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检索条件"任意字段=Proceedings of the 2023 International Conference on Communication Network and Machine Learning"

共 13503 条记录，以下是71-80 订阅

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machine learning Based Algorithm for Earthquake Monitoring 12

Machine Learning Based Algorithm for Earthquake Monitoring

引用

12th IEEE international conference on communication Systems and network Technologies, CSNT 2023

作者： Varshney, Neeraj Kumar, Gaurav Kumar, Ankit Pandey, Saroj Kumar Singh, Teekam Singh, Kamred Udham Gla University Department of Computer Engineering & Apllications UP Mathura India Graphic Era Deemed to Be University Dehradun Department of Computer Science and Engineering India Graphic Era Hill University School of Computing Dehradun India

ISBN: (纸本)9781665462617

In this paper, we are trying to predict the Earthquake before it happens using seismic indicators anywhere around the world. It is an important task, on detecting seismic events from seismic time series using machine learning techniques. When we train a dataset for any one location it should also be applicable to detect at the other locations. Earthquakes are one of the most dangerous and deadly natural disasters. It is very important for the safety of all human beings as well as all the animals living on the planet to predict the earthquake way before it is going to happen. The population of the earth is increasing at a very rapid rate so the human population is also settling in the areas where the region is at very high risk of earthquakes which also causes more people to be affected by the impact of the earthquakes and more people to die. During this research various machine learning techniques were used such as Support Vector machine, Random Forest, Neural network, Convolutional Neural network, Quantum machine learning, K-Nearest Neighbor, machine learning Risk Prediction Algorithm, Deep learning Model, and Regression. © 2023 IEEE.

关键词： Earthquakes

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A Light-Weighted machine learning Based ECU Identification for Automotive CAN Security

A Light-Weighted Machine Learning Based ECU Identification f...

引用

2023 international conference on networking and network Applications, NaNA 2023

作者： Li, Jini Zhang, Man Lai, Yu School of Electronics and Communication Engineering Guangzhou University Guangzhou China

ISBN: (纸本)9798350327380

The rise of artificial intelligence brings information security challenges for intelligent connected vehicles. Securing the CAN is crucial to ensuring the overall security of the in-vehicle network. Traditional cryptography technology faces challenges of low computational efficiency and excessive data load when identifying ECU. This paper proposes a light-weighted machine learning based identification algorithm that leverages the physical characteristics of ECU. By analyzing the CAN voltage signals in the time and frequency domains, reducing the data load and choosing a suitable classification model, this method achieves high accuracy, high efficiency and low load for safety identification in-vehicle networks. The experimental results on the data sets of both actual vehicles and CAN bus prototypes have verified the rationality and feasibility of the method. © 2023 IEEE.

关键词： machine learning

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Research Progress of machine learning in Software Defect Prediction 12

Research Progress of Machine Learning in Software Defect Pre...

引用

12th IEEE international conference on communication Systems and network Technologies, CSNT 2023

作者： Meng, Fanqi Li, Yongjun Wang, Jingdong Northeast Electric Power University School of Computer Science Jilin China

ISBN: (纸本)9781665462617

Software defect prediction is of great significance to ensuring software security. Appropriate metrics are an effective way to predict whether a program contains software flaws. In recent years, machine learning techniques have been widely used to measure programs to predict software defects and have achieved good results. In order to clarify the research progress of software defect prediction based on machine learning, this paper reviews the research results in this field in the past three years from two aspects: within-project defect prediction and cross-project defect prediction. Firstly, the software defect prediction framework based on machine learning and its development are introduced. Secondly, this paper summarizes the research results in the field of defect prediction in the past three years from two aspects: within-project defect prediction and cross-project defect prediction. Finally, based on the research results so far, the status and hot spots of machine learning in software defect prediction are discussed, which provides possible help for the development direction of machine learning in software defect prediction. © 2023 IEEE.

关键词： machine learning

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A review on analysis and prediction methodologies of crop yield pattern 5th

A review on analysis and prediction methodologies of crop yi...

引用

5th international conference on Advances in AI for Biomedical Instrumentation, Electronics and Computing, ICABEC 2023

作者： Neha Ahlawat, Anil Chaudhary, Himanshu Department of Computer Science Engineering KIET Group of Institutions Delhi NCR Ghaziabad India Department of Electronics and Communication Engineering KIET Group of Institutions Delhi NCR Ghaziabad India

ISBN: (纸本)9781032642987

Making supportive decisions for crop production, such as crop name recommendations and forecasts for crop production, requires machine learning implementation. Numerous machine learning classifiers and algorithms are employed in this. Climatic factors (temperature, rainfall, precipitation, sun radiation, and humidity), edaphic factors (pH, kind, and proportions of nitrogen, phosphorus, and potassium in the soil), and physiographic factors (altitude, steepness of slope, exposure to light, and wind) are some of the variables or factors that influence crop production. However, the most common variables are temperature, soil pH, precipitation, and humidity, and the most common algorithms are Examples of artificial neural networks and deep learning algorithms include Random Forest, Support Vector machine, Linear Regression, Long Short-Term Memory, Convolution Neural network, and Deep Neural network. As part of this study, we reviewed the literature to ascertain the techniques and influencing factors that have been used in agricultural output prediction. © 2024 The Author(s).

关键词： Support vector machines

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network Traffic Classification Using Supervised learning Algorithms

Network Traffic Classification Using Supervised Learning Alg...

引用

international conference on Computer, Electrical and communication Engineering (ICCECE)

作者： Choudhury, Mira Rani Muraleedharan, N. Acharjee, Parimal George, Aleena Terese Natl Inst Technol Dept Elect Engn Durgapur India Ctr Dev Adv Comp C DAC Bangalore Karnataka India

ISBN: (纸本)9781665452519

network traffic classification is crucial for traffic monitoring and application-based policy enforcement. However, the widespread use of encrypted protocols has greatly challenged conventional traffic classification techniques using packet payload and port numbers. For the network application in this paper, two machine learning algorithms, Decision Tree (DT) and Random Forest (RF) are used. An open-access Kaggle dataset with six different types of applications is used for this study. To achieve the best values for model training, loop iteration is used rather than the hyper-parameter optimization technique. When compared to DT, RF has the highest accuracy (99.72%). In order to improve the classification process and various hidden patterns connected with the statistical features, more statistical features were taken into account in comparison to other related works that had already been done. The outcomes demonstrate the potency of supervised learning algorithms for categorizing network traffic.

关键词： Traffic classification supervised learning machine learning hyper-parameter tuning decision tree random forest

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Research on intrusion detection method of party building network in colleges and universities based on machine learning

Research on intrusion detection method of party building net...

引用

2024 international conference on Signal Processing and communication Security, ICSPCS 2024

作者： Fan, Chaozhi Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9781510681699

The rapid development of the Internet and the intelligent information age have provided more possibilities for data leakage, network attacks and other behaviors of malicious persons. The network security of party building in colleges and universities faces severe challenges. Intrusion detection is a new information security technology used to detect intrusions in computer network systems. Intrusion detection methods based on machine learning can better identify unknown anomalies than rule-based methods. The essence of the intrusion detection problem is a classification problem. There are many classification methods in machine learning, including convolutional neural networks, naive Bayes, SVM, etc. These algorithms have good classification performance on balanced data sets. How to extract more representative attribute features from complex data sets to more accurately describe normal behavior and intrusion behavior has become an important breakthrough point in current intrusion detection solutions based on machine learning. In order to solve the problem of network intrusion for party building in universities, this paper proposes to apply the convolutional neural network algorithm and random forest algorithm in machine learning to the intrusion detection model of party building network in universities, which effectively improves the overall classification accuracy of the model and the recognition rate of minority classes. © 2024 SPIE.

关键词： Intrusion detection

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Exploiting Drift Detection Techniques for Next Generation Radio Access networks 15

Exploiting Drift Detection Techniques for Next Generation Ra...

引用

15th international conference on communication Systems and networks (COMSNETS)

作者： Gudepu, Venkateswarlu Chintapalli, Venkatarami Reddy Valcarenghi, Luca Kondepu, Koteswararao Indian Inst Technol Dharwad Dharwad Karnataka India Indian Inst Technol Hyderabad Hyderabad Telangana India Scuola Super Sant Anna Pisa Italy

ISBN: (纸本)9781665477062

Next Generation Radio Access network (NG-RAN) is expected to support extremely high data rates, low-latency applications, and massive machine communication. The increasing growth of network complexity and the highly dynamic service demands make NG-RAN to incorporate Artificial Intelligence and machine learning (AI/ML) algorithms due to their ability to deal with complex network architecture and making intelligent decisions. However, AI/ML model performance degradation (i.e., drift) is prevalent in NG-RAN due to their highly dynamic service demands. This paper proposes a novel drift handling mechanism based on the average root mean square error (RMSE) over a defined window. The proposed drift handling mechanism is compared with one class drift detector (OCDD) and evaluated over channel quality indicator (CQI) use-case. The results showed that proposed drift handling mechanism could outperform whenever a model performance degrades.

关键词： Degradation machine learning algorithms Heuristic algorithms machine learning Detectors Complexity theory Root mean square

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Design of Computer Complex Spam Filtering System Based on machine learning Algorithm 5

Design of Computer Complex Spam Filtering System Based on Ma...

引用

5th international conference on Applied machine learning, ICAML 2023

作者： Yang, Yong Chongqing Three Gorges Vocational College Changqing City404000 China

ISBN: (纸本)9798350341416

SMS and E-mail have become common text communication methods in daily life because of their low rate, high speed and convenient sending and receiving. Sending spam not only consumes a lot of network resources and storage space, but also wastes users' precious time and disrupts the life and work of network users. The common contents of spam include advertising information, pornographic information, fake winning information, fraudulent information, pranks and so on. This paper studies the computer complex spam filtering system based on machine learning (ML) algorithm, analyzes the key technologies of the system and the problems existing in the original system, and puts forward the method of log feedback to analyze the filtered spam, extract keywords and update the filtered keyword database, so as to improve the system. When a new email arrives, we can extract feature words from it, and judge whether it is spam or not by calculating the obvious degree of spam features of feature words. Experiments and analysis show that the algorithm in this paper can effectively identify users who send spam, and at the same time, it can effectively control the rate of misidentifying normal users as spam users. © 2023 IEEE.

关键词： machine learning

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Research on Intelligent Power Grid Attack Detection System Based on machine learning 24

Research on Intelligent Power Grid Attack Detection System B...

引用

international conference on machine Intelligence and Digital Applications (MIDA)

作者： Zhang, Ning Zhu, Liang HeiLongJiang Univ Technol Sch Elect & Informat Engn Jixi Peoples R China

ISBN: (纸本)9798400718144

The development of smart grids, coupled with the advancements in communication, network, and automation control technologies, has led to the integration of physical devices in power systems with communication networks, enhancing efficiency and intelligence. However, this integration also makes the power system more susceptible to network attacks. This study is dedicated to detecting False Data Injection Attacks (FDIA) in smart grids using machine learning. A large amount of node state variable sample data was employed to train Random Forest classifiers and XGBoost algorithms, with parameter optimization significantly enhancing their accuracy in various scenarios. The study found that while Random Forest excels in parallel training and noise resistance, XGBoost is more suited for short-term state prediction based on historical data. Both algorithms demonstrated effective performance and high accuracy in FDIA detection through extensive simulation. Furthermore, the paper explores the construction, implementation, and detection methods of FDIA, vital for the safe operation of smart grids. The research in this paper not only provides robust cybersecurity measures for smart grids but also lays the groundwork for future research on FDIA in more complex power system environments.

关键词： Smart grid False data injection attacks communication FDIA model Attack detection

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Prediction of Diabetes by machine learning Algorithm

Prediction of Diabetes by Machine Learning Algorithm

引用

2025 international conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Reddy, L. Rajeshwar Akhila, V. Nivas, L. Laxmi Tharun, P. Vignana Bharathi Institute of Technology Department of Electronics and Communication Engineering Telangana Ghatkesar India

ISBN: (纸本)9798331509828

Diabetes is the serious and widespread disease worldwide. Common ingredients in modern diets, like sugar and fat, increase the risk of diabetes. Recognizing the symptoms is essential to predict and prevent the disease. machine learning (ML) techniques have become essential tool for disease detection. This article introduces a model developed to predict diabetes through a hybrid machine learning approach. The framework combines two model types: Support Vector machine (SVM) and Artificial Neural network (ANN) are two popular techniques in machine learning. These models analyse data to ascertain whether a diabetes diagnosis is positive or negative. The dataset used in the study is divided for training purposes. © 2025 IEEE.

关键词： Adversarial machine learning

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