International Journal of Innovative Computing and Applications (5 papers in press)

Regular Issues

• Automatic Speech Recognition of Gujarati Digits using Wavelet Coefficients in Machine Learning algorithms  
  by Purnima Pandit, Shardav Bhatt 
  Abstract: In today's world, Automatic Speech Recognition (ASR) is an important task implemented via Machine Learning (ML) to assist Artificial Intelligence (AI). It has diverse applications such as humanmachine interactions, handsfree computing, voice search, domestic appliance control and many more. Speech recognition in an Indian regional language becomes a very necessary task in order to facilitate people, who can communicate only using their mother tongue and the disabled ones. In this article, we have proposed and performed experiments of speech recognition for Gujarati language, particularly for Gujarati digits. The recorded speech is preprocessed and then speech features are extracted from it using Mel-Frequency Discrete Wavelet Coefficient (MFDWC). These features are trained using Artificial Neural Networks (ANN) for classification. Two ANN architectures namely, Multi-layer Perceptrons (MLP) and Radial Basis Function Networks (RBFN) are used for training and recognition. The experimental results obtained in this work are compared with our previous experimental results.
  Keywords: Automatic Speech recognition (ASR); Machine Learning (ML); Artificial Neural Networks (ANN); Radial Basis Function Networks (RBFN).
  
  
• Prioritizing Test Cases to Improve the Software Fault Detection using MCDM Methods  
  by Maryam Mohammadi Sarpiri, Keyvan Mohebbi, Ali Jamshidi 
  Abstract: To decrease the cost of software testing, we can run a subset of test cases, but this may result in residual faults. To keep the efficiency of testing, the most important test cases should be selected through a prioritization approach. Such prioritization requires the assessment of different criteria, so it can be formulated as a multi-criteria decision-making (MCDM) problem. This research proposes an approach to select the proper subset of test cases using the MCDM methods. Three MCDM methods, namely, Fuzzy SAW, Fuzzy VIKOR, and Fuzzy TOPSIS are applied to prioritize the test cases concerning various criteria. To select a subset of test cases, a threshold is determined for different pairs of the most important criteria. The proposed approach is applied to an actual e-government software system with two variants. The experimental evaluations indicate the efficiency of this approach with respect to both the failure rate and the average percentage of fault detection metrics.
  Keywords: Software Testing; Test Case Prioritization; Multi-Criteria Decision Making; Fault Failure Rate; and Average Percentage Fault Detection.
  
  
• Deep Learning Intelligence for Influencer-based Topological Classification for Online Social Networks  
  by Somya Jain, Adwitiya Sinha 
  Abstract: Social network analysis provides quantifiable methods and topological metrics to examine the networked structure for several interdisciplinary applications. In our research, a social network of GitHub community is constructed that forms a dense network of 37700 developers with 289003 associations amongst them. The research involves finding the central developers in the GitHub network using graph analytics and benchmark centrality metrics, including Degree, Betweenness, Closeness, PageRank and Eigenvector; which is based upon network structural information. Our research methodology provides a breakthrough towards predicting the classification of GitHub users using artificial intelligence-based learning model trained with derived topological network centrality metrics. The proposed approach performs feature extraction for the developers by computing centrality score of each user followed by building correlation matrix using centrality parameters based on network topology. Further, the derived topological centrality scores were used as input features to train and build artificial intelligence-based models for classification. Our experimentation shows better performance of artificial neural network over autoencoders, logistic regression and hyper-parameter tuned support vector machine. Certain intermediate outcomes include correlation, principal component analysis, loss monitoring, etc. The performance evaluation was performed in terms of macro and weighted F1-score, recall, precision, and accuracy.
  Keywords: Online social network; GitHub community; user influence computing; network centrality; artificial intelligence; topological classification.
  
  
• Fuzzy based MPPT Control for solar PV Applications  
  by RAVINDRANATH TAGORE YADLAPALLI  
  Abstract: Nowadays, the solar energy is playing a vital role as compared to the fossil fuels due to their several advantages such as more reliable, inexhaustible, low running cost, creating job opportunities and energy independence. However, the solar based power generation is affected by the atmospheric conditions. Therefore, the imperative aspect is to track maximum power from the solar PV panel with respect to the changing environmental conditions. This paper mainly focuses on the various MPPT techniques for the solar panel. It includes the implementation of the MPPT algorithms based on the Perturb & Observe (P&O), incremental-conductance (INC) and fuzzy logic control (FLC). At first, the solar PV panel is modelled based on the single-diode model. The P-V and I-V curves are shown with different solar irradiation variations. The main objective here is to analyse the dynamics in terms of the tracking time as well as the steady state error. The exhaustive simulation results are presented for the boost converter based on the INC, P&O and FLC MPPT algorithms.
  Keywords: solar panel; dynamics; fuzzy control.
  DOI: 10.1504/IJICA.2023.10051410
   
  
• Whale Optimization Algorithm Based on Kent Mapping and Adaptive Parameters  
  by Benjia Hu, Zhiyong Wu, Wen Gao, Ke Meng, Dayin Shi, Xiuwei Hu, Yilong Sun 
  Abstract: Aiming at the shortcomings of whale optimisation algorithm, such as easy to fall into local optimisation and slow convergence speed in the later stage, an optimisation method based on three improved strategies is proposed. Firstly, Kent mapping is introduced to initialise the population and enrich the diversity of the population; Secondly, a nonlinear convergence factor strategy is proposed to improve the global search speed and local optimisation accuracy; finally, inertia weight is added to maintain the balance between global search and local optimisation. Simulation experiments with 13 standard test functions show that the proposed algorithm has remarkable performance in global search, convergence speed and optimisation accuracy. In addition, through its application in path planning, the feasibility and effectiveness of the algorithm proposed in this paper are further verified.
  Keywords: Kent mapping; nonlinear factor; inertia weight; whale optimisation algorithm; WOA; global optimisation; route planning.
  DOI: 10.1504/IJICA.2023.10058069