Valentina E. Balas

Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis.

Geospatial data analytical model is developed in this paper to model the spatial suitability of malaria outbreak in Vellore, Tamil Nadu, India. In general, Disease control strategies are only the spatial information like landscape, weather and climate, but also spatially explicit information like socioeconomic variable, population density, behavior and natural habits of the people. The spatial multi-criteria decision analysis approach combines the multi-criteria decision analysis and geographic information system (GIS) to model the spatially explicit and implicit information and to make a practical decision under different scenarios and different environment. Malaria is one of the emerging diseases worldwide; the cause of malaria is weather & climate condition of the study area. The climate condition is often called as spatially implicit information, traditional decision-making models do not use the spatially implicit information it most often uses spatially explicit information such as socio-economic, natural habits of the people. There is need to develop an integrated approach that consists of spatially implicit and explicit information. The proposed approach is used to identity an effective control strategy that prevents and control of malaria. Inverse Distance Weighting (IDW) is a type of deterministic method used in this paper to assign the weight values based on the neighborhood locations. ArcGIS software is used to develop the geospatial habitat suitability model.

Classification of EEG-Based Brain–Computer Interfaces

This chapter demonstrates the development of a brain computer interface (BCI) decision support system for controlling the movement of a wheelchair for neurologically disabled patients using their Electroencephalography (EEG). The subject was able to imagine his/her hand movements during EEG experiment which made EEG oscillations in the signal that could be classified by BCI. The BCI will translate the patient’s thoughts into simple wheelchair commands such as “go” and “stop”. EEG signals are recorded using 59 scalp electrodes. The acquired signals are artifacts contaminated. These artifacts were removed using blind source separation (BSS) by independent component analysis (ICA) to get artifact-free EEG signal from which certain features are extracted by applying discrete wavelet transformation (DWT). The extracted features were reduced in dimensionality using principal component analysis (PCA). The reduced features were fed to neural networks classifier yielding classification accuracy greater than 95 %.

Indian Sign Language Recognition Using Optimized Neural Networks

Recognition of sign languages has gained reasonable interest by the researchers in the last decade. An accurate sign language recognition system can facilitate more accurate communication of deaf and dumb people. The wide variety of Indian Sign Language (ISL) led to more challenging learning process. In the current work, three novel methods was reported to solve the problem of recognition of ISL gestures effectively by combining Neural Network (NN) with Genetic Algorithm (GA), Evolutionary algorithm (EA) and Particle Swarm Optimization (PSO) separately to attain novel NN-GA, NN-EA and NN-PSO methods; respectively. The input weight vector to the NN has been optimized gradually to achieve minimum error. The proposed methods performance was compared to NN and the Multilayer Perceptron Feed-Forward Network (MLP-FFN) classifiers. Several performance metrics such as the accuracy, precision, recall, F-measure and kappa statistic were calculated. The experimental results established that the proposed algorithm achieved considerable improvement over the performance of existing works in order to recognize ISL gestures. The NN-PSO outperformed the other approaches with 99.96 accuracy, 99.98 precision, 98.29 recall, 99.63 F-Measure and 0.9956 Kappa Statistic.

Comparing Haar-Hilbert and Log-Gabor based iris encoders on Bath Iris Image Database

This papers introduces a new family of iris encoders which use 2-dimensional Haar Wavelet Transform for noise attenuation, and Hilbert Transform to encode the iris texture. In order to prove the usefulness of the newly proposed iris encoding approach, the recognition results obtained by using these new encoders are compared to those obtained using the classical Log-Gabor iris encoder. Twelve tests involving single/multienrollment and conducted on Bath Iris Image Database are presented here. One of these tests achieves an Equal Error Rate comparable to the lowest value reported so far for this database. New Matlab tools for iris image processing are also released together with this paper: a second version of the Circular Fuzzy Iris Segmentator (CFIS2), a fast Log-Gabor encoder and two Haar-Hilbert based encoders.

Driver Assisting by Inverse Time to Collision

The paper is proposing a specific indicator, the inverse time to collision TTC-1, useful when analyzing the highway traffic. The advantage of TTC-1 vs. TTC is a direct and continuous dependence with the collision risk. TTC-1 could be used as an input in car following algorithms. Because the automate driving is yet in a research stage, a feasible application for TTC-1 would be rather assisting the driver of the following car at the choice of the distance gap towards the first car.

Optimization of 5.5-GHz CMOS LNA parameters using firefly algorithm

This paper presents an optimal design of low noise amplifier (LNA) using an efficient swarm-based optimizer called firefly algorithm (FA). Many researchers have used firefly algorithm to solve various nonlinear engineering problems and reported outstanding results. In view of this, FA is implemented for the first time in this paper to optimize the parameters of LNA like gain and noise figure (NF). Two case studies have been performed which includes the minimization of NF and maximization of gain. Optimization of these two parameters has been carried out by considering each parameter as a single objective function. Penalty factor method is considered for handling the constraints. Other parameters of LNA like power consumption, linearity, and stability are also discussed for both the cases. The designed LNA has a cascode structure with inductive source degeneration topology and is implemented in UMC 0.18-μm CMOS technology using CADENCE software. LNA is designed for 5.5-GHz frequency. The performance of FA in optimizing the parameters of LNA is also compared with the performance of other similar contemporary algorithms like particle swarm optimization (PSO), human behavior PSO (HB-PSO), backtracking search algorithm, and cuckoo search algorithm (CSA). The optimized value of LNA parameter using FA and other algorithms when simulated in MATLAB environment is compared with the simulated result of CADENCE. Statistical analysis is also performed for each case study, and the results are compared with the above-mentioned optimization algorithms. Simulation results, comparative study, and statistical analysis confirm the superiority of FA over other methods in terms of its computational efficiency, consistency, and robustness.

Rule-based back propagation neural networks for various precision rough set presented KANSEI knowledge prediction: a case study on shoe product form features extraction

Abstract Nonlinear operators for KANSEI evaluation dataset were significantly developed such as uncertainty reason techniques including rough set, fuzzy set and neural networks. In order to extract more accurate KANSEI knowledge, rule-based presentation was concluded a promising way in KANSEI engineering research. In the present work, variable precision rough set was applied in rule-based system to reduce the complexity of the knowledge database from normal item dataset to high frequent rule set. In addition, evidence theory’s reliability indices, namely the support and confidence for rule-based knowledge presentation, were proposed by using back propagation neural network with Bayesian regularization algorithm. The proposed method was applied in shoes KANSEI evaluation system; for a certain KANSEI adjective, the key form features of products were predicted. Some similar algorithms such as Levenberg–Marquardt and scaled conjugate gradient were also discussed and compared to establish the effectiveness of the proposed approach. The experimental results established the effectiveness and feasibility of the proposed algorithms customized for shoe industry, where the proposed back propagation neural network/Bayesian regularization approach achieved superior performance compared to the other algorithms in terms of the performance, gradient, Mu, Effective number of parameter, and the sum square parameter in KANSEI support and confidence time series prediction.

Linguistic Hedges Fuzzy Feature Selection for Differential Diagnosis of Erythemato-Squamous Diseases

The differential diagnosis of erythemato-squamous diseases is a real challenge in dermatology. In diagnosing of these diseases, a biopsy is vital. However, unfortunately these diseases share many histopathological features, as well. Another difficulty for the differential diagnosis is that one disease may show the features of another disease at the beginning stage and may have the characteristic features at the following stages. In this paper, a new Feature Selection based on Linguistic Hedges Neural-Fuzzy classifier is presented for the diagnosis of erythemato-squamous diseases. The performance evaluation of this system is estimated by using four training-test partition models: 50–50%, 60–40%, 70–30% and 80–20%. The highest classification accuracy of 95.7746% was achieved for 80–20% training-test partition using 3 clusters and 18 fuzzy rules, 93.820% for 50–50% training-test partition using 3 clusters and 18 fuzzy rules, 92.5234% for 70–30% training-test partition using 5 clusters and 30 fuzzy rules, and 91.6084% for 60–40% training-test partition using 6 clusters and 36 fuzzy rules. Therefore, 80–20% training-test partition using 3 clusters and 18 fuzzy rules are the best classification accuracy with RMSE of 6.5139e-013. This research demonstrated that the proposed method can be used for reducing the dimension of feature space and can be used to obtain fast automatic diagnostic systems for other diseases.

8-valent fuzzy logic for iris recognition and biometry

This paper shows that maintaining logical consistency of an iris recognition system is a matter of finding a suitable partitioning of the input space in enrollable and unenrollable pairs by negotiating the user comfort and the safety of the biometric system. In other words, consistent enrollment is mandatory in order to preserve system consistency. A fuzzy 3-valent disambiguated model of iris recognition is proposed and analyzed in terms of completeness, consistency, user comfort and biometric safety. It is also shown here that the fuzzy 3-valent model of iris recognition is hosted by an 8-valent Boolean algebra of modulo 8 integers that represents the computational formalization in which a biometric system (a software agent) can achieve the artificial understanding of iris recognition in a logically consistent manner.

Grid Color Moment Features in Glaucoma Classification

Abstract — Automated diagnosis of glaucoma disease is focused on the analysis of the retinal images to localize, perceive and evaluate the optic disc. Clinical decision support system (CDSS) is used for glaucoma classification in human eyes. This process depends mainly on the feature type that can be morphological or non-morphological. It is originated in the retinal image analysis technique that used color feature, texture features, extract structure, or contextual. This work proposes an empirical study on the retinal nerve fiber layer (RNFL) thickness or the ocular a narrative automated glaucoma diagnosis, classification system based on both Grid Color Moment method as a feature vector to extract the color features (non-morphological) and neural network classifier. Consequently, these features are fed to the back propagation neural network (BPNN) classifier for automated diagnosis. The proposed system was tested using an open RIM-ONE database with accurate gold standards of the optic nerve head. This work classifies both normal and abnormal defected retina with glaucoma images. The experimental results achieved an accuracy of 87.47%. Thus, the proposed system can detect the early glaucoma stage with good accuracy.