Broca’s Area of Brain to Analyze the Language Impairment Problem and Behavior Analysis of Autism

Islam, Md. Ashiqul; Karim, Rafat; Ahmed, Faruq; Maksuda,; Hossen, Md. Sagar; Akter, Shamima

doi:10.1007/978-3-030-82763-2_19

“…A total number of 100 epochs have been used to train the proposed model and each epoch has provided a different number of accuracy and loss values which mainly de ne the training performance of the trained model [30].…”

Section: Results Analysismentioning

confidence: 99%

Comprehensive Analysis of CNN and YOLOv5 Object Detection Model to Classify Phytomedicine Tree’s Leaf Disease

Islam

¹

,

Haque

²

,

Rahaman

³

et al. 2022

Preprint

0

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Phytomedicine is not only known to the poor people of East Bengal and West Bengal as a medicine but also as a beacon of hope for survival in a diseased life. In this approach, Terminalia Arjuna, Lawsonia inermis, Azadirachta indica, and Ocimum tenuiflorum Phytomedicine tree datasets. Terminalia Arjuna mainly protects gastric mucosa from oxidative damage and it makes it strong so that the stomach gets protection against ulcers. It also exhibits strong antibacterial and antimicrobial properties which help to fight against stomach infections like gastric, diarrhea, and dysentery. There are various uses of Lawsonia inermis. It is known as a powerful detoxifier and it helps to prefer healthy gum, and healthy hair and reduces arthritis pain. The flower of Lawsonia inermis can be used for headaches due to the heat of the sun. Neem is known as Phytomedicine for all diseases and the most common diseases are treated with acne, nourishing the skin, treating wounds, use to reduce dandruff and joint pain, etc. Mainly it increases immunity and provides a healthy life. Another famous Phytomedicine known as Ocimum tenuiflorum is used to treat kidney stones, alleviate fever and headaches, provide a healthy heart, healthy eye, healthy oral, and fight against acne, etc. It is also known as a big source of vitamin K. The most famous and fast object detection algorithms as YOLOv5 and Keras pre-trained models as Xception, ResNet152V2, VGG19, and InceptionResNetV2 have been developed to predict disease-affected or disease-free leaves. The implemented approach has gained a height accuracy of 97.00% from Xception and 95% accuracy from YOLOv5.

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“…Broca's area damage can lead to expressive aphasia, that is only can use simple and limited vocabulary repeatedly, but not use complex grammar and syntax. They are able to understand language, but they are unable to respond (Islam et al, 2022). Channels 15, 40, and 41 are located in area 22 of Brodmann area, which are located in the superior temporal gyrus.…”

Section: Discussionmentioning

confidence: 99%

Identification of autism spectrum disorder based on functional near-infrared spectroscopy using adaptive spatiotemporal graph convolution network

Zhang

¹

,

Xu²,

Yu³

et al. 2023

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The accurate diagnosis of autism spectrum disorder (ASD) is of great practical significance in clinical practice. The spontaneous hemodynamic fluctuations were collected by functional near-infrared spectroscopy (fNIRS) from the bilateral frontal and temporal cortices of typically developing (TD) children and children with ASD. Since traditional machine learning and deep learning methods cannot make full use of the potential spatial dependence between variable pairs, and require a long time series to diagnose ASD. Therefore, we use adaptive spatiotemporal graph convolution network (ASGCN) and short time series to classify ASD and TD. To capture spatial and temporal features of fNIRS multivariable time series without the pre-defined graph, we combined the improved adaptive graph convolution network (GCN) and gated recurrent units (GRU). We conducted a series of experiments on the fNIRS dataset, and found that only using 2.1 s short time series could achieve high precision classification, with an accuracy of 95.4%. This suggests that our approach may have the potential to detect pathological signals in autism patients within 2.1 s. In different brain regions, the left frontal lobe has the best classification effect, and the abnormalities occur more frequently in left hemisphere and frontal lobe region. Moreover, we also found that there were correlations between multiple channels, which had different degrees of influence on the classification of ASD. From this, we can also generalize to a wider range, there may be potential correlations between different brain regions. This may help to better understand the cortical mechanism of ASD.

show abstract

Comprehensive Analysis of CNN and YOLOv5 Object Detection Model to Classify Phytomedicine Tree’s Leaf Disease

Islam

¹

,

Haque

²

,

Rahaman

³

et al. 2022

Preprint

1

0

View full text Add to dashboard Cite

Phytomedicine is not only known to the poor people of East Bengal and West Bengal as a medicine but also as a beacon of hope for survival in a diseased life. In this approach, Terminalia Arjuna, Lawsonia inermis, Azadirachta indica, and Ocimum tenuiflorum Phytomedicine tree datasets. Terminalia Arjuna mainly protects gastric mucosa from oxidative damage and it makes it strong so that the stomach gets protection against ulcers. It also exhibits strong antibacterial and antimicrobial properties which help to fight against stomach infections like gastric, diarrhea, and dysentery. There are various uses of Lawsonia inermis. It is known as a powerful detoxifier and it helps to prefer healthy gum, and healthy hair and reduces arthritis pain. The flower of Lawsonia inermis can be used for headaches due to the heat of the sun. Neem is known as Phytomedicine for all diseases and the most common diseases are treated with acne, nourishing the skin, treating wounds, use to reduce dandruff and joint pain, etc. Mainly it increases immunity and provides a healthy life. Another famous Phytomedicine known as Ocimum tenuiflorum is used to treat kidney stones, alleviate fever and headaches, provide a healthy heart, healthy eye, healthy oral, and fight against acne, etc. It is also known as a big source of vitamin K. The most famous and fast object detection algorithms as YOLOv5 and Keras pre-trained models as Xception, ResNet152V2, VGG19, and InceptionResNetV2 have been developed to predict disease-affected or disease-free leaves. The implemented approach has gained a height accuracy of 97.00% from Xception and 95% accuracy from YOLOv5.

show abstract

Broca’s Area of Brain to Analyze the Language Impairment Problem and Behavior Analysis of Autism

Cited by 3 publications

References 25 publications

Comprehensive Analysis of CNN and YOLOv5 Object Detection Model to Classify Phytomedicine Tree’s Leaf Disease

Comprehensive Analysis of CNN and YOLOv5 Object Detection Model to Classify Phytomedicine Tree’s Leaf Disease

Identification of autism spectrum disorder based on functional near-infrared spectroscopy using adaptive spatiotemporal graph convolution network

Comprehensive Analysis of CNN and YOLOv5 Object Detection Model to Classify Phytomedicine Tree’s Leaf Disease

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