A Perspective on Intervention Approaches for Children with Autism Spectrum Disorder

Balaji, Saishashank; Sanfilippo, Filippo; Gerdes, Martin; Prattichizzo, Domenico

doi:10.1007/978-3-031-10525-8_11

Cited by 2 publications

(1 citation statement)

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“…For example, the analysis of electronic health records can help identify disease patterns and predict outcomes, leading to more targeted and effective treatments. The use of genomics data can also help develop personalised treatments and therapies for patients based on their genetic makeup.There is an increasing body of research indicating that mobile health (mHealth) systems' technological advancements can be leveraged to create diverse intervention approaches to aid individuals with autism spectrum disorder (ASD) in coping with their problem behaviours and adapting to their surroundings [4]- [6]. These advancements in healthcare data analytics are transforming the way healthcare is delivered and hold the promise to reshape the field.…”

Section: Introductionmentioning

confidence: 99%

Early Mental Stress Detection Using Q-Learning Embedded Starling Murmuration Optimiser-Based Deep Learning Model

Moosavi,

Zafar,

Sanfilippo

et al. 2023

IEEE Access

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Stress affects individual of all ages as a regular part of life, but excessive and chronic stress can lead to physical and mental health problems, decreased productivity, and reduced quality of life. By identifying stress at an early stage, individuals can take steps to manage it effectively and improve their overall wellbeing. Feature selection is a critical aspect of early stress detection because it helps identify the most relevant and informative features that can differentiate between stressed and non-stressed individuals. This paper firstly proposes a variance based feature selection technique that uses q-learning embedded Starling Murmuration Optimiser (QLESMO) to choose relevant features from a publicly available dataset in which stresses experienced by nurses working during the Covid'19 Pandemic is recorded using bio-signals and user surveys. Furthermore, a comparative study with other metaheuristic based feature selection techniques have been demonstrated. Next, to evaluate the efficacy of the proposed algorithm, 10 benchmark test functions have been used. The reduced feature subset is then classified through a 1D convolutional neural network (CNN) model (QLESMO-CNN) and is seen to perform well in terms of the evaluation metrics in comparison to other competitive algorithms. Finally, the proposed technique is compared with the State-of-the-Art methodologies present in literature. The experiments provide a strong basis to determine features that are most relevant for early mental stress classification using a hybrid model combining CNN, Reinforcement Learning and metaheuristic algorithms.

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Section: Introductionmentioning

confidence: 99%