Titlebook: Intelligent Systems and Applications; Proceedings of the 2 Kohei Arai Conference proceedings 2024 The Editor(s) (if applicable) and The Aut

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Remote Learning of Mathematics for Visually Impaired Students During COVID-19: Exploring Online Intrises, social distancing, increases in food demand, job losses and closure of educational institutions. This led to a rapid transition from face-to-face to remote teaching. Most remote learning platforms are designed primarily for sighted students and are less useful for visually-impaired students.

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Optimization of Lacrimal Aspect Ratio for Explainable Eye Blinking, lacrimal area for detection. This paper proposes a new eye blinking detection method using a novel lacrimal aspect ratio (LAR) strategy that utilises eyebrow movement and eyes. The proposed algorithm estimates facial landmarks using an automatic facial landmark detector to extract a single scalar q

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SIM_RL: A New Approach for Integrating Simulation with Reinforcement Learning,ystem becomes more complex, the simulation engine must run numerous times in response to the increasing complexity of the input and the simulation process. Additionally, an expensive physical experiment needs to be performed to validate the results. This paper demonstrates an innovative, general-pur

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Grouping Shapley Value Feature Importances of Random Forests for Explainable Yield Prediction,a variety of yield prediction scenarios. The data included for the prediction of yields are intricate and the models are often difficult to understand. However, understanding the models can be simplified by using natural groupings of the input features. Grouping can be achieved, for example, by the

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Mining Interesting Aggregate Tuples, because of the exponential number of cuboids. This number is not only an important problem of computing, but also of searching for what is interesting or useful in the data cube. This paper presents the concept of interesting aggregate tuple that can be useful for managers to decide on their busine

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Algorithmic Fairness in Healthcare Data with Weighted Loss and Adversarial Learning,lthcare domain. Group fairness is considered as one of the principal criteria. However, most of the prevailing mitigation techniques emphasize on tuning the training algorithms while overlooking the fact that the training data may possibly be the primary reason for the biased outcomes. In this work,

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Application of Mean-Variance Cloning Technique to Investigate the Comparative Performance Analysis In this case, the researcher is faced with dual problems such as (i) biasedness towards the majority group over the minority group and (ii) dimensionality or singularity of the covariance matrix. In such situations, the classical classifiers dependent on the sample mean and covariance matrix are im

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