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2015 IEEE SENSORS

作者： Zhang, Jie Dong, Fang Feng, Gaoang Shen, Chong Hainan Univ Coll Informat Sci & Technol Haikou Peoples R China

ISBN: (纸本)9781479982035

With the development of wireless communication technology and the improvement of data processing capability, as a new value-added service, location service has a broad market prospect, its basic principle is using the existing wireless sensor network and the position information of nodes, combining TOA (Time of Arrival), TDOA (Time Difference of Arrival) algorithms, to achieve locating of nodes. In this paper, the UWB RTLS (Ultra Wide Band-Real Time Location Systems) based on the wireless location algorithm of TDOA is studied[1]. We analyze the Kalman filtering algorithm, and propose three kinds of new locating algorithms based on Kalman filtering, at the same time, we carry on the simulation for them and compare advantages and disadvantages of different algorithms. The MATLAB simulation and hardware test results show that, in the same condition, a self-adapting joint algorithm shows a better performance, and eliminates the NLOS error of transmission process effectively.

关键词： UWB RTLS TDOA NLOS multiple algorithms

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Exploring multiple algorithms for leaf classification:A comparative study

Exploring multiple algorithms for leaf classification:A comp...

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作者： Haoyang Pan School of Mechano-Electronic Engineering Xidian University

In the world there are an estimated nearly half a million plant species,and the leaves of each plant have their own unique ***,leaf classification has historically been problematic,with some similar-looking leaves being repeatedly identified and errors may even *** the past,people used precision instruments,chemical analysis,and other methods to improve the success rate of resolution,but this often required a certain amount of professional knowledge,sometimes it was trouble to *** the development of computer in recent years makes the leaf classification problem can be solved better through the image processing and machine learning ***,the topic of this paper is to classify leaves by different algorithms and compare the classification results,which include KNN,Random Forest,the neural network algorithm ANN,and *** can be seen through experiments that the classification results obtained by ANN have a higher accuracy rate among these algorithms.

关键词： Leaf Classification Machine Learning multiple algorithms Validation Accuracy

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A Bayesian Bradley-Terry model to compare multiple ML algorithms on multiple data sets

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JOURNAL OF MACHINE LEARNING RESEARCH 2023年 24卷

作者： Wainer, Jacques Univ Estadual Campinas Inst Comp Artificial Intelligence Lab RECOD AI BR-13083970 Campinas Brazil

This paper presents a Bayesian model, called the Bayesian Bradley Terry (BBT) model, for comparing multiple algorithms on multiple data sets based on any metric. The model is an extension of the Bradley Terry model, which tracks the number of wins each algorithm has on different data sets. Unlike frequentist methods such as Demsar tests on mean rank or multiple pairwise Wilcoxon tests, the Bayesian approach provides a more nuanced understanding of the algorithms' performance and allows for the definition of the "region of practical equivalence" (ROPE) for two algorithms. Additionally, the paper introduces the concept of "local ROPE," which assesses the significance of the difference in mean measure between two algorithms using effect sizes, and can be applied in frequentist approaches as well. Both an R package and a Python program implementing the BBT are available for use.

关键词： Bayesian Bradley-Terry model Comparison of classifiers Comparison of regressors multiple data sets multiple algorithms

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A Bayesian Bradley-Terry model to compare multiple ML algorithms on multiple data sets

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 16307-16340页

作者： Jacques Wainer Artificial Intelligence Lab RECOD.ai Institute of Computing University of Campinas Campinas Brazil

关键词： Bayesian Bradley-Terry model comparison of classifiers comparison of regressors multiple data sets multiple algorithms

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VERTEXICA: Your Relational Friend for Graph Analytics!

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PROCEEDINGS OF THE VLDB ENDOWMENT 2014年第13期7卷 1669-1672页

作者： Jindal, Alekh Rawlani, Praynaa Wu, Eugene Madden, Samuel Deshpande, Amol Stonebraker, Mike MIT CSAIL Cambridge MA 02139 USA Univ Maryland Baltimore MD 21201 USA

In this paper, we present VERTEXICA, a graph analytics tools on top of a relational database, which is user friendly and yet highly efficient. Instead of constraining programmers to SQL, VERTEXICA offers a popular vertex-centric query interface, which is more natural for analysts to express many graph queries. The programmers simply provide their vertex-compute functions and VERTEXICA takes care of efficiently executing them in the standard SQL engine. The advantage of using VERTEXICA is its ability to leverage the relational features and enable much more sophisticated graph analysis. These include expressing graph algorithms which are difficult in vertex centric but straightforward in SQL and the ability to compose end to-end data processing pipelines, including pre- and post-processing of graphs as well as combining multiple algorithms for deeper insights. VERTEXICA has a graphical user interface and we outline several demonstration scenarios including, interactive graph analysis, complex graph analysis, and continuous and time series analysis.

关键词： Graph theory Data handling Graphical user interfaces Pipeline processing systems Query processing Time series analysis Data processing pipelines Graph algorithms Graph analytics multiple algorithms Post processing Query interfaces Relational Database Relational features

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Multi-Layer Anomaly Detection for Internet Traffic Based on Data Mining 9

Multi-Layer Anomaly Detection for Internet Traffic Based on ...

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9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS)

作者： Cui, Baojiang He, Shanshan Jin, Haifeng Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China Nation Engn Lab Mobile Network Secur Beijing Peoples R China

ISBN: (纸本)9781479988730

the large number of internet traffic has highlighted the importance of traffic detection. Anomaly detection is playing an increasingly important role in network security. Feature matching, statistics rules and data mining are widely used in traditional anomaly detection systems, but they have numerous disadvantages, such as low accuracy, over consumption of processing resources. For the complexities of irregular situation, we propose a new model for anomaly traffic detection in this paper. This study combine feature matching module, statistics rules module and data mining module under fully considering the advantages and disadvantages of these three detection methods. Moreover, a multi-layer detection scheme was introduced to enhance system accuracy and reduce the complexity at the same time. Data mining module is the core of the model, Naive Bayes, decision tree and clustering algorithms are used in this module. The results of this system are produced by integrating the detection results of multi detection modules and proved that it has more accuracy than separate module.

关键词： multi-layer data mining feature matching statistics rules multiple algorithms

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Cryptography using Artificial Intelligence

Cryptography using Artificial Intelligence

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International Joint Conference on Neural Networks (IJCNN)

作者： Blackledge, Jonathan Bezobrazov, Sergei Tobin, Paul Univ KwaZulu Natal Sch Math Stat & Comp Sci Durban South Africa Brest State Tech Univ Intelligent Informat Technol Dept Brest BELARUS Dublin Inst Technol Sch Elect & Elect Engn Dublin Ireland

ISBN: (纸本)9781479919598

This paper presents and discusses a method of generating encryption algorithms using neural networks and evolutionary computing. Based on the application of natural noise sources obtained from data that can include atmospheric noise (generated by radio emissions due to lightening, for example), radioactive decay, electronic noise and so on, we 'teach' a system to approximate the input noise with the aim of generating an output nonlinear function. This output is then treated as an iterator which is subjected to a range of tests to check for potential cryptographic strength in terms of metric such as a (relatively) large positive Lyapunov exponent, high information entropy, a high cycle length and key diffusion characteristics, for example. This approach provides the potential for generating an unlimited number of unique Pseudo Random Number Generator (PRNG) that can be used on a I-to-l basis. Typical applications include the encryption of data before it is uploaded onto the Cloud by a user that is provided with a personalized encryption algorithm rather than just a personal key using a 'known algorithm' that may be subject to a ' known algorithm attack' and/or is 'open' to the very authorities who are promoting its use.

关键词： coding and encryption artificial intelligence multiple algorithms personalised encryption engines artificial neural networks evolutionary computing

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Adaptive Load Allocation for Combining Anomaly Detectors Using Controlled Skips

Adaptive Load Allocation for Combining Anomaly Detectors Usi...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Berger, Mario Erlacher, Felix Sommer, Christoph Dressler, Falko Barracuda Networks Innsbruck Austria Univ Innsbruck Inst Comp Sci Comp & Commun Syst A-6020 Innsbruck Austria

ISBN: (纸本)9781479923588

Traditional Intrusion Detection Systems (IDS) can be complemented by an Anomaly Detection Algorithm (ADA) to also identify unknown attacks. We argue that, as each ADA has its own strengths and weaknesses, it might be beneficial to rely on multiple ADAs to obtain deeper insights. ADAs are very resource intensive;thus, real-time detection with multiple algorithms is even more challenging in high-speed networks. To handle such high data rates, we developed a controlled load allocation scheme that adaptively allocates multiple ADAs on a multi-core system. The key idea of this concept is to utilize as many algorithms as possible without causing random packet drops, which is the typical system behavior in overload situations. We developed a proof of concept anomaly detection framework with a sample set of ADAs. Our experiments confirm that the detection performance can substantially benefit from using multiple algorithms and that the developed framework is also able to cope with high packet rates.

关键词： multiprocessing systems real-time systems resource allocation security of data ADA IDS adaptive load allocation anomaly detection algorithm controlled load allocation controlled skips high-speed networks intrusion detection systems multicore system multiple algorithms real-time detection resource intensive unknown attacks High-speed networks Intrusion detection Probabilistic logic Reliability Uplink World Wide Web

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Dec :: Tech Reports :: Src-RR-98

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2016年

Dec :: Tech Reports :: Src-RR-98 by published by

关键词： algorithm algorithm animation algorithms animation animation festival animations code code view festival find closest graph vbt multiple algorithms participants programming language reports software system src src algorithm src-rr-98 steve glassman user zeus

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RGN: A Triple Hybrid Algorithm for Multi-level Image Segmentation with Type II Fuzzy Sets

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AIN SHAMS ENGINEERING JOURNAL 2024年第11期15卷

作者： Salgotra, Rohit Mittal, Nitin Almazyad, Abdulaziz S. Mohamed, Ali Wagdy AGH Univ Krakow Fac Phys & Appl Comp Sci Krakow Poland Univ Technol Sydney Data Sci Inst 15 Broadway Ultimo NSW 2007 Australia Middle East Univ MEU Res Unit Amman Jordan Shri Vishwakarma Skill Univ Skill Fac Engn & Technol Palwal 121102 Haryana India King Saud Univ Coll Comp & Informat Sci Dept Comp Engn POB 51178 Riyadh 11543 Saudi Arabia Cairo Univ Fac Grad Studies Stat Res Operat Res Dept Giza 12613 Egypt

This paper presents a study focused on enhancing the effectiveness of cuckoo search (CS). The goal is to improve its performance in avoiding local optima, improve the exploration and exploit potentially new solutions. To achieve this, we incorporate three additional algorithms - grey wolf optimizer (GWO), red panda optimization (RPO), and naked mole rat algorithm (NMRA) - into the basic CS framework to strengthen its exploration and exploitation capabilities. The resulting hybrid algorithm is named RGN, standing for red panda, grey wolf and naked mole-rat. To make the parameters of the RGN algorithm adaptable, six new mutation operators and inertia weights are added to the proposed RGN algorithm. The proposed algorithm is tested on CEC 2005, CEC 2014, and CEC 2022 benchmark problems to prove its effectiveness. Friedman test and Wilcoxon rank-sum tests, are done to analyse the significance of the proposed RGN algorithm statistically. It has been found that the proposed RGN is significantly better with respect to LSHADE-SPACMA, SaDE, SHADE, CMA-ES, extended GWO, hierarchical learning particle swarm optimization (FHPSO), Kepler optimization algorithm (KOA), improved chef-based optimization algorithm (CBOADP), improved symbiotic herding optimization (IMEHO), blended-biogeography based optimization (B-BBO), and Laplacian BBO (LX-BBO), among others. Application of the proposed algorithm RGN for Multilevel Image Thresholding with Type II Fuzzy Sets, shows that it is better than other algorithms over various performance matrices including mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similitude index (SSIM). Experimentally and statistically, it has been proved that the proposed RGN algorithm can be considered as a better alternative for optimization research.

关键词： multiple algorithms Image thresholding Numerical optimization Parametric adaptation Naked mole rat algorithm

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