Enhancing Autism Severity Classification: Integrating LSTM into CNNs for Multisite Meltdown Grading

International Journal of Advanced Computer Science and Applications, Journal Year: 2023, Volume and Issue: 14(12)

Published: Jan. 1, 2023

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social interaction, verbal and non-verbal communication, often associated with cognitive neurobehavioral challenges. Timely screening diagnosis of ASD are crucial for early educational planning, treatment, family support, timely medical intervention. Manual diagnostic methods time-consuming labor-intensive, underscoring the need automated approaches to assist caretakers parents. While various researchers have employed machine learning deep techniques diagnosis, existing models fall short capturing complexity multisite meltdowns fully leveraging interdependence among these severity assessment acquired facial images children, hindering development comprehensive grading system. This paper introduces novel approach using Long Short Term Memory (LSTM) integrated Convolution Neural Network (CNN) designed identify exploit their ASD. The process begins image pre-processing, involving discrete convolution filters noise removal contrast enhancement improve quality. enhanced then undergoes instance segmentation Segment Anything model significant regions child's image. segmented region subjected principal component analysis feature extraction, features utilized LSTM-integrated CNN meltdown detection classification. trained children's extracted from videos, testing performed on videos captured during observations. Performance reveals superior results, training accuracy 88% validation 84%, outperforming conventional methods. innovative not only enhances efficiency but also provides more nuanced understanding impact severity, contributing robust

Language: Английский

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Assessing the Impact of Climate Change on Agriculture: Farm-Level Evidence from Karnataka, India

Universal Journal of Agricultural Research, Journal Year: 2024, Volume and Issue: 12(1), P. 76 - 86

Published: Feb. 1, 2024

This study examines the impact of climate change on agriculture and explores role technology in its adaptation.For this purpose, primary data are collected from ecologically sensitive coastal western ghat regions state Karnataka.The applied Ricardian approach to estimate sensitivity region.A structured questionnaire with 98 questions various information farmer households.Farmers' responses these presented as frequency tables cross-sectional regression is under approach.The empirical results confirm that farmers aware change.However, there a lack understanding adaptation change.Most feel since farming largely relies nature, it impossible adapt vagaries.Therefore, greater need educate possible strategies.Further, no variation crops cultivated farmers' across different districts area.There lot scope for using risks strategies.Results model reveal erratic rainfall has negative farmland value.Among socioeconomic variables, land belonging socially backward communities commands less value than forward communities.Variables like education households practising main profession positively influenced values.Based findings, several policy implications highlighted, prominent being helping diversify earnings, communicating standard strategies cost-effective communications reach.

Language: Английский

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A review of using deep learning from an ecology perspective to address climate change and air pollution

Global NEST Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 16, 2024

<p>Artificial intelligence techniques that can shatter pattern recognition accuracy records, has recently attracted a lot of attention. With its flexibility and capacity to handle massive complicated datasets, deep learning transformed numerous academic domains, including bioinformatics medicine, in few years. We think ecologists also benefit from these methods, since ecological datasets are getting bigger more complicated. This review examined current implementations demonstrates how been effectively applied classify animal activity, identify species, estimate biodiversity big such as audio recordings, videos, camera-trap photos. paper show most disciplines, contexts like management conservation, learning. frequent problems concerning the application learning, what is process for building network, resources available, kind data processing power needed. To assist utilising it offer guidelines, suggestions, helpful materials, reference flowchart. contend potential become an extremely useful tool at time when automated population monitoring creates amounts humans no longer able interpret efficiently.</p>

Language: Английский

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Design of Web Based Recommendation System for Farmers using Machine Learning

Published: June 7, 2024

Language: Английский

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Impact of Climate Change Over Food Chain Supply: An Analysis of Machine Learning Techniques

SSRN Electronic Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

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