关键词： decision tree algorithm Flask web framework XGboost algorithm Random Forest algorithms

来源：评论

学校读者我要写书评

暂无评论

An SDN-based decision tree Detection (DTD) Model for Detecting DDoS Attacks in Cloud Environment

引用

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第7期13卷 54-64页

作者： Praba, J. Jeba Sridaran, R. Christ Coll Dept Comp Applicat Rajkot India Marwadi Univ Rajkot Gujarat India Marwadi Univ Fac Comp Applicat Rajkot Gujarat India

Detecting Distributed Denial of Service (DDoS) attacks has become a significant security issue for various network technologies. This attack has to be detected to increase the system's reliability. Though various traditional studies are present, they suffer from data shift issues and accuracy. Hence, this study intends to detect DDoS attacks by classifying the normal and malicious traffic. The study aims to solve the data shift issues by using the introduced decision tree Detection (DTD) model encompassing of Greedy Feature Selection (GFS) algorithm and decision tree algorithm (DTA). It also attempts to enhance the proposed model's detection rate (accuracy) above 90%. Various processes are involved in DDoS attack detection. Initially, the gureKddcup dataset is loaded to perform pre-processing. This process is essential for removing noisy data. After this, feature selection is performed to select only the relevant features, removing the irrelevant data. This is then fed into the train and test split. Following this, Software Defined Networking (SDN) based DTA is used to classify the normal and malicious traffic, then given to the trained model for predicting this attack. Performance analysis is undertaken by comparing the proposed model with existing systems in terms of accuracy, MCC (Matthew's Correlation Coefficient), sensitivity, specificity, error rate, FAR (False Alarm Rate), and AUC (Area under Curve). This analysis is carried out to evaluate the efficacy of the proposed model, which is verified through the results.

关键词： -Distributed denial of service attack greedy feature selection decision tree algorithm software defined networking cloud and decision tree detection

来源：评论

学校读者我要写书评

暂无评论

Detection of DDoS Attack using Machine Learning

Detection of DDoS Attack using Machine Learning

引用

2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Prathiksha, S.V. Meenakshi Sundaram, A. Reva University Bangalore India

ISBN: (纸本)9798350308167

In particular, Distributed Denial of Service (DDoS) attacks have become more sophisticated due to the internet's rapid growth. It is essential for dependable internet services to identify and stop these attacks. This study combines the Naive Bayes and decision tree machine learning methods to distinguish between DDoS attacks and safe traffic. Through recursive partitioning, the Naive Bayes classification method provides interpretability whereas the decision tree classifier performs well for high-dimensional datasets. We suggest a technique that makes use of both techniques, and we evaluate how well it works using a large amount of data. After training on the same data, the algorithms identify attack patterns using the features capturing network properties that were extracted. Through the implementation of feature engineering and preprocessing methods, the interaction between DDOS and BENIGN patterns is highlighted, improving accuracy. © 2023 IEEE.

关键词： classification DDoS attack decision tree algorithm machine learning Naïve Bayes algorithm network security

来源：评论

学校读者我要写书评

暂无评论

Spectral fusion-based machine learning classifiers for discriminating membrane breakage in multiple scenarios

引用

WATER RESEARCH 2024年 257卷 121714页

作者： Yu, Yang Jia, Hui Gao, Fei Zhu, Haifeng Zhang, Lei Wang, Jie Tiangong Univ State Key Lab Separat Membranes & Membrane Proc Tianjin 300387 Peoples R China Tiangong Univ Sch Environm Sci & Engn Tianjin 300387 Peoples R China Shenyang Acad Environm Sci Shenyang 110167 Peoples R China Cangzhou Inst Tiangong Univ Hebei Ind Technol Res Inst Membranes Cangzhou 061000 Peoples R China

Membrane breakage can lead to filtration failure, which allows harmful substances to enter the effluent, posing potential hazards to human health and the environment. This study is an innovative combination of fluorescence and ultraviolet-visible (UV-Vis) spectroscopy to identify membrane breakage. It aims to unravel more comprehensive information, improve detection sensitivity and selectivity, and enable real-time monitoring capabilities. Fluorescence and UV-Vis data are extracted through variance partitioning analysis (VPA) and integrated through a decision tree algorithm to form a superior system with enhanced discrimination capabilities. VPA improves discrimination efficiency by extracting key information from spectral data and eliminating redundancy. The decision tree algorithm, on the other hand, can process large amounts of data simultaneously. In addition, the method has a wide range of applications and can be used in various scenarios accurately. The scenarios include domestic sewage, micropollutant water, aquaculture wastewater, and secondary treated sewage. The experimental results validate the application of machine learning classifiers in membrane breakage detection with an accuracy rate of 96.8 % to 97.4 %.

关键词： Membrane breakage Fluorescence spectroscopy Ultraviolet -visible spectroscopy decision tree algorithm Spectral fusion

来源：评论

学校读者我要写书评

暂无评论

Research on E-learning interactive English vocabulary recommendation education system based on naive Bayes algorithm

引用

ENTERTAINMENT COMPUTING 2024年 51卷

作者： Xi, Wang Changzhou Vocat Inst Mechatron Technol Changzhou 213164 Jiangsu Peoples R China

E-learning is an interactive online learning mode that can enhance students' interest in learning in an entertainment environment. This article aims to develop an English vocabulary learning recommendation system based on decision tree algorithm and naive Bayesian algorithm to provide personalized learning suggestions and help learners learn and remember words more effectively. Firstly, a large amount of English vocabulary data was collected, preprocessed, and feature extracted. decision tree algorithms were selected as the foundation, and through analysis of existing data, a decision tree was generated to classify new data. Using learners' characteristics (such as age, learning objectives, etc.) and learning history (such as learned words, learning time, etc.) as inputs, a personalized recommendation model was constructed, This model can recommend suitable learning content for learners based on their personalized needs and learning situation. In order to better understand learners' learning progress, a naive Bayesian model was trained using learners' learning history and progress information to analyze their current learning progress and predict their future learning situation. Through testing and evaluation of actual learners, the recommendation system performs well in providing personalized learning suggestions, and has a significant improvement effect on learners' vocabulary learning.

关键词： decision tree algorithm Naive Bayesian algorithm E-learning Recommendation system

来源：评论

学校读者我要写书评

暂无评论

A Method for Evaluating the Performance of Unmanned Aerial Navigation Systems Based on Fuzzy decision trees 13th

A Method for Evaluating the Performance of Unmanned Aerial N...

引用

13th China Satellite Navigation Conference (CSNC) - Digital Economy and Intelligent Navigation

作者： Chen, Changyuan Shi, Chunlin Liu, Teng Li, Haojie Li, Qiyang Ma, Li Informat Engn Univ Zhengzhou Peoples R China Northwestern Polytech Univ Xian Peoples R China Coll Aeronaut Engn Xian Peoples R China Changchun Univ Sci & Technol Changchun Peoples R China

ISBN: (纸本)9789811925887;9789811925870

In the process of application, unmanned aerial vehicles (UAVs) are faced with the threat of a complex electromagnetic environment, causing the airborne navigation system to fail to achieve ideal working conditions, and seriously restricting the overall effectiveness of the UAV system. Therefore, accurate assessment of UAV navigation system performance is an important prerequisite for scientific decision-making on the use of UAVs in complex environments in the future. In system design and experimentation, the more realistic electromagnetic interference environments that UAVs may be exposed to are difficult to build, resulting in insufficient data being collected to verify UAV navigation system performance. However, as real data from UAVs is continuously accumulated in various application scenarios at a later stage, it is necessary to explore a data mining-based method for UAV navigation system performance evaluation. In this study, a decision tree algorithm was used to construct a UAV navigation performance level classification assessment model. UAV performance was analysed through UAV navigation capability and survivability indicators. Finally, simulation experiments were conducted to further develop the construction and testing of the decision tree model. The experimental results showed that the model accuracy is above 90%, validating the feasibility of the method.

关键词： UAV navigation Electromagnetic environment Performance evaluation Data mining decision tree 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：