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Publisher: Inderscience Publishers   (Total: 440 journals)

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Showing 1 - 200 of 440 Journals sorted alphabetically
African J. of Accounting, Auditing and Finance     Hybrid Journal   (Followers: 13)
African J. of Economic and Sustainable Development     Hybrid Journal   (Followers: 14)
Afro-Asian J. of Finance and Accounting     Hybrid Journal   (Followers: 8, SJR: 0.195, CiteScore: 0)
American J. of Finance and Accounting     Hybrid Journal   (Followers: 23)
Asian J. of Management Science and Applications     Hybrid Journal   (Followers: 4)
Atoms for Peace: an Intl. J.     Hybrid Journal   (Followers: 3)
Electronic Government, an Intl. J.     Hybrid Journal   (Followers: 16, SJR: 0.424, CiteScore: 1)
EuroMed J. of Management     Hybrid Journal  
European J. of Cross-Cultural Competence and Management     Hybrid Journal   (Followers: 7)
European J. of Industrial Engineering     Hybrid Journal   (Followers: 10, SJR: 0.595, CiteScore: 1)
European J. of Intl. Management     Hybrid Journal   (Followers: 1, SJR: 0.3, CiteScore: 1)
Global Business and Economics Review     Hybrid Journal   (Followers: 3, SJR: 0.154, CiteScore: 0)
Interdisciplinary Environmental Review     Hybrid Journal   (Followers: 3)
Intl. J. of Abrasive Technology     Hybrid Journal   (Followers: 2, SJR: 0.279, CiteScore: 0)
Intl. J. of Accounting and Finance     Hybrid Journal   (Followers: 17)
Intl. J. of Accounting, Auditing and Performance Evaluation     Hybrid Journal   (Followers: 15, SJR: 0.14, CiteScore: 0)
Intl. J. of Ad Hoc and Ubiquitous Computing     Hybrid Journal   (Followers: 8, SJR: 0.21, CiteScore: 1)
Intl. J. of Adaptive and Innovative Systems     Hybrid Journal   (Followers: 1)
Intl. J. of Additive and Subtractive Materials Manufacturing     Hybrid Journal   (Followers: 5)
Intl. J. of Advanced Intelligence Paradigms     Hybrid Journal   (Followers: 5, SJR: 0.144, CiteScore: 1)
Intl. J. of Advanced Mechatronic Systems     Hybrid Journal   (Followers: 3, SJR: 0.132, CiteScore: 0)
Intl. J. of Advanced Media and Communication     Hybrid Journal   (Followers: 25, SJR: 0.124, CiteScore: 0)
Intl. J. of Advanced Operations Management     Hybrid Journal   (Followers: 8, SJR: 0.163, CiteScore: 0)
Intl. J. of Aerodynamics     Hybrid Journal   (Followers: 31)
Intl. J. of Agent-Oriented Software Engineering     Hybrid Journal   (Followers: 3)
Intl. J. of Agile and Extreme Software Development     Hybrid Journal   (Followers: 6)
Intl. J. of Agile Systems and Management     Hybrid Journal   (Followers: 6, SJR: 0.878, CiteScore: 3)
Intl. J. of Agricultural Resources, Governance and Ecology     Hybrid Journal   (Followers: 2, SJR: 0.152, CiteScore: 0)
Intl. J. of Agriculture Innovation, Technology and Globalisation     Hybrid Journal  
Intl. J. of Alternative Propulsion     Hybrid Journal   (Followers: 12)
Intl. J. of Applied Cryptography     Hybrid Journal   (Followers: 10, SJR: 0.455, CiteScore: 3)
Intl. J. of Applied Decision Sciences     Hybrid Journal   (Followers: 1, SJR: 0.275, CiteScore: 1)
Intl. J. of Applied Management Science     Hybrid Journal   (Followers: 4, SJR: 0.229, CiteScore: 0)
Intl. J. of Applied Nonlinear Science     Hybrid Journal   (Followers: 1)
Intl. J. of Applied Pattern Recognition     Hybrid Journal   (Followers: 5)
Intl. J. of Applied Systemic Studies     Hybrid Journal   (SJR: 0.129, CiteScore: 0)
Intl. J. of Arab Culture, Management and Sustainable Development     Hybrid Journal   (Followers: 7)
Intl. J. of Artificial Intelligence and Soft Computing     Hybrid Journal   (Followers: 11)
Intl. J. of Arts and Technology     Hybrid Journal   (Followers: 6, SJR: 0.225, CiteScore: 1)
Intl. J. of Auditing Technology     Hybrid Journal   (Followers: 5)
Intl. J. of Automation and Control     Hybrid Journal   (Followers: 11, SJR: 0.189, CiteScore: 1)
Intl. J. of Automation and Logistics     Hybrid Journal   (Followers: 5)
Intl. J. of Automotive Composites     Hybrid Journal   (Followers: 5)
Intl. J. of Automotive Technology and Management     Hybrid Journal   (Followers: 6, SJR: 0.374, CiteScore: 1)
Intl. J. of Autonomic Computing     Hybrid Journal   (Followers: 2)
Intl. J. of Autonomous and Adaptive Communications Systems     Hybrid Journal   (Followers: 3, SJR: 0.128, CiteScore: 0)
Intl. J. of Aviation Management     Hybrid Journal   (Followers: 6)
Intl. J. of Banking, Accounting and Finance     Hybrid Journal   (Followers: 16, SJR: 0.137, CiteScore: 0)
Intl. J. of Behavioural Accounting and Finance     Hybrid Journal   (Followers: 11)
Intl. J. of Behavioural and Healthcare Research     Hybrid Journal   (Followers: 8)
Intl. J. of Bibliometrics in Business and Management     Hybrid Journal   (Followers: 2)
Intl. J. of Big Data Intelligence     Hybrid Journal   (Followers: 22)
Intl. J. of Bio-Inspired Computation     Hybrid Journal   (Followers: 1, SJR: 0.721, CiteScore: 4)
Intl. J. of Bioinformatics Research and Applications     Hybrid Journal   (Followers: 15, SJR: 0.157, CiteScore: 0)
Intl. J. of Biomechatronics and Biomedical Robotics     Hybrid Journal   (Followers: 4)
Intl. J. of Biomedical Engineering and Technology     Hybrid Journal   (Followers: 4, SJR: 0.205, CiteScore: 1)
Intl. J. of Biomedical Nanoscience and Nanotechnology     Hybrid Journal   (Followers: 6)
Intl. J. of Biometrics     Hybrid Journal   (Followers: 4, SJR: 0.155, CiteScore: 0)
Intl. J. of Biotechnology     Hybrid Journal   (Followers: 5, SJR: 0.269, CiteScore: 1)
Intl. J. of Bonds and Derivatives     Hybrid Journal   (Followers: 1)
Intl. J. of Business and Data Analytics     Hybrid Journal  
Intl. J. of Business and Emerging Markets     Hybrid Journal   (Followers: 2)
Intl. J. of Business and Globalisation     Hybrid Journal   (Followers: 3, SJR: 0.263, CiteScore: 1)
Intl. J. of Business and Systems Research     Hybrid Journal   (Followers: 1, SJR: 0.104, CiteScore: 0)
Intl. J. of Business Competition and Growth     Hybrid Journal   (Followers: 5)
Intl. J. of Business Continuity and Risk Management     Hybrid Journal   (Followers: 15)
Intl. J. of Business Environment     Hybrid Journal   (Followers: 3)
Intl. J. of Business Excellence     Hybrid Journal   (Followers: 4, SJR: 0.274, CiteScore: 1)
Intl. J. of Business Forecasting and Marketing Intelligence     Hybrid Journal   (Followers: 6)
Intl. J. of Business Governance and Ethics     Hybrid Journal   (Followers: 5, SJR: 0.171, CiteScore: 0)
Intl. J. of Business Information Systems     Hybrid Journal   (Followers: 16, SJR: 0.266, CiteScore: 1)
Intl. J. of Business Innovation and Research     Hybrid Journal   (Followers: 11, SJR: 0.28, CiteScore: 1)
Intl. J. of Business Intelligence and Data Mining     Hybrid Journal   (Followers: 30, SJR: 0.249, CiteScore: 2)
Intl. J. of Business Intelligence and Systems Engineering     Hybrid Journal  
Intl. J. of Business Performance and Supply Chain Modelling     Hybrid Journal   (Followers: 18, SJR: 0.18, CiteScore: 0)
Intl. J. of Business Performance Management     Hybrid Journal   (Followers: 9, SJR: 0.197, CiteScore: 1)
Intl. J. of Business Process Integration and Management     Hybrid Journal   (Followers: 12, SJR: 0.149, CiteScore: 1)
Intl. J. of Chinese Culture and Management     Hybrid Journal   (Followers: 4)
Intl. J. of Circuits and Architecture Design     Hybrid Journal   (Followers: 6)
Intl. J. of Cloud Computing     Hybrid Journal   (Followers: 25)
Intl. J. of Cognitive Biometrics     Hybrid Journal   (Followers: 3)
Intl. J. of Cognitive Performance Support     Hybrid Journal   (Followers: 4)
Intl. J. of Collaborative Engineering     Hybrid Journal   (Followers: 1)
Intl. J. of Collaborative Enterprise     Hybrid Journal   (Followers: 1)
Intl. J. of Collaborative Intelligence     Hybrid Journal   (Followers: 3)
Intl. J. of Communication Networks and Distributed Systems     Hybrid Journal   (Followers: 7, SJR: 0.177, CiteScore: 1)
Intl. J. of Comparative Management     Hybrid Journal  
Intl. J. of Competitiveness     Hybrid Journal   (Followers: 3)
Intl. J. of Complexity in Applied Science and Technology     Hybrid Journal  
Intl. J. of Complexity in Leadership and Management     Hybrid Journal   (Followers: 28)
Intl. J. of Computational Biology and Drug Design     Hybrid Journal   (Followers: 1, SJR: 0.231, CiteScore: 1)
Intl. J. of Computational Complexity and Intelligent Algorithms     Hybrid Journal   (Followers: 2)
Intl. J. of Computational Economics and Econometrics     Hybrid Journal   (Followers: 5)
Intl. J. of Computational Intelligence in Bioinformatics and Systems Biology     Hybrid Journal   (Followers: 11)
Intl. J. of Computational Intelligence Studies     Hybrid Journal   (Followers: 3)
Intl. J. of Computational Materials Science and Surface Engineering     Hybrid Journal   (Followers: 6, SJR: 0.135, CiteScore: 0)
Intl. J. of Computational Science and Engineering     Hybrid Journal   (Followers: 2, SJR: 0.373, CiteScore: 1)
Intl. J. of Computational Systems Engineering     Hybrid Journal   (Followers: 1)
Intl. J. of Computational Vision and Robotics     Hybrid Journal   (Followers: 6, SJR: 0.129, CiteScore: 0)
Intl. J. of Computer Aided Engineering and Technology     Hybrid Journal   (Followers: 3, SJR: 0.131, CiteScore: 0)
Intl. J. of Computer Applications in Technology     Hybrid Journal   (Followers: 1, SJR: 0.225, CiteScore: 1)
Intl. J. of Computers in Healthcare     Hybrid Journal   (Followers: 3)
Intl. J. of Computing Science and Mathematics     Hybrid Journal   (Followers: 1, SJR: 0.299, CiteScore: 1)
Intl. J. of Continuing Engineering Education and Life-Long Learning     Hybrid Journal   (Followers: 5, SJR: 0.196, CiteScore: 0)
Intl. J. of Convergence Computing     Hybrid Journal   (Followers: 2)
Intl. J. of Corporate Governance     Hybrid Journal   (Followers: 4)
Intl. J. of Corporate Strategy and Social Responsibility     Hybrid Journal   (Followers: 5)
Intl. J. of Creative Computing     Hybrid Journal   (Followers: 1)
Intl. J. of Critical Accounting     Hybrid Journal   (Followers: 3)
Intl. J. of Critical Computer-Based Systems     Hybrid Journal   (Followers: 1, SJR: 0.127, CiteScore: 0)
Intl. J. of Critical Infrastructures     Hybrid Journal   (Followers: 2, SJR: 0.173, CiteScore: 1)
Intl. J. of Data Analysis Techniques and Strategies     Hybrid Journal   (Followers: 3, SJR: 0.23, CiteScore: 0)
Intl. J. of Data Mining and Bioinformatics     Hybrid Journal   (Followers: 18, SJR: 0.217, CiteScore: 1)
Intl. J. of Data Mining, Modelling and Management     Hybrid Journal   (Followers: 14, SJR: 0.209, CiteScore: 0)
Intl. J. of Data Science     Hybrid Journal   (Followers: 10)
Intl. J. of Decision Sciences, Risk and Management     Hybrid Journal   (Followers: 9)
Intl. J. of Decision Support Systems     Hybrid Journal   (Followers: 1)
Intl. J. of Design Engineering     Hybrid Journal   (Followers: 12)
Intl. J. of Digital Culture and Electronic Tourism     Hybrid Journal   (Followers: 6)
Intl. J. of Digital Enterprise Technology     Hybrid Journal   (Followers: 1)
Intl. J. of Digital Signals and Smart Systems     Hybrid Journal   (Followers: 1)
Intl. J. of Diplomacy and Economy     Hybrid Journal   (Followers: 6)
Intl. J. of Dynamical Systems and Differential Equations     Hybrid Journal   (Followers: 1, SJR: 0.184, CiteScore: 0)
Intl. J. of Earthquake and Impact Engineering     Hybrid Journal   (Followers: 4)
Intl. J. of Economic Policy in Emerging Economies     Hybrid Journal   (Followers: 4, SJR: 0.134, CiteScore: 1)
Intl. J. of Economics and Accounting     Hybrid Journal   (Followers: 1)
Intl. J. of Economics and Business Research     Hybrid Journal   (Followers: 5, SJR: 0.129, CiteScore: 0)
Intl. J. of Education Economics and Development     Hybrid Journal   (Followers: 5, SJR: 0.156, CiteScore: 0)
Intl. J. of Electric and Hybrid Vehicles     Hybrid Journal   (Followers: 9, SJR: 0.225, CiteScore: 1)
Intl. J. of Electronic Banking     Hybrid Journal   (Followers: 5)
Intl. J. of Electronic Business     Hybrid Journal   (Followers: 2, SJR: 0.24, CiteScore: 0)
Intl. J. of Electronic Customer Relationship Management     Hybrid Journal   (Followers: 3, SJR: 0.148, CiteScore: 0)
Intl. J. of Electronic Democracy     Hybrid Journal   (Followers: 2)
Intl. J. of Electronic Finance     Hybrid Journal   (Followers: 5, SJR: 0.155, CiteScore: 0)
Intl. J. of Electronic Governance     Hybrid Journal   (SJR: 0.142, CiteScore: 1)
Intl. J. of Electronic Healthcare     Hybrid Journal   (Followers: 2, SJR: 0.254, CiteScore: 1)
Intl. J. of Electronic Marketing and Retailing     Hybrid Journal   (Followers: 7, SJR: 0.249, CiteScore: 1)
Intl. J. of Electronic Security and Digital Forensics     Hybrid Journal   (Followers: 9, SJR: 0.137, CiteScore: 0)
Intl. J. of Electronic Transport     Hybrid Journal   (Followers: 9)
Intl. J. of Embedded Systems     Hybrid Journal   (Followers: 5, SJR: 0.48, CiteScore: 1)
Intl. J. of Emergency Management     Hybrid Journal   (Followers: 11, SJR: 0.185, CiteScore: 0)
Intl. J. of Energy Technology and Policy     Hybrid Journal   (Followers: 7, SJR: 0.224, CiteScore: 0)
Intl. J. of Engineering Management and Economics     Hybrid Journal   (Followers: 4)
Intl. J. of Engineering Systems Modelling and Simulation     Hybrid Journal   (Followers: 8, SJR: 0.175, CiteScore: 0)
Intl. J. of Enterprise Network Management     Hybrid Journal   (SJR: 0.118, CiteScore: 0)
Intl. J. of Entrepreneurial Venturing     Hybrid Journal   (Followers: 1, SJR: 0.308, CiteScore: 1)
Intl. J. of Entrepreneurship and Innovation Management     Hybrid Journal   (Followers: 27, SJR: 0.255, CiteScore: 1)
Intl. J. of Entrepreneurship and Small Business     Hybrid Journal   (Followers: 28, SJR: 0.401, CiteScore: 1)
Intl. J. of Environment and Health     Hybrid Journal   (Followers: 5, SJR: 0.181, CiteScore: 0)
Intl. J. of Environment and Pollution     Hybrid Journal   (Followers: 2, SJR: 0.215, CiteScore: 1)
Intl. J. of Environment and Sustainable Development     Hybrid Journal   (Followers: 17, SJR: 0.132, CiteScore: 0)
Intl. J. of Environment and Waste Management     Hybrid Journal   (Followers: 4, SJR: 0.175, CiteScore: 0)
Intl. J. of Environment, Workplace and Employment     Hybrid Journal   (Followers: 6, SJR: 0.117, CiteScore: 0)
Intl. J. of Environmental Engineering     Hybrid Journal   (Followers: 6)
Intl. J. of Environmental Policy and Decision Making     Hybrid Journal   (Followers: 2)
Intl. J. of Environmental Technology and Management     Hybrid Journal   (Followers: 1, SJR: 0.141, CiteScore: 0)
Intl. J. of Exergy     Hybrid Journal   (Followers: 3, SJR: 0.396, CiteScore: 1)
Intl. J. of Experimental and Computational Biomechanics     Hybrid Journal   (Followers: 8)
Intl. J. of Experimental Design and Process Optimisation     Hybrid Journal   (Followers: 6)
Intl. J. of Export Marketing     Hybrid Journal   (Followers: 3)
Intl. J. of Financial Engineering and Risk Management     Hybrid Journal   (Followers: 4)
Intl. J. of Financial Innovation in Banking     Hybrid Journal   (Followers: 3)
Intl. J. of Financial Markets and Derivatives     Hybrid Journal   (Followers: 4)
Intl. J. of Financial Services Management     Hybrid Journal   (Followers: 1)
Intl. J. of Food Safety, Nutrition and Public Health     Hybrid Journal   (Followers: 19)
Intl. J. of Forensic Engineering     Hybrid Journal   (Followers: 3)
Intl. J. of Forensic Engineering and Management     Hybrid Journal   (Followers: 3)
Intl. J. of Foresight and Innovation Policy     Hybrid Journal   (Followers: 7, SJR: 0.115, CiteScore: 0)
Intl. J. of Functional Informatics and Personalised Medicine     Hybrid Journal   (Followers: 4)
Intl. J. of Fuzzy Computation and Modelling     Hybrid Journal   (Followers: 2)
Intl. J. of Gender Studies in Developing Societies     Hybrid Journal   (Followers: 5)
Intl. J. of Global Energy Issues     Hybrid Journal   (Followers: 8, SJR: 0.199, CiteScore: 0)
Intl. J. of Global Environmental Issues     Hybrid Journal   (Followers: 3, SJR: 0.153, CiteScore: 0)
Intl. J. of Global Warming     Hybrid Journal   (Followers: 2, SJR: 0.259, CiteScore: 1)
Intl. J. of Globalisation and Small Business     Hybrid Journal   (Followers: 14, SJR: 0.233, CiteScore: 1)
Intl. J. of Governance and Financial Intermediation     Hybrid Journal  
Intl. J. of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 2)
Intl. J. of Green Economics     Hybrid Journal   (Followers: 6, SJR: 0.209, CiteScore: 0)
Intl. J. of Grid and Utility Computing     Hybrid Journal   (SJR: 0.341, CiteScore: 2)
Intl. J. of Happiness and Development     Hybrid Journal   (Followers: 8)
Intl. J. of Healthcare Technology and Management     Hybrid Journal   (Followers: 7, SJR: 0.139, CiteScore: 0)
Intl. J. of Heavy Vehicle Systems     Hybrid Journal   (Followers: 7, SJR: 0.23, CiteScore: 0)
Intl. J. of High Performance Computing and Networking     Hybrid Journal   (Followers: 4, SJR: 0.428, CiteScore: 1)
Intl. J. of High Performance Systems Architecture     Hybrid Journal   (Followers: 6, SJR: 0.116, CiteScore: 0)
Intl. J. of Higher Education and Sustainability     Hybrid Journal   (Followers: 5)
Intl. J. of Hospitality and Event Management     Hybrid Journal   (Followers: 4)
Intl. J. of Human Factors and Ergonomics     Hybrid Journal   (Followers: 18, SJR: 0.117, CiteScore: 0)
Intl. J. of Human Factors Modelling and Simulation     Hybrid Journal   (Followers: 15)
Intl. J. of Human Resources Development and Management     Hybrid Journal   (Followers: 28, SJR: 0.162, CiteScore: 0)
Intl. J. of Human Rights and Constitutional Studies     Hybrid Journal   (Followers: 14)
Intl. J. of Humanitarian Technology     Hybrid Journal  
Intl. J. of Hybrid Intelligence     Hybrid Journal  
Intl. J. of Hydrology Science and Technology     Hybrid Journal   (Followers: 8, SJR: 0.43, CiteScore: 2)
Intl. J. of Hydromechatronics     Hybrid Journal  
Intl. J. of Image Mining     Hybrid Journal   (Followers: 1)
Intl. J. of Immunological Studies     Hybrid Journal   (Followers: 1)
Intl. J. of Indian Culture and Business Management     Hybrid Journal  
Intl. J. of Industrial and Systems Engineering     Hybrid Journal   (Followers: 11, SJR: 0.34, CiteScore: 1)
Intl. J. of Industrial Electronics and Drives     Hybrid Journal   (Followers: 3)
Intl. J. of Information and Coding Theory     Hybrid Journal   (Followers: 6)

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Similar Journals
Journal Cover
International Journal of Computational Systems Engineering
Number of Followers: 1  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2046-3391 - ISSN (Online) 2046-3405
Published by Inderscience Publishers Homepage  [440 journals]
  • A computerised framework for prediction of fatty and dense breast tissue
           using principal component analysis and multi-resolution texture
           descriptors
    • Authors: Indrajeet Kumar, H.S. Bhadauria, Jitendra Virmani
      Pages: 73 - 85
      Abstract: The present work proposes a computerised framework for prediction of fatty and dense breast tissue using principal component analysis and multi-resolution texture descriptors. For this study, 480 MLO view digitised screen film mammograms have been taken from the DDSM dataset. A fixed ROIs size of 128 × 128 pixels are cropped from the centre location of each mammographic image. Three texture features are computed in multi-resolution transform domain, where each ROI is decomposed up to 2nd level using ten different compact support wavelet filters resulting 16 sub-band feature images. Two step feature optimisation approach (feature pruning followed by feature space dimensionality reduction using PCA) is applied. In feature pruning stage, the TFV corresponding to best basis feature is selected; result of feature pruning stage is PTFV. This PTFV is subjected to PCA for feature space dimensionality reductions. After the application, PCA accuracy increases from 92.1% to 97.9%.
      Keywords: mammography; breast density classification; multi-resolution texture descriptors; principal component analysis; PCA; support vector machine classifier; SVM
      Citation: International Journal of Computational Systems Engineering, Vol. 4, No. 2/3 (2018) pp. 73 - 85
      PubDate: 2018-04-30T23:20:50-05:00
      DOI: 10.1504/IJCSYSE.2018.091386
      Issue No: Vol. 4, No. 2/3 (2018)
       
  • GPU-based focus-driven multi-coordinates viewing system for large volume
           data visualisation
    • Authors: Piyush Kumar, Anupam Agrawal
      Pages: 86 - 95
      Abstract: In this paper, we are presenting a generalised contactless interactive graphics processing unit (GPU) accelerated compute unified device architecture (CUDA) based focus and context visualisation approach with displaying the inner anatomy of the large scale visible human male dataset in multi-coordinate viewing system (MCVS). The focusing area has been achieved by 3D Cartesian region of interest (ROI). The large dataset has been structured by using Octree method. The volume rendering part has been done by using an improved ray intersection cube method for voxels with the ray casting algorithm. The final results would allow the doctors to diagnose and analyse the atlas of 8-bit CT-scan data visualisation with efficient frame rates of rendering speed. The system is tested on ten multiple sized of 3D medical datasets ranging from 10 MB to 3.15 GB. The scope of this system is to explore of the human body for surgery purpose.
      Keywords: volume visualisation; focus-driven; multi-coordinate viewing system; MCVS; focus and context; MRI dataset
      Citation: International Journal of Computational Systems Engineering, Vol. 4, No. 2/3 (2018) pp. 86 - 95
      PubDate: 2018-04-30T23:20:50-05:00
      DOI: 10.1504/IJCSYSE.2018.091387
      Issue No: Vol. 4, No. 2/3 (2018)
       
  • Multimodality medical image fusion using non-subsampled rotated wavelet
           transform for cancer treatment
    • Authors: Satishkumar S. Chavan, Abhijit Pawar, Sanjay N. Talbar
      Pages: 96 - 105
      Abstract: This paper presents non-subsampled rotated wavelet transform based feature extraction approach to multimodality medical image fusion. The non-subsampled rotated wavelet filters are designed to extract textural and edge features from axial brain images from the modalities viz. computed tomography and magnetic resonance imaging. These extracted features are selected using entropy-based fusion rule to form composite spectral plane. Entropy-based fusion rule preserves dominant spectral features and imparts all relevant information from both the modalities to the fused image. The fused image is reconstructed using inverse transform from the composite spectral slice. The proposed algorithm is evaluated using 39 sets of pilot images of 23 patients subjectively and objectively. Three expert radiologists have verified the subjective quality of the fused images to ascertain abnormality. Subjective and objective evaluation reveal that the fused images using proposed algorithm are superior in terms of visualisation of abnormalities over the state of the art wavelet based algorithms.
      Keywords: multimodality medical image fusion; MMIF; discrete wavelet transform; DWT; rotated wavelet filters; RWFs; non-subsampled rotated wavelet transform; NSRWT; cancer treatment; radiotherapy
      Citation: International Journal of Computational Systems Engineering, Vol. 4, No. 2/3 (2018) pp. 96 - 105
      PubDate: 2018-04-30T23:20:50-05:00
      DOI: 10.1504/IJCSYSE.2018.091389
      Issue No: Vol. 4, No. 2/3 (2018)
       
  • Comparison of feature extraction techniques for classification of hardwood
           species
    • Authors: Arvind R. Yadav, R.S. Anand, M.L. Dewal, Sangeeta Gupta, Jayendra Kumar
      Pages: 106 - 119
      Abstract: The texture of an image plays an important role in identification and classification of images. The hardwood species of an image contains four key elements namely: vessels (popularly known as pores in cross-section view), fibres, parenchyma's and rays, useful in its identification and classification. Further, the arrangements of all these elements posses texture rich features. Thus, in this work investigation of existing texture feature extraction techniques for the classification of hardwood species have been done. The texture features are extracted from greyscale images of hardwood species to reduce the computational complexity. Further, linear support vector machine (SVM), radial basis function (RBF) kernel SVM, random forest (RF) and linear discriminant analysis (LDA) have been employed as classifiers to investigate the efficacy of the texture feature extraction techniques. The classification accuracy of the existing texture descriptors has been compared. Further, principal component analysis (PCA) and minimal-redundancy-maximal-relevance (mRMR) feature selection method is employed to select the best subset of feature vector data. The PCA reduced feature vector data of co-occurrence of adjacent local binary pattern (CoALBP24) texture feature extraction technique has attained maximum classification accuracy of 96.33 ± 1.14% with the help of LDA classifier.
      Keywords: texture features; support vector machine; SVM; feature selection; hardwood species
      Citation: International Journal of Computational Systems Engineering, Vol. 4, No. 2/3 (2018) pp. 106 - 119
      PubDate: 2018-04-30T23:20:50-05:00
      DOI: 10.1504/IJCSYSE.2018.091390
      Issue No: Vol. 4, No. 2/3 (2018)
       
  • Myoelectric control of upper limb prostheses using linear discriminant
           analysis and multilayer perceptron neural network with back propagation
           algorithm
    • Authors: Sachin Negi, Yatindra Kumar, V.M. Mishra
      Pages: 120 - 126
      Abstract: Electromyogram (EMG) signals or myoelectric signals (MESs) have two prominent areas in the field of biomedical instrumentation. EMG signals are primarily used to analyse the neuromuscular diseases such as myopathy and neuropathy. In addition, the EMG signal can be utilised in myoelectric control systems - where the external devices like upper limb prostheses, intelligent wheelchairs, and assistive robots can be controlled by acquiring surface EMG signals. The aim of present work is to obtain classification accuracy first by using linear discriminant analysis (LDA) classifier where principal component analysis (PCA) and uncorrelated linear discriminant analysis (ULDA) feature reduction techniques are used for upper limb prostheses control application. Next, the multilayer perceptron (MLP) neural network with back propagation algorithm is used to calculate the classification accuracy for upper limb prostheses control.
      Keywords: electromyogram; EMG; myoelectric control system; MCS; linear discriminant analysis; LDA; principal component analysis; PCA; uncorrelated linear discriminant analysis; ULDA; multilayer perceptron; MLP; back propagation
      Citation: International Journal of Computational Systems Engineering, Vol. 4, No. 2/3 (2018) pp. 120 - 126
      PubDate: 2018-04-30T23:20:50-05:00
      DOI: 10.1504/IJCSYSE.2018.091392
      Issue No: Vol. 4, No. 2/3 (2018)
       
  • Volumetric tumour detection using improved region grow algorithm
    • Authors: Shitala Prasad, Shikha Gupta
      Pages: 127 - 139
      Abstract: This paper works on segmentation of brain pathological tissues (tumour, edema and narcotic core) and visualise it in 3D for their better physiological understanding. We propose a novel approach which combines threshold and region grow algorithm for tumour detection. In this proposed system, FLAIR and T2 modalities of MRI are used due to their unique ability to detect the high and low contrast lesions with great accuracy. In this approach, first the tumour is segmented from an image which is a combination of FLAIR and T2 image using a threshold value, selected automatically based on the intensity variance of tumour and normal tissues in 3D MR images. Then the tumour part is extracted from the actual 3D MRI of brain by selecting the largest connected volume. To correctly detect tumour 26 connected neighbours are used. The method is evaluated using a publically available BRAT dataset of 80 different patients having gliomas tumours. The accuracy in terms of detection is reached to 97.5% which is best compared to other state-of-the-art in given time frame. The algorithm takes 4-5 minutes for generating the 3D visualisation for final output.
      Keywords: 3D volumetric; brain tumour; region growing algorithm; thresholding; voxel seeding
      Citation: International Journal of Computational Systems Engineering, Vol. 4, No. 2/3 (2018) pp. 127 - 139
      PubDate: 2018-04-30T23:20:50-05:00
      DOI: 10.1504/IJCSYSE.2018.091388
      Issue No: Vol. 4, No. 2/3 (2018)
       
  • Comparative study of LVQ and BPN ECG classifier
    • Authors: Ashish Nainwal, Yatindra Kumar, Bhola Jha
      Pages: 140 - 145
      Abstract: ECG is the electrical waveform of heart activity. It contains much information on heart disease. It is very important to diagnosis the heart disease as soon as possible otherwise it can be harmful to patient. This paper presents to classify ECG signal using learning vector quantisation and back propagation neural network and feature of ECG (morphology and frequency domain) features. In this paper, the 45 ECG signals from MIT-BIH arrhythmia database are used to classify in to two classes, one is normal and another one is abnormal using above mentioned classifier. Out of 45 signals 25 are normal and 20 are abnormal according to MIT-BIH. Twenty-eight morphological features and four frequency domain features are set as an input to the classifier. The performance of classifier measures in the terms of sensitivity (SE), positive predictivity (PP) and specificity (SP). The system performance is achieved with 82.35% accuracy using LVQ and 94.11% using BPN.
      Keywords: back propagation neural network; learning vector quantisation; LVQ; electrocardiogram; ECG; MIT-BIH
      Citation: International Journal of Computational Systems Engineering, Vol. 4, No. 2/3 (2018) pp. 140 - 145
      PubDate: 2018-04-30T23:20:50-05:00
      DOI: 10.1504/IJCSYSE.2018.091393
      Issue No: Vol. 4, No. 2/3 (2018)
       
  • Automatic feature extraction of ECG signal based on adaptive window
           dependent differential histogram approach and validation with CSE database
           
    • Authors: Basudev Halder, Sucharita Mitra, Madhuchhanda Mitra
      Pages: 146 - 155
      Abstract: A very simple and novel idea based on adaptive window dependent differential histogram approach has been proposed for automatic detection and identification of ECG waves with its characteristic features. To facilitate the estimation of the waves, the normalised signal has been divided into a few small windows by an adaptive window selection technique. By counting the number of changes between successive samples as frequency, the differential histogram has been plotted. Some of the zones having an area more than a pre-defined threshold are depicted as QRS zones. The local maxima of these zones are referred as the R-peaks. T and P peaks are also detected. Baseline point and clinically significant time plane features have been computed and validated with reference values of the CSE database. The proposed technique achieved better performance in comparison with CSE groups. Its accuracy is achieved in sensitivity (99.86%), positive productivity (99.76%) and detection accuracy (99.8%).
      Keywords: adaptive window; differential histogram; CSE database; baseline; sensitivity; ECG signal; QRS zones; R-peaks; distinctive point's; sample values
      Citation: International Journal of Computational Systems Engineering, Vol. 4, No. 2/3 (2018) pp. 146 - 155
      PubDate: 2018-04-30T23:20:50-05:00
      DOI: 10.1504/IJCSYSE.2018.091394
      Issue No: Vol. 4, No. 2/3 (2018)
       
  • A comparative study on Kapur's and Tsallis entropy for multilevel
           thresholding of MR images via particle swarm optimisation technique
    • Authors: Taranjit Kaur, Barjinder Singh Saini, Savita Gupta
      Pages: 156 - 164
      Abstract: The present paper explores both the Kapur's and Tsallis entropy for a three level thresholding of brain MR images. The optimal thresholds are obtained by the maximisation of these entropies using a population-based search technique called as particle swarm optimisation (PSO). The algorithm is implemented for the segregation of various tissue constituents, i.e., cerebral spinal fluid (CSF), white matter (WM) and grey matter (GM) region from the simulated images obtained from the brain web database. The efficacy of the thresholding methods was evaluated by the measure of the spatial overlap, i.e., the Dice coefficient (Dice). The experimental results show that: 1) for both the WM and CSF the Tsallis entropy outperforms the Kapur's entropy by achieving an average value of 0.967279 and 0.878031 respectively; 2) for the GM, the Kapur's entropy is more beneficial which is duly justified by the mean value of Dice which was 0.851025 for this case.
      Keywords: Kapur's; Tsallis; multilevel thresholding; particle swarm optimisation; PSO
      Citation: International Journal of Computational Systems Engineering, Vol. 4, No. 2/3 (2018) pp. 156 - 164
      PubDate: 2018-04-30T23:20:50-05:00
      DOI: 10.1504/IJCSYSE.2018.091395
      Issue No: Vol. 4, No. 2/3 (2018)
       
  • Comparative studies of discrete cosine transform and lifting wavelet
           transform techniques for compression of blood pressure signal in salt
           sensitive Dahl rat
    • Authors: Vibha Aggarwal, Manjeet Singh Patterh, Virinder Kumar Singla
      Pages: 165 - 170
      Abstract: This paper introduces a study based on quality controlled discrete cosine transform (DCT) and lifting wavelet transform (LWT) based compression method for blood pressure signal compression in salt sensitive Dahl rat. The transformed coefficients are thresholded using the bisection algorithm to match the predefined user specified percentage root mean square difference (PRD) within the tolerance. Then, the binary lookup table is made to store the position map for zero and non-zero coefficients (NZC). The NZC are quantised by Max-Lloyd quantiser followed by arithmetic coding. Lookup table is encoded by Huffman coding. The results are presented on different blood pressure signals of varying characteristic. There is no significant difference in before quantisation PRD (BPRD) and after quantisation PRD (QPRD) in various signals in both transforms. Mean compression ratio increases with an increase in user define PRD (UPRD).
      Keywords: blood pressure signal; salt sensitive Dahl rat; compression; nonlinear transform; linear transform
      Citation: International Journal of Computational Systems Engineering, Vol. 4, No. 2/3 (2018) pp. 165 - 170
      PubDate: 2018-04-30T23:20:50-05:00
      DOI: 10.1504/IJCSYSE.2018.091396
      Issue No: Vol. 4, No. 2/3 (2018)
       
  • B-mode breast ultrasound image segmentation techniques: an investigation
           and comparative analysis
    • Authors: Madan Lal, Lakhwinder Kaur, Savita Gupta
      Pages: 171 - 184
      Abstract: Breast cancer is the second leading reason for death among women. A commonly used method for detection of breast cancer is ultrasound imaging. Ultrasonic imaging is a low cost, easy to use, non-invasive and portable process, but it suffers from acoustic interferences (speckle noise) and other artefacts. As a result, it becomes difficult for the experts to directly identify the exact shapes of abnormalities in these images. Numerous techniques have been proposed by different researchers for visual enhancement and for segmentation of lesion regions in breast ultrasound images. In this work, different automatic and semi-automatic breast ultrasound image segmentation techniques have been reviewed with a brief explanation of their different technological aspects. Performance of selected methods has been evaluated on a database of 45 B-mode breast ultrasound images containing benign and malignant tumours (25 benign and 20 malignant). For performance analysis of the segmentation methods, results are taken in terms of area and boundary error based quantitative metrics using manually delineated images (by an expert Radiologist) as ground truth/ reference images.
      Keywords: B-mode breast ultrasound image; breast tumour; speckle noise; image segmentation; thresholding; region growing; fuzzy clustering; watershed; neural networks; active contour; level set
      Citation: International Journal of Computational Systems Engineering, Vol. 4, No. 2/3 (2018) pp. 171 - 184
      PubDate: 2018-04-30T23:20:50-05:00
      DOI: 10.1504/IJCSYSE.2018.091401
      Issue No: Vol. 4, No. 2/3 (2018)
       
  • An improved unsupervised mapping technique using AMSOM for
           neurodegenerative disease detection
    • Authors: Isha Suwalka, Navneet Agrawal
      Pages: 185 - 194
      Abstract: The most challenging aspect in medical imaging is the accuracy of detection of neurodegenerative diseases. The advent of new imaging techniques has yet limited manual evaluations, manual reorientation and other time consuming limitations with reduced resolution. Therefore, there is a need to develop efficient algorithm for proper detection with quantitative information of significance for the clinicians. The proposed algorithm includes improved adaptive moving self organising mapping (AMSOM) which trains the extracted features along with mini-mental state examination (MMSE) factor and volumetric parameter using volume-based method (VBM) for computing feature dataset which in total improves time iteration rate, mean square error, sensitivity and accuracy. The algorithm is an improved version of moving mapping method which on one hand tackles drawback of SOM of fixed grid mapping and improves neighbourhood function of neuron which provides better detection and classification yielding promising results. It further improves performance of AMSOM by better visualisation of the input dataset and provides a framework for determining the efficient parameters. This paper uses real MRI dataset taken from OASIS having a cross-sectional collection of 416 subjects aged 18 to 96. The analysis includes different comparison of mapping approaches that reveals features associated to the Alzheimer disease.
      Keywords: self organising mapping for MRI image; hierarchical mapping with GHSOM; e-database using OASIS; moving neuron concept using AMSOM; clustering for detection of Alzheimer disease
      Citation: International Journal of Computational Systems Engineering, Vol. 4, No. 2/3 (2018) pp. 185 - 194
      PubDate: 2018-04-30T23:20:50-05:00
      DOI: 10.1504/IJCSYSE.2018.091402
      Issue No: Vol. 4, No. 2/3 (2018)
       
  • Active contours using global models for medical image segmentation
    • Authors: Ramgopal Kashyap, Vivek Tiwari
      Pages: 195 - 201
      Abstract: Accurate segmentation with denoising is the subject of research in the field of medical imaging and computer vision. This paper presents an enhanced energy based active contour model with a level set detailing. Local energy fitting term impacts neighbourhood drive to pull the shape and restrict it to protest limits. Thus, the global intensity fitting term drives the movement of contour at distant from the object boundaries. The global energy term depends on worldwide division calculation, which can better catch energy data of picture than Chan-Vese (CV) model. Both neighbourhood and worldwide terms are commonly absorbed to build a vitality work in light of a level set plan to portion images with force inhomogeneity, experiments demonstrate that the proposed model has the upside of commotion resistance and is better than conventional image segmentation. Results demonstrate that the proposed method performs better both subjectively and quantitatively contrasted with other best in class methods.
      Keywords: denoising; energy based active contour; image segmentation; intensity inhomogeneity; local binary fitting; LBF; local region based active contour
      Citation: International Journal of Computational Systems Engineering, Vol. 4, No. 2/3 (2018) pp. 195 - 201
      PubDate: 2018-04-30T23:20:50-05:00
      DOI: 10.1504/IJCSYSE.2018.091404
      Issue No: Vol. 4, No. 2/3 (2018)
       
  • Application of ensemble artificial neural network for the classification
           of white blood cells using microscopic blood images
    • Authors: Jyoti Rawat, Annapurna Singh, H.S. Bhadauria, Jitendra Virmani, Jagtar Singh Devgun
      Pages: 202 - 216
      Abstract: In order to overcome the problems of manual diagnosis in recognising the morphology of blood cells, the automated analysis is frequently used by a pathologist. So this work gives a semi-automated technique to identify and classify white blood cell. In this work, a k-means clustering algorithm is used to segment the nucleus by upgrading the district of the white blood cell nucleus and stifling the other components of the blood smear images. From each cell, various shape, chromatic and texture features are extracted. This feature set was used to train the classifier to determine different classes of WBC. Performance of this model indicates that CAC system design based on the ensemble artificial neural network is the most suitable model for the four class white cell classification, with an accuracy of 95%. The proposed method represents a medicinal method to avoid the plentiful drawbacks associated with the labour-intensive examination of WBCs.
      Keywords: white blood cell; segmentation; k-means clustering; texture features; shape features; chromatic features; artificial neural network classifier; ANN
      Citation: International Journal of Computational Systems Engineering, Vol. 4, No. 2/3 (2018) pp. 202 - 216
      PubDate: 2018-04-30T23:20:50-05:00
      DOI: 10.1504/IJCSYSE.2018.091407
      Issue No: Vol. 4, No. 2/3 (2018)
       
 
 
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