Publisher: Inderscience Publishers   (Total: 448 journals)

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Showing 1 - 200 of 448 Journals sorted alphabetically
African J. of Accounting, Auditing and Finance     Hybrid Journal   (Followers: 15)
African J. of Economic and Sustainable Development     Hybrid Journal   (Followers: 18)
Afro-Asian J. of Finance and Accounting     Hybrid Journal   (Followers: 9, SJR: 0.195, CiteScore: 0)
American J. of Finance and Accounting     Hybrid Journal   (Followers: 25)
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: 17, 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: 3, 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: 19)
Intl. J. of Accounting, Auditing and Performance Evaluation     Hybrid Journal   (Followers: 16, 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: 7)
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: 27, SJR: 0.124, CiteScore: 0)
Intl. J. of Advanced Operations Management     Hybrid Journal   (Followers: 10, SJR: 0.163, CiteScore: 0)
Intl. J. of Aerodynamics     Hybrid Journal   (Followers: 34)
Intl. J. of Agent-Oriented Software Engineering     Hybrid Journal   (Followers: 3)
Intl. J. of Agile and Extreme Software Development     Hybrid Journal   (Followers: 5)
Intl. J. of Agile Systems and Management     Hybrid Journal   (Followers: 5, 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: 9, 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: 8)
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: 12)
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: 4)
Intl. J. of Automotive Technology and Management     Hybrid Journal   (Followers: 7, 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: 7)
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: 24)
Intl. J. of Big Data Management     Hybrid Journal   (Followers: 2)
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: 16, 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: 8)
Intl. J. of Biometrics     Hybrid Journal   (Followers: 5, SJR: 0.155, CiteScore: 0)
Intl. J. of Biotechnology     Hybrid Journal   (Followers: 6, SJR: 0.269, CiteScore: 1)
Intl. J. of Blockchains and Cryptocurrencies     Hybrid Journal   (Followers: 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: 16)
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: 7, SJR: 0.171, CiteScore: 0)
Intl. J. of Business Information Systems     Hybrid Journal   (Followers: 17, 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: 19, 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: 29)
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: 6)
Intl. J. of Computational Intelligence in Bioinformatics and Systems Biology     Hybrid Journal   (Followers: 13)
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 Medicine and Healthcare     Hybrid Journal   (Followers: 1)
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: 2)
Intl. J. of Computational Vision and Robotics     Hybrid Journal   (Followers: 5, 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: 2, 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: 5)
Intl. J. of Corporate Strategy and Social Responsibility     Hybrid Journal   (Followers: 6)
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: 17, 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: 10)
Intl. J. of Decision Support Systems     Hybrid Journal   (Followers: 2)
Intl. J. of Design Engineering     Hybrid Journal   (Followers: 11)
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: 2)
Intl. J. of Diplomacy and Economy     Hybrid Journal   (Followers: 7)
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: 10, SJR: 0.225, CiteScore: 1)
Intl. J. of Electronic Banking     Hybrid Journal   (Followers: 6)
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: 8, SJR: 0.137, CiteScore: 0)
Intl. J. of Electronic Transport     Hybrid Journal   (Followers: 9)
Intl. J. of Embedded Systems     Hybrid Journal   (Followers: 6, SJR: 0.48, CiteScore: 1)
Intl. J. of Emergency Management     Hybrid Journal   (Followers: 12, 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: 29, SJR: 0.255, CiteScore: 1)
Intl. J. of Entrepreneurship and Small Business     Hybrid Journal   (Followers: 32, 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: 7)
Intl. J. of Export Marketing     Hybrid Journal   (Followers: 3)
Intl. J. of Financial Engineering and Risk Management     Hybrid Journal   (Followers: 5)
Intl. J. of Financial Innovation in Banking     Hybrid Journal   (Followers: 4)
Intl. J. of Financial Markets and Derivatives     Hybrid Journal   (Followers: 5)
Intl. J. of Financial Services Management     Hybrid Journal   (Followers: 1)
Intl. J. of Food Safety, Nutrition and Public Health     Hybrid Journal   (Followers: 22)
Intl. J. of Forensic Engineering     Hybrid Journal   (Followers: 3)
Intl. J. of Forensic Engineering and Management     Hybrid Journal   (Followers: 3)
Intl. J. of Forensic Software Engineering     Hybrid Journal   (Followers: 3)
Intl. J. of Foresight and Innovation Policy     Hybrid Journal   (Followers: 6, 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: 6)
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: 3)
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 Policy     Hybrid Journal   (Followers: 1)
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: 6)
Intl. J. of Hospitality and Event Management     Hybrid Journal   (Followers: 4)
Intl. J. of Human Factors and Ergonomics     Hybrid Journal   (Followers: 21, SJR: 0.117, CiteScore: 0)
Intl. J. of Human Factors Modelling and Simulation     Hybrid Journal   (Followers: 18)
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   (Followers: 1)
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)

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Similar Journals
Journal Cover
Electronic Government, an International Journal
Journal Prestige (SJR): 0.424
Citation Impact (citeScore): 1
Number of Followers: 17  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1740-7494 - ISSN (Online) 1740-7508
Published by Inderscience Publishers Homepage  [448 journals]
  • A cooperative GA-SM-based prediction model for healthcare services
    • Authors: M. Durgadevi, R. Kalpana
      Pages: 7 - 24
      Abstract: Diabetes mellitus is a major health challenge around the world. The blood glucose level is one of the major factors in the human body and a significant increase in its level can cause many harmful effects in human life. It is expected that early diagnosis of diabetes mellitus can lead to rapid and effective treatment of glycemic control. As the number of people who suffer from diabetes mellitus increases significantly, a study on diabetes mellitus prediction was done through well-known methods in data mining (DM). In this paper, a genetic algorithm (GA)-based suppressor mutation (SM) optimisation rule miner has been proposed as a cooperative approach for prediction of diabetes mellitus. A novel fitness function has been incorporated into the GA-SM approach to generate a comprehensive optimal rule set while balancing accuracy, sensitivity and specificity. The proposed rule miner was compared against three rule-based algorithms, namely CN2, J48 and BF tree on the Pima Indians Diabetes Dataset with 768 patient records using ten-fold cross validation. The results obtained prove that the proposed GA-SM approach has outperformed CN2, J48 and BF tree with respect to accuracy and kappa.
      Keywords: diabetes mellitus; data mining; genetic algorithm; suppressor mutation
      Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 7 - 24
      PubDate: 2020-02-22T23:20:50-05:00
      DOI: 10.1504/EG.2020.105251
      Issue No: Vol. 16, No. 1/2 (2020)
       
  • An analysis of parallel ensemble diabetes decision support system based on
           voting classifier for classification problem
    • Authors: S. Sathurthi, K. Saruladha
      Pages: 25 - 38
      Abstract: Diabetes mellitus is one of the prominent health challenges in the world. Diabetes is a dangerous, metabolic disease that caused by human blood sugar level and progresses throughout life. In supervised learning-based systems have been proposed that incorporate ensemble learning techniques for diabetes prediction depends upon the diagnostic measurement of the diabetes patient. In this paper, voting classifier were used for combining the various ensemble and base classifiers for designing diabetes disease prediction. Voting mechanism helps to build the multiple ensemble and base classifier model. The accuracy of ensemble of ensemble classifiers has resulted in high rate of accuracy (79%) when compared to the ensemble of base classifiers (77%) with majority rule voting (MRV) and weighted majority voting (WMV) models. Hence, ensemble of ensemble classifier was chosen as the best model for diabetes healthcare prediction. This system has been experimented with Pima Indian diabetes UCI dataset and its implemented in python language.
      Keywords: base classifiers; ensemble classifiers; cross validation; bagging; boosting; decision tree; majority rule voting; MRV; weighted majority voting; WMV
      Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 25 - 38
      PubDate: 2020-02-22T23:20:50-05:00
      DOI: 10.1504/EG.2020.105250
      Issue No: Vol. 16, No. 1/2 (2020)
       
  • Identification and characterisation of choroidal neovascularisation using
           e-Health data through an optimal classifier
    • Authors: G. Anitha, Mohamed Ismail, S.K. Lakshmanaprabu
      Pages: 39 - 55
      Abstract: Over the years, health informatics and eHealth gained more popularity in health care application. The collection of eHealth data becomes easier due to the advancement of digital technology. In this paper, the e-Health based supporting system is developed for the classification of a retinal disease called CNV. CNV is a retinal disease caused due to the growth of abnormal blood vessels in the choroidal layer. A good classifier for CNV data makes the process of identifying the disease easier and it will help the medical practitioners to give the treatment at the right time. A comparison has been done among different machine learning classifiers such as support vector machine (SVM), k-nearest neighbours (kNN), neural network (NN), ensemble and naive Bayes classifiers and they are tested and evaluated based on accuracy and training time. From the results, it is observed that kNN classifier outperforms the other classifiers in all aspects.
      Keywords: choroidal neovascularisation; optical coherence tomography; OCT; machine learning classifiers; support vector machine; SVM; kNN classifier; naive Bayes classifier
      Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 39 - 55
      PubDate: 2020-02-22T23:20:50-05:00
      DOI: 10.1504/EG.2020.105254
      Issue No: Vol. 16, No. 1/2 (2020)
       
  • An efficient healthcare framework for kidney disease using hybrid harmony
           search algorithm
    • Authors: Prasad Koti, P. Dhavachelvan, T. Kalaipriyan, Sariga Arjunan, J. Uthayakumar, Pothula Sujatha
      Pages: 56 - 68
      Abstract: Prediction of kidney disease (KD) gains more importance in the medical decision support systems. As the medical dataset are massive in size, effective techniques are required to produce accurate results. This paper proposes a hybrid harmony search (HM-L) algorithm with Levi distribution to properly predict KD at appropriate time. In this research work, correlation-based feature selection (CFS) is used as a feature selection technique. The effectiveness of hybrid harmony search (HS) algorithm is validated by employing it against a set of dataset. The obtained results of applied datasets without and with feature selection are compared to one another. The experimental results imply that the HM-L algorithm attains significant results than existing methods such as HS algorithm, biogeography optimisation algorithm (BBO), grey wolf optimisation (GWO), AL particle swarm optimisation algorithm (ALPSO) and artificial bee colony (ABC) algorithm. The presented HM-L model attains a sensitivity of 96, specificity of 93.33, accuracy of 95, F-score of 96 and kappa value of 0.89.
      Keywords: feature selection; harmony search algorithm; intelligent algorithms; Levi distribution
      Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 56 - 68
      PubDate: 2020-02-22T23:20:50-05:00
      DOI: 10.1504/EG.2020.105236
      Issue No: Vol. 16, No. 1/2 (2020)
       
  • Internet of medical things with cloud-based e-health services for brain
           tumour detection model using deep convolution neural network
    • Authors: M. Ganesan, N. Sivakumar, M. Thirumaran
      Pages: 69 - 83
      Abstract: In the present days, e-health services offer various decision support systems in healthcare sector. These systems make use of internet of medical things (IoMT) devices and cloud platform to offer services to millions of people. In this paper, we develop an IoT with cloud-based brain tumour detection model using convolution neural network (CNN). Here, the input MRI brain images are captured by the use of medical equipments as well as IoT devices is used to transmit data to the cloud. In the cloud, the D-CNN model can be executed to identify the presence of disease and classify the brain tumour as malignant or benign. The presented D-CNN model is employed to a set of benchmark BRATS 2015 challenge dataset. The presented model attains maximum classifier performance with the sensitivity value of 97.17, specificity of 98.77 and accuracy of 98.07.
      Keywords: e-health services; brain tumour; cloud computing; convolution neural network; CNN; decision support; internet of medical things; IoMT
      Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 69 - 83
      PubDate: 2020-02-22T23:20:50-05:00
      DOI: 10.1504/EG.2020.105240
      Issue No: Vol. 16, No. 1/2 (2020)
       
  • A framework for e-healthcare management service using recommender
           system
    • Authors: P. Nagaraj, P. Deepalakshmi
      Pages: 84 - 100
      Abstract: Recommender systems are emerging up very popular as they are able to predict the preferences of a user. This makes the proposal dependent on the user profile, past evaluations or/and extra learning, for example, user logical information and instructions. It can also be applied to healthcare area by taking advantage of environment information to sustain health improvement and disease prohibition. The utilisation of recommender framework has been efficient in both industry and the intellectual world. Recommender framework has additionally discovered its way into the healthcare discipline interval with shifted applications. The motivation behind this investigation is to comprehend the pattern of recommender framework applications in healthcare services by analysing and to give specialists and research knowledge and future supervision. The proposed work centres on giving a complete outline of the recommender frameworks in e-health service information to patients whenever, wherever and whatever. Health information systems are turning into an essential stage for healthcare services. In this specific circumstance, health recommender system is exhibited as correlative toll in decision-making process in healthcare services and administrations. This paper exhibits current improvements in the market, challenges and opportunities in e-health concerning health recommender system and developing methodologies.
      Keywords: recommender system; health recommender system; e-healthcare; healthcare integrated information systems
      Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 84 - 100
      PubDate: 2020-02-22T23:20:50-05:00
      DOI: 10.1504/EG.2020.105256
      Issue No: Vol. 16, No. 1/2 (2020)
       
  • Smart learning using personalised recommendations in web-based learning
           systems using artificial bee colony algorithm to improve learning
           performance
    • Authors: Maganti Venkatesh, S. Sathyalakshmi
      Pages: 101 - 117
      Abstract: Many of e-learning systems in their web-based courses do not have personalisation based on individual needs and their capabilities. Main challenging aspect of personalised delivery of e-learning is concerned with an adaptive course delivery along with content delivery. Personalised e-learning environment provide recommendations to learning community for supporting and also helping them go through the process of e-learning, as it plays a crucial role in promotion of smart learning in smart cities. In this work, a novel framework namely, personalised bee recommender for e-learning (PBReL) based on artificial bee colony (ABC) optimisation is proposed to build a structure of recommendation by using K-means clustering. Many other recommender system are available that made use of ABC to identify its optimal learning path. Experiments are carried out by using web links and contents of Moodle-based learning management system (LMS). Results show that the proposed framework obtains higher precision and coverage.
      Keywords: e-learning; personalisation; smart cities; smart learning; personalised bee recommender for e-learning; PBReL; K-means clustering; artificial bee colony; ABC
      Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 101 - 117
      PubDate: 2020-02-22T23:20:50-05:00
      DOI: 10.1504/EG.2020.105253
      Issue No: Vol. 16, No. 1/2 (2020)
       
  • Empower good governance with public assessed schemes by improved sentiment
           analysis accuracy
    • Authors: Akula V.S. Siva Rama Rao, P. Ranjana
      Pages: 118 - 136
      Abstract: Many government schemes were unsuccessful because lack of proper feedback on the ongoing schemes, where billion dollars investment is going to be in vain. Sentiment analysis is one of best approach to analyse opinions of the peoples on various government schemes. Sentiment analysis and machine learning techniques emerged to analyse huge social media corpora to track people's views on government policies, products and services. Sentiment analysis process consists of various phases which include data discovery, data collection, data pre-processing, and data analysis. Stemming is a process to generate the morphemes in natural language sentences for various applications such as sentiment analysis, information retrieval, and domain analysis. The stemming process involved two major errors, which are over-stemming and under-stemming errors. Most of sentiment analysis natural languages processing applications used Lancaster and Porter stemming algorithms where more than one word inflected into same morpheme, which causes the etymology behaviour of the stemming word and prone to classify the tweets false positives and false negative. The proposed un-prejudice light stemming algorithm prevent etymology behaviour of morpheme and sustain its meaning during stemming process by selecting a word which has maximum number of synonyms in lexical database.
      Keywords: empower government; government schemes; NLP; social media networks; under-stemming; over-stemming; stemmer weight; sentiment analysis
      Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 118 - 136
      PubDate: 2020-02-22T23:20:50-05:00
      DOI: 10.1504/EG.2020.105252
      Issue No: Vol. 16, No. 1/2 (2020)
       
  • Social internet of things using big data analytics and security aspects
           - a review
    • Authors: S. Deva Arul, Meenakshisundaram Iyapparaja
      Pages: 137 - 154
      Abstract: The rapid development of technologies in today's world has become interesting that made millions of people to utilise the major advantages in it. Two main technologies that were emerging in modern society are big data and the social internet of things. Several researchers have studied and developed a major concept of using big data with SIoT and the security development of maintain a large amount of data. In this paper, deep survey regarding the concepts behind the big data analytics with the social internet of things (SIoT) was studied and analysed. Furthermore, the machine learning techniques that were used in previous works were analysed and comparisons of various methods are discussed. The performance comparison of various classifiers on different datasets is shown and SVM has more than 90% of accuracy when compared with other algorithms. KNN has 64% of accuracy which is lowest of any classifier than NB and NN.
      Keywords: big data; social internet of things; SIoT; frequent itemset mining; FIM; machine learning
      Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 137 - 154
      PubDate: 2020-02-22T23:20:50-05:00
      DOI: 10.1504/EG.2020.105238
      Issue No: Vol. 16, No. 1/2 (2020)
       
  • Managing natural hazards in smart cities in Kingdom of Saudi Arabia using
           a technique based on interior search algorithm
    • Authors: S. Deva Arul, Meenakshisundaram Iyapparaja
      Pages: 155 - 169
      Abstract: A smart city is a modern habitat that uses internet of things (IOT) to collect data and manage resources to provide high level of services to people. Information and communication technology (ICT) is a technique to enhance the quality of a smart city. Semantic web and cloud servers are the resources to provide data for smart city management tools. Riyadh is one of the smart cities in Kingdom of Saudi Arabia. Dust storm and floods are the most common hazards in the city. Existing smart city management could not provide an effective solution for the natural hazards. There is a necessity for smart city applications to optimise data and provide an optimum accuracy in output. The objective of the paper is to provide a solution for natural hazards and provide effective management of Riyadh city. A machine learning technique, interior search algorithm is used in the proposed study. It is used in the research for the extraction of knowledge from complex data. The efficiency of proposed method is compared with state of the art algorithms. The proposed method has achieved an accuracy of 87% in the management of natural hazards.
      Keywords: smart city; internet of things; IOT; machine learning; artificial intelligence; search techniques; information and communication technology; ICT; Kingdom of Saudi Arabia
      Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 155 - 169
      PubDate: 2020-02-22T23:20:50-05:00
      DOI: 10.1504/EG.2020.105260
      Issue No: Vol. 16, No. 1/2 (2020)
       
  • Optimal parameter tuning for PID controller using accelerated grey wolf
           optimisation in smart sensor environments
    • Authors: R. Rajakumar, D. Sivanandakumar, J. Uthayakumar, T. Vengattaraman, K. Dinesh
      Pages: 170 - 189
      Abstract: System lifetime is the crucial problem of wireless sensor networks (WSNs), and exploiting environmental energy provides a potential solution. Boost convertor can be employed in WSN to achieve energy efficiency. In order to achieve better performance, PID controller is combined with boost convertor. However, tuning the PID controller is crucial task whenever the input voltage fluctuates. In this work, a novel algorithm namely accelerated grey wolf optimisation (AGWO) is proposed to improve the convergence speed and to eradicate the local optima stagnation. AGWO algorithm utilises a balanced intensification and diversification techniques to eradicate the local optima struck. The observed results conveys that AGWO achieves minimum percentage overshoot (9%), settling time (0.894), rise time (0.50) and peak time (0.57) which is better compared to other comparative algorithms. Additionally, it has been observed that AGWO is able to achieve comparatively better success performance in a complex environment.
      Keywords: wireless sensor network; WSN; smart sensors; PID controller; grey wolf optimisation; GWO; boost convertor problem
      Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 170 - 189
      PubDate: 2020-02-22T23:20:50-05:00
      DOI: 10.1504/EG.2020.105237
      Issue No: Vol. 16, No. 1/2 (2020)
       
  • Social spider-based unequal clustering protocol for wireless sensor
           environment for smart cities
    • Authors: R. Buvanesvari, A. Rijuvana Begum
      Pages: 190 - 209
      Abstract: Wireless sensor networks involve a massive number of sensor nodes often deployed to observe the physical world. Energy efficiency is the challenging issue in the design of WSN. For attaining energy efficient characteristic, clustering techniques has been employed. But, it suffers from hot spot issue which defines that the cluster heads (CHs) closet to base station are burdened with more traffic compared to CHs located far away from BS due to multihop communication. To resolving this, we present a social spider-based unequal clustering protocol (SSUCP) for WSN. SSUCP is based on the nature of social spiders to select the proper CHs and cluster size. Based on the fitness functions and node parameters, the interested decision of selecting the proper CHs and cluster size were made. The SSUCP is implemented in MATLAB and an extensive experimentation takes place under three situations based on distance to BS for ensuring the consistent results of the proposed method. In addition, the SSUCP is validated in terms of energy efficiency and network lifetime analysis. The attained experimental outcome verified that the SSUCP is the superior one over the compared methods.
      Keywords: smart cities; WSN; social spider; hot spot issue; clustering
      Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 190 - 209
      PubDate: 2020-02-22T23:20:50-05:00
      DOI: 10.1504/EG.2020.105235
      Issue No: Vol. 16, No. 1/2 (2020)
       
  • A performance analysis of stereo matching algorithms for stereo vision
           applications in smart environments
    • Authors: V. Kavitha, G. Balakrishnan
      Pages: 210 - 221
      Abstract: Stereo vision is a subfield of computer vision that tends to an essential research issue of reproducing the three-dimensional directions and focuses for depth estimation. This paper gives a relative investigation of stereo vision and matching techniques, utilised to resolve the correspondence problem. The investigation of matching algorithms is done by the use of extensive experiments on the Middlebury benchmark dataset. The tests concentrated on an examination of three stereovision techniques namely mean shift algorithm (MSA), seed growing algorithm (SGA) and multi-curve fitting (MCF) algorithm. With a specific end goal to evaluate the execution, some statistics related insights were computed. The experimental results demonstrated that best outcome is attained by the MCF algorithm in terms of depth estimation, disparity estimation and CT. The presented MCF algorithm attains a minimum computation time (CT) of 2 s whereas the other MSA and SGA require a maximum CT of 8.9 s and 7 s, respectively. The simulation results verified that the MCF algorithm reduces the processing time in a significant way than the compared methods.
      Keywords: stereo matching; stereo vision; multi-fitting; Middlebury; smart cities
      Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 210 - 221
      PubDate: 2020-02-22T23:20:50-05:00
      DOI: 10.1504/EG.2020.105245
      Issue No: Vol. 16, No. 1/2 (2020)
       
 
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