Browsing by Author "Prasad, Mukesh"

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A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance
(2018-01-01)
Rajora, Shantanu
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Li, Dong-Lin
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Jha, Chandan
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Bharill, Neha
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Patel, Om Prakash
This paper proposes a comparative performance of ten different machine learning algorithms, done on a credit card fraud detection application. The machine learning methods have been classified into two groups namely classification algorithms and ensemble learning group. Each group is comprised of five different algorithms. Besides, the 'Time' feature is introduced in the data set and performances of the algorithms are studied with and without the 'Time' feature. Two algorithms of the ensemble learning group have been found to perform better when the used dataset does not include the 'Time' feature. However, for the classification algorithms group, three classifiers are found to show better predictive accuracies when all attributes are included in the used dataset. The rest of the machine learning models have approximate similar scores between these datasets.
A fast and self-adaptive on-line learning detection system
(2018-01-01)
Prasad, Mukesh
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Zheng, Ding-Rong
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Mery, Domingo
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Puthal, Deepak
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Sundaram, Suresh
This paper proposes a method to allow users to select target species for detection, generate an initial detection model by selecting a small piece of image sample and as the movie plays, continue training this detection model automatically. This method has noticeable detection results for several types of objects. The framework of this study is divided into two parts: the initial detection model and the online learning section. The detection model initialization phase use a sample size based on the proportion of users of the Haar-like features to generate a pool of features, which is used to train and select effective classifiers. Then, as the movie plays, the detecting model detects the new sample using the NN Classifier with positive and negative samples and the similarity model calculates new samples based on the fusion background model to calculate a new sample and detect the relative similarity to the target. From this relative similarity-based conservative classification of new samples, the conserved positive and negative samples classified by the video player are used for automatic online learning and training to continuously update the classifier. In this paper, the results of the test for different types of objects show the ability to detect the target by choosing a small number of samples and performing automatic online learning, effectively reducing the manpower needed to collect a large number of image samples and a large amount of time for training. The Experimental results also reveal good detection capability. (C) 2018 The Authors. Published by Elsevier Ltd.
A fast fused part-based model with new deep feature for pedestrian detection and security monitoring
(2020-02-01)
Cheng, Eric Juwei
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Prasad, Mukesh
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Yang, Jie
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Khanna, Pritee
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Chen, Bing-Hong
In recent years, pedestrian detection based on computer vision has been widely used in intelligent transportation, security monitoring, assistance driving and other related applications. However, one of the remaining open challenges is that pedestrians are partially obscured and their posture changes. To address this problem, deformable part model (DPM) uses a mixture of part filters to capture variation in view point and appearance and achieves success for challenging datasets. Nevertheless, the expensive computation cost of DPM limits its ability in the real-time application. This study propose a fast fused part-based model (FFPM) for pedestrian detection to detect the pedestrians efficiently and accurately in the crowded environment. The first step of the proposed method trains six Adaboost classifiers with Haar-like feature for different body parts (e.g., head, shoulders, and knees) to build the response feature maps. These six response feature maps are combined with full-body model to produce spatial deep features. The second step of the proposed method uses the deep features as an input to support vector machine (SVM) to detect pedestrian. A variety of strategies is introduced in the proposed model, including part-based to full-body method, spatial filtering, and multi-ratios combination. Experiment results show that the proposed FFPM method improves the computation speed of DPM and maintains the performance in detection. (C) 2019 Elsevier Ltd. All rights reserved.
A Generalized Enhanced Quantum Fuzzy Approach for Efficient Data Clustering
(2019-01-01)
Bharill, Neha
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Patel, Om Prakash
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Tiwari, Aruna
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Mu, Lifeng
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Li, Dong-Lin
Data clustering is a challenging task to gain insights into data in various fields. In this paper, an Enhanced Quantum-Inspired Evolutionary Fuzzy C-Means (EQIE-FCM) algorithm is proposed for data clustering. In the EQIE-FCM, quantum computing concept is utilized in combination with the FCM algorithm to improve the clustering process by evolving the clustering parameters. The improvement in the clustering process leads to improvement in the quality of clustering results. To validate the quality of clustering results achieved by the proposed EQIE-FCM approach, its performance is compared with the other quantum-based fuzzy clustering approaches and also with other evolutionary clustering approaches. To evaluate the performance of these approaches, extensive experiments are being carried out on various benchmark datasets and on the protein database that comprises of four superfamilies. The results indicate that the proposed EQIE-FCM approach finds the optimal value of fitness function and the fuzzifier parameter for the reported datasets. In addition to this, the proposed EQIE-FCM approach also finds the optimal number of clusters and more accurate location of initial cluster centers for these benchmark datasets. Thus, it can be regarded as a more efficient approach for data clustering.
A Layered-Coevolution-Based Attribute-Boosted Reduction Using Adaptive Quantum-Behavior PSO and Its Consistent Segmentation for Neonates Brain Tissue
(2018-06-01)
Ding, Weiping
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Lin, Chin-Teng
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Prasad, Mukesh
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Cao, Zehong
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Wang, Jiandong
The main challenge of attribute reduction in large data applications is to develop a new algorithm to deal with large, noisy, and uncertain large data linking multiple relevant data sources, structured or unstructured. This paper proposes a new and efficient layered-coevolution-based attribute-boosted reduction algorithm (LCQ-ABR*) using adaptive quantum-behavior particle swarm optimization (PSO). First, the quantum rotation angle of an evolutionary particle is updated by a dynamic change of self-adapting step size. Second, a self-adaptive partitioning strategy is employed to group particles into different memeplexes, and the quantum-behavior mechanism with the particles' states depicted by the wave function cooperates to achieve superior performance in their respective memeplexes. Third, a new layered coevolutionary model with multiagent interaction is constructed to decompose a complex attribute set, and it can self-adapt the attribute sizes among different layers and produce the reasonable decompositions by exploiting any interdependence among multiple relevant attribute subsets. Fourth, the decomposed attribute subsets are evolved to compute the positive region and discernibility matrix by using their best quantum particles, and the global optimal reduction set is induced successfully. Finally, extensive comparative experiments are provided to illustrate that LCQ-ABR* has better feasibility and effectiveness of attribute reduction on large-scale and uncertain dataset problems with complex noise as compared with representative algorithms. Moreover, LCQ-ABR* can be successfully applied in the consistent segmentation for neonatal brain three-dimensional MRI, and the consistent segmentation results further demonstrate its stronger applicability.
A METHOD TO ENHANCE THE DEEP LEARNING IN AN AERIAL IMAGE
(2017-01-01)
Chou, Kuang-Pen
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Li, Dong-Lin
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Lin, Wen-Chieh
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Prasad, Mukesh
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Lin, Chin-Teng
In this paper, we propose a kind of pre-processing method which can be applied to the depth learning method for the characteristics of aerial image. This method combines the color and spatial information to do the quick background filtering. In addition to increase execution speed, but also to reduce the rate of false positives
A Novel Fuzzy Logic Model for Pseudo-Relevance Feedback-Based Query Expansion
(2016-12-01)
Singh, Jagendra
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Prasad, Mukesh
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Prasad, Om Kumar
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Joo, Er Meng
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Saxena, Amit Kumar
In this paper, a novel fuzzy logic-based expansion approach considering the relevance score produced by different rank aggregation approaches is proposed. It is well known that different rank aggregation approaches yield different relevance scores for each term. The proposed fuzzy logic approach combines different weights of each term by using fuzzy rules to infer the weights of the additional query terms. Experimental results demonstrate that the proposed approach achieves significant improvement over individual expansion, aggregated and other related state-of-the-arts methods.
A Novel Fuzzy Logic Model for Pseudo-Relevance Feedback-Based Query Expansion
(2016-12)
Singh, Jagendra
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Prasad, Mukesh
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Prasad, Om Kumar
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Joo, Er Meng
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Saxena, Amit Kumar
In this paper, a novel fuzzy logic-based expansion approach considering the relevance score produced by different rank aggregation approaches is proposed. It is well known that different rank aggregation approaches yield different relevance scores for each term. The proposed fuzzy logic approach combines different weights of each term by using fuzzy rules to infer the weights of the additional query terms. Experimental results demonstrate that the proposed approach achieves significant improvement over individual expansion, aggregated and other related state-of-the-arts methods.
A novel online self-learning system with automatic object detection model for multimedia applications
(1970-01-01)
Cheng, Eric Juwei
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Prasad, Mukesh
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Yang, Jie
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Zheng, Ding Rong
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Tao, Xian
This paper proposes a novel online self-learning detection system for different types of objects. It allows users to random select detection target, generating an initial detection model by selecting a small piece of image sample and continue training the detection model automatically. The proposed framework is divided into two parts: First, the initial detection model and the online reinforcement learning. The detection model is based on the proportion of users of the Haar-like features to generate feature pool, which is used to train classifiers and get positive-negative (PN) classifier model. Second, as the videos plays, the detecting model detects the new sample by Nearest Neighbor (NN) Classifier to get the PN similarity for new model. Online reinforcement learning is used to continuously update classifier, PN model and new classifier. The experiment shows the result of less detection sample with automatic online reinforcement learning is satisfactory.
A robust real-time facial alignment system with facial landmarks detection and rectification for multimedia applications
(1970-01-01)
Chou, Kuang Pen
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Prasad, Mukesh
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Yang, Jie
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Su, Sheng-Yao
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Tao, Xian
Face detection often plays the first step in various visual applications. Large variants of facial deformations due to head movements and facial expression make it difficult to identify appropriate face region. In this paper, a robust real-time face alignment system, including facial landmarks detection and face rectification, is proposed. A facial landmarks detection model based on regression tree is utilized in the proposed system. In face rectification framework, 2-D geometrical analysis based on pitch, yaw and roll movements is designed to solve the misalignment problem in face detection. The experiments on the two datasets verify the performance significantly improved by the proposed method in the facial recognition task and outperform than those obtained by other alignment methods. Furthermore, the proposed method can achieve robust recognition results even if the amount of training images is not large.
An Improved Polynomial Neural Network Classifier Using Real-Coded Genetic Algorithm
(2015-11-01)
Lin, Chin-Teng
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Prasad, Mukesh
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Saxena, Amit
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資訊工程學系
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電機工程學系
In this paper, a novel approach is proposed to improve the classification performance of a polynomial neural network (PNN). In this approach, the partial descriptions (PDs) are generated at the first layer based on all possible combinations of two features of the training input patterns of a dataset. The set of PDs from the first layer, the set of all input features, and a bias constitute the chromosome of the real-coded genetic algorithm (RCGA). A system of equations is solved to determine the values of the real coefficients of each chromosome of the RCGA for the training dataset with the mean classification accuracy (CA) as the fitness value of each chromosome. To adjust these values for unknown testing patterns, the RCGA is iterated in the usual manner using simple selection, crossover, mutation, and elitist selection. The method is tested extensively with the University of California, Irvine benchmark datasets by utilizing tenfold cross validation of each dataset, and the performance is compared with various well-known state-of-the-art techniques. The results obtained from the proposed method in terms of CA are superior and outperform other known methods on various datasets.
Attribute Equilibrium Dominance Reduction Accelerator (DCCAEDR) Based on Distributed Coevolutionary Cloud and Its Application in Medical Records
(2016-03)
Ding, Wei-Ping
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Lin, Chin-Teng
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Prasad, Mukesh
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Chen, Sen-Bo
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Guan, Zhi-Jin
Aimed at the tremendous challenge of attribute reduction for big data mining and knowledge discovery, we propose a new attribute equilibrium dominance reduction accelerator (DCCAEDR) based on the distributed coevolutionary cloud model. First, the framework of N-populations distributed coevolutionary MapReduce model is designed to divide the entire population into N subpopulations, sharing the reward of different subpopulations\' solutions under a MapReduce cloud mechanism. Because the adaptive balancing between exploration and exploitation can be achieved in a better way, the reduction performance is guaranteed to be the same as those using the whole independent data set. Second, a novel Nash equilibrium dominance strategy of elitists under the N bounded rationality regions is adopted to assist the subpopulations necessary to attain the stable status of Nash equilibrium dominance. This further enhances the accelerator\'s robustness against complex noise on big data. Third, the approximation parallelism mechanism based on MapReduce is constructed to implement rule reduction by accelerating the computation of attribute equivalence classes. Consequently, the entire attribute reduction set with the equilibrium dominance solution can be achieved. Extensive simulation results have been used to illustrate the effectiveness and robustness of the proposed DCCAEDR accelerator for attribute reduction on big data. Furthermore, the DCCAEDR is applied to solve attribute reduction for traditional Chinese medical records and to segment
Block-based Feature Extraction Model for Early Fire Detection
(2017-01-01)
Chou, Kuang-Pen
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Prasad, Mukesh
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Gupta, Deepak
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Sankar, Sharmi
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Xu, Ting-Wei
Every year the fire disaster always causes a lot of casualties and property damage. Many researchers are involved in the study of related disaster prevention. Early warning systems and stable fire can significantly reduce the damage caused by fire. Many existing image-based early warning systems can perform well in a particular field. In this paper, we propose a general framework that can be applied in most realistic environments. The proposed system is based on a block-based feature extraction method, which analyses local information in separate regions leading to a reduction in computing data. Local features of fire block are extracted from the detailed characteristics of fire objects, which include fire color, fire source immobility, and disorder. Each local feature has high detection rate and filter out different false-positive cases. Global analysis with fire texture and non-moving properties are applied to further reduce false alarm rate. The proposed system is composed of algorithms with low computation. Through a series of experiments, it can be observed that Experimental results show that the proposed system has higher detection rate and low false alarm rate under various environment.
Deep Sparse Representation Classifier for facial recognition and detection system
(2019-07-01)
Cheng, Eric-Juwei
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Chou, Kuang-Pen
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Rajora, Shantanu
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Jin, Bo-Hao
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Tanveer, M.
This paper proposes a two-layer Convolutional Neural Network (CNN) to learn the high-level features which utilizes to the face identification via sparse representation. Feature extraction plays a vital role in real-world pattern recognition and classification tasks. The details description of the given input face image, significantly improve the performance of the facial recognition system. Sparse Representation Classifier (SRC) is a popular face classifier that sparsely represents the face image by a subset of training data, which is known as insensitive to the choice of feature space. The proposed method shows the performance improvement of SRC via a precisely selected feature exactor. The experimental results show that the proposed method outperforms other methods on given datasets. (C) 2019 Elsevier B.V. All rights reserved.
Designing Mamdani Type Fuzzy Rule Using a Collaborative FCM Scheme
(2013)
Lin, Chin-Teng
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Prasad, Mukesh
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Chang, Jyh-Yeong
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
This paper presents a new approach for generating fuzzy rules for fuzzy inference system by using collaborative fuzzy c-mean (CFCM). In order to do any mode of integration between datasets, there is a need to define the common feature between datasets by using some kind of collaborative process and also need to preserve the privacy and security at higher levels. This collaboration process gives a common structure between datasets which helps to define an appropriate number of rules for structural learning and also improve the accuracy of the system modeling. This all consideration bring the concept of collaborative fuzzy rule generation process with a quality measuring.
EEG-based Driving Fatigue Prediction System Using Functional-link-based Fuzzy Neural Network
(2014)
Liu, Yu-Ting
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Lin, Yang-Yin
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Wu, Shang-Lin
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Chuang, Chun-Hsiang
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Prasad, Mukesh
This study presents a fuzzy prediction system for the forecasting and estimation of driving fatigue, which utilizes a functional-link-based fuzzy neural network (FLFNN) to predict the drowsiness (DS) level in car driving task. The cognitive state in car driving task is one of key issue in cognitive neuroscience because fatigue driving usually causes enormous losses nowadays. The damage can be extremely decreased by the assistant of various artificial systems. Many Electroencephalography (EEG)based interfaces have been widely developed recently due to its convenient measurement and real-time response. However, the improvement of recognition accuracy is still confined to some specific problems (e.g., individual difference). In order to solve this issue, the proposed methodology in this paper utilizes a non-linear fuzzy neural network structure to increase the adaptability in the real-world environment. Therefore, this study is further to analysis the brain activities in car driving, which is constructed in a simulated three-dimensional virtual-reality (VR) environment. Finally, through the development of brain cognitive model in car driving task, this system can predict the cognitive state effectively before drivers\' action and then provide correct feedback to users. This study also compared the result with the-state-of-art systems, including Linear Regression (LR), Multi-Layer Perceptron Neural Network (MLPNN) and Support Vector Regression (SVR). Results of this study demonstrate the effectiveness of the proposed FLFNN model.
EOG-Based Eye Movement Classification and Application on HCI Baseball Game
(2019-01-01)
Lin, Chin-Teng
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King, Juang-Tai
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Bharadwaj, Priyanka
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Chen, Chih-Hao
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Gupta, Akshansh
Electrooculography (EOG) is considered as the most stable physiological signal in the development of human-computer interface (HCI) for detecting eye-movement variations. EOG signal classification has gained more traction in recent years to overcome physical inconvenience in paralyzed patients. In this paper, a robust classification technique, such as eight directional movements is investigated by introducing a concept of buffer along with a variation of the slope to avoid misclassification effects in EOG signals. Blinking detection becomes complicated when the magnitude of the signals are considered. Hence, a correction technique is introduced to avoid misclassification for oblique eye movements. Meanwhile, a case study has been considered to apply these correction techniques to HCI baseball game to learn eye-movements.
Estimation of SSVEP-based EEG Complexity using Inherent Fuzzy Entropy
(2017-01-01)
Cao, Zehong
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Prasad, Mukesh
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Lin, Chin-Teng
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腦科學研究中心
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Brain Research Center
This study considers the dynamic changes of complexity feature by fuzzy entropy measurement and repetitive steady-state visual evoked potential (SSVEP) stimulus. Since brain complexity reflects the ability of the brain to adapt to changing situations, we suppose such adaptation is closely related to the habituation, a form of learning in which an organism decreases or increases to respond to a stimulus after repeated presentations. By a wearable electroencephalograph (EEG) with Fpz and Oz electrodes, EEG signals were collected from 20 healthy participants in one resting and five-times 15 Hz SSVEP sessions. Moreover, EEG complexity feature was extracted by multi-scale Inherent Fuzzy Entropy (IFE) algorithm, and relative complexity (RC) was defined the difference between resting and SSVEP. Our results showed the enhanced frontal and occipital RC was accompanied with increased stimulus times. Compared with the 1st SSVEP session, the RC was significantly higher than the 5th SSVEP session at frontal and occipital areas (p < 0.05). It suggested that brain has adapted to changes in stimulus influence, and possibly connected with the habituation. In conclusion, effective evaluation of IFE has a potential EEG signature of complexity in the SSEVP-based experiment.
Face recognition using nonparametric-weighted Fisherfaces
(2012)
Li, Dong-Lin
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Prasad, Mukesh
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Hsu, Sheng-Chih
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Hong, Chao-Ting
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Lin, Chin-Teng
This study presents an appearance-based face recognition scheme called the nonparametric-weighted Fisherfaces (NW-Fisherfaces). Pixels in a facial image are considered as coordinates in a high-dimensional space and are transformed into a face subspace for analysis by using nonparametric-weighted feature extraction (NWFE). According to previous studies of hyperspectral image classification, NWFE is a powerful tool for extracting hyperspectral image features. The Fisherfaces method maximizes the ratio of between-class scatter to that of within-class scatter. In this study, the proposed NW-Fisherfaces weighted the between-class scatter to emphasize the boundary structure of the transformed face subspace and, therefore, enhances the separability for different persons' face. The proposed NW-Fisherfaces was compared with Orthogonal Laplacianfaces, Eigenfaces, Fisherfaces, direct linear discriminant analysis, and null space linear discriminant analysis methods for tests on five facial databases. Experimental results showed that the proposed approach outperforms other feature extraction methods for most databases.
Fast Deformable Model for Pedestrian Detection with Haar-like Features
(2017-01-01)
Chou, Kuang-Pen
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Prasad, Mukesh
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Puthal, Deepak
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Chen, Ping-Hung
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Vishwakarma, Dinesh Kumar
This paper proposes a novel Fast Deformable Model for Pedestrian Detection (FDMPD) to detect the pedestrians efficiently and accurately in the crowded environment. Despite of multiple detection methods available, detection becomes difficult due to variety of human postures and perspectives. The proposed study is divided into two parts. First part trains six Adaboost classifiers with Haar-like feature for different body parts (e.g., head, shoulders, and knees) to build the response feature maps. Second part uses these six response feature maps with full-body model to produce spatial deep features. The combined deep features are used as an input to SVM to judge the existence of pedestrian. As per the experiments conducted on the INRIA person dataset, the proposed FDMPD approach shows greater than 44.75 % improvement compared to other state-of-the-art methods in terms of efficiency and robustness.