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Publisher: Inderscience Publishers   (Total: 436 journals)

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Showing 1 - 200 of 436 Journals sorted alphabetically
African J. of Accounting, Auditing and Finance     Hybrid Journal   (Followers: 12)
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: 22)
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: 1, 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: 15)
Intl. J. of Accounting, Auditing and Performance Evaluation     Hybrid Journal   (Followers: 14, 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: 22, 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 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: 10, 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: 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: 5)
Intl. J. of Banking, Accounting and Finance     Hybrid Journal   (Followers: 15, SJR: 0.137, CiteScore: 0)
Intl. J. of Behavioural Accounting and Finance     Hybrid Journal   (Followers: 9)
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: 21)
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: 14, 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 Emerging Markets     Hybrid Journal   (Followers: 2)
Intl. J. of Business and Globalisation     Hybrid Journal   (Followers: 2, 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: 4)
Intl. J. of Business Continuity and Risk Management     Hybrid Journal   (Followers: 14)
Intl. J. of Business Environment     Hybrid Journal   (Followers: 2)
Intl. J. of Business Excellence     Hybrid Journal   (Followers: 3, 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: 4, SJR: 0.171, CiteScore: 0)
Intl. J. of Business Information Systems     Hybrid Journal   (Followers: 15, SJR: 0.266, CiteScore: 1)
Intl. J. of Business Innovation and Research     Hybrid Journal   (Followers: 10, 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: 17, 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: 2)
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: 3)
Intl. J. of Computational Intelligence in Bioinformatics and Systems Biology     Hybrid Journal   (Followers: 10)
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: 4)
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: 16, SJR: 0.217, CiteScore: 1)
Intl. J. of Data Mining, Modelling and Management     Hybrid Journal   (Followers: 13, 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: 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: 5)
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: 2, 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: 4, 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: 1, SJR: 0.24, CiteScore: 0)
Intl. J. of Electronic Customer Relationship Management     Hybrid Journal   (Followers: 2, SJR: 0.148, CiteScore: 0)
Intl. J. of Electronic Democracy     Hybrid Journal   (Followers: 1)
Intl. J. of Electronic Finance     Hybrid Journal   (Followers: 4, 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: 7)
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: 27, 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: 16, 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: 5, 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: 3)
Intl. J. of Environmental Technology and Management     Hybrid Journal   (Followers: 1, SJR: 0.141, CiteScore: 0)
Intl. J. of Exergy     Hybrid Journal   (Followers: 2, 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: 5, 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: 4)
Intl. J. of Global Energy Issues     Hybrid Journal   (Followers: 9, 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: 12, 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: 4, 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: 7)
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: 26, 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 Hydrology Science and Technology     Hybrid Journal   (Followers: 7, 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)
Intl. J. of Information and Communication Technology     Hybrid Journal   (Followers: 8, SJR: 0.13, CiteScore: 0)
Intl. J. of Information and Computer Security     Hybrid Journal   (Followers: 17, SJR: 0.141, CiteScore: 0)
Intl. J. of Information and Decision Sciences     Hybrid Journal   (Followers: 9, SJR: 0.233, CiteScore: 1)

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Journal Cover
International Journal of Business Intelligence and Data Mining
Journal Prestige (SJR): 0.249
Citation Impact (citeScore): 2
Number of Followers: 30  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1743-8187 - ISSN (Online) 1743-8195
Published by Inderscience Publishers Homepage  [436 journals]
  • Effective optimisation of Honda algorithm for rear end collision avoidance
           system with genetic algorithm
    • Authors: K.G. Manjunath, N. Jaisankar
      Pages: 3 - 12
      Abstract: Honda is one of the most popular algorithm for rear end collision avoidance system, but does not intend to avoid all the accidents, this due to that it gives a smaller range of warning which cannot avoid the accidents at high speed, to overcome this issue we proposing the optimisation of warning range with a genetic algorithm to minimise the probability of collision. The results show that proposed algorithm avoids 7% more accident than the standard Honda algorithm. We presented the MATLAB implementation of proposed work with examples to show the efficiency of our work.
      Keywords: mutation; warning range; collision avoidance; vehicle velocity
      Citation: International Journal of Business Intelligence and Data Mining, Vol. 14, No. 1/2 (2019) pp. 3 - 12
      PubDate: 2018-12-11T23:20:50-05:00
      DOI: 10.1504/IJBIDM.2019.096845
      Issue No: Vol. 14, No. 1/2 (2018)
       
  • Comparison between optimised genetic-based Honda algorithm and Honda
           algorithm for collision avoidance system
    • Authors: K.G. Manjunath, N. Jaisankar
      Pages: 13 - 24
      Abstract: As the driver's mental state is among the primary factors responsible for collision, most of the sectors now install an automated approach to prevent the collision. The automatic approaches halt the vehicle unit on sensing the state of collision. Anti-collision system is an automatic approach that monitors the various factors of a vehicle unit on a gradual basis. The system is powered with Honda algorithm that computes the minimum safe distance from these factors. As the efficiency of algorithm is subjected to accuracy of sensor readings, the environmental tragedies and precision of sensors can deliver false readings that affects the performance of algorithm. In this paper genetic algorithm is implemented to optimise these values. The optimised modification of readings discards the probability of false reading obtained due to environmental noise. The evaluation parameters indicate that GA performs with almost same accuracy for optimisation.
      Keywords: crossover; selection; warning range; collision avoidance; vehicle velocity
      Citation: International Journal of Business Intelligence and Data Mining, Vol. 14, No. 1/2 (2019) pp. 13 - 24
      PubDate: 2018-12-11T23:20:50-05:00
      DOI: 10.1504/IJBIDM.2019.096843
      Issue No: Vol. 14, No. 1/2 (2018)
       
  • A novel multi-class ensemble model based on feature selection using Hadoop
           framework for classifying imbalanced biomedical data
    • Authors: Thulasi Bikku, N. Sambasiva Rao, Ananda Rao Akepogu
      Pages: 25 - 39
      Abstract: Due to the exponential growth of biomedical repositories such as PubMed and Medline, an accurate predictive model is essential for knowledge discovery in Hadoop environment. Traditional decision tree models such as multivariate Bernoulli model, random forest and multinominal naïve Bayesian tree use attribute selection measures to decide best split at each node of the decision tree. Also, the efficiency of document analysis in Hadoop framework is limited mainly due to the class imbalance problem and large candidate sets. In this paper, we proposed a two phase map-reduce framework with text preprocessor and classification model. In the first phase, mapper based preprocessing method was designed to eliminate irrelevant features, missing values and outliers from the biomedical data. In the second phase, a map-reduce based multi-class ensemble decision tree model was designed and implemented on the preprocessed mapper data to improve the true positive rate and computational time. The experimental results on the complex biomedical datasets show that the performance of our proposed Hadoop based multi-class ensemble model significantly outperforms state-of-the-art baselines.
      Keywords: ensemble model; Hadoop; imbalanced data; medical databases; textual decision patterns
      Citation: International Journal of Business Intelligence and Data Mining, Vol. 14, No. 1/2 (2019) pp. 25 - 39
      PubDate: 2018-12-11T23:20:50-05:00
      DOI: 10.1504/IJBIDM.2019.096801
      Issue No: Vol. 14, No. 1/2 (2018)
       
  • Network affinity aware energy efficient virtual machine placement
           algorithm
    • Authors: R. Ranjana, S. Radha, J. Raja
      Pages: 40 - 53
      Abstract: Efficient mapping of virtual machine request to the available physical machine is an optimisation problem in data centres. It is solved by aiming to minimise the number of physical machines and utilising them to their maximum capacity. Another avenue of optimisation in data centre is the energy consumption. Energy consumption can be reduced by using fewer physical machines for a given set of VM requests. An attempt is made in this work to propose an energy efficient VM placement algorithm that is also network affinity aware. Considering the network affinity between VMs during the placement will reduce the communication cost and the network overhead. The proposed algorithm is evaluated using the Cloudsim toolkit and the performance in terms of energy consumed, communication cost and number of active PMs, is compared with the standard first fit greedy algorithm.
      Keywords: virtualisation; affinity aware; cloud computing; virtual machine placement; network affinity
      Citation: International Journal of Business Intelligence and Data Mining, Vol. 14, No. 1/2 (2019) pp. 40 - 53
      PubDate: 2018-12-11T23:20:50-05:00
      DOI: 10.1504/IJBIDM.2019.096803
      Issue No: Vol. 14, No. 1/2 (2018)
       
  • Dynamic runtime protection technique against the file system injecting
           malwares
    • Authors: Easwaramoorthy Arul, Venugopal Manikandan
      Pages: 54 - 61
      Abstract: Malwares enters into the victim system by injecting the code into victim system executable files or well-known files or folders. In this paper, the proposed dynamic runtime protection technique (DRPT) will ensure for protection of all the modes of the malware entering into the system. In the affected system, the behaviours of the injected file are monitored and controlled and the malware spreads either through online or offline modes via files. The DRPT unpack the malware, continuously monitors and analyses the windows application programming interface (API) calls in the imported and exported dynamic link library (DLL's) of the malwares to find the injection code. DRPT also protects against the malware spread into the other files and the stealing of information from the victim machine. The DRPT tested with 1,517 executable files, among which 811 malicious files have been taken with different malware families. The result of DRPT shows true positive of 94.20% and false positive of 0.05%.
      Keywords: malware; dynamic runtime protection technique; DRPT; dynamic link library; DLL; application programming interface; API
      Citation: International Journal of Business Intelligence and Data Mining, Vol. 14, No. 1/2 (2019) pp. 54 - 61
      PubDate: 2018-12-11T23:20:50-05:00
      DOI: 10.1504/IJBIDM.2019.096809
      Issue No: Vol. 14, No. 1/2 (2018)
       
  • An efficient clustering approach for fair semantic web content retrieval
           
    • Authors: R. Bhavani, V. Prakash, K. Chitra
      Pages: 62 - 88
      Abstract: Web pages are heterogeneous and complex and there exists complicated associations within one web pages and linking to the others. The high interactions between terms in pages demonstrate vague and ambiguous meanings. Efficient and effective clustering methods are needed to discover latent and coherent meanings in context are necessary. This paper proposes an efficient clustering approach for fair semantic web content retrieval based on tri-level ontology construction model with hybrid dragonfly algorithm. Initially the query processing phase, by making use of systematic adaptive hierarchy method (SAHM) efficient ontology selection process is carried out by means of matching keywords retrieved form user query. Secondly, fuzzy sensitive near-neighbour influence (FSNI) based clustering approach relied on the ontology driven fuzzy linguistic measure, applied to estimate the uncertainty that may be relevant to the semantic content which belongs to the user quires. The proposed FSNI clustering approach with HDA algorithm performance is be evaluated and compared with existing clustering approaches in terms of retrieval accuracy and surfing time.
      Keywords: systematic adaptive hierarchy method; SAHM; linear projection based self organised map; SOM; additive normalised-point wise mutual information; AN-PMI; hybrid dragonfly algorithm; HAD; tri-level ontology model construction; fuzzy sensitiv
      Citation: International Journal of Business Intelligence and Data Mining, Vol. 14, No. 1/2 (2019) pp. 62 - 88
      PubDate: 2018-12-11T23:20:50-05:00
      DOI: 10.1504/IJBIDM.2019.096836
      Issue No: Vol. 14, No. 1/2 (2018)
       
  • Knowledge transfer for efficient cross domain ranking using AdaRank
           algorithm
    • Authors: N. Geetha, P.T. Vanathi
      Pages: 89 - 105
      Abstract: Learning-to-rank has been an exciting topic of research exclusively in hypothetical and the productions in the information retrieval practices. Usually, in the learning-based ranking procedures, it is expected the training and testing data are recovered from the identical data delivery. However those existing research methods do not work well in case of multiple documents retrieved from the cross domains (different domains). In this case ranking of documents would be more difficult where the contents are described in multiple documents from different cross domains. The main goal of this research method is to rank the documents gathered from the multiple domains with improved learning rate by learning features from different domains. The feature level information allocation and instance level information relocation are achieved with four learners namely RankNet, ranking support vector machine (SVM), RankBoost and AdaRank. The estimation results presented that the AdaRank algorithm achieves good performance.
      Keywords: learning-to-rank; knowledge transfer; RankNet; ranking SVM; RankBoost; AdaRank
      Citation: International Journal of Business Intelligence and Data Mining, Vol. 14, No. 1/2 (2019) pp. 89 - 105
      PubDate: 2018-12-11T23:20:50-05:00
      DOI: 10.1504/IJBIDM.2019.096808
      Issue No: Vol. 14, No. 1/2 (2018)
       
  • Enhanced R package-based cluster analysis fault identification models for
           three phase power system network
    • Authors: K. Nithiyananthan, Pratap Nair, Raman Raguraman, Tan Yong Sing, Syahrel Emran Bin Siraj
      Pages: 106 - 120
      Abstract: The main objective of this research work is to develop an R-based fault identification model for power system in a cluster analysis environment. Cluster analysis-based data mining techniques model has been implemented to locate the three-phase transmission lines fault in IEEE 30 bus power system. Power World version 18 software was used to simulate the IEEE 30 bus power system and the three-phase transmission lines fault. The bus voltages at fault were collected and import to R statistical software to identify the faults at buses. Through cluster analysis using squared euclidean distance method, fault has been identified at each bus. Then the data also imported to R statistical package to compute the cophenetic distance of dendrogram and check the accuracy of clustering. This meant that the application of data mining techniques yields a huge potential in solving complex problems related to power system, it not only yield an accurate result but also fast computation. The proposed innovative successful model was able to locate the fault at each bus by bus nominal voltage comparison method.
      Keywords: power system transmission lines faults; data mining; cluster analysis; R; SPSS
      Citation: International Journal of Business Intelligence and Data Mining, Vol. 14, No. 1/2 (2019) pp. 106 - 120
      PubDate: 2018-12-11T23:20:50-05:00
      DOI: 10.1504/IJBIDM.2019.096844
      Issue No: Vol. 14, No. 1/2 (2018)
       
  • Probabilistic variable precision fuzzy rough set technique for discovering
           optimal learning patterns in e-learning
    • Authors: K.S. Bhuvaneshwari, D. Bhanu, S. Sophia, S. Kannimuthu
      Pages: 121 - 137
      Abstract: In e-learning environment, optimal learning patterns are discovered for realising and understanding the effective learning styles. The value of uncertain and imprecise knowledge collected has to be categorised into classes known as membership grades. Rough set theory is potential in categorising data into equivalent classes and fuzzy logic may be applied through soft thresholds for refining equivalence relation that quantifies correlation between each class of elucidated data. In this paper, probabilistic variable precision fuzzy rough set technique (PVPFRST) is proposed for deriving robust approximations and generalisations that handles the types of uncertainty namely stochastic, imprecision and noise in membership functions. The result infers that the degree of accuracy of PVPFRST is 21% superior to benchmark techniques. Result proves that PVPFRST improves effectiveness and efficiency in identifying e-learners styles and increases the performance by 27%, 22% and 25% in terms of discrimination rate, precision and recall value than the benchmark approaches.
      Keywords: inclusion degree; probabilistic fuzzy information system; fuzzy membership grade; crispness coefficient; probabilistic variable precision fuzzy rough set; inclusion function
      Citation: International Journal of Business Intelligence and Data Mining, Vol. 14, No. 1/2 (2019) pp. 121 - 137
      PubDate: 2018-12-11T23:20:50-05:00
      DOI: 10.1504/IJBIDM.2019.096807
      Issue No: Vol. 14, No. 1/2 (2018)
       
  • Enhancing the JPEG image steganography security by RSA and attaining high
           payload using advanced DCT replacement method and modified quantisation
           table
    • Authors: J. Hemalatha, M.K. Kavitha Devi, S. Geetha
      Pages: 138 - 154
      Abstract: Steganography deal with hiding information science, which offers an ultimate security in defence, profitable usages, thus sending the imperceptible information, will not be bare or distinguished by others. The aim of this paper is to propose a novel steganographic method in JPEG images to highly enrich a data security by RSA algorithm and attains higher payload by modified quantisation table. The goals of this paper are to be recognised through: 1) modify the quantisation table of the JPEG-JSTEG tool, hiding secret message with its middle frequency to offer great embedding capacity; 2) for challenge, secure RSA algorithm is used to prevent data from extraction. A broad experimental evaluation compares the performance of our proposed work with existing JSTEG was conducted. This algorithm resulted in greater PSNR values and steganogram histogram is more similar. Experimental results reported that the proposed system is a state-of-the model, contributing abundant payload and beating the statistical revealing. Besides, our method has better in all the parameters than JPEG-JSTEG method.
      Keywords: RSA; information forensics; robustness; DCT; JPEG; quantisation table
      Citation: International Journal of Business Intelligence and Data Mining, Vol. 14, No. 1/2 (2019) pp. 138 - 154
      PubDate: 2018-12-11T23:20:50-05:00
      DOI: 10.1504/IJBIDM.2019.096812
      Issue No: Vol. 14, No. 1/2 (2018)
       
  • A fuzzy approach to prioritise DEA ranked association rules
    • Authors: Shekhar Shukla, B.K. Mohanty, Ashwani Kumar
      Pages: 155 - 176
      Abstract: Association rule mining discovers interesting information from large databases. Frequency, reliability and domain knowledge form the multiple criteria for evaluation these association rules. Data envelopment analysis (DEA) is a popular technique used to rank association rules based on the previously mentioned multiple criteria. A decision maker might be interested to have a priority list of these ranked rules based on business and situational requirements. We present an approach to prioritise DEA ranked association rules based on the preference and desirability of the decision maker for different criteria. A modified generalised fuzzy evaluation method (MGFEM) obtains vector-valued fuzzy scores of a group of decision makers and aggregate them to form a preference. A fuzzy logic-based decision support mechanism prioritises these rules based on the decision maker's desirability using the membership function and preference obtained from MGFEM. An example of DEA ranked association rules is presented to explain this innovative approach for prioritisation.
      Keywords: association rules; data envelopment analysis; DEA; multiple criteria; fuzzy logic; modified generalised fuzzy evaluation method; MGFEM
      Citation: International Journal of Business Intelligence and Data Mining, Vol. 14, No. 1/2 (2019) pp. 155 - 176
      PubDate: 2018-12-11T23:20:50-05:00
      DOI: 10.1504/IJBIDM.2019.096842
      Issue No: Vol. 14, No. 1/2 (2018)
       
  • BAIT: behaviour aided intruder testimony technique for attacker intention
           prediction in business data handling
    • Authors: K. Narasimha Mallikarjunan, S. Mercy Shalinie, A. Bhuvaneshwaran
      Pages: 177 - 198
      Abstract: During business transactions there are lot of opportunity for data theft and data misinterpretation. Mostly, the legitimate users act like malicious users and try to misuse their privileges. So, it is very important to know their intentions and different strategies they apply for business data theft. In this paper, we develop an information analytics based technique for inferring attacker intent objectives and strategies (AIOS). The input to the model is the alert logs in real-world attack-defense scenario and output are the discovered attack strategies or patterns. The implementation of this model is done on a real-world attack-defence scenario to increase the learning efficiency of the technique. Experimental results on expected impact and attack path shows that the technique provides better results than conventional intrusion detection systems.
      Keywords: attacker behaviour analysis; information analytics; AIOS; attacker category; pattern mining
      Citation: International Journal of Business Intelligence and Data Mining, Vol. 14, No. 1/2 (2019) pp. 177 - 198
      PubDate: 2018-12-11T23:20:50-05:00
      DOI: 10.1504/IJBIDM.2019.096835
      Issue No: Vol. 14, No. 1/2 (2018)
       
  • A secured best data centre selection in cloud computing using encryption
           technique
    • Authors: A. Prabhu, M. Usha
      Pages: 199 - 217
      Abstract: In this work, we have proposed an approach for providing very high security to the cloud system. Our proposed method comprises of three phases namely authentication phase, cloud data centre selection phase and user related service agreement phase. For the purpose of accessing data from the cloud server, we will need a secure authentication key. In the authentication phase, the user authentication is verified and gets the key then encrypts the file using blowfish algorithm. Before encryption the input data is divided into column-wisely with the help of pattern matching approach. In the approach, the encryption and decryption processes are carried out by employing the blowfish algorithm. We can optimally select the cloud data centre to store the data; here the position is optimally selected with the help of bat algorithm. In the final phase, the user service agreement is verified. The implementation will be done by cloud sim simulator.
      Keywords: authentication key; blowfish; bat algorithm; pattern match; cloud data centre selection
      Citation: International Journal of Business Intelligence and Data Mining, Vol. 14, No. 1/2 (2019) pp. 199 - 217
      PubDate: 2018-12-11T23:20:50-05:00
      DOI: 10.1504/IJBIDM.2019.096804
      Issue No: Vol. 14, No. 1/2 (2018)
       
  • Minimal constraint based cuckoo search algorithm for removing transmission
           congestion and rescheduling the generator units
    • Authors: N. Chidambararaj, K. Chitra
      Pages: 218 - 237
      Abstract: In the paper, a minimal constraint based cuckoo search (CS) algorithm is proposed for solving transmission congestion problem by considering both increase and decrease in generation power. Thus, the proposed algorithm is used to optimise the real power changes of generator while transmission congestion occurred. Then, the power loss, generator sensitivity factor and congestion management cost of the system is evaluated by the proposed algorithm according to the transmission congestion. The proposed method is implemented in MATLAB working platform and their congestion management performance is analysed. The performance of the proposed method is compared with the other existing methods such as fuzzy adaptive bacterial foraging (FABF), simple bacterial foraging (SBF), particle swarm optimisation (PSO), and artificial neural network (ANN)-CS respectively. The congestion management cost is reduced up to 26.169%. Through the analysis of comparison, it is shown that the proposed technique is better and outperforms other existing techniques in terms of congestion management measures.
      Keywords: minimal constraint based CS algorithm; particle swarm optimisation; PSO; artificial neural network; ANN; real power; congestion management; power loss and congestion management cost
      Citation: International Journal of Business Intelligence and Data Mining, Vol. 14, No. 1/2 (2019) pp. 218 - 237
      PubDate: 2018-12-11T23:20:50-05:00
      DOI: 10.1504/IJBIDM.2019.096796
      Issue No: Vol. 14, No. 1/2 (2018)
       
  • Modelling economic choice under radical uncertainty: machine learning
           approaches
    • Authors: N. Chidambararaj, K. Chitra
      Pages: 238 - 253
      Abstract: This paper utilises a novel experimental dataset on consumer choice to investigate and benchmark the performance of alternative statistical models under conditions of extreme uncertainty. We compare the results of logistic regression, discriminant analysis, naïve Bayes classifier, neural network, support vector machine (SVM), decision tree, and random forest (RF) to discover that the RF model robustly registers the highest classification accuracy. Variable importance analysis reveals that apart from demographic and situational factors, consumer choice is highly dependent on social network effects and subject to numerous non-linearities, thus making machine learning approaches the best modelling choice.
      Keywords: choice; decision-making; machine learning; uncertainty; social network; logistic regression; neural network; random forest; consumer choice; modelling
      Citation: International Journal of Business Intelligence and Data Mining, Vol. 14, No. 1/2 (2019) pp. 238 - 253
      PubDate: 2018-12-11T23:20:50-05:00
      DOI: 10.1504/IJBIDM.2019.096794
      Issue No: Vol. 14, No. 1/2 (2018)
       
  • OLAP technology and machine learning as the tools for validation of the
           numerical models of convective clouds
    • Authors: Elena N. Stankova, Andrey V. Balakshiy, Dmitry A. Petrov, Vladimir V. Korkhov, Andrey V. Shorov
      Pages: 254 - 266
      Abstract: In the present work we use the technologies of machine learning and OLAP for more accurate forecasting of such phenomena as a thunderstorm, hail, heavy rain, using the numerical model of convective cloud. Three methods of machine learning: support vector machine, logistic regression and ridge regression are used for making the decision on whether or not a dangerous convective phenomenon occurs at present atmospheric conditions. The OLAP technology is used for development of the concept of multidimensional data base intended for distinguishing the types of the phenomena (thunderstorm, heavy rainfall and light rain). Previously developed complex information system is used for collecting the data about the state of the atmosphere and about the place and at the time when dangerous convective phenomena are recorded.
      Keywords: online analytical processing; OLAP; online analytical processing; machine learning; validation of numerical models; numerical model of convective cloud; weather forecasting; thunderstorm; multidimensional data base; data mining
      Citation: International Journal of Business Intelligence and Data Mining, Vol. 14, No. 1/2 (2019) pp. 254 - 266
      PubDate: 2018-12-11T23:20:50-05:00
      DOI: 10.1504/IJBIDM.2019.096793
      Issue No: Vol. 14, No. 1/2 (2018)
       
  • Data cubes retrieval and design in OLAP systems: from query analysis
           to visualisation tool
    • Authors: Rahma Djiroun, Kamel Boukhalfa, Zaia Alimazighi
      Pages: 267 - 298
      Abstract: Business intelligence systems provide an effective solution from large volumes of data for multidimensional online computing and analysis. Usually, in a decision-making process, organisations and enterprises, require several internal and/or external cubes which are often heterogeneous. Most of the time, the structure of these cubes is unknown to the decision-makers. To analyse a phenomenon, the decision-maker seeks among sets of cubes, in a collection, the cube which responds better to his need. In this context, we propose an approach that enables decision-makers to express their needs via a query expressed in a natural language, returns top-K relevant cubes and designs/constructs new cubes when no, or few deployed cubes are relevant. We propose a tool called RD-cubes-query implementing our approach in a ROLAP architecture. We use this tool in some experiments to validate our approach.
      Keywords: cubes design; cubes search; online analytical processing; OLAP; top-K; query analysis; visualisation tool
      Citation: International Journal of Business Intelligence and Data Mining, Vol. 14, No. 1/2 (2019) pp. 267 - 298
      PubDate: 2018-12-11T23:20:50-05:00
      DOI: 10.1504/IJBIDM.2019.096813
      Issue No: Vol. 14, No. 1/2 (2018)
       
 
 
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