Forthcoming and Online First Articles

International Journal of Data Analysis Techniques and Strategies

International Journal of Data Analysis Techniques and Strategies (IJDATS)

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International Journal of Data Analysis Techniques and Strategies (4 papers in press)

Regular Issues

  • Sentiment analysis of customer reviews for Algerian dialect using DziriBERT model   Order a copy of this article
    by Fateh Bougamouza, Samira Hazmoune 
    Abstract: The increasing volume of daily comments and tweets presents a valuable resource for improving various processes, from business strategies to service management. However, the Algerian Dialect, despite its growing presence on social media, has been overlooked in sentiment analysis. This study addresses this gap by proposing an approach for sentiment analysis of Algerian Dialect feedback, specifically from customers of Algerian telephone operators (Djezzy, Mobilis, and Ooredoo). Leveraging Transfer Learning, the pre-trained DziriBERT model was fine-tuned, with experiments refining data preprocessing techniques and hyperparameters. The outcome is an impressive 82.01% accuracy rate, offering promising insights into sentiment analysis in the Algerian Dialect and highlighting its potential significance for companies and researchers in the field.
    Keywords: Sentiment Analysis; Algerian Arabic Dialect; DziriBERT; Transfer Learning; Algerian telephone operators; Emoji categorization.
    DOI: 10.1504/IJDATS.2024.10062272
     
  • Boosting CNN Network Performance for Face Recognition in an Authentication System   Order a copy of this article
    by Hamza Benyezza, Reda Kara, Mounir Bouhedda, Zine Eddine Safar Zitoun, Samia Rbouh 
    Abstract: Face recognition technology has made significant advancements through the utilisation of Convolutional Neural Networks (CNN) in various applications. However, accurately identifying individuals from similar backgrounds remains a notable challenge due to inherent similarities in facial features among individuals with shared genetic ancestry or cultural heritage. This paper addresses the limitations of traditional CNN in accurately identifying individuals from the same origins and presents an approach to enhance the performance of CNN networks and improve the reliability of face recognition in authentication systems. The proposed approach incorporates advanced face detection and identification algorithm based on the VGG-Face CNN descriptor model, along with the cosine distance algorithm. Promising results were obtained through a prototype implementation on a Raspberry Pi 4. Comparative evaluations against alternative face recognition strategies showcased exceptional performance, achieving an accuracy rate of 96.33% for positive pairs and 95.38% for negative pairs at an optimal threshold of 20.
    Keywords: Smart Authentication system; Face detection and identification; VGG-Face CNN descriptor; IoT; Cosine distance algorithm.
    DOI: 10.1504/IJDATS.2024.10062942
     
  • Optimizing IPL Squad Composition: A Mathematical Framework for Efficient Team Selection on a Limited Budget in a Multi-Criteria, Multi-Objective Environment   Order a copy of this article
    by Pabitra Kumar Dey, Abhijit Banerjee, Dipendra Nath Ghosh 
    Abstract: Selection of the finest cricket squads for Twenty-20 cricket while considering multiple criteria and a limited budget is indeed a challenging problem for team management. For the formation of the best team squads, the objectives could include maximising batting and bowling strength, considering player performances, experiences, age, and captaincy capabilities while spending the minimum amount. To tackle this problem, a multi-objective optimisation approach can be valuable to find the best possible team composition. A comprehensive approach for the selectors was proposed by combining the multi-objective genetic algorithm in a multi-criteria environment. Overall, the aims of this research work are to provide selectors with a mathematical framework that can assist them in choosing the best cricket squad with a lower budget. This approach can help automate the process of selecting teams in a multi-criteria environment, such as player auctions, and provide selectors with a range of optimal options to consider.
    Keywords: Optimum Team Selection; Modified Group Decision Algorithm (MGDA); Modified Multi-Objective Genetic Algorithm (MMOGA); Non-Dominated Sorting Genetic Algorithm-II; IPL T-20 Cricket; Strategy Planning.
    DOI: 10.1504/IJDATS.2024.10062994
     
  • CEVAB: NIR-VIS Face Recognition using Convolutional Encoder-based Visual Attention Block   Order a copy of this article
    by Patil Jayashree Madhukara, Ashok Kumar P. M, Raju Anitha 
    Abstract: Recent research in night vision face recognition has spiked due to the rise of night-time surveillance in public areas, where cameras often use near infr-red (NIR) images. This paper presents a new face recognition method, the convolutional encoder-based visual attention block (CEVAB), optimised for NIR and visible spectrum (VIS) images. CEVAB combines a convolutional encoder with an attention-based architecture, focusing on critical facial features to enhance accuracy against watchlists. Tested on the FaceSurv dataset with over 132,000 images, CEVAB outshines traditional methods in VIS, achieving 95.08% Rank 1 accuracy at close distances, and in NIR, with 74.00% Rank 1 accuracy, surpassing competitors like Verilook and ResNet-50. These results prove CEVABs exceptional adaptability and performance in various imaging conditions, significantly advancing night vision face recognition technology.
    Keywords: Deep learning; Face recognition; NIR Images; Visual Attention.
    DOI: 10.1504/IJDATS.2024.10063484
     

 

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