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Interest on palmprint biometrics has experimented a strong growth in the last decades due to its useful characteristics as uniqueness, permanence, reliability, user-friendliness, acceptability, non-intrusiveness, and low cost of the acquisition devices, which make it attractive for civil and commercial applications. Accordingly, a wide research has been developed in this field. Nevertheless, there...
Geriatric depression is a disease prevailing in the elderly. It is characterized by typical symptoms of lower functioning, diminished interest in activities, insomnia or hypersomnia, fatigue or loss of energy and observable psycho motor agitation or retardation. Many studies exist with an aim to predict the geriatric depression from the perspective of healthcare informatics based on data mining analytics...
This research proposes a reliable machine learning based computational solution for human detection. The proposed model is specifically applicable for illumination-variant natural scenes in big data video frames. In order to solve the illumination variation problem, a new feature set is formed by extracting features using histogram of gradients (HoG) and linear phase quantization (LPQ) techniques,...
Automatic voiceprint recognition, posited on human speech signal, serves many salient practical applications. A number of studies are undertaken on the basis of normal speech. This research intends to develop automatic voiceprint recognition system on the basis of emotion speech signal in Indonesia language. The study is limited to four different people with speeches of four distinctive emotional...
It is well-known that the precision of data, weight vector, and internal representations employed in learning systems directly impacts their energy, throughput, and latency. The precision requirements for the training algorithm are also important for systems that learn on-the-fly. In this paper, we present analytical lower bounds on the precision requirements for the commonly employed stochastic gradient...
Diagnosing liver disease is the challenging task for many public health physicians. In this study, we propose the framework to diagnose the hepatitis disease. For this study the adaptive rule based induction were formulated and the adaptive rule implemented in combined Robust BoxCox Transformation (RBCT) and Neural Network (NN) methods. The performance of proposed model is compared and results are...
This paper describes an algorithm that parallelizes support vector machines. The data is split into subsets and optimized separately with multiple SVMs, instead of analyzing the whole training set in one optimization step. The partial results are combined and filtered in a cascade of SVMs. The process terminates when the global optimum is reached. The Cascade SVM is spread over multiple processors...
Predicting stock market accurately has always fascinated the market analysts. During the previous few decades assorted machine learning techniques (Regression, RBFN, SOM, BN and SVM) have been applied to examine the highly debatable nature of stock market by capturing and using repetitive patterns. Our main aim is to accurately predict value for the future and maximum amount of profit for a holder...
Predicting price has now become an important task in the operation of electrical power system. Day-ahead prediction provides forecast prices for a day ahead that is useful for daily operation and decision-making. The main challenge for day ahead price forecasting is the accuracy and efficiency. Lower accuracy is produced due to the nature of electricity price that is highly volatile compared to load...
Most of the intrusion detection systems analyze all network traffic features to identify intrusions with different classification techniques. Any intrusion detection model developed has to provide maximum accuracy with minimal false alarms. Identifying the optimal feature subset for classification is an important task for improved classification. In this paper, consistency based feature selection...
Analysis of a medical dataset having missing values and then filling the missing values through different approaches exists in the literature. However, the classification accuracies achieved using these approaches have not been so promising when analyzed. It is this reason; which implicitly motivated us to study and address new methods for imputation. In this paper, we propose an approach for efficient...
This paper reviews the comparative performance of Support Vector Machine (SVM) using four different kernels, i.e., Linear, Polynomial, Radial Basis Function (RBF) and Sigmoid. Overall accuracy (OA), Kappa Index Analysis (KIA), Receiver Operating Characteristic (ROC) and Precision (P) have been considered as evaluation parameters in order to assess the predictive accuracy of SVM. Both high resolution...
Email is a rapid and cheap communication medium for sending and receiving information where spam is becoming a nuisance for such communication. A good spam filtering cannot only be achieved by high performance accuracy but low false positive is also necessary. This paper presents a combining classifiers approach with committee selection mechanism where the main objective is to combine individual decisions...
In this paper an effective and most reliable method for appropriate classification of cardiac arrhythmia using automated based Artificial Neural Network (ANN) has been proposed. The results are encouraging and are found to have produced a very confident and efficient arrhythmia classification, which is easily applicable in diagnostic decision support system. In this paper the authors have employed...
Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classifier. Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifiers, and finally the classification results are combined following either the one-against-one...
Brain-computer Interfaces (BCIs) are control and communication systems based on acquisition and processing of brain signals to control a computer or an external device. Usually, BCI is focused in recognizing acquired events by different neuroimage methods, but the most used is the electroencephalography (EEG). Feature extraction over EEG signals for BCI systems is crucial to the classification performance...
For hyperspectral image classification, feature extraction is a crucial pre-process for avoiding the Hughes phenomena. Some feature extraction methods such as linear discriminant analysis (LDA), nonparametric weighted feature extraction (NWFE), and their kernel versions, generalized discriminant analysis (GDA) and kernel nonparametric weighted feature extraction method (KNWFE) have been shown that...
Band selection is an effective solutions for dimensionality reduction in hyperspectral imagery. In this paper, a novel band weighting and selection method is proposed based on maximizing margin in support vector machine (SVM). The goal is to reduce high dimensionality if hyperspectral data while achieving accuracy classification performance. This method computes the weights of the samples to maximize...
We propose a superpixel-based composite kernel framework for hyperspectral image (HSI) classification. Composite kernel methods can utilize both the spectral and the spatial information for the HSI classification. However, setting the optimal spatial neighborhood for different spatial structures is a non-trivial issue. In order to adaptively exploit the spatial contextual information, we utilize superpixel...
Hyperspectral imaging enables detailed ground cover classification with hundreds of spectral bands at each pixel. Rich spectral information can be a drawback since supervised classification of a hyperspectral image requires a balance between the number of training samples and its dimension. Achieving this balance requires a large number of training or ground truth samples, which is generally difficult,...
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