An AI-Enabled Internet of Things Based Autism Care System for Improving Cognitive Ability of Children with Autism Spectrum Disorders

Hameed, Mohamed Abdel; Hassaballah, M.; Hosney, Mosa E.; Alqahtani, Abdullah

doi:10.1155/2022/2247675

Cited by 28 publications

(10 citation statements)

References 48 publications

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“…AI's impact on autism research is substantial, with a growing interest in its applications [65][66][67]. From diagnostics to IoT integrations, AI's transformative influence spans various healthcare facets, marking a remarkable evolution [68,69]. Machine learning (ML) and deep learning, especially in neural networks, play pivotal roles in addressing autism spectrum disorder (ASD) challenges [65].…”

Section: Contextualizing Our Study: a Comparative Analysis With Diver...mentioning

confidence: 99%

“…ML excels in early ASD detection via behavioral and physiological data analysis, while predictive modeling tailors support strategies [66]. Naturalistic behavioral analysis, powered by computer vision and ML, informs interventions by decoding subtle cues [69]. Deep learning contributes to understanding communication challenges and identifying genetic markers associated with autism [65].…”

Section: Contextualizing Our Study: a Comparative Analysis With Diver...mentioning

confidence: 99%

“…Deep learning contributes to understanding communication challenges and identifying genetic markers associated with autism [65]. AI promises transformative potential in ASD research, from early detection to personalized interventions, exemplifying technology's capacity to improve lives [65][66][67][68][69].…”

Section: Contextualizing Our Study: a Comparative Analysis With Diver...mentioning

confidence: 99%

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Breaking Barriers—The Intersection of AI and Assistive Technology in Autism Care: A Narrative Review

Iannone,

Giansanti

2023

JPM

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(Background) Autism increasingly requires a multidisciplinary approach that can effectively harmonize the realms of diagnosis and therapy, tailoring both to the individual. Assistive technologies (ATs) play an important role in this context and hold significant potential when integrated with artificial intelligence (AI). (Objective) The objective of this study is to analyze the state of integration of AI with ATs in autism through a review. (Methods) A review was conducted on PubMed and Scopus, applying a standard checklist and a qualification process. The outcome reported 22 studies, including 7 reviews. (Key Content and Findings) The results reveal an early yet promising interest in integrating AI into autism assistive technologies. Exciting developments are currently underway at the intersection of AI and robotics, as well as in the creation of wearable automated devices like smart glasses. These innovations offer substantial potential for enhancing communication, interaction, and social engagement for individuals with autism. Presently, researchers are prioritizing innovation over establishing a solid presence within the healthcare domain, where issues such as regulation and acceptance demand increased attention. (Conclusions) As the field continues to evolve, it becomes increasingly clear that AI will play a pivotal role in bridging various domains, and integrated ATs with AI are positioned to act as crucial connectors.

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Section: Contextualizing Our Study: a Comparative Analysis With Diver...mentioning

confidence: 99%

Section: Contextualizing Our Study: a Comparative Analysis With Diver...mentioning

confidence: 99%

Section: Contextualizing Our Study: a Comparative Analysis With Diver...mentioning

confidence: 99%

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Breaking Barriers—The Intersection of AI and Assistive Technology in Autism Care: A Narrative Review

Iannone,

Giansanti

2023

JPM

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“…The SVM technique has been selected due to its high performance with small data sets [ 76 ]. We have used a linear kernel, as shown in Equation (11).…”

Section: Design Of the Asd Detection Schemementioning

confidence: 99%

Energy-Efficient EEG-Based Scheme for Autism Spectrum Disorder Detection Using Wearable Sensors

Alhassan

Soudani

AlMusallam

2023

Sensors

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The deployment of wearable wireless systems that collect physiological indicators to aid in diagnosing neurological disorders represents a potential solution for the new generation of e-health systems. Electroencephalography (EEG), a recording of the brain’s electrical activity, is a promising physiological test for the diagnosis of autism spectrum disorders. It can identify the abnormalities of the neural system that are associated with autism spectrum disorders. However, streaming EEG samples remotely for classification can reduce the wireless sensor’s lifespan and creates doubt regarding the application’s feasibility. Therefore, decreasing data transmission may conserve sensor energy and extend the lifespan of wireless sensor networks. This paper suggests the development of a sensor-based scheme for early age autism detection. The proposed scheme implements an energy-efficient method for signal transformation allowing relevant feature extraction for accurate classification using machine learning algorithms. The experimental results indicate an accuracy of 96%, a sensitivity of 100%, and around 95% of F1 score for all used machine learning models. The results also show that our scheme energy consumption is 97% lower than streaming the raw EEG samples.

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“…The survey of skin disease detection and classification shows that there are various studies based on supervised, semi-supervised, and unsupervised approaches [ 3 ]. The performance of supervised-based methods is high compared to the semi-supervised and unsupervised approaches [ 6 ]. Thus, the current study considered the supervised-based approach to accurately detect skin diseases and classify them into their skin disease family.…”

Section: Introductionmentioning

confidence: 99%

Attention Cost-Sensitive Deep Learning-Based Approach for Skin Cancer Detection and Classification

Ravi

2022

Cancers

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Deep learning-based models have been employed for the detection and classification of skin diseases through medical imaging. However, deep learning-based models are not effective for rare skin disease detection and classification. This is mainly due to the reason that rare skin disease has very a smaller number of data samples. Thus, the dataset will be highly imbalanced, and due to the bias in learning, most of the models give better performances. The deep learning models are not effective in detecting the affected tiny portions of skin disease in the overall regions of the image. This paper presents an attention-cost-sensitive deep learning-based feature fusion ensemble meta-classifier approach for skin cancer detection and classification. Cost weights are included in the deep learning models to handle the data imbalance during training. To effectively learn the optimal features from the affected tiny portions of skin image samples, attention is integrated into the deep learning models. The features from the finetuned models are extracted and the dimensionality of the features was further reduced by using a kernel-based principal component (KPCA) analysis. The reduced features of the deep learning-based finetuned models are fused and passed into ensemble meta-classifiers for skin disease detection and classification. The ensemble meta-classifier is a two-stage model. The first stage performs the prediction of skin disease and the second stage performs the classification by considering the prediction of the first stage as features. Detailed analysis of the proposed approach is demonstrated for both skin disease detection and skin disease classification. The proposed approach demonstrated an accuracy of 99% on skin disease detection and 99% on skin disease classification. In all the experimental settings, the proposed approach outperformed the existing methods and demonstrated a performance improvement of 4% accuracy for skin disease detection and 9% accuracy for skin disease classification. The proposed approach can be used as a computer-aided diagnosis (CAD) tool for the early diagnosis of skin cancer detection and classification in healthcare and medical environments. The tool can accurately detect skin diseases and classify the skin disease into their skin disease family.

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An AI-Enabled Internet of Things Based Autism Care System for Improving Cognitive Ability of Children with Autism Spectrum Disorders

Cited by 28 publications

References 48 publications

Breaking Barriers—The Intersection of AI and Assistive Technology in Autism Care: A Narrative Review

Breaking Barriers—The Intersection of AI and Assistive Technology in Autism Care: A Narrative Review

Energy-Efficient EEG-Based Scheme for Autism Spectrum Disorder Detection Using Wearable Sensors

Attention Cost-Sensitive Deep Learning-Based Approach for Skin Cancer Detection and Classification

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