Forthcoming and Online First Articles

International Journal of Innovative Computing and Applications

International Journal of Innovative Computing and Applications (IJICA)

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International Journal of Innovative Computing and Applications (3 papers in press)

Regular Issues

  • An ensemble framework of decision trees for class imbalance using partitioning   Order a copy of this article
    by Vijayakumar Kadappa, Shankru Guggari, Rajeshwari Devi D. V 
    Abstract: Decision tree classifiers are widely used in machine learning and data mining due to their intuitiveness. However, they do not perform well for class-imbalanced data due to bias creation towards the majority class. Therefore, handling class-imbalanced data is an active research area in many applications of machine learning. Feature set partitioning paradigm is proven to be effective for classification by many researchers. In this paper, we extend the ideas of partitioning to propose a decision tree ensemble to overcome class-imbalance issues. The framework consists of balancing the data, partitioning the feature set, building local decision trees, then combining decisions. Some instances of the proposed framework (e.g., Ferrer diagram-based approach) are used to validate the performance of the framework. An empirical study is carried out using University of California Irvine (UCI), Knowledge Extraction Evolutionary Learning (KEEL), and other datasets. The performance of the instances of the proposed framework demonstrates improved performance compared to other benchmark machine learning approaches.
    Keywords: machine learning; decision tree; feature set partitioning; class imbalance; ensemble method; classification.
    DOI: 10.1504/IJICA.2024.10063631
     
  • Prediction of Variants of DDoS Attacks based on Statistical Analysis and Machine Learning Algorithms   Order a copy of this article
    by Anupama Mishra, Neena Gupta, Brij B. Gupta, Karamjit Bhatia, Mahendra Singh Aswal 
    Abstract: Protecting our servers and machines is vital since they store our data and resources. Attackers use the latest tools and technologies to launch the attack. Among, several cyberattacks, DDoS attacks are the worst. DDoS attackers employ a variety of methods to exploit machines and consume all server resources to block authorized users. Current DDoS detection methods depend on network topology, cannot detect all types of attacks, use outdated or invalid datasets, and require powerful and expensive infrastructure hardware. In our research, we filter non-legitimate traffic and use machine learning classifiers to predict attack types from attack traffic. These two processes together reduce attack volume and identify attack type to provide a comprehensive DDoS protection and enable targeted reaction and mitigation. We used CIC DDoS 2019 data for the experiment. It records MSSQL, PortMap, LDAP, NetBIOS, Syn, UDP, UDPLag, and benign traffic attacks. Experiments yield promising and satisfying results.
    Keywords: DDoS; entropy; machine learning; CISDDoS2019.
    DOI: 10.1504/IJICA.2024.10066155
     
  • Performance Analysis of Fuzzy-SMC based MPPT Algorithm for Solar PV Systems   Order a copy of this article
    by Ravindranath Tagore Yadapalli, Rajani Kandipati, RamaKoteswara Rao Alla 
    Abstract: Nowadays the solar power generation is dominant due to their phenomenal contributions such as low-lying running expenditures and inexhaustible source of energy as compared to the fossil fuel based power generation However, the degraded issue connected with the solar connected power generation is the unstable atmospheric circumstances This necessitates the maximum power to be gleaned from the solar panel against the uncertain environmental situations Therefore, this paper articulates the distinct maximum power point techniques (MPPTs) for the boost converter based solar panel It is based on the sliding mode control (SMC) and the integrated fuzzy-sliding mode control (SMC) MPPT techniques In view of the above, the modelling of the solar PV panel is burnished with the aid of single-diode model The appropriate fuzzy rules are framed along with the SMC design The key parameters under consideration are the tracking time and error The extensive simulation end results are delineated based on the SMC and integrated fuzzy-SMC MPPT algorithms along with their performance comparison.
    Keywords: Solar PV system; fuzzy logic control; sliding mode control.
    DOI: 10.1504/IJICA.2024.10067589
     

 

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