Abstract
A hybridised image classification technique based on discrete wavelet auto encoder, principal component analysis, and extreme learning machine is proposed. The suggested approach is split into three stages: (a) The subsequent stage in the data preprocessing phase is to extract features from the preprocessed pictures. This may be performed through the use of several approaches such as object recognition, image segmentation, and extraction of features with the help of deep learning models. (b) Principal component analysis to transform a dataset into a lower-dimensional space while retaining the most significant properties of the data. PCA can help simplify huge dataset processing and minimise the computational complexity of machine learning methods. (c) Following feature extraction, the features are sent into extreme learning machines (ELMs) for categorisation. The hidden layer employs a random matrix to translate the input parameters to a higher dimensional space, while the output layer conducts classification based on the mapped features. Therefore, it can be stated that PCA-ELM provides higher generalisation performance.
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Nagendrudu, P.V.V.S.D., Sekhar, C.C., Kandru, J.L., Revanth, P.V. (2024). A Framework for Picture Categorisation Primarily Examines the Possibilities of ELM. In: Swaroop, A., Polkowski, Z., Correia, S.D., Virdee, B. (eds) Proceedings of Data Analytics and Management. ICDAM 2023. Lecture Notes in Networks and Systems, vol 786. Springer, Singapore. https://doi.org/10.1007/978-981-99-6547-2_28
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DOI: https://doi.org/10.1007/978-981-99-6547-2_28
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