This paper presents the expectation-maximization (EM) variant of probabilistic neural network (PNN) as a step toward creating an autonomous and deterministic PNN. In the real world, faulty reading sensors can happen and will create input vectors with missing features yet they should not be discarded. To overcome this, regularized EM is put in place as a preprocessing step to impute the missing values. The problem faced by users when using random initialization is that they have to define the number of clusters through trial and error, which makes it stochastic in nature. Global k-means is used to autonomously find the number of clusters using a selection criterion and deterministically provide the number of clusters needed to train the model. In addition, fast Global k-means will be tested as an alternative to Global k-means to help reduce computational time. Tests are conducted on both homoscedastic and heteroscedastic PNNs. Benchmark medical datasets and also vibration data collected from a US Navy CH-46E helicopter aft gearbox known as Westland were used. The tests' results fully support the usage of fast Global k-means and regularized EM as preprocessing steps to aid the EMtrained PNN.

Hand gesture recognition (HGR) is a crucial area of research that enhances communication by overcoming language barriers and facilitating human-computer interaction. Although previous works in HGR have employed deep neural networks, they fail to encode the orientation and position of the hand in the image. To address this issue, this paper proposes HGR-ViT, a Vision Transformer (ViT) model with an attention mechanism for hand gesture recognition. Given a hand gesture image, it is first split into fixed size patches. Positional embedding is added to these embeddings to form learnable vectors that capture the positional information of the hand patches. The resulting sequence of vectors are then served as the input to a standard Transformer encoder to obtain the hand gesture representation. A multilayer perceptron head is added to the output of the encoder to classify the hand gesture to the correct class. The proposed HGR-ViT obtains an accuracy of 99.98%, 99.36% and 99.85% for the American Sign Language (ASL) dataset, ASL with Digits dataset, and National University of Singapore (NUS) hand gesture dataset, respectively.

An alternative learning framework utilizing a natural user interface (NUI) can be applied in the context of education for students whose needs are not catered for in the current learning environment in Malaysia. The alternative learning program would still follow the current subject syllabus, but with differences in how the lessons are delivered, learned and executed. A conceptual framework that enables the adoption of the alternative learning program using Microsoft Kinect in Malaysian education system was then proposed to minimize the gaps found in the current learning setting to cater for the special needs of physically disabled children in Malaysia, in particular, primary school children, using the National Curriculum as a guide. Since this is a new framework, the validity and reliability of the proposed framework will be analyzed. A usability test would also be conducted to gauge the acceptance of the proposed framework amongst the children.

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