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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

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Modeling Approaches and Algorithms for Advanced Computer applications 1

引用

丛书名： Studies in Computational Intelligence

2013年

作者： Frédéric Boniol (auth.), Abdelmalek Amine, Ait Mohamed Otmane, Ladjel Bellatreche (eds.)

ISBN: (数字)9783319005607

ISBN: (纸本)9783319005591;9783319005607

"During the last decades Computational Intelligence has emerged and showed its contributions in various broad research communities (computer science, engineering, finance, economic, decision making, etc.). This was done by proposing approaches and algorithms based either on turnkey techniques belonging to the large panoply of solutions offered by computational intelligence such as data mining, genetic algorithms, bio-inspired methods, Bayesian networks, machine learning, fuzzy logic, artificial neural networks, etc. or inspired by computational intelligence techniques to develop new ad-hoc algorithms for the problem under consideration. This volume is a comprehensive collection of extended contributions from the 4th International conference on Computer Science and Its applications (CiiA2013) organized into four main tracks: Track 1: Computational Intelligence, Track 2: Security & Network Technologies, Track 3: Information Technology and Track4: Computer Systems and applications. This book presents recent advances in the use and exploitation of computational intelligence in several real world hard problems covering these tracks such as image processing, Arab text processing, sensor and mobile networks, physical design of advanced databases, model matching, etc. that require advanced approaches and algorithms borrowed from computational intelligence for solving them.

关键词： Computer science

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Comparative Study of Plant Disease Detection Techniques: A Performance-Based Review

Comparative Study of Plant Disease Detection Techniques: A P...

引用

Advancement in Electronics & Communication Engineering (AECE), International conference on

作者： Sarita Singh Noopur Goel Veer Bahadur Singh Purvanchal University Department of Computer Applications Veer Bahadur Singh Purvanchal University

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Agriculture is essential for human civilization, contributing to the economy and ensuring food supply. However, plant diseases can hinder growth and reduce crop yield. It is crucial to identify and categorize these diseases as soon as possible and accurately. Manual methods for disease prediction and classification may lead to inaccuracies and tedium. Therefore, implementing computerized image processing methods in agriculture can help to minimize losses and increase productivity. To detect and categorize plant illnesses on the basis of photos of sick leaves or crops, a variety of approaches, including DL and ML techniques like k-means clustering, NB and convolutional neural networks have been investigated. Even though there has been a lot of development in this area, talks and changing technologies call for ongoing improvements. The particular problem, data accessibility, and processing capacity all influence which of the conventional machine learning and deep learning approaches are the best. When a large amount of data and computational resources are available, deep learning, especially with CNNs, is generally the method of choice for picture identification and classification. This paper introduced a brief overview of the latest research on identifying and categorizes crop diseases and illness using image processing in the field of artificial intelligence. It covers the performance, assessment criteria, and findings of different approaches, aiming to keep future researchers informed about the progress in this area.

关键词： Deep learning Plant diseases Adaptation models Computer vision Reviews Computational modeling neural networks Feature extraction Agriculture Convolutional neural networks

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A Dynamic Analysis of a Dual Memristive Hopfield neural Network

A Dynamic Analysis of a Dual Memristive Hopfield Neural Netw...

引用

neural networks, Information and Communication Engineering (NNICE), International conference on

作者： Na Li Zhaoxing Wang Lili Huang School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China Collaborative Innovation Center of Memristive Computing Application (CICCA) Qilu Institute of Technology Jinan China

The human brain, as a large and highly complex nonlinear dynamical system, has a close connection with human intelligence activities. The artificial neural network based on memristors has shown excellent performance in simulating the working mechanism of the human brain. Therefore, in the HNN neural network, both hyperbolic tangent memristors and quadratic nonlinear memristors are introduced to couple with it. Using basic dynamical analysis methods, the paper discusses the dynamical behavior of the system under different coupling strengths and studies the behavior of attractor symmetry under different initial values. The experimental results show that under different memristor parameters, by observing the changes in the phase trajectory diagram, Lyapunov exponent, and bifurcation diagram of the system, it is found that it has periodic doubling bifurcation, chaos, periodic window, and double vortex attractor symmetry behavior. At the same time, it also emphasizes its full sensitivity to initial values. At the same time, its complex dynamical characteristics also provide research support for applications in data processing, image encryption, biomedical, and other fields.

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Recognition of Moving Objects by Intelligent Distributed Information and Measurement System

Recognition of Moving Objects by Intelligent Distributed Inf...

引用

International conference on Industrial Engineering, applications and Manufacturing (ICIEAM)

作者： E. Zakasovskaya V. Tarasov Far Eastern Regional Educational and Research Center for Information Security Far Eastern Federal University (FEFU) Vladivostok Russia

ISBN: (纸本)9781538643082

The problem of pattern recognition for a distributed fiber-optic measuring system is considered in the article. At present, this complex problem of recognizing moving images has no general solution, but in the case of a number of applied problems, the research of approaches based on the use of methods and technologies of artificial intelligence can be useful for this purpose. When monitoring moving objects for pattern recognition, an integrated approach to solving data processing problems is advisable, which was implemented in this work by creating an expert system with the use of neural networks for a distributed fiber optic measurement network (DFOMN). The system for rapid decision making with reference to the object class and its parameters has a software module that extracts the characteristics of the object from the output data from the measuring lines and forms the models of the image. A comparative analysis of the results obtained with the reference data is made.

关键词： Training Time measurement Monitoring Expert systems Optical variables measurement Biological neural networks

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ANN based three-value logic SVPWM control in CSR

ANN based three-value logic SVPWM control in CSR

引用

International conference on Bio-Inspired Computing: Theories and applications, BICTA

作者： Jinbang Xu Anwen Zhizhuo Wu Jun Yang Xuan Yang Department of Control Science & Technology Huazhong University of Science & Technology Wuhan China Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Department of Control Science & Technology Huazhong University of Science & Technology 430074 Wuhan China

To achieve better performance with various load and system parameters in controlling a current-source rectifier (CSR) with less computing cost, a neural-network-based implementation of three-logic space-vector modulation (SVM) is proposed in this research, and the random weight change (RWC)algorithm is employed for on-line parameter tuning. The scheme has been simulated in SABER simulation software and the result is compared with the conventional SVM method. The advantage of the method is explicit with a better performance under a non-rated system load.

关键词： Support vector machines artificial neural networks Computational modeling

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Resource-Aware Heterogeneous Cluster Scheduling System for Deep neural Network Training Tasks

Resource-Aware Heterogeneous Cluster Scheduling System for D...

引用

International conference on Computer and Communications (ICCC)

作者： Xiaoqing Gao Chuxuan Zeng Dan Huang Liang Du Nong Xiao School of Computer Science and Engineering Sun Yat-sen University Guangzhou China China Unicom Guangdong Branch Guangzhou China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Deep neural networks (DNNs) have become integral to various modern artificial intelligence applications. As DNN models grow in complexity, diverse accelerators are increasingly utilized for training. Current heterogeneous cluster schedulers recognize performance disparities among models across different GPU types, managing costly GPU resources for users. However, these schedulers often neglect the significance of auxiliary resources in allocation decisions, relying on a GPU-based proportional distribution for CPU and memory resources. This study investigates the sensitivity of various DNN models to auxiliary resources through empirical experiments, revealing that training throughput for image and speech models is highly reliant on CPU allocation, while language models exhibit lower sensitivity. In response, we developed Nexus, a resource-aware scheduler designed to optimize resource utilization in heterogeneous GPU clusters. Experimental results demonstrate that Nexus can reduce average task completion time by up to 2.26x compared to traditional scheduling systems.

关键词： Training Sensitivity Computational modeling Graphics processing units artificial neural networks Throughput Complexity theory Resource management artificial intelligence

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Computer vision & fuzzy logic based offline signature verification and forgery detection

Computer vision & fuzzy logic based offline signature verifi...

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IEEE International conference on Computational Intelligence and Computing Research

作者： Gautam S. Prakash Shanu Sharma CSE Department Amity University Noida Uttar Pradesh India

Automated signature verification and forgery detection has many applications in the field of Bank-cheque processing, document authentication, ATM access etc. Handwritten signatures have proved to be important in authenticating a person's identity, who is signing the document. In this paper a Fuzzy Logic and artificial neural Network Based Off-line Signature Verification and Forgery Detection System is presented. As there are unique and important variations in the feature elements of each signature, so in order to match a particular signature with the database, the structural parameters of the signatures along with the local variations in the signature characteristics are used. These characteristics have been used to train the artificial neural network. The system uses the features extracted from the signatures such as centroid, height - width ratio, total area, I st and ii nd order derivatives, quadrant areas etc. After the verification of the signature the angle features are used in fuzzy logic based system for forgery detection.

关键词： Feature extraction artificial neural networks Testing Forgery Training Computer vision

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Japanese phonetic feature extraction for automatic speech recognition

Japanese phonetic feature extraction for automatic speech re...

引用

International conference on Signal and image processing (ICSIP)

作者： Manoj Banik Qamrun Nahar Eity Nusrat Jahan Lisa Foyzul Hassan Aloke Kumar Saha Mohammad Nurul Huda Department of CSE Ahsanullah University of Science and Technology Dhaka Bangladesh Enosis Solutions Dhaka Bangladesh Department of CSE University of Asia Pacific Dhaka Bangladesh Department of CSE United International University Dhaka Bangladesh

This paper presents a method for extracting distinctive phonetic features (DPFs) for automatic speech recognition (ASR). The method comprises three stages: i) a acoustic feature extractor, ii) a multilayer neural network (MLN) and iii) a hidden Markov model (HMM) based classifier. At first stage, acoustic features, local features (LFs), are extracted from input speech. On the other stage, MLN generates a 45-dimentional DPF vector from the LFs of 75- dimentions. Finally, these 45-dimentional DPF vector is inserted into an HMM-based classifier to obtain phoneme strings. From the experiments on Japanese Newspaper Article Sentences (JNAS), it is observed that the proposed DPF extractor provides a higher phoneme correct rate and accuracy with fewer mixture components in the HMMs compared to the method based on mel frequency cepstral coefficients (MFCCs). Moreover, a higher correct rate for each phonetic feature is obtained using the proposed method.

关键词： Feature extraction Speech Speech recognition Hidden Markov models artificial neural networks Mel frequency cepstral coefficient

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Eigenspace Estimation with Missing Values and its Application to Eigenvoice Adaptation for Speech Recognition

Eigenspace Estimation with Missing Values and its Applicatio...

引用

2008 International conference on Audio，Language and image processing(2008国际声音、语言、图像过程大会)

作者： Zhijian Ou Jun Luo Department of Electronic Engineering Tsinghua Unversity Beijing 100084 China

Eigenspace estimation via principal component analysis (PCA) has been used in many applications, e.g., in eigenvoice modeling for speaker adaptation. Here the data of interest are the speaker supervectors, where each supervector is a concatenation of all the mean vectors in the speaker's speaker-dependent (SD) model. One problem is that we often do not have enough speaker-specific data to establish the individual SD models (having unseen phones). To address this issue, an approach to eigenspace estimation by expectation-maximization (EM) algorithm in situations where the training samples contain missing values is proposed, applied to eigenvoice adaptation, and experimentally evaluated in this paper.

关键词： Estimation Adaptation model artificial neural networks Training Hidden Markov models Covariance matrix Maximum likelihood estimation

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Countering Adversarial Zeroth Order Optimization Attacks Based on neural-Cleanse, Gaussian and Poisson Noise Adding

Countering Adversarial Zeroth Order Optimization Attacks Bas...

引用

International conference on Industrial Engineering, applications and Manufacturing (ICIEAM)

作者： Igor Kotenko Igor Saenko Vladimir Sadovnikov Laboratory of Computer Security Problems St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS) Saint-Petersburg Russia

ISBN: (数字)9798350395013

ISBN: (纸本)9798350395020

Nowadays, machine learning is becoming an increasingly widely used artificial intelligence technology and is being actively implemented in various fields of science and technology, such as defense against cyber attacks, image recognition, computer vision, autonomous vehicles and other complex tasks. However, despite the benefits of machine learning, it attracts the attention of attackers. Cyberattacks against machine learning models, such as classifiers or artificial neural networks, can seriously distort the results of these models and cause irreversible damage to machine learning-based decision-making systems. Therefore, research aimed at countering cyber attacks using machine learning models is becoming very important nowadays. The paper presents a method for protecting against adversarial attacks, Zeroth Order Optimization (ZOO), which is based on the use of neural-Cleanse technology and the addition of Gaussian and Poisson noise. The proposed approach is aimed at increasing the resistance of neural networks to malicious attacks by additional processing of input data and complicating the optimization problem for attackers. An experimental evaluation of the proposed approach was carried out on the PC Parts images Dataset using the K-Nearest Neighbors, Random Forest, Naive Bayes Classifier, and Decision Trees classifiers. The study results showed the high efficiency of the proposed approach to protecting models from the effects of adversarial ZOO attacks. The findings may be useful for developing more reliable systems and improving security in the field of machine learning.

关键词： image recognition Computational modeling Noise Naive Bayes methods Security Decision trees Task analysis

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