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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

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Using Ensemble learning for Securing Public Area Networks from VPN Malicious Transmissions

Using Ensemble Learning for Securing Public Area Networks fr...

引用

2023 IEEE international conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Khan, Aadil Sharma, Ishu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350305708

In this paper, a new paradigm for network security is developed via the use of ensemble learning. The primary objective of this study is to identify and stop the involvement of malicious communication into network configurations, especially in public areas that are vulnerable to cyber-attacks. The use of sophisticated machine learning in this research improves the safety and protection of networks. The increasing usage of virtual private networks (VPNs) on public networks is causing problems. Virtual private networks (VPNs) improve connection speeds and privacy, but they may also make it easier for criminals to get unwanted access. The purpose of this research is to enhance network safety by identifying potential vulnerabilities and analyzing trends in virtual private network (VPN) use. The complicated network traffic patterns are analyzed using a combination of several different machine learning algorithms. This enables the computer to swiftly detect deviations from norms, allowing for the identification of fraudulent data and the facilitation of proactive response. By addressing vulnerabilities in VPNs, the strategy enhances digital security, lowers the risk of cyberattacks, and stops unlawful activities. The results of this experiment demonstrate that the proposed algorithm is able to identify and prevent VPN connections and other potentially harmful data infiltrations. The effectiveness of ensemble learning was shown by its high level of accuracy. This research improves network security measures that may be used to defend public networks from cyberattacks. © 2023 IEEE.

关键词： Ensemble learning Malicious Data Public Area Network Random Forest Classifier Security Virtual Private Network

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Bearing and distance Formation Control of Rigid Bodies in SE(3) with Bearing and distance Constraints 9

Bearing and Distance Formation Control of Rigid Bodies in SE...

引用

9th international conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Mansourinasab, Sara Sojoodi, Mahdi Moghadasi, S. Reza Tarbiat Modares University Department of Electrical and Computer Engineering Tehran Iran Sharif University of Technology Department of Mathematical Sciences Tehran Iran

ISBN: (纸本)9798350311402

Rigidity of the interaction graph is a fundamental condition for achieving the desired formation which can be defined in terms of distance or bearing constraints between agents. In this paper, for reaching a unique formation with the same scaling and orientation as the target formation, both distance and bearing constraints are considered for defining the desired formation. Besides, both distance and bearing measurements are also available. Each agent is able to gather the measurements with respect to other agents in its own body frame. So, the agents are coordinated-free concerning a global reference frame. On the other hand, the framework is embedded in SE(3). The control signal is designed based on a gradient descent method by introducing a cost function. Firstly, the formation problem is considered for bearing-only constraints in SE(3) configuration. Then, the formation control is expressed for the general case of both bearing and distance constraints. Furthermore, the essential conditions that guarantee reaching the desired formation is discussed. Finally, the validity of the proposed formation control is verified by numerical simulations. © 2023 IEEE.

关键词： Cost functions

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Adaptive Step-Size Sparse Millimeter Wave Imaging Algorithm Based on Approximate Observations 11

Adaptive Step-Size Sparse Millimeter Wave Imaging Algorithm ...

引用

11th IEEE international conference on computer Science and Network Technology, ICCSNT 2023

作者： Wang, Dianhao Huang, Xinqi Yu, Canping Mao, Qi Li, Yingsong College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350311594

The currently constructed millimeter wave imaging system has the problems of long sampling time and more sampling points of antenna units, and the use of compressed perception algorithm can improve the imaging quality when the number of sampling points are much smaller than the Nyquist distance sampling. The traditional compressed perception algorithm can achieve better sparse recovery than the matched filter imaging algorithm, but there is a large dimension of the measurement matrix and high computational complexity. For the problems of difficult data processing and large dimensions of measurement matrix, a sparse imaging regularization model is constructed based on approximate observation, and an improved soft threshold iterative algorithm is used with adaptive step size, which improves the convergence performance, reduces the computational complexity of the sparse recovery dramatically and achieves a better quality of sparse imaging than the traditional compressed perception algorithm. © 2023 IEEE.

关键词： Computational complexity

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Unconditional Generation of Scalable Chinese Characters as Bézier Curve Sequences 3

Unconditional Generation of Scalable Chinese Characters as B...

引用

3rd international conference on Artificial Intelligence and Signal Processing, AISP 2023

作者： Lasrado, Rohan Nolan Vijayasherly, V. VIT University School of Computer Science and Engineering Vellore India VIT University Department of Software Systems Vellore India

ISBN: (纸本)9798350320749

Neural generative modelling of sketches has been an active research direction. SketchRNN set a milestone with their sequence-to-sequence variational autoencoder architecture being able to generate hand drawn sketches in various classes by modelling them as sequences of displacements between consecutive stroke points. The diversity and variety in the set of handwritten Chinese characters makes them a good candidate for such generative modelling enabling their unconditional generation. However, modelling them as sequences of points causes coarse looking strokes and much longer sequences, consequently requiring use of polygonal approximation algorithms to cut down on points. Instead, we propose and investigate the modelling of Chinese characters as sequences of Bézier curves using the SketchRNN architecture with a few modifications, to allow the model to directly generate smooth curves. This way the encoded representation is smaller while more of the stroke's characteristics are retained and the generated characters are truly scalable. We also suggest the appropriate preprocessing strategy for the KanjiVG dataset to make it suitable for this purpose. Qualitative evaluation of the results suggests the model demonstrates generation of characters with mostly well-structured and ordered strokes. This was substantiated by quantitative evaluation based on the FrFréchetchet Inception distance score. © 2023 IEEE.

关键词： learning systems

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Plant Disease Classification Using Machine learning

Plant Disease Classification Using Machine Learning

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2023 international conference on Disruptive Technologies, ICDT 2023

作者： Singla, Ravi Shankar Gupta, Amit Gupta, Richa Tripathi, Vikas Naruka, Mahaveer Singh Awasthi, Shashank Graphic Era Deemed to Be University Department of Computer Science and Engineering India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Gl Bajaj Institute of Technology and Management Greater Noida India Computer Science and Engineering Gl Bajaj Institute of Technology and Management Greater Noida India

ISBN: (纸本)9798350323887

In the event of a sharp rise in global population, agriculture strives to provide food to it. In agriculture, the detection and diagnosis of diseases occurring in the plants continues to be the arduous task. That is why it is endorsed to predict the diseases when the crops are in early stage. This work is done to develop and implement a disease prediction system by using different machine learning algorithms and a convolutional neural network. The objective of the paper is to grab the attention among the organisations to employ innovative technologies to decrease the diseases that are persistent in plants. The different approaches of ML and image processing with the algorithm that will provide explicit results to recognize the leaves that are healthy and classification algorithms and techniques so that we come to a result of categorically conclusive factor that the leaf is infected by any disease or not. Firstly, the dataset of leaves is divided into directories based on some features extracted from the leaves. Now, the logistic regression estimates the probability of the leave being healthy, based on the dataset of independent variables. The same dataset is also provided to Neural Networks (NN), Support Vector Machines (SVM) and Naïve Bayes algorithms. All the models are analysed with the confusion matrix and K-Fold Cross validation techniques. The proposed model gave the accuracy of 94% using Neural Networks. © 2023 IEEE.

关键词： Support vector machines

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Vehicle Theft Detection Using Neural Pattern Recognition 3

Vehicle Theft Detection Using Neural Pattern Recognition

引用

3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Jane Rubel Angelina, J. Subhashini, S.J. Kumar Rapuri, Anil Ravva, Vennela Rasala, Raghavendra Ray Adurgam, Sai Dcepak Kalasalingam Academy of Research and Education Dept of Computer Science and Engineering TamilNadu Krishnankoil India SRM Madurai College for Engineering and Technology Dept of Computer Science and Engineering TamilNadu Sivagangai India

ISBN: (纸本)9798350322842

Finding stolen cars is becoming increasingly important in many urban regions. An automated system for scanning license plates can recognize vehicle numbers without the need for human interaction. This work proposes a vehicle theft detection system based on neural pattern recognition, gaussian filter, and equilibrium optimization. The proposed architecture has significant speedup and higher accuracy rates. The proposed number plate recognition method has a maximum accuracy rate of 94% and an average reduction in the processing time of 32%. Pattern recognition is the process of detecting regularities and patterns in data using machine learning. These analogies may now be uncovered via statistical analysis, historical data, or machine-generated knowledge. © 2023 IEEE.

关键词： Image segmentation

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Developing AI-based Fraud Detection Systems for Banking and Finance 5

Developing AI-based Fraud Detection Systems for Banking and ...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Dash, Samikshya Das, Simanchala Sivasubramanian, S. Kalyana Sundaram, N. Harsha, G.K. Sathish, T. School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India Kl Business School Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India New Prince Shri Bhavani College of Engineering and Technology Department of Information Technology Tamil Nadu Chennai600073 India Jss Academy of Technical Education Department of Computer Science Uttar Pradesh Noida201301 India Saveetha School of Engineering Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350321425

Safeguarding financial institutions and their consumers against fraudulent activity makes fraud detection a top priority in the banking and finance business. There has been a rise in the development of artificial intelligence-based fraud detection systems in tandem with the popularity of machine learning methods. This study presents a comprehensive evaluation of modern machine learning approaches like neural networks in comparison to more conventional ones like logistic regression and decision trees. These techniques are tested using financial and banking data from the real world, and the findings indicate that neural networks are superior to more conventional approaches. In addition, our research emphasizes the significance of data gathering and administration in the evolution of fraud detection systems. © 2023 IEEE.

关键词： Logistic regression

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Phishing Website Detection Using Machine learning Methods

Phishing Website Detection Using Machine Learning Methods

引用

2023 international conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Anakal, Sudhir Maka, Kiran Tadkal, Arun Humanabad, Sunil Anakal, Sridhar Laxmikant, E. Sharnbasva University Department of Computer Applications Kalaburagi India Sharnbasva University Department of Engineering and Technology Kalaburagi India

ISBN: (纸本)9798350315455

As the internet promotes the growth of ecommerce businesses, the security of financial and personal data becomes a concern. Phishing attacks are becoming more complex and difficult to detect. Several Machine learning (ML) algorithms that gather data from multiple sources, such as website addresses, search engines, and other internet resources, might be useful in distinguishing a legitimate website from a phishing website. This paper investigates supervised ML techniques such as Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbors (KNN), Gradient Boosting (GB), and AdaBoost that are used to detect phishing websites. After training the ML models, the top-performing ML model is deployed using Streamlit so that it can be accessed to check the legitimacy of the website before it is accessed. © 2023 IEEE.

关键词： Machine learning Phishing Streamlit Supervised URL

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computer and information technologies in the agro-industrial complex at the present stage 3

Computer and information technologies in the agro-industrial...

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3rd international conference on Optics, computer Applications, and Materials Science, CMSD 2023

作者： Tuskov, Andrey Efimov, Ivan Efimov, Petr Saparova, Botagoz S. Saparova, Altynai A. Saparova, Aigul A. Penza State University 40 Krasnaya Street Penza440026 Russia K.G. Razumovsky Moscow State University of Technologies and Management 73 Zemlyanoy Val Street Moscow109004 Russia Mordovian State University N. P. Ogaryova 68/1 Bolshevik street Saransk430005 Russia Eurasian National University Named after L.N. Gumilyov Kazhymukan Kazakhstan Narxoz University 55 Zhandosova street Almaty Kazakhstan

ISBN: (纸本)9781510674486

This article is devoted to the consideration of how information technologies are used in practice in the agro-industrial complex at the present time. The main purpose of the study is to substantiate the effectiveness of these methods and to identify the main areas of use. The paper discusses the use of machine learning models to work with data in agriculture. The relevance of the study is justified by the fact that industry, as well as agriculture, are one of the most priority areas in our country. There is a need for the use of adaptive devices and new methods that will make it possible to achieve significant success in this area. The article is devoted to the consideration of the code implemented in Python, which can predict crop yields with high accuracy. By applying adaptive learning models and comparing different statistical models with each other, it is possible to identify the most accurate predictive indicators. © 2024 SPIE. All rights reserved.

关键词： Complex networks

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Detection of epileptic seizure using hybrid machine learning algorithms 12

Detection of epileptic seizure using hybrid machine learning...

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12th IEEE international conference on Advanced Computing, ICoAC 2023

作者： Velvizhy, P. Len, Ria Bas Rajeshwari, N. Kanimozhi, K. Anna University Department of Computer Science and Engineering Chennai25 India

ISBN: (纸本)9798350318210

A brain condition known as epilepsy is characterized by frequent seizures. A seizure is an abrupt change in behavior brought on by a brief disturbance in the electrical activity of the brain. For those who are experiencing seizures, abrupt and synchronized electrical energy bursts that impact their consciousness, movements, and sensations disturb the usual electrical pattern. Even now, it is simple to detect an epileptic seizure automatically because the existing detection techniques are time-consuming and inaccurate. In order to effectively identify seizures, machine learning and deep learning algorithms are integrated. Using features retrieved from EEG data where the MLP+CNN+SVM model was used, we present a method in this study for detecting epileptic seizures from EEG signals using three hybrid machine learning classification networks, namely SVM+CNN, MLP+CNN, and SVM+MLP+CNN. © 2023 IEEE.

关键词： Brain

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