Bio-Inspired Optimization Algorithms for Arabic Handwritten Characters

2017 ◽  
pp. 897-914 ◽  
Author(s):  
Ahmed.T. Sahlol ◽  
Aboul Ella Hassanien
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Random Forests ◽  
Classification Accuracy ◽  
Processing Time ◽  
Optimization Algorithms ◽  
Nearest Neighbors ◽  
Benchmark Dataset ◽  
K Nearest Neighbors ◽  
Linear Discriminant

There are still many obstacles for achieving high recognition accuracy for Arabic handwritten optical character recognition system, each character has a different shape, as well as the similarities between characters. In this chapter, several feature selection-based bio-inspired optimization algorithms including Bat Algorithm, Grey Wolf Optimization, Whale optimization Algorithm, Particle Swarm Optimization and Genetic Algorithm have been presented and an application of Arabic handwritten characters recognition has been chosen to see their ability and accuracy to recognize Arabic characters. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classification accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classification accuracy and the processing time. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classification accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classification accuracy and the processing time.

scholarly journals Using Linear Discriminant Analysis for Dimensionality Reduction for Predicting Anomalies of BGP data

2019 ◽  
Vol 8 (11) ◽  
pp. 1989-1995
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
High Dimensions ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
Data Packets ◽  
Vector Machines

Border Gateway Protocol (BGP) is a vital protocol on the internet for transfer of data packets among Autonomous System (AS). Security is a major concern for the transmission of BGP packets which are often attacked by worms or are hijacked by an attacker which results in requests entering black holes or loss of connection to the particular sites. The BGP anomalies can be reduced by analyzing the BGP datasets. Since, ASes communicate through messages, therefore, the anomalies can be reduced by identifying the corrupted BGP message in the dataset. In this paper, BGP anomalies have been classified by applying Machine learning (ML) algorithms. The dataset contains information about the sending and receiving time between ASes. The classifiers were used to predict the anomalies. Since the dataset had high dimensions, the dimensions were reduced using Linear Discriminant Analysis (LDA) and then Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Linear Regression, Logistic Regression and Multi-Layer Perceptron (MLP) have been used to classify the anomalies.

Equivalence between LDA/QR and Direct LDA

2012 ◽  
pp. 338-353
Author(s):  
Rong-Hua Li ◽  
Shuang Liang ◽  
George Baciu ◽  
Eddie Chan
Keyword(s):  
Discriminant Analysis ◽  
Dimension Reduction ◽  
Linear Discriminant Analysis ◽  
Classification Accuracy ◽  
Qr Decomposition ◽  
Linear Discriminant ◽  
Qr Algorithm ◽  
Special Cases ◽  
Benchmark Datasets ◽  
Pseudo Inverse

Singularity problems of scatter matrices in Linear Discriminant Analysis (LDA) are challenging and have obtained attention during the last decade. Linear Discriminant Analysis via QR decomposition (LDA/QR) and Direct Linear Discriminant analysis (DLDA) are two popular algorithms to solve the singularity problem. This paper establishes the equivalent relationship between LDA/QR and DLDA. They can be regarded as special cases of pseudo-inverse LDA. Similar to LDA/QR algorithm, DLDA can also be considered as a two-stage LDA method. Interestingly, the first stage of DLDA can act as a dimension reduction algorithm. The experiment compares LDA/QR and DLDA algorithms in terms of classification accuracy, computational complexity on several benchmark datasets and compares their first stages. The results confirm the established equivalent relationship and verify their capabilities in dimension reduction.

scholarly journals Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6763
Author(s):  
Mads Jochumsen ◽  
Imran Khan Niazi ◽  
Muhammad Zia ur Rehman ◽  
Imran Amjad ◽  
Muhammad Shafique ◽  
...  
Keyword(s):  
Neural Networks ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Intraclass Correlation ◽  
Motor Impairment ◽  
Wrist Flexion ◽  
Stroke Patients ◽  
Linear Discriminant

Brain- and muscle-triggered exoskeletons have been proposed as a means for motor training after a stroke. With the possibility of performing different movement types with an exoskeleton, it is possible to introduce task variability in training. It is difficult to decode different movement types simultaneously from brain activity, but it may be possible from residual muscle activity that many patients have or quickly regain. This study investigates whether nine different motion classes of the hand and forearm could be decoded from forearm EMG in 15 stroke patients. This study also evaluates the test-retest reliability of a classical, but simple, classifier (linear discriminant analysis) and advanced, but more computationally intensive, classifiers (autoencoders and convolutional neural networks). Moreover, the association between the level of motor impairment and classification accuracy was tested. Three channels of surface EMG were recorded during the following motion classes: Hand Close, Hand Open, Wrist Extension, Wrist Flexion, Supination, Pronation, Lateral Grasp, Pinch Grasp, and Rest. Six repetitions of each motion class were performed on two different days. Hudgins time-domain features were extracted and classified using linear discriminant analysis and autoencoders, and raw EMG was classified with convolutional neural networks. On average, 79 ± 12% and 80 ± 12% (autoencoders) of the movements were correctly classified for days 1 and 2, respectively, with an intraclass correlation coefficient of 0.88. No association was found between the level of motor impairment and classification accuracy (Spearman correlation: 0.24). It was shown that nine motion classes could be decoded from residual EMG, with autoencoders being the best classification approach, and that the results were reliable across days; this may have implications for the development of EMG-controlled exoskeletons for training in the patient’s home.

IMPROVED PSEUDOINVERSE LINEAR DISCRIMINANT ANALYSIS METHOD FOR DIMENSIONALITY REDUCTION

2012 ◽  
Vol 26 (01) ◽  
pp. 1250002 ◽  
Author(s):  
KULDIP K. PALIWAL ◽  
ALOK SHARMA
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Classification Accuracy ◽  
Classical Method ◽  
Small Sample Size ◽  
Small Sample ◽  
Analysis Method ◽  
Small Sample Size Problem ◽  
Linear Discriminant ◽  
Discriminant Analysis Method

Pseudoinverse linear discriminant analysis (PLDA) is a classical method for solving small sample size problem. However, its performance is limited. In this paper, we propose an improved PLDA method which is faster and produces better classification accuracy when experimented on several datasets.

scholarly journals THE EFFECT OF USING HAMMING WINDOW AND LINEAR PREDICTIVE CODING MODEL IN EEG-P300 SIGNALS CLASSIFICATION

2015 ◽  
pp. 97-106
Author(s):  
Ali Momennezhad ◽  
Mousa Shamsi ◽  
Hossein Ebrahimnezhad ◽  
Lida Asgharian
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Classification Accuracy ◽  
Predictive Coding ◽  
Convergence Time ◽  
Linear Predictive Coding ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Maximum Accuracy ◽  
Fisher Linear Discriminant Analysis

In this paper, Fisher linear discriminant analysis (FLDA) is used to classify the EEGP-300 signals which are extracted from brain activities. In this case, at first the preprocessing algorithms such as filtering and referencing are applied to the raw EEG signal. Then, in order to create a model out of the signal, a linear predictive coding model with 6 order is used. So that the signal is reconstructed by extracting linear predictive coding (LPC) model parameters of each single trial, and then every signal trial is passed through the Hamming window by length 9. At last Fisher Linear Discriminant Analysis is used for classifying. In this paper, classification accuracy, the maximum bit rate and the convergence time to achieve stability in maximum accuracy of classification are computed to compare performance of the proposed method, Fisher Linear Discriminant Analysis with Linear Predictive Coding Model and Hamming Window (LPC+HAMMING+FLDA), to FLDA and LPC+FLDA. The implementation results show that the efficiency of the proposed method in terms of classification accuracy and convergence time to achieve stability in maximum accuracy is better than the other two mentioned algorithms. As example, at the proposed algorithm with 8 electrode configuration the S2 converges to the maximum accuracy after eleventh Block while this happens for two other algorithms after fourteenth Block and the total classification accuracy for this person at proposed algorithm is improved as 2.2% and 4% than respectively LPC+FLDA and FLDA algorithms.

scholarly journals Luwak Coffee Classification Using UV-Vis Spectroscopy Data: Comparison of Linear Discriminant Analysis and Support Vector Machine Methods

2018 ◽  
Vol 7 (2) ◽  
pp. 115-121
Author(s):  
Diding Suhandy ◽  
Meinilwita Yulia
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Classification Accuracy ◽  
Quality Evaluation ◽  
Support Vector ◽  
Classification Result ◽  
Linear Discriminant ◽  
Data Comparison ◽  
Vis Spectroscopy ◽  
Vector Machines

UV-Vis spectroscopy has been used as a promising method for coffee quality evaluation including in authentication of several high-economic coffee types. In this paper, we have compared the abilities of linear discriminant analysis (LDA) and support vector machines classification (SVMC) methods for Luwak coffee classification. UV-Vis spectral data of 50 samples of pure Luwak coffee and 50 samples of pure non-Luwak coffee were acquired using a UV-Vis spectrometer in transmittance mode. The results show that UV-Vis spectroscopy combined with LDA and SVMC was an effective method to classify Luwak and non-Luwak coffee samples. The classification result was acceptable and yielded 100% classification accuracy for both LDA and SVMC methods. However, due to the simplicity and volume of the required calculation, in this present study LDA method is superior to SVMC method.

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