Forthcoming and Online First Articles

International Journal of Artificial Intelligence and Soft Computing

International Journal of Artificial Intelligence and Soft Computing (IJAISC)

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International Journal of Artificial Intelligence and Soft Computing (3 papers in press)

Regular Issues

  • Application of Machine Learning in Grain-Related Clustering of Laue Spots in a Polycrystalline Energy Dispersive Laue Pattern   Order a copy of this article
    by Amir Tosson, Mohammad Shokr, Mahmoud Al Humaidi, Eduard Mikayelyan, Christian Gutt, Ulrich Pietsch 
    Abstract: We address the identification of grain-corresponding Laue reflections in energy dispersive Laue diffraction (EDLD) experiments by formulating it as a clustering problem solvable through unsupervised machine learning (ML). To achieve reliable and efficient identification of grains in a Laue pattern, we employ a combination of clustering algorithms, namely hierarchical clustering (HC) and K-means. These algorithms allow us to group together similar Laue reflections, revealing the underlying grain structure in the diffraction pattern. Additionally, we utilise the elbow method to determine the optimal number of clusters, ensuring accurate results. To evaluate the performance of our proposed method, we conducted experiments using both simulated and experimental datasets obtained from nickel wires. The simulated datasets were generated to mimic the characteristics of real-world EDLD experiments, while the experimental datasets were obtained from actual measurements.
    Keywords: machine learning; Laue diffraction; X-ray; hierarchical clustering; K-means; crystallography; artificial intelligence; synchrotron radiation; polycrystalline material.
    DOI: 10.1504/IJAISC.2024.10065524
     
  • Modelling the Profitability of Soyabean Farming in Zambia using Machine Learning   Order a copy of this article
    by Naomi M. Mwaba, Derrick Ntalasha 
    Abstract: Planning crops for any upcoming season has proven to be a tough chore for farmers since it is challenging to forecast the profits that their crops will fetch after harvest as most farmers base their choice of crop to grow on high-yield crops and previous farming season profit margins. This introduces a problem of profit determination over time. This study uses soya bean data for the past ten farming seasons to develop and compare two machine learning (ML) models using support vector machine regression (SVMR) and autoregressive integrated moving average (ARIMA) to forecast the profitability of soya beans in Zambia. The experimental results demonstrate that the SVMR is best suited as it predicted profits with an accuracy of 79.25% compared to ARIMA with an accuracy of only 34.2%. This suggests that the SVMR model is efficient to use as a financial analysis tool to allow farmers to make informed decisions.
    Keywords: profit prediction; machine learning; support vector machine regression; SVMR; autoregressive integrated moving average; ARIMA; Zambia.
    DOI: 10.1504/IJAISC.2024.10065638
     
  • License Plate Redaction Algorithm: A Privacy-Preserving Approach   Order a copy of this article
    by Divyanka Thakur, Bhagyalakshmi V, Sonali Deshpande, Sunil Chavan 
    Abstract: License plates are assigned to every vehicle for identification which displays unique letter-number combinations containing sensitive owner details. Redaction is masking of the sensitive information in media and is essential in surveillance to protect privacy. This research proposes an automated license plate redaction system using NVIDIA DeepStream. Leveraging object detection and recognition models, the system identifies and redacts license plates in realtime video footage. A dataset of various vehicles is compiled, annotated, and trained on YOLO-V5s, achieving 97% accuracy in license plate detection. Integrated with DeepStream and PaddleOCR for recognition, a redaction algorithm applies anonymisation using Python's computer vision tools. The system ensures efficient, reliable license plate redaction with minimal computational resource demands. Applicable in law enforcement, traffic analysis, parking, and toll systems, this tool enhances privacy and public safety while improving operational efficiency.
    Keywords: license plate redaction; NVIDIA Deepstream; Yolo model; PaddleOCR; artificial intelligence; video analytics; computer vision; object detection; deep learning.
    DOI: 10.1504/IJAISC.2024.10068008
     

 

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