Forthcoming articles

International Journal of Intelligence and Sustainable Computing

International Journal of Intelligence and Sustainable Computing (IJISC)

These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Intelligence and Sustainable Computing (5 papers in press)

Regular Issues

  • WAVELET ANALYSIS OF EEG FOR THE IDENTIFICATION OF ALCOHOLICS USING PROBABILISTIC CLASSIFIERS AND NEURAL NETWORKS   Order a copy of this article
    by Malar E, Gauthaam M 
    Abstract: Electroencephalography (EEG) is the process of recording the complex activity of the brain in the form of signals. EEG primarily has delta, theta, alpha, beta and gamma frequency bands whose presence and strength describes changes in brain under different kinds of activities. On the other hand alcohol consumption leads to depression and confusion which reduces the activity of the nervous system thereby affecting the brain. Alcoholics are identified from normal persons by multi-resolution and multi-scale analysis of EEG. In our research, EEG is decomposed into sub frequency bands using wavelet. The effect of alcohol on each of these wave bands is identified using power spectral density analysis. These evident variations in EEG are manifested due to depression in brain activity caused by intake of alcohol. The first order and second order statistical measures of the EEG signal are selected as features. Classifiers such as Bayes, Naive Bayes, radial basis function network (RBFN), multilayer perceptron (MLP) and extreme learning machine (ELM) are used for classification. Results show that our proposed EEG analysis acts as an effective bio-marker for differentiating alcoholics from non-alcoholics and extreme learning machine provides higher classification efficiency (87.6%) compared to other classifiers used.
    Keywords: EEG; Frequency Bands; Power Spectral Density; Wavelet Decomposition; Extreme Learning Machine.
    DOI: 10.1504/IJISC.2018.10017098
     
  • Design and development of a novel MOSFET structure for reduction of reverse bias pn junction leakage current   Order a copy of this article
    by Debasis Mukherjee, B. V. R. Reddy 
    Abstract: Present world is acquainted with the plethora of battery operated portable electronic goods in leaps and bounds. For long life of battery, it is very imperative to minimise the leakage current in devices. Amount of leakage in scaled deep-submicron VLSI1 CMOS circuitry has already occupied a momentous part of the total power consumption, and likely to amplify in future with technology scaling. Top three dominant components of transistor leakage current are gate leakage, subthreshold leakage and p-n junction leakage. We report our study of constructional modification of MOSFET transistor to control p-n junction leakage current. TCAD simulation was performed on a 20 nm NMOS, following the rules of International Technology Roadmap for Semiconductors (ITRS). As substrate is the common terminal for this kind of leakage, substrate current was measured to note the effectiveness of the proposed methodology. A 52% reduction in substrate leakage current was noted after applying the proposed methodology.
    Keywords: 20 nm; band-to-band tunneling; BTBT; bulk MOSFET; CMOS; device simulation; junction; leakage current; TCAD; VLSI.
    DOI: 10.1504/IJISC.2018.10019643
     
  • Optimization of Multibody fishbot undulatory swimming speed based on SOLEIL and BhT simulators   Order a copy of this article
    by Raja Mohamed, P. Raviraj 
    Abstract: Robotic fish design is an upcoming and interesting research area with lot of challenging tasks due to the impulsive dynamics of water space. In this paper an evolutionary computational approach is performed to design caudal fins under carangi form and sub-carangi form swimming modes. Size and Shape with SOLEIL and multi-body evolutionary experiments were carried out using Euler-Lagrangian equation-based BhT tool to experiment and validate the hydrodynamic effects of caudal fin by avoiding complex and time consuming CFD simulations to achieve realistic motion. To improve average velocity of robotic fish two approaches have been suggested, one is a hill climbing algorithm to find optimal shape with standard stiffness whereas the second approach considers both shape and stiffness together in a genetic algorithm. Finally simulated fin models are compared against physical models to identify the correlation and performances of both to accurately approximate real world performances in a simulated environment leading to design optimised caudal fins for robotic fish.
    Keywords: bio-hydrodynamics; carangiform; body-caudal fin; pressure sensing; multi-body segments; fin flexibility.
    DOI: 10.1504/IJISC.2018.10021268
     
  • Palm Pattern Recognition using Scale Invariant Feature Transform (SIFT)   Order a copy of this article
    by Kasiselvanathan M, Sangeetha V, Kalaiselvi A 
    Abstract: In this research paper, we propose an efficient scale invariant feature transform (SIFT) for palm pattern recognition. A fingerprint recognition which is efficient for individual authentication based on fingerprint pattern. This method leads to fraudulent because it could be extracted easily from individuals. The SIFT method based on feature detection overcomes the above problem and is a combination of fast key point detector and visual descriptor. Using SIFT method contactless palm pattern images can be acquired, matched, recognised, authenticated and their matching performance are simulated using OpenCV. The experimental results show that SIFT method provides significantly fast and improved performance than the conventional methods like oriented FAST and rotated BRIEF (ORB).
    Keywords: Image Matching; Biometrics; Palm Pattern; Feature Detection; SIFT; ORB.
    DOI: 10.1504/IJISC.2018.10023048
     
  • Mutation based PSO Techniques for Optimal Location and Parameter Settings of STATCOM under Generator Contingency   Order a copy of this article
    by Jayachitra Selvaraj, Amin Mohammed 
    Abstract: This article addresses the efficient contribution of particle swarm optimisation (PSO) and its variants such as constrained factor-PSO (CF-PSO), Cauchy mutation-CFPSO (CM-CFPSO) and Gaussian mutation-CFPSO (GM-CFPSO) algorithm to choose suitable placement and rating of static synchronous compensator (STATCOM) based on novel index called unification index (UI). Minimisation of real and reactive power loss, voltage deviation reduction and augmentation of voltage stability are considered for this research work. UI is computed with respect to normal and generator contingency condition. Based on the index value, the ranking of severe lines is made. The purpose of embedding PSO with mutation is to expand the search space particularly to avoid being trapped in local optima. IEEE 30 bus system is chosen to assess the potency of the propound method using MATLAB working platform against generator bus contingency with and without STATCOM. This proposed approach yields promising result and their performances were presented and compared with other methodologies.
    Keywords: Generator Contingency; Mutation; PSO; STATCOM; Unification Index.
    DOI: 10.1504/IJISC.2019.10023049