Journal of Intelligent & Fuzzy Systems - Volume 34, issue 3

Authors: Aliakhmet, Kamilla | Sadykova, Diana | Mathew, Joshin | James, Alex Pappachen

Article Type: Research Article

Abstract: Image blurring artifact is the main challenge to any spatial, denoising filters. This artifact is contributed by the heterogeneous intensities within the given neighborhood or window of fixed size. Selection of most similar intensities (G-Neighbors) helps to adapt the window shape which is of edge-aware nature and subsequently reduce this blurring artifact. The paper presents a memristive circuit design to implement this variable pixel G-Neighbor filter. The memristive circuits exhibits parallel processing capabilities (near real-time) and neuromorphic architectures. The proposed design is demonstrated as simulations of both algorithm (MATLAB) and circuit (SPICE). Circuit design is evaluated for various parameters such …as processing time, fabrication area used, and power consumption. Denoising performance is demonstrated using image quality metrics such as peak signal-to-noise ratio (PSNR), mean square error (MSE), and structural similarity index measure (SSIM). Combining adaptive filtering method with mean filter resulted in average improvement of MSE to about 65% reduction, increase of PSNR and SSIM to nearly 18% and 12% correspondingly. Show more

Keywords: Denoising filter, G-neighbor, memristor, SPICE

DOI: 10.3233/JIFS-169459

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1653-1667, 2018

Authors: Narang, Ankit | Batra, Bhumika | Ahuja, Arpit | Yadav, Jyoti | Pachauri, Nikhil

Article Type: Research Article

Abstract: EEG is the most effective diagnostic technique to determine epilepsy in a patient. The objective of this research work is to apply classification techniques on EEG signals to determine whether the patient has suffered from epileptic seizure. This is carried out through the extraction of various time and frequency domain features. The two classifiers, i.e. Artificial Neural Network (ANN) and Support Vector Machine (SVM) are used and compared using various evaluation parameters. The simulation results and corresponding quantitative analysis shows that ANN classifier is superior to SVM.

Keywords: Artificial neural network, support vector machine, EEG signal

DOI: 10.3233/JIFS-169460

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1669-1677, 2018

Authors: Isaac, Meera Mary | Wilscy, M.

Article Type: Research Article

Abstract: Today’s Image processing tools have matured to a level where its users can effortlessly modify or enhance the images according to their requirement. A misuse of such tools has created a necessity for authenticating images to ensure its correctness. Image Forensics deals with the study of different kinds of manipulation on images and their detection. Image forgery detection algorithms detect forgery related artifacts which can be distinguished using specific image properties. Texture-based features have been widely used to detect forgery induced texture variations in the images. In this paper, we propose Region and Texture combined features for Image Forgery Detection. …The Region-based approaches like – Edge-based Region Detection, Saliency-based Region Detection, and Wavelet-based Region Detection are captured, and on these regions, the texture feature- Rotation invariant Co-occurrences among adjacent LBP (RiCoLBP) is applied. The features thus obtained are optimized using Non-Negative Matrix Factorization and fed to a Support Vector Machine (SVM) for classification. The method is extensively evaluated on three benchmark datasets for image forgery detection namely CASIA v1.0, CASIA v2.0 and CUISDE. The performance reveals improved detection accuracies when compared to the state-of-the-art methods in detecting forged and authentic images. Show more

Keywords: Image forgery detection, edge-based features, Saliency, Wavelets, RiCLBP

DOI: 10.3233/JIFS-169461

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1679-1690, 2018

Authors: Islam, Mohiul | Roy, Amarjit | Laskar, Rabul Hussain

Article Type: Research Article

Abstract: In this paper, a robust image watermarking technique has been proposed in lifting wavelet transform (LWT) domain. Neural network is incorporated in the watermark extraction process to achieve improved robustness against different attacks. The integration of neural network with LWT makes the system robust to various attacks maintaining an adequate level of imperceptibility. The 3-level LWT coefficients are randomized and arranged in 2×2 non-overlapping blocks. Each block is modified according to a binary watermark bit. Randomization of coefficients and blocks has been done to enhance the security of the system. The binary watermark bit is also encrypted using another key. …The scheme provides an average imperceptibility of 43.88 dB for a watermark capacity of 512 bits. The robustness has been observed against all the intentional and non-intentional attacks. The technique provides satisfactory robustness against different attacks such as noising attacks, de-noising attacks, lossy compression attacks, image processing attacks and some geometric attacks. The algorithm has been tested on a large image database containing different class of images. Show more

Keywords: Lifting wavelet transform (LWT), image watermarking, artificial neural network (ANN), peak signal to noise ratio (PSNR), normalized cross-correlation (NC)

DOI: 10.3233/JIFS-169462

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1691-1700, 2018

Authors: Ganga Gowri, B. | Soman, K.P.

Article Type: Research Article

Abstract: In this paper, a two step approach using Variational Mode Decomposition (VMD) and ℓ1 trend filter is proposed for enhancing speech signals degraded by white Gaussian noise. In the first step, VMD decomposes the noisy speech signal into Intrinsic Mode Functions (IMFs) corresponding to different frequency components of the signal. In the second step, ℓ1 trend filter retrieves the speech information by filtering out the noisy sub frames. The noisy sub frames are identified using a threshold based on noise variance in the corresponding IMFs. The proposed work experiments on speech signals degraded by white Gaussian noise in …the range 10 dB– 30 dB. The performance of proposed method is compared with some of the well known speech enhancement techniques: Spectral Subtraction (SS) and Minimized Mean Square Error (MMSE) using the subjective and objective quality measures. The proposed, two-step approach of VMD-ℓ1 trend filter achieves better performance compared to the considered methods. Show more

Keywords: Speech enhancement, white Gaussian noise, variational mode decomposition (VMD), ℓ1 trend filter

DOI: 10.3233/JIFS-169463

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1701-1711, 2018

Authors: Kulkarni, Nilima | Amudha, J.

Article Type: Research Article

Abstract: Optic disc (OD) detection is an important step in a number of algorithms developed for automatic extraction of anatomical structures and retinal lesions. In this article, a novel system, eye gaze– based OD detection, is presented for detecting OD in fundus retinal image using the knowledge developed from the expert’s eye gaze data. The eye gaze data are collected from expert optometrists and non-experts group while viewing the fundus retinal images. The task given to them is to spot the OD in fundus retinal images. Eye gaze fixations were used to identify the target and distractor regions. The image-based features …were extracted from the identified regions. The top-down (TD) knowledge is developed using feature ranking and fuzzy system. This TD knowledge is further used for building the TD map. The success rates for various standard datasets are: DRIVE dataset, 100%; DRIONS-DB, 98.2%; INSPIRE, 97.5%; High Resolution Fundus Images, 100%; DIRECTDB0, 96.9%; ONHSD, 91.9% and STARE, 81.4%. Show more

Keywords: Optic disc detection, fuzzy system, eye gaze, fixations, regions identification, top-down knowledge

DOI: 10.3233/JIFS-169464

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1713-1722, 2018

Authors: Shanmugha Sundaram, G.A. | Reshma, R.

Article Type: Research Article

Abstract: Satellites that orbit the Earth at lower altitudes are the predominant type that are deployed in remote sensing missions. Although thee are many well documented advantages in having the Earth-observing satellites in such lower orbits, the altitude parameter often introduces significant variabilities to the orbital elements, the predominant among them being the perturbative forces due to atmospheric drag. The drag parameter causes deviation of the satellite from its actual orbital trajectory. While updated near-Earth atmospheric drag models have helped resolve this issue, there are the other forces that are secondary in importance, such as the non-spherical Earth’s shape factor effect …and luni-solar perturbations that also affect on remote sensing satellites, particularly of the low-Earth Orbit (LEO) and geosynchronous transfer orbiting (GTO) types, that are considered for this particular study. After verifying the equivalent perturbed acceleration terms using the Cowell’s method, variations caused to the classical orbital elements in general, and the altitude element in particular, are characterised in terms of the corresponding distortions caused in the imaging data obtained by the Landsat platform, that are then shown as resulting in a image quality degradation. Show more

Keywords: LEO and GTO satellites, PAN image, orbital perturbations, Keplerian elements, imaging sensors

DOI: 10.3233/JIFS-169465

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1723-1730, 2018

Authors: Kaur, Parampreet | Sobti, Rajeev

Article Type: Research Article

Abstract: Automotive embedded applications are gaining widespread importance worldwide. Almost every car is equipped with hundreds of software-enabled technologies which are expected to have a drastic spike in the coming years ahead. With the advent of more and more automatic and autonomous techniques, there arises an extensive requirement of applying latest development and testing strategies. Apart from using single traditional V-Model, the design and test cases are generated based on more advanced double and triple V-models. The front panel of one of the autonomous car systems is designed and more refinement is obtained for optimum test path generation using prefix graph …algorithm and edge-pair coverage criteria. Show more

Keywords: Design model, automotive, double V-model, testing, edge-pair coverage criteria

DOI: 10.3233/JIFS-169466

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1731-1742, 2018

Authors: Chandrika, K.R. | Amudha, J.

Article Type: Research Article

Abstract: A quality software development is inclined to the software developer skills. The research focus on recommending the skills of individuals based on the eye movement data. The paper sketches a study conducted on students who are future developers. A fuzzy based recommendation system was implemented to recommend two skills, code coverage and debugging skills that are primary in source code review. The code coverage inference system recommends individual code coverage as maximum, average and minimum and the debugging fuzzy inference system recommends debugging skills as skilled, unskilled and expert.

Keywords: Software engineering, recommendation system, eye tracking, source cod review, code coverage, debugging, skills

DOI: 10.3233/JIFS-169467

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1743-1754, 2018

Authors: Malhotra, Ruchika | Khanna, Megha

Article Type: Research Article

Abstract: Determination of change prone classes is crucial in providing guidance to software practitioners for efficient allocation of limited resources and to develop favorable quality software products with optimum costs. Previous literature studies have proposed successful use of design metrics to predict classes which are more prone to change in an Object-Oriented (OO) software. However, the use of evolution-based metrics suite, which quantifies history of changes in a software, release by release should also be evaluated for effective prediction of change prone classes. Evolution-based metrics are representative of evolution characteristics of a class over all its previous releases and are important …in order to understand progression and change-prone nature of a class. This study evaluates the use of evolution-based metrics when used in conjunction with OO metrics for prediction of classes which are change prone in nature. In order to empirically validate the results, the study uses two application packages of the Android software namely Contacts and Gallery2. The results indicate that evolution based metrics when used in conjunction with OO metrics are the best predictors of change prone classes. Furthermore, the study statistically evaluates the superiority of this combined metric suite for change proneness prediction. Show more

Keywords: Change proneness, evolution-based metrics, empirical validation, machine learning techniques

DOI: 10.3233/JIFS-169468

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1755-1766, 2018