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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

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Classification of start-ups' digital marketing adoption experiences: an investigation of characteristics and interactions

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FUTURE BUSINESS JOURNAL 2025年第1期11卷 1-10页

作者： El-Shihy, Dina Hassan, Noha NewGiza Univ NGU Sch Business & Finance km 22 Cairo Alex Desert Rd Cairo Egypt

PurposeThis study aims to classify various start-ups based on their experiences with digital marketing adoption. It examines the characteristics of these start-ups and how they interact to influence the adoption ***/methodology/approachA quantitative research approach was adopted, utilizing questionnaires administered to 205 founders and managers of Egyptian start-ups. Data were collected on the usage of digital marketing tools, level of utilization, duration of adoption, resource allocation, and budget. A nonparametric decision tree algorithm, visualized using R, was employed for data *** study identifies key characteristics of start-ups that influence their classification in terms of digital marketing adoption. Factors such as digital marketing experience, time and budget allocation, and utilization levels significantly impact this classification. Furthermore, website usage emerged as a crucial criterion distinguishing basic from moderate digital marketing adopters among Egyptian *** research enhances the understanding of digital marketing adoption among Egyptian start-ups. By classifying start-ups based on demographics and digital marketing practices using a decision tree algorithm, it offers a novel perspective on the topic.

关键词： Start-up characteristics Digital marketing adoption Digital marketing tools decision tree algorithm R visualization Entrepreneurial ecosystem Cost-effectiveness Marketing strategy Adoption years Budget allocation

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Analysing Effectiveness of Sentiments in Social Media Data Using Machine Learning Techniques

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Journal of Computer and Communications 2025年第1期13卷 136-151页

作者： Thambusamy Velmurugan Mohandas Archana Ajith Singh Nongmaithem PG & Research Department of Computer Science D.G.Vaishnav College Chennai India PG Department of IT & BCA D.G.Vaishnav College Chennai India Department of Computer Science Souht East Manipur College Manipur India

Every second, a large volume of useful data is created in social media about the various kind of online purchases and in another forms of reviews. Particularly, purchased products review data is enormously growing in different database repositories every day. Most of the review data are useful to new customers for theier further purchases as well as existing companies to view customers feedback about various products. Data Mining and Machine Leaning techniques are familiar to analyse such kind of data to visualise and know the potential use of the purchased items through online. The customers are making quality of products through their sentiments about the purchased items from different online companies. In this research work, it is analysed sentiments of Headphone review data, which is collected from online repositories. For the analysis of Headphone review data, some of the Machine Learning techniques like Support Vector Machines, Naive Bayes, decision trees and Random Forest algorithms and a Hybrid method are applied to find the quality via the customers’ sentiments. The accuracy and performance of the taken algorithms are also analysed based on the three types of sentiments such as positive, negative and neutral.

关键词： Support Vector Machine Random Forest algorithm Naive Bayes algorithm Machine Learning Techniques decision tree algorithm

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Design and Optimization of Personalized Education System Based on Intelligent algorithms

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Procedia Computer Science 2024年 243卷 514-522页

作者： Jie Zhu Yahong Liao Danni Lu Wuhan Railway Vocational College of Technology Wuhan 430205 Hubei China Xiamen Jin Shang Middle School Xiamen 361000 Fujian China Hubei Vocational College of Railway Transportation Wuhan 430070 Hubei China

The education industry, as the top priority of social operation, is constantly emerging with education systems or online education platforms based on internet technology. However, most of them are facing problems of rigidity, stiff, or resource scarcity. Therefore, this article aimed to establish a personalized education system to solve this problem and optimize the system based on intelligent algorithms. At the end of this article, a comparison was made on the algorithm performance of the decision tree algorithm in the intelligent algorithm. Compared with the original algorithm of the system, the accuracy increased from 70.35% to 75.68%. The system based on the intelligent algorithm also helped the students in the experimental class improve their grades, and even cleared the score record below 40 points, helping to improve the overall performance of the entire class.

关键词： Intelligent algorithm decision tree algorithm Personalized Education System Particle Swarm Optimization algorithm

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Fault Detection, Classification and Localization Along the Power Grid Line Using Optimized Machine Learning algorithms

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2024年第1期17卷 49-49页

作者： Najafzadeh, Masoud Pouladi, Jaber Daghigh, Ali Beiza, Jamal Abedinzade, Taher Islamic Azad Univ Dept Elect Engn Shabestar Branch Shabestar Iran

Distributed energy generation increases the need for smart grid monitoring, protection, and control. Localization, classification, and fault detection are essential for addressing any problems immediately and resuming the smart grid as soon as possible. Simultaneously, the capacity to swiftly identify smart grid issues utilizing sensor data and easily accessible frequency and voltage data from PMU devices is a prerequisite of this task. Therefore, this paper proposes new methods using fuzzy logic and adaptive fuzzy neural networks as well as machine learning and meta-heuristic algorithms. First, line voltage is used by a fuzzy thresholding method to estimate when a transmission line defect would develop in less than 1.2 clock cycles. Next, features taken from frequency signals in the real-time interval are utilized to classify the type of error using machine learning systems (decision tree algorithm and random forest algorithm) optimized with wild horse meta-heuristic algorithm. To locate the precise problem location, we finally use a neural fuzzy inference system that is capable of adapting to new data. We employ a simulated power transmission system in MATLAB to test our proposed solutions. Mean square error (MSE) and confusion matrix are used to assess the efficiency of a classifier or detector. For the decision tree algorithm method, the detector attained an acceptable MSE of 2.34e-4 and accuracy of 98.1%, and for the random forest algorithm method, an acceptable MSE of 3.54e-6 and accuracy of 100%. Furthermore, the placement error was less than 153.6 m in any direction along the line.

关键词： Fault detection and classification Smart network Fault localization Wild horse optimization algorithm Fuzzy neural inference system decision tree algorithm Random forest algorithm

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Machine learning based cardiovascular disease prediction

Machine learning based cardiovascular disease prediction

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International Conference on Advanced Materials for Innovation and Sustainability (ICAMIS)

作者： Chinnasamy, P. Kumar, S. Arun Navya, V. Priya, K. Lakshmi Boddu, Siva Sruthi MLR Inst Technol Dept Comp Sci & Engn Hyderabad India Bethesda Inst Technol & Sci Dept Comp Sci & Engn Gwalior India East Point Coll Engn & Technol Dept Elect & Commun Engn Bangalore India PERI Inst Technol Dept Elect & Commun Engn Chennai India MLR Inst Technol Dept Comp Sci & Engn Hyderabad India

In past few years, one of the really tough situations in medicine is already making predictions cardiac dysfunction. Almost any moment, roughly one patient dies through myocardial infarction with in present era. Due to the difficulty of forecasting cardiac sickness, it is vital to systematize the procedure in place to minimize the risk associated with and to educate the patient well enough in preparation. The risk of cardiovascular disease in the World is tremendous. The accurate and thorough assessment of a physician's cardiovascular risk is critical for reducing the incidence and severity attack and stroke and improving cardiovascular protection. In order to solve this issue, we are proposing method to discover these heart abnormalities as soon as reasonably practicable to avert fatal consequences. The next step is to determine whether or not the user is at risk of developing cardiovascular disease. It also answers difficult problems concerning heart detection of diseases, allowing clinicians to make better clinical decisions. Medical specialists have accumulated a large number of medical data that can be analysed and important fact retrieved. Machine learning approaches can improve in the prognosis and early identification of heart illness after hypothesis testing. Copyright (c) 2022 Elsevier Ltd. All rights *** and peer-review under responsibility of the scientific committee of the International Conference on Advanced Materials for Innovation and Sustainability.

关键词： Heart disease Machine learning K -nearest Classifier Ensemble classifier decision tree algorithm

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Clustering-Based Network Intrusion Detection System 5

Clustering-Based Network Intrusion Detection System

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5th IEEE Conference on Dependable and Secure Computing (IEEE DSC)

作者： Fan, Chun-I Lai, Yen-Lin Shie, Cheng-Han Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung Taiwan

ISBN: (数字)9781665421416

ISBN: (纸本)9781665421416

The increasing sophistication of network attacks and the inability of traditional defensive techniques such as firewalls or weak passwords against them have led researchers to propose network intrusion detection systems. Many network intrusion detection systems using machine learning techniques have been proposed, but the detection performance of some systems can be further improved. In addition, many systems adopted multiple machine learning classifiers to cooperate in generating detection results, but the individual classifiers in the system are often difficult to operate independently, limiting the flexibility of the system. This paper presents a Clustering-Based Network Intrusion Detection System, which applies the concept of clustering to detect network attacks by using the K-Nearest Neighbor algorithm for the initial detection of network attack types, and the decision tree algorithm specializes in detecting specific types of attacks. This improves the detection performance of the system and maintains the usability of an individual classifier.

关键词： Intrusion Detection System Machine Learning Clustering K-Nearest Neighbor algorithm decision tree algorithm

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Design, Fabrication, and Performance Analysis of Intelligent Mesoscale Capacitive Accelerometer for Vibration Measurement

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JOURNAL OF TESTING AND EVALUATION 2021年第4期49卷 2407-2424页

作者： Gomathi, K. Sakthivel, R. Marshal, S. Joseph John Pratheep, V. G. Kongu Engn Coll Dept Mechatron Engn Perundurai 638060 Tamil Nadu India Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Mech Engn Coimbatore 641112 Tamil Nadu India Karunya Inst Technol & Sci Dept Mech Engn Coimbatore 641114 Tamil Nadu India

This paper portrays an accelerometer as a motion sensor used to measure acceleration, vibration, and tilt angles which plays a vital role in the constructional field, inertial measurements, automotive safety system, biomedical, and yet other safety applications. A small packaged accelerometer can be made larger in size by acquiring an external power source or battery. The designed micro-accelerometer is fabricated using clean room technology, which is not quite cost effective, and even the silicon base is replaced by steel because of steel's high yield strength and low cost to fabricate a mesoscale accelerometer. Design parameters are optimized by a decision tree algorithm where the optimized dimensions are used to model the mesoscale accelerometer in INTELLISUITE 8.6 software. A Wire-cut discharger is used for the fabrication of mechanical elements of an accelerometer with a spring steel foil to reduce fabrication cost. Finally, the fabricated accelerometer with a mesoscale was made to undergo experimentation with a hammer drill and compared with simulation results to evaluate its performance in a harsh construction environment. 0.576 (mu m/g) of mechanical sensitivity and 0.629 (pF/g) of electrical sensitivity is achieved.

关键词： mesoscale capacitive accelerometer decision tree algorithm INTELLISUITE sensitivity electro-discharge-machining

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Mozambique Flood (2019) Caused by Tropical Cyclone Idai Monitored From Sentinel-1 and Sentinel-2 Images

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 8761-8772页

作者： Guo, Jinyun Luan, Yujie Li, Zhen Liu, Xin Li, Chengming Chang, Xiaotao Shandong Univ Sci & Technol Coll Geodesy & Geomat Qingdao 266590 Shandong Peoples R China Chinese Acad Surveying & Mapping Beijing 100036 Peoples R China Land Satellite Remote Sensing Applicat Ctr MNR Beijing 100048 Peoples R China

The tropical cyclone Idai caused severe floods in Mozambique in March 2019. Sentinel-1 and Sentinel-2 images are processed to monitor the flood disaster in Pungue and Buzi rivers by combining the object-based image analysis approach and the decision tree algorithm. Water is preliminarily extracted from Sentinel-1 image, and shadows of mountain and buildings in the study area are extracted from Sentinel-2 image. Mountain shadow is extracted based on the decision tree classification rules constructed by the digital elevation model and the index model, while building shadow is extracted by constructing the decision tree classification rules using the features of objects. Water extraction results are combined with shadows to eliminate confusing shadows in the preliminary extraction to obtain the accurate flood extent. Sentinel-2 images are used to classify land use types in multiple periods before and after the flood and analyze the disaster by combining with the change information of the water. Land use type changes are analyzed and predicted with the CA-Markov model. The results show that the maximum submerged area was 3.5 times of the normal water area, and that most areas were submerged for more than 10 d. The area between Pungue and Buzi rivers were severely affected, where a large number of crops and villages were submerged. Grassland and cropland were the most seriously submerged. The overall accuracy of extracted water results ranges from 86% to 92%. The results indicated that the combining method can effectively suppress the influence of shadows on water extraction results.

关键词： Floods Monitoring Satellites Data mining Rivers Feature extraction Synthetic aperture radar decision tree algorithm flood Mozambique object-based image analysis (OBIA) Sentinel-1 Sentinel-2 tropical cyclone Idai

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Resource provisioning using workload clustering in cloud computing environment: a hybrid approach

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2021年第1期24卷 319-342页

作者： Shahidinejad, Ali Ghobaei-Arani, Mostafa Masdari, Mohammad Islamic Azad Univ Dept Comp Engn Qom Branch Qom Iran Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran

In recent years, cloud computing paradigm has emerged as an internet-based technology to realize the utility model of computing for serving compute-intensive applications. In the cloud computing paradigm, the IT and business resources, such as servers, storage, network, and applications, can be dynamically provisioned to cloud workloads submitted by end-users. Since the cloud workloads submitted to cloud providers are heterogeneous in terms of quality attributes, management and analysis of cloud workloads to satisfy Quality of Service (QoS) requirements can play an important role in cloud resource management. Therefore, it is necessary for the provisioning of proper resources to cloud workloads using clustering of them according to QoS metrics. In this paper, we present a hybrid solution to handle the resource provisioning issue using workload analysis in a cloud environment. Our solution utilized the Imperialist Competition algorithm (ICA) and K-means for clustering the workload submitted by end-users. Also, we use a decision tree algorithm to determine scaling decisions for efficient resource provisioning. The effectiveness of the proposed approach under two real workloads traces is evaluated. The simulation results demonstrate that the proposed solution reduces the total cost by up to 6.2%, and the response time by up to 6.4%, and increases the CPU utilization by up to 13.7%, and the elasticity by up to 30.8% compared with the other approaches.

关键词： Cloud computing Workload clustering Resource provisioning Imperialist competition algorithm decision tree algorithm

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