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A compositional Multiple-F0 estimation algorithms for computer-synthesized music

A compositional Multiple-F0 estimation algorithms for comput...

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2nd Annual conference on Electrical and Control Engineering, ICECE 2011

作者： Guo, Yi Bai, Xinyue Hou, Xiaorong Xu, Hongbing Chengdu 611731 China

ISBN: (纸本)9781424481637

Multiple fundamental frequency estimation, or Multiple-F0 estimation, is one of the most important problem on automatic music transcription, but it has not been well resolved up to now. This paper presents a machine learning methods using harmonic matching and iterative deletion for computer-synthesized music specifically to Multiple-F0 estimation. Computer-synthesized music is almost free from similar instruments of the differences between different individuals, so it is a good research object. It can be shown in this paper that the experimental results indicate that this method has very good recognition results. © 2011 ieee.

关键词： pattern recognition

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An Intelligent Framework for the Classification of Premium and Regular Gasoline for Arson and Fuel Spill Investigation Based on Extreme learning machines

An Intelligent Framework for the Classification of Premium a...

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2nd international conference on Computational Intelligence Communication Systems and Networks (CICSyN)

作者： Olatunji, Sunday O. Adeleke, Imran A. Univ Technol Malaysia Fac Comp Sci & Informat Syst Utm Skudai 81310 Johor Malaysia

ISBN: (纸本)9780769541587

Detection and correct identification of gasoline types during Arson and Fuel Spill Investigation are very important in forensic science. As the number of arson and spillage becomes a common place, it becomes more important to have an accurate means of detecting and classifying gasoline found at such sites of incidence. However, currently only a very few number of classification models have been explored in this germane field of forensic science, particularly as relates to gasoline identification. In this work, we developed extreme learning machines (ELM) based identification model for identifying gasoline types. The model was constructed using gas chromatography-mass spectrometry (GC-MS) spectral data obtained from gasoline sold in Canada over one calendar year. Prediction accuracy of the model was evaluated and compared with earlier used methods on the same datasets. Empirical results from simulation showed that the proposed ELM based model achieved better performance compared to other earlier implemented techniques.

关键词： Extreme learning machine gas chromatography-mass spectrometry pattern recognition Principal Component Analysis Artificial Neural Networks

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Digital Image Forgery Detection Using Local Binary pattern (LBP) and Harlick Transform with classification 2

Digital Image Forgery Detection Using Local Binary Pattern (...

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2nd ieee international Power and Renewable Energy conference (IPRECON)

作者： Sabeena, M. Abraham, Lizy Varghese, Anju Coll Engn Dept Elect & Commun Karunagappally Kerala India LBS Inst Technol Women Thiruvananthapuram Kerala India APJ Abdul Kalam Technol Univ Thiruvananthapuram Kerala India LBS Inst Technol Women Dept Elect & Commun Thiruvananthapuram Kerala India

ISBN: (纸本)9781665401371

Images are a natural carrier of information. Images are used in an immense range of applications nowadays, including military purposes, surveillance systems, insurance processing, the internet, television, advertising media, forensic investigation, and so on. However, because powerful, low-cost image editing tools are readily available, these images can be easily tampered with. Therefore, the authenticity of images has become questionable. In this age of advanced PC innovation, digital picture and video have high significance in our everyday life. For editing or modifying the original multimedia contents, a range of low-cost multimedia content handling tools, techniques, and applications with various advanced features are available on the Internet, such as Adobe Photoshop. To handle this issue, numerous investigations have been centered around how to identify, this kind of controlled media. Existing computerized fraud identification techniques are grouped into two significant classes: active and passive. This paper proposes a technique of copy-move forgery detection in which feature extraction is done by Local Binary patterns (LBP) and Harlick features. For the verification of the authenticity of the image various supervised machine learning classifiers like Support Vector machine (SVM), Random Forest (RF) & Gradient Boost classifiers are used and the performance of forgery detection is based on these classifiers are analyzed.

关键词： Copy move forgery local binary pattern Harlick transform SVM RF GBC

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Topology distance for manifold clustering

Topology distance for manifold clustering

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2010 2nd international conference on Computer Engineering and Technology, ICCET 2010

作者： Peng, Yuan Guo, Qiyong Shen, I-Fan Chen, Wenbin School of Computer Science Fudan University Shanghai China Department of Computer and Information Engineering Shanghai Univ. of Electric Power Shanghai China Shandong Branch China Construction Bank Jinan China School of Mathematical Sciences Fudan University Shanghai China

ISBN: (纸本)9781424463503

Manifold clustering is a widely used techniques in pattern recognition and machine learning. It partition a set of input data into several clusters each of which contains data points from a separate, simple low-dimensional manifold. In order to cluster manifold, we propose a novel distance measure based on topology structure that can efficiently represent the underlying manifold. Under this distance measure, data points belong to the same clusters are more closed and that of the different clusters are farther apart. By using normalized cut on similarity matrix, clusters can be found with ease. Experiments on both synthetic data and real data show that our method is feasible and promising in manifold clustering. © 2010 ieee.

关键词： pattern recognition

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AL-XAI-MERS: Unveiling Alzheimer's Mysteries with Explainable AI 2

AL-XAI-MERS: Unveiling Alzheimer's Mysteries with Explainabl...

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2nd international conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Deshmukh, Afif Kallivalappil, Neave D'Souza, Kyle Kadam, Chinmay Fr. Conceicao Rodrigues College of Engineering Computer Engineering Department Mumbai India

ISBN: (纸本)9798350328202

Alzheimer's disease poses an escalating global health challenge, necessitating accurate and timely diagnosis for effective intervention. This study presents a novel approach to Alzheimer's detection utilising advanced machine learning techniques applied to brain MRI scans. Leveraging Explainable Artificial Intelligence (XAI) methods, the developed model not only detects Alzheimer's disease but also offers transparent insights into the intricate patterns within the MRI data. In an era where Alzheimer's prevalence is rising, our methodology provides a valuable tool for clinicians and patients. By employing XAI, individuals can gain a comprehensive understanding of their MRI results, enabling them to seek second opinions and fostering a deeper comprehension of their condition. This research marks a significant step towards democratising medical diagnostics, empowering individuals with knowledge and promoting informed decision-making in Alzheimer's diagnosis and management. © 2024 ieee.

关键词： pattern recognition

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Online Incremental learning of Inverse Dynamics Incorporating Prior Knowledge

Online Incremental Learning of Inverse Dynamics Incorporatin...

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8th international conference on Image Analysis and recognition (ICIAR) / 2nd international conference on Autonomous and Intelligent Systems (AIS)

作者： de la Cruz, Joseph Sun Kulic, Dana Owen, William Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Univ Waterloo Dept Mech & Mechatron Engn Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9783642215377;9783642215384

Recent approaches to model-based manipulator control involve data-driven learning of the inverse dynamics relationship of a manipulator, eliminating the need for any knowledge of the system model. Ideally, such algorithms should be able to process large amounts of data in an online and incremental manner, thus allowing the system to adapt to changes in its model structure or parameters. Locally Weighted Projection Regression (LWPR) and other non-parametric regression techniques have been applied to learn manipulator inverse dynamics. However, a common issue amongst these learning algorithms is that the system is unable to generalize well outside of regions where it has been trained. Furthermore, learning commences entirely from 'scratch,' making no use of any a-priori knowledge which may be available. In this paper, an online, incremental learning algorithm incorporating prior knowledge is proposed. Prior knowledge is incorporated into the LWPR framework by initializing the local linear models with a first order approximation of the available prior information. It is shown that the proposed approach allows the system to operate well even without any initial training data, and further improves performance with additional online training.

关键词： Robotics learning Control

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Simulation Study of Financial Securities Market Volatility Model Based on BP Neural Network 2

Simulation Study of Financial Securities Market Volatility M...

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2nd international Signal Processing, Communications and Engineering Management conference, ISPCEM 2022

作者： Xiaofang, Niu Beijing Junyu International Import and Export Trading Co. Limited Beijing102600 China

ISBN: (纸本)9781665492713

With the continuous development and improvement of financial markets, the study of the nature and laws of the stock market has put forward higher requirements, of which predicting stock market volatility has become an important financial topic. The volatility of the stock market is its essential feature and attribute, but if the price volatility shows a sharp amplitude and too fast frequency, it may lead to the price distortion of the market, and then lead to the violent stock market shocks. Neural network is a powerful and widely used machine learning algorithm that is very good at discovering nonlinear relationships between data and is suitable for stock price prediction problems. The paper takes into account the factors influencing stock prices, extracts trading indicators and financial indicators, reduces the dimensionality through principal component analysis, and uses the reduced-dimensional principal components as the input variables of the network. Based on the actual stock trading market, this paper establishes a multilayer neural network classifier and uses four learning algorithms in BP neural network to fit the simulated data, and the results show that the algorithm has high accuracy for volatility pattern recognition. Under the modern fierce market competition, enterprises must strengthen the control of corporate financial risks and financial crisis prevention if they want to survive, develop and make profits. © 2022 ieee.

关键词： Financial markets

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recognition of Greek Alphabet Characters with Memristive Neuromorphic Circuit 2

Recognition of Greek Alphabet Characters with Memristive Neu...

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2nd Edition ieee international conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023

作者： Chatzinikolaou, Theodoros Panagiotis Chatzipaschalis, Ioannis K. Tsakalos, Karolos-Alexandros Karamani, Rafailia-Eleni Fyrigos, Iosif-Angelos Kitsios, Stavros Bousoulas, Panagiotis Tsoukalas, Dimitris Sirakoulis, Georgios Ch. Democritus University of Thrace Department of Electrical and Computer Engineering Xanthi Greece Universitat Politécnica de Catalunya Department of Electronic Engineering Barcelona Spain National Technical University of Athens Department of Applied Physics Athens Greece

ISBN: (纸本)9798350300802

pattern recognition is a critical aspect of modern computing and artificial intelligence, enabling robotic and computing systems to classify and categorize information based on its patterns and relationships. In this context, memristors are emerging as a promising new type of device for pattern recognition due to their memory capabilities as non-volatile two-terminal elements and in-memory computing potential. In this work, we present a novel approach to character recognition using CBRAM memristors by combining traditional machine learning techniques with a memristor-based oscillator exhibiting neuromorphic properties. The proposed approach demonstrates the feasibility of a pattern recognition specific hardware based on memristors that can be integrated in edge computing applications, aiming to bring computation and data storage closer to the source of data, rather than relying on centralized data centers, and consequently, leading to improved latency, increased reliability, and reduced costs. © 2023 ieee.

关键词： memristive neuromorphic circuit memristive oscillators neuromorphic computing pattern recognition

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The compound effect of boosting and stratified sampling on decision tree accuracy

The compound effect of boosting and stratified sampling on d...

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2nd international conference on Emerging Technologies

作者： Gill, Abdul Aziz Smith, George D. SZABIST Dept Comp Sci Islamabad Pakistan Univ E Anglia Sch Comp Sci Norwich NR4 7TJ Norfolk England

ISBN: (纸本)9781424405039

It is generally recognized that recursive partitioning, as used in the construction of class claaification trees, is inherently, unstable, particularly for small data sets. Classification accuracy and, by implication, tree structure, are sensitive to changes in the training data. Successful approaches to counteract this effect include multiple classifiers, e.g. boosting, bogging or windowing. The downside o(c) these multiple classification models, however, is the plethora of trees that result, often making it difficult to extract the classifier in a meaningful manner. We show that, by using some very weak knowledge in the sampling stage, when the data set is partitioned into the training and test sets, a more consistent and improved performance is achieved by a single decision tree classifier. The reductions in error rate attained are comparable with those attained using boosting. In addition, we demonstrate that the combination of such sampling, combined with boosting, yields significant reductions in error rates.

关键词： data mining pattern recognition machine learning

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Research on Improved GA-BP pattern recognition Algorithm for Quality Analysis 2

Research on Improved GA-BP Pattern Recognition Algorithm for...

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2nd international conference on Artificial Intelligence and Computer Information Technology, AICIT 2023

作者： Guo, Xiaoke Zhang, Niansong Wang, Aimin Nanjing University of Science and Technology Department of Mechanical Engineering Nanjing China Beijing Institute of Technology Department of Mechanical Engineering Beijing China

ISBN: (纸本)9798350302950

Utilizing control chart pattern recognition techniques allows for determination of whether the current production process is performing abnormally, thereby enabling quality analysis and facilitating worker identification of anomalies in a timely manner. The GA-BP algorithm is a control chart pattern recognition algorithm. However, it exhibits certain shortcomings such as slow convergence speed and a fixed maximum number of iterations. The fixed maximum number of iterations implies that the algorithm may terminate before convergence, resulting in decreased accuracy. In response to these problems, this paper proposes an improved GA-BP algorithm, which employs adaptive learning rate and adaptive iteration number methods. Specifically, the algorithm first uses a GA algorithm to obtain the optimal initial weight values of the BP network. Based on this, the algorithm updates the weight values using adaptive learning rate methods and updates the maximum number of iterations using adaptive iteration number methods. These improvements significantly increase the convergence speed of the algorithm while also avoiding early termination of iterations. Experimental results demonstrate that the proposed algorithm significantly improves classification accuracy and reduces training time. © 2023 ieee.

关键词： Iterative methods

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