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نتیجه جستجو - Discrete wavelet transform

تعداد مقالات یافته شده: 14
ردیف عنوان نوع
1 Plant leaf disease detection using computer vision and machine learning algorithms
تشخیص بیماری برگ گیاه با استفاده از بینایی کامپیوتری و الگوریتم های یادگیری ماشین-2022
Agriculture provides food to all the human beings even in case of rapid increase in the population. It is recom- mended to predict the plant diseases at their early stage in the field of agriculture is essential to cater the food to the overall population. But it unfortunate to predict the diseases at the early stage of the crops. The idea behind the paper is to bring awareness amongst the farmers about the cutting-edge technologies to reduces diseases in plant leaf. Since tomato is merely available vegetable, the approaches of machine learning and image processing with an accurate algorithm is identified to detect the leaf diseases in the tomato plant. In this investigation, the samples of tomato leaves having disorders are considered. With these disorder samples of tomato leaves, the farm- ers will easily find the diseases based on the early symptoms. Firstly, the samples of tomato leaves are resized to 256 × 256 pixels and then Histogram Equalization is used to improve the quality of tomato samples. The K-means clustering is introduced for partitioning of dataspace into Voronoi cells. The boundary of leaf samples is extracted using contour tracing. The multiple descriptors viz., Discrete Wavelet Transform, Principal Component Analysis and Grey Level Co-occurrence Matrix are used to extract the informative features of the leaf samples. Finally, the extracted features are classified using machine learning approaches such as Support Vector Machine (SVM), Convolutional Neural Network (CNN) and K-Nearest Neighbor (K-NN). The accuracy of the proposed model is tested using SVM (88%), K-NN (97%) and CNN (99.6%) on tomato disordered samples.
keywords: شبکه های عصبی کانولوشنال | تبدیل موجک گسسته | تجزیه و تحلیل مؤلفه های اصلی | نزدیکترین همسایه | بیماری برگ | Convolutional Neural Networks | Discrete Wavelet Transform | Principal Component Analysis | Nearest Neighbor | Leaf disease
مقاله انگلیسی
2 Soft biometric based keystroke classification using PSO optimized neural network
طبقه بندی نرم افزاری بیومتریک با استفاده از شبکه عصبی بهینه شده PSO-2021
In this work, variable length login-id and password belonging to the user were analyzed for bringing forth a more secure verification system. Soft biometrics such as age group and gender are estimated from key- stroke dynamics patterns when he/she types a given password or login id on a keyboard. Experiments were carried on GREYC a web-based keystroke dataset by exploiting the features from DWT of keystroke dynamics and provides classification results using PSO optimized neural network. Experiments done using PSO-NN resulted in 94% accuracy which clearly out performs the BPNN and GA-NN classifiers.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Emerging Trends in Materials Science, Technology and Engineering.
Keywords: Soft biometric | Discrete wavelet transform (DWT) | Genetic Algorithm optimized neural network (GA-NN) | Back propagation neural network (BPNN) | Particle Swarm Optimized neural network(PSO-NN)
مقاله انگلیسی
3 EBAPy: A Python framework for analyzing the factors that have an influence in the performance of EEG-based applications
EBAPy: یک چارچوب پایتون برای تجزیه و تحلیل عوامل موثر بر عملکرد برنامه های مبتنی بر EEG-2021
EBAPy is an easy-to-use Python framework intended to help in the development of EEG-based applications. It allows performing an in-depth analysis of factors that influence the performance of the system and its computational cost. These factors include recording time, decomposition level of Discrete Wavelet Transform, and classification algorithm. The ease-of-use and flexibility of the presented framework have allowed reducing the development time and evaluating new ideas in developing biometric systems using EEGs. Furthermore, different applications that classify EEG signals can use EBAPy because of the generality of its functions. These new applications will impact human–computer interaction in the near future.Code metadataCurrent code version v1.1Permanent link to code/repository used for this code version https://github.com/SoftwareImpacts/SIMPAC-2021-2Permanent link to Reproducible Capsule https://codeocean.com/capsule/4497139/tree/v1Legal Code License MITCode versioning system used gitSoftware code languages, tools, and services used Python Compilation requirements, operating environments & dependencies If available Link to developer documentation/manualSupport email for questions dustin.carrion@gmail.com
Keywords: EEG-based applications | Recording time | Discrete wavelet transform
مقاله انگلیسی
4 A novel biometric recognition method based on multi kernelled bijection octal pattern using gait sound
یک روش جدید بیومتریک شناختی مبتنی بر الگوی هشت جداره چند هسته ای با استفاده از صدای راه رفتن-2021
Background: Many gait based methods have been presented about biometric identification in the literature. Gait recognition methods have generally used images and sensors signals. In this work, a novel gait based biometric recognition method is presented. A novel Multi Kernelled Bijection Octal Pattern (MK- BOP) is presented in this study. Object: The main aim of the proposed MK-BOP is to extract distinctive and comprehensive features from a signal (gait sound). By using the proposed MK-BOP, a novel biometric recognition method is proposed. Gait sounds are collected, and two novel datasets are collected. The first dataset is a noisy and heterogeneous dataset. The second dataset is a clear and homogenous dataset. A multileveled method is presented to authenticate subjects from these datasets. One dimensional discrete wavelet transform (1D-DWT) is applied to sound signal with Symlet 6 (sym6) filter, and levels are calculated. Conclusion: The proposed MK-BOP generates features from each level signals, and the generated features are concatenated. A hybrid feature selector (RFNCA) selects the most discriminative feature, and selected most discriminative features are forwarded to classifiers. 0.980 and 0.949 success rates were achieved for clear and noisy datasets, respectively.© 2020 Elsevier Ltd. All rights reserved.
Keywords: Gait recognition | Biometrics | Multi kernelled bijection octal pattern | Information fusion | Sound recognition
مقاله انگلیسی
5 012-S00304018210
012-S00304018210-2021
A novel multiple-single-channel color image cryptosystem based on unequal spectrum decomposition (USD) and 2D sine improved 1ogistic iterative chaotic map with infinite collapse modulation map (2D-SLIM) is proposed. In this method, R, G, and B channels of each color image of an authorized user is fused with corresponding LL sub-band of a gray-scale carrier image by inverse discrete wavelet transform (DWT) to obtain single-channel and watermarked image. The individual biometric key of an authorized user is produced by his/her phase- encoded irisprint and then modulated by chaotic random phase mask (CRPM). So the parameters of CRPM are used as decryption keys with the uniqueness of the irisprint. The watermarked image as input image is normalized, multiplied with biometric key, and then Fresnel transformed. The Fresnel spectrum is divided into two complex-value masks by using unequal spectrum decomposition in which one phase mask is exploited as decryption key and other as ciphertext. The final ciphertext is obtained by adding individual ciphertexts. The encryption and decryption process can be realized with a hybrid optoelectronic system. Numerical simulations have been performed to verify the validity and feasibility of the proposed system.
Keywords: 2D-SLIM | Unequal spectrum decomposition | Biometric keys
مقاله انگلیسی
6 Alignment-free cancelable fingerprint templates with dual protection
الگوهای اثر انگشت قابل انعطاف بدون تراز با محافظت دوگانه-2021
Cancelable fingerprint templates Discrete wavelet transform Attacks via record multiplicity Cancelable biometrics is an important biometric template protection technique. However, many existing cancelable fingerprint templates suffer post-transformation performance deterioration and the attacks via record multiplicity (ARM). In this paper, we design alignment-free cancelable fingerprint templates with dual protection, which is composed of the window-shift-XOR model and the partial discrete wavelet transform. The former defuses the ARM threat and is combined with the latter to provide dual protection and enhance matching performance. The designed cancelable templates meet the requirements of non-invertibility, diversity and revocability and demonstrate superior recognition accuracy, when evaluated over public databases; for example, the Equal Error Rate of the proposed method in the lost-key scenario under the 1vs1 protocol is 0% for both FVC2002 DB1 and DB2, 1.63% for FVC2002 DB3, 7.35% for FVC2004 DB1 and 4.69% for FVC2004DB2.© 2020 Elsevier Ltd. All rights reserved.
Keywords: Cancelable biometrics | Alignment-free | Cancelable fingerprint templates | Discrete wavelet transform | Attacks via record multiplicity
مقاله انگلیسی
7 Analysis of factors that influence the performance of biometric systems based on EEG signals
تجزیه و تحلیل عوامل موثر بر عملکرد سیستم های بیومتریک بر اساس سیگنال های EEG-2021
Searching for new biometric traits is currently a necessity because traditional ones such as fingerprint, voice, or face are highly prone to forgery. For this reason, the study of bioelectric signals has the potential to develop new biometric systems. A motivation for using electroencephalogram signals is that they are unique to each person and are much more difficult to replicate than conventional biometrics. The objective of this study is to analyze the factors that influence the performance of a biometric system based on electroencephalogram signals. This work uses six different classifiers to compare several decomposition levels of the discrete wavelet transform as a preprocessing technique and also explores the importance of the recording time. These classifiers are Gaussian Naïve Bayes Classifier, K-Nearest Neighbors, Random Forest, AdaBoost, Support Vector Machine, and Multilayer Perceptron. This work proves that the decomposition level does not have a high impact on the overall result of the system. On the other hand, the recording time of electroencephalograms has a significant impact on the performance of the classifiers. It is worth mentioning that this study used two different datasets to validate the results. Finally, our experiments show that Support Vector Machine and AdaBoost are the bestclassifiers for this particular problem since they achieved a sensitivity, specificity, and accuracy of 85.94 ± 1.8, 99.55 ± 0.06, 99.12 ± 0.11 and 95.54 ± 0.53, 99.91 ± 0.01, and 99.83 ± 0.02 respectively.
Keywords: Biometrics | Electroencephalogram | Discrete Wavelet Transform | Performance factors
مقاله انگلیسی
8 Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals
تشخیص خودکار آریتمی با استفاده از الگوی جدید موضعی hexadecimal و تبدیل موجک چند سطحی با سیگنالهای ECG-2019
Electrocardiography (ECG) is widely used for arrhythmia detection nowadays. The machine learning methods with signal processing algorithms have been used for automated diagnosis of cardiac health using ECG signals. In this article, discrete wavelet transform (DWT) coupled with novel 1-dimensional hexadecimal local pattern (1D-HLP) technique are employed for automated detection of arrhythmia detection. The ECG signals of 10 s duration are subjected to DWT to decompose up to five levels. The 1D-HLP extracts 512 dimensional features from each level of the five levels of low pass filter. Then, these extracted features are concatenated to obtain 512 × 6 = 3072 dimensional feature set. These fused features are subjected to neighborhood component analysis (NCA) feature reduction technique to obtain 64, 128 and 256 features. Finally, these features are subjected to 1 nearest neighborhood (1NN) classifier for classification with 4 distance metrics namely city block, Euclidean, spearman and cosine. We have obtained a classification accuracy of 95.0% in classifying 17 arrhythmia classes using MIT-BIH Arrhythmia ECG dataset. Our results show that the proposed method is more superior than other already reported classical ensemble learning and deep learning methods for arrhythmia detection using ECG signals.
Keywords: Hexadecimal local pattern | Multilevel DWT | ECG classification | Pattern recognition | Biomedical engineering
مقاله انگلیسی
9 Combining Principal Component Analysis, Discrete Wavelet Transform and XGBoost to trade in the financial markets
ترکیب تجزیه و تحلیل مؤلفه های اصلی ، تبدیل موجک گسسته و XGBoost برای تجارت در بازارهای مالی-2019
When investing in financial markets it is crucial to determine a trading signal that can provide the in- vestor with the best entry and exit points of the financial market, however this is a difficult task and has become a very popular research topic in the financial area. This paper presents an expert system in the financial area that combines Principal Component Analysis (PCA), Discrete Wavelet Transform (DWT), Extreme Gradient Boosting (XGBoost) and a Multi-Objective Optimization Genetic Algorithm (MOO-GA) in order to achieve high returns with a low level of risk. PCA is used to reduce the dimensionality of the financial input data set and the DWT is used to perform a noise reduction to every feature. The re- sultant data set is then fed to an XGBoost binary classifier that has its hyperparameters optimized by a MOO-GA. The importance of the PCA is analyzed and the results obtained show that it greatly improves the performance of the system. In order to improve even more the results obtained in the system using PCA, the PCA and the DWT are then applied together in one system and the results obtained show that this system is capable of outperforming the Buy and Hold (B&H) strategy in three of the five analyzed financial markets, achieving an average rate of return of 49.26% in the portfolio, while the B&H achieves on average 32.41%.
Keywords: Financial markets | Principal Component Analysis (PCA) | Discrete Wavelet Transform (DWT) | Extreme Gradient Boosting (XGBoost) | Multi-Objective Optimization Genetic | Algorithm (MOO-GA)
مقاله انگلیسی
10 A unique feature extraction using MRDWT for automatic classification of abnormal heartbeat from ECG big data with Multilayered Probabilistic Neural Network classifier
استخراج ویژگی منحصر به فرد با استفاده از MRDWT برای طبقه بندی خودکارضربان قلب غیر طبیعی از داده های بزرگ ECG با چند لایه طبقه بندی احتمالی شبکه عصبی-2018
This paper employs a novel adaptive feature extraction techniques of electrocardiogram (ECG) signal for detection of cardiac arrhythmias using multiresolution discrete wavelet transform from ECG big data. In this paper, five types ECG arrhythmias including normal beats have been classified. The MIT-BIH database of 48 patient records is utilized for detection and analysis of cardiac arrhythmias. Proposed feature extraction utilizes Daubechies as wavelet function and extracts 21 feature points which include the QRS complex of ECG signal. The Multilayered Probabilistic Neural Network (MPNN) classifier is pro posed as the best-suited classifier for the proposed feature. Total 1700 ECG betas were tested using MPNN classifier and compared with other three classifiers Back Propagation (BPNN), Multilayered Perceptron (MLP) and Support Vector Machine (SVM). The system efficiency and performance have been evaluated using seven types of evaluation criteria: precision (PR), F-Score, positive predictivity (PP), sensitivity (SE), classification error rate (CER) and specificity (SP). The overall system accuracy, using MPNN technique utilizing the proposed feature, obtained is 99.53% whereas using BPNN, MLP and SVM provide 97.94%, 98.53%, and 99%. The processing time using MPNN classifier is only 3 s which show that the proposed techniques not only very accurate and efficient but also very quick.
Keywords: Signal processing ، Artificial intelligence ، Pattern recognition ، Soft computing ، Wavelet transform
مقاله انگلیسی
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