Journal of Intelligent & Fuzzy Systems - Volume 38, issue 5

Authors: Jyotsna, C. | Amudha, J. | Rao, Raghavendra | Nayar, Ravi

Article Type: Research Article

Abstract: Trail making test is a cognitive impairment test used for understanding the visual attention during the visual search task. The classical paper pencil method measures the completion time of the participant and there was no mechanism for comparison across the participant with similar feature. The psychologist has to observe the reactions of the participants during the trial process and there is no mechanism to capture it. This study made an attempt to resolve the above problem and tried to infer additional parameters which can support psychologist to understand the participant performance in trail making test. The insight provided by the …approach is to extract various features which helps a psychologist by providing individual profiling and group profiling of a person and can understand the group of people who show similar cognitive impairment while performing trail Making Test. The proposed Intelligent Gaze Tracking approach could classify the participant into three different groups like low, high and medium cognitive impairment based on the extracted gaze features. The proposed approach has been compared across existing literature survey to significantly show the advantage of the system in terms of identifying the people with similar characteristics in terms of cognitive impairment. Show more

Keywords: Eye tracking, cognitive impairment, trail making test, area of interest, scanpath, fixation, adaptive neuro fuzzy inference system, k-means clustering

DOI: 10.3233/JIFS-179711

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6299-6310, 2020

Authors: Amudha, J. | Divya, K.V. | Aarthi, R.

Article Type: Research Article

Abstract: Top–down influences play a major role in the primate’s visual attention mechanism. Design of top-down influences for target search problems is the recommended approach to develop better computational models. Existing top down computational visual attention models mainly exploit three factors namely the context information, target information and task demands. Here in this paper we propose a Fuzzy based System for Target Search (FSTS) which makes use of target information as the top-down factor. The system uses Fuzzy logic to predict the salient locations in an image based on the prior information about a target object to be detected in a …scene or frame. The performance of the system was analysed using multiple evaluation parameters and is found to have a better average hit number, number of first hits and elapsed CPU time than the existing system. The saliency map comparison is performed with human eye fixation map and is found to predict the human fixations with better accuracy than existing systems. Show more

Keywords: Visual attention, saliency, regions of interest, fuzzy system, computer vision

DOI: 10.3233/JIFS-179712

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6311-6323, 2020

Authors: Navdeep, | Singh, Vijander | Rani, Asha | Goyal, Sonal

Article Type: Research Article

Abstract: This paper presents an improved hyper smoothing function based methodology for efficient edge detection. The main aim of this work is to obtain localized edges of noisy and blurred images without duplicate ones and integrating them into meaningful object boundaries. Therefore, logarithmic hyper-smoothing function is introduced in local binary pattern leading to improved hyperfunction based local binary pattern (IHLBP) algorithm. The proposed technique uses an improved counting scheme to correctly evaluate the number of image points having pixel value greater than or equal to the central pixel. The IHLBP algorithm is tested on synthetic images, radiography images, real-life pictures from …USC-SIPL and BSDS database. Improved local binary pattern (ILBP), hyper local binary pattern (HLBP), Canny and Sobel methods are also used for comparative analysis. The results reveal that the proposed algorithm performs well on all synthetic and real images in the presence of blur and salt & pepper noise. Thus IHLBP proves to be an effective approach for edge detection in comparison to conventional methods. Show more

Keywords: Edge detection, digital radiography images, real images, noise images

DOI: 10.3233/JIFS-179713

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6325-6335, 2020

Authors: Jahnavi, B. Sai | Supraja, B. Sai | Lalitha, S.

Article Type: Research Article

Abstract: The main motive of this work is to discriminate a vital neurodegenerative condition of Parkinson Disease (PD) affected patients from individuals with no history of such a disorder. Excitation source features, voice quality features and prosodic features are the speech constituents considered. Voice samples of PD patients are extracted from the University of California-Irvine (UCI) Machine Learning Parkinson’s database. Random Forest (RF) decision trees and Support Vector Machine (SVM) are considered for classification. Feature reduction is applied with the Correlation based Feature Selection (CFS) attribute selector classifier that utilizes Best First Selector (BFS) as a search algorithm. The work involves …recognizing a PD patient from a healthy individual using only two speech sounds of /a/ and /o/. The speech sounds are extracted without the association of a certified clinician, that adds novelty. The proposed algorithm is non-invasive and accomplished 94.77% accuracy with feature selection process and applying RF classifier. Show more

Keywords: Best first selector, correlation based feature selection, feature reduction, parkinson, random forest, support vector machine

DOI: 10.3233/JIFS-179714

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6337-6345, 2020

Authors: Singh, Utkarsh | Gupta, Akshay | Bisharad, Dipjyoti | Arif, Wasim

Article Type: Research Article

Abstract: Speech analysis for extracting attributes such as the speaker, gender, accent and like has been a field of great interest and has been widely studied. The paper presents a novel architecture for accent identification by using a cascade of two deep-learning architecture. We design and test our proposed architecture on common voice dataset. The architecture consists of a cascade of Convolutional Neural Network (CNN) and Convolutional Recurrent Neural Network (CRNN). It is trained on Mel-spectrogram of the audios. We consider five of the most popular English accents groups namely India, Australia, US, England, Canada in this study. The proposed model …has an accuracy of 78.48% using CNN and 83.21% using CRNN. Show more

Keywords: Mel-spectrogram, deep neural networks, foreign accent classification, recurrent neural network

DOI: 10.3233/JIFS-179715

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6347-6352, 2020

Authors: Vyshnav, M.T. | Sachin Kumar, S. | Mohan, Neethu | Soman, K.P.

Article Type: Research Article

Abstract: The present paper proposes Random Kitchen Sink based music/speech classification. The temporal and spectral features such as spectral centroid, Spectral roll-off, spectral flux, Mel-frequency cepstral coefficients, entropy, and Zero-crossing rate are extracted from the signals. In order to show the competence of the proposed approach, experimental evaluations and comparisons are performed. Even though both speech and music signals differ in their production mechanisms, those share many common characteristics such as a common spectrum of frequency and are comparatively non-stationary which makes the classification difficult. The proposed approach explicitly maps the data to a feature space where it is linearly separable. …The evaluation results shows that the proposed approach provides competing scores with the methods in the available literature. Show more

Keywords: Music/speech, random kitchen sink, feature vector, GTZAN database, S&S database, spectral features

DOI: 10.3233/JIFS-179716

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6353-6363, 2020

Authors: Shinde, Hemendra Vijay | Patil, Devashri Manohar | Edla, Damodar Reddy | Bablani, Annushree | Mahananda, Malkauthekar

Article Type: Research Article

Abstract: Background: Students have to manage the strain of rising education level and their future career, accompanying the hormonal changes during their pubescence. This creates a great impact on their education as well as personal life. In this paper, an analysis has been made to study the impact of yoga on engineering students. To understand the impact. Brain-Computer Interface (BCI) approaches have been utilized. An EEG based BCI is used which will give a direct view of whats going on in the students’ brains. Methodology: In this work, an experiment has been performed on engineering students and their brain …activity is recorded before and after practicing yoga. In the experimental procedure, EEG signals are acquired from 8 electrodes which are associated with the cognitive and memory-related tasks of the brain. During each trial, participants solve the set of mathematical questionnaire. EEG signals are acquired during test trials before and after the yoga session. A bandpass filter is applied to preprocess the EEG signals. A discrete wavelet transform is implemented for feature extraction of the preprocessed signals. Results: Different classification algorithms are applied to classify the EEG signals before and after the yoga session. To measure the classification performance, measures such as accuracy, sensitivity, and specificity are presented in the paper. The highest accuracy of 95 % is achieved with Probabilistic Neural Network. Classification concluded the variations in signals before and after yoga. Further, in this work analysis of frequency bands, accuracy and score of the subjects before and after the yoga session are also done. Show more

Keywords: Brain Computer Interface, EEG signals, yoga, wavelets, Probabilistic Neural Network

DOI: 10.3233/JIFS-179717

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6365-6376, 2020

Authors: Manu, D.K. | Karthik, P.

Article Type: Research Article

Abstract: Increasingly challenging problems have been addressed in the field of underwater acoustics. The critical topics of research have been increasing the sensitivity of Sensors, measurement of sound intensity, locating target from the source, measurement of radiating power, etc. In this proposed research work, the fiber optic hydrophone sensitivity is increased by varying different parameters like dimensions of the materials, Poisson’s Ratio and Young’s Modulus of the mandrel. The fiber optic hydrophone is composed of different materials- Nylon, Aluminum, Polystyrene, Fiber, and Polyurethane. The design of the hydrophone is carried out using finite element analysis tools. The parameters of the hydrophone …(mandrel) have been varied, and the analytical result shows that there is a considerable increase in sensitivity. These results demonstrate that there is an improvement in the hydrophone sensitivity by around 20 db in contrast with the existing hydrophone. From this result, we are now focusing on customizing the design and further validating the design, in the future. Show more

Keywords: Fiber optic-hydrophone, sensitivity, poissons ratio, Young’s modulus, mandrel, finite element analysis

DOI: 10.3233/JIFS-179718

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6377-6382, 2020

Authors: Chaithanya Krishna, D.C. | Tripathi, Shikha

Article Type: Research Article

Abstract: A hybrid architecture for transforms such as N-point Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Discrete Sine Transform (DST) and Discrete Wavelet Transform (DWT) has been proposed and implemented using triple matrix product method. There is limited work reported on efficient single architecture that can perform multiple transforms simultaneously or serially depending on the application requirement. The Hybrid architecture implemented, can compute various transforms efficiently. A controller is designed which can perform different transforms using the hybrid architecture based on the input provided. The implemented systolic array can be used for computing the diagonal elements of triple-matrix product. The …designed architecture produces the output of transform sequence in order, which avoids reordering at output. The implemented architecture can be used to handle large sized transforms by repeatedly using fixed size architecture for a large number of points without increasing the number of Processing Elements (PEs). The proposed architecture has been validated with a watermarking algorithm that uses DCT and DWT transforms and its performance analyzed. The proposed hybrid architecture is implemented on Spartan-7 xc7s100fgga676-1. The simulation results are given and analyzed against standalone architecture. Show more

Keywords: Hybrid architecture, transforms implementation, triple matrix product method

DOI: 10.3233/JIFS-179719

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6383-6390, 2020

Authors: Remya Revi, K. | Wilscy, M.

Article Type: Research Article

Abstract: Nowadays the manipulations of digital images are common due to easy access of many online photo editing applications and image editing softwares. Forged images are widely used in social media for creating deceitful propaganda of an individual or a particular event and for cooking up fake evidences even in court proceedings. Hence ensuring the integrity of digital images is of prime significance and it has become a hot research area. In this paper, a novel technique for image forgery detection is proposed. The method utilizes the layer activation of inception-ResNet-v2, a pretrained Convolutional Neural Network(CNN)to extract the deep textural features …from Rotation Invariant – Local Binary Pattern (RI-LBP) map of the chrominance image. Non-negative Matrix Factorization (NMF) technique is used to reduce the dimensionality of the extracted features. The dimensionality reduced features are used to train a quadratic Support Vector Machine(SVM) classifier to classify images into forged or authentic. The method is assessed on four benchmark datasets (CASIA ITDE v1.0, CASIA ITDE v2.0, CUISDE and IFS-TC). Extensive experimental analysis is done and the results show an improved detection accuracy compared to the state-of-the-art methods. Show more

Keywords: Deep learning, rotation invariant-local binary pattern, pretrained convolutional neural etworks, deep textual features, image forgery detection

DOI: 10.3233/JIFS-179720

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6391-6401, 2020