You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Accurate classification of self-care problems in children who suffer from physical and motor affliction is an important problem in the healthcare industry. This is a difficult and a time consumming process and it needs the expertise of occupational therapists. In recent years, healthcare professionals have opened up to the idea of using expert systems and artificial intelligence in the diagnosis and classification of self care problems. In this study, we propose a new deep learning based approach named Care2Vec for solving these kind of problems and use a real world self care activities dataset that is based on a conceptual framework designed by the World Health Organization (WHO). Care2Vec is a mix of unsupervised and supervised learning where we use Autoencoders and Deep neural networks as a two step modeling process. We found that Care2Vec has a better prediction accuracy than some of the traditional methods reported in the literature for solving the self care classification problem viz. Decision trees and Artificial neural networks.

Cited Abstract(s):
[1]: 

[2]: Smart environment has been evolved as a hot research topic with the development of machine learning algorithms and wireless communication technologies. Existing smart home solutions usually need inhabitants to operate device controllers directly. However, for people with disabilities, it is inconvenient and difficult to perform such manual operations. Therefore, it is important to develop automatic and intelligent services to reduce operation inconvenience and improve comfort level. In this paper, an Improved Hidden Markov Model (IHMM) is presented to support personalized behavior prediction for people with disabilities. The model can learn behavior patterns of users and provide services to inhabitants automatically. Moreover, by breaking the time invariant hypothesis in Hidden Markov Model, we incorporate time information as positions of states, and develop a temporal state transition matrix to replace the fixed state transition matrix to demonstrate the probabilities of state transitions. As a result, different values of daily temperature sections are characterized and identified as hidden variables, which guide user activities. Evaluation of proposed work has shown that IHMM improves the prediction accuracy by at least 10 compared to the traditional HMM.

[3]: This paper presents a novel feature learning model for cyber security tasks. We propose to use Auto-encoders (AEs), as a generative model, to learn latent representation of different feature sets. We show how well the AE is capable of automatically learning a reasonable notion of semantic similarity among input features. Specifically, the AE accepts a feature vector, obtained from cyber security phenomena, and extracts a code vector that captures the semantic similarity between the feature vectors. This similarity is embedded in an abstract latent representation. Because the AE is trained in an unsupervised fashion, the main part of this success comes from appropriate original feature set that is used in this paper. It can also provide more discriminative features in contrast to other feature engineering approaches. Furthermore, the scheme can reduce the dimensionality of the features thereby signicantly minimising the memory requirements. We selected two different cyber security tasks: networkbased anomaly intrusion detection and Malware classication. We have analysed the proposed scheme with various classifiers using publicly available datasets for network anomaly intrusion detection and malware classifications. Several appropriate evaluation metrics show improvement compared to prior results.
[3] worked on an intelligent decision support system for the evaluation of the degree of hearing loss. [2] worked on learning the behavior patterns of people with disabilities using machine learning methods such as Hidden markov models. [1] implemented multiple machine learning algorithms viz. Decision trees, Support vector machine and k nearest neighbors for identification of children with reading disability.