Artificial intelligence and internet of things in screening and management of autism spectrum disorder

Ghosh, Tapotosh; Banna, Md. Hasan Al; Rahman, Md. Sazzadur; Kaiser, M. Shamim; Mahmud, Mufti; Hosen, A. S. M. Sanwar; Cho, Gihwan

doi:10.1016/j.scs.2021.103189

Cited by 86 publications

(22 citation statements)

References 95 publications

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“…LR is an approach of data categorization that divides data into distinct categories. LR employs the logistic sigmoid function to convert its output to a probability value which may subsequently be transferred to two or more discrete classifications [40].…”

Section: Classification In Wekamentioning

confidence: 99%

“…The SMO method is a decomposition technique in which a multi-variable optimization problem is divided into a series of sub-problems. Every subproblem optimizes an optimal solution with a limited number of variables, usually just one, while the rest of the variables are treated as constants [40]. The SMO method is built by taking the decomposition strategy to its logical conclusion and optimizing a tiny subset of only two points at each iteration [40].…”

Section: Classification In Wekamentioning

confidence: 99%

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Data Mining for Autism Spectrum Disorder detection among Adults

Al-Qazzaz

Jaffer

Abdulazez

et al. 2022

NJES

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Autism Spectrum Disorder (ASD) is one of the most common children's neurodevelopmental disorders (NDD) with an estimated global incidence of 1% to 2%. There are two aims for this research, first, to propose a data mining architecture that combines behavioural and clinical characteristics with demographic data. Second, to provide a quick, acceptable and easy way to support the ASD diagnosis. this can be performed by conducting a comparison study to determine the efficacy of four possible classifiers: logistic regression (LR), sequential minimum optimization (SMO), naïve Bayes, and instance-based technique based on k-neighbors (IBK). These classifiers have been performed with Waikato Environment for Knowledge Analysis (WEKA) tools to distinguish autistic adults from healthy, normal subjects. The results showed that, with 99.71%, SMO classification accuracy was 99.71, which exceeded the accuracy of other classifiers. The proposed architecture allows for early detection of ASD, distinguishing between ASD and healthy control subjects. This study could help doctors and clinicians by giving them a better idea of what the future holds for people with autism spectrum disorder (ASD) and by improving therapy programs, allowing people with ASD to live a long and happy life.

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Section: Classification In Wekamentioning

confidence: 99%

Section: Classification In Wekamentioning

confidence: 99%

Data Mining for Autism Spectrum Disorder detection among Adults

Al-Qazzaz

Jaffer

Abdulazez

et al. 2022

NJES

View full text Add to dashboard Cite

show abstract

“…In recent years artificial intelligence (AI) has been applied in diverse problem domains to solve various challenging problems, including text classification [13,14,15,16], cyber security [17,18,19,20], neurological disease detection [21,22,23,12] and management [24,25,26,27,28,29], elderly care [30,31], fighting pandemic [32,33,34,35,36,37,38], and healthcare service delivery [39,40,41]. In particular, deep learning (DL) has attracted a lot of attention [6,7].…”

Section: Related Workmentioning

confidence: 99%

Four-Way Classification of Alzheimer's Disease using Deep Siamese Convolutional Neural Network with Triplet-Loss Function

Hajamohideen

Shaffi

Mahmud

et al. 2022

Preprint

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Alzheimer's disease (AD) is a neurodegenerative disease that causes irreversible damage to several brain regions, including the hippocampus causing impairment in cognition, function, and behaviour. Early diagnosis of the disease will reduce the suffering of the patients and their family members. Towards this aim, in this paper, we propose a Siamese Convolutional Neural Network (CNN) architecture that employs the triplet-loss function for the representation of input MRI images as k-dimensional embeddings. We used both pre-trained and non-pre-trained CNNs to transform images into the embedding space. These embeddings are subsequently used for the 4-way classification of Alzheimer's Disease. The model efficacy was tested using the ADNI and OASIS datasets which produced an accuracy of 91.83% and 93.85%, respectively. Furthermore, obtained results are compared with similar methods proposed in the literature.

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“…Another method of diagnosis of ASD is from different modalities of neuroimaging data such as Magnetic resonance imaging (MRI), Electroencephalography (EEG), Electrocorticography (ECoG), Functional near-infrared spectroscopy (fNIRS), and Magnetoencephalography (MEG) [ 10 ]. However, such techniques are often not affordable to the people who live in economically depressed areas.…”

Section: Introductionmentioning

confidence: 99%

Empirical Study of Autism Spectrum Disorder Diagnosis Using Facial Images by Improved Transfer Learning Approach

et al. 2022

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Autism spectrum disorder (ASD) is a neurological illness characterized by deficits in cognition, physical activities, and social skills. There is no specific medication to treat this illness; only early intervention can improve brain functionality. Since there is no medical test to identify ASD, a diagnosis might be challenging. In order to determine a diagnosis, doctors consider the child’s behavior and developmental history. The human face can be used as a biomarker as it is one of the potential reflections of the brain and thus can be used as a simple and handy tool for early diagnosis. This study uses several deep convolutional neural network (CNN)-based transfer learning approaches to detect autistic children using the facial image. An empirical study is conducted to select the best optimizer and set of hyperparameters to achieve better prediction accuracy using the CNN model. After training and validating with the optimized setting, the modified Xception model demonstrates the best performance by achieving an accuracy of 95% on the test set, whereas the VGG19, ResNet50V2, MobileNetV2, and EfficientNetB0 achieved 86.5%, 94%, 92%, and 85.8%, accuracy, respectively. Our preliminary computational results demonstrate that our transfer learning approaches outperformed existing methods. Our modified model can be employed to assist doctors and practitioners in validating their initial screening to detect children with ASD disease.

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Artificial intelligence and internet of things in screening and management of autism spectrum disorder

Cited by 86 publications

References 95 publications

Data Mining for Autism Spectrum Disorder detection among Adults

Data Mining for Autism Spectrum Disorder detection among Adults

Four-Way Classification of Alzheimer's Disease using Deep Siamese Convolutional Neural Network with Triplet-Loss Function

Empirical Study of Autism Spectrum Disorder Diagnosis Using Facial Images by Improved Transfer Learning Approach

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