Multimodal depressive dictionary learning method for performing prediction of social media mental disorder, such as cyber relationship addiction, involves using benchmark depression and non-depression datasets that applying Social Network Mental Disorder Detection on large-scale datasets
	KALAISELVI B; UMAMAHESHWARI M; SUBBULAKSHM S; SIVANNARAYANA K; KRISHNA G M; SAGAR K S S; KRISHNA J M
	2023-10-16
专利权人	BHARATH HIGHER EDUCATION & RES INST (BHAR-Non-standard)
申请日期	2023-10-16
专利号	IN202341069608-A
成果简介	NOVELTY - The method involves using benchmark depression and non-depression datasets as well as well-defined discriminative depression-oriented feature groups, and apply Social Network Mental Disorder Detection on large-scale datasets and analyze the characteristics of the social network mental disorders types. USE - Multimodal depressive dictionary learning method for performing prediction of social media mental disorder, such as cyber relationship addiction, information overload, and net compulsion, among youngsters using machine learning. ADVANTAGE - The method ensuring accurately identify potential cases of social network mental disorders, thus ensuring to identify online social network users with potential social network mental disorders.
IPC 分类号	A61K-031/519 ; G06N-010/00 ; G06N-003/04 ; G06Q-050/00 ; H04L-067/55
国家	印度
专业领域	信息技术
语种	英语
成果类型	专利
文献类型	科技成果
条目标识符	http://119.78.100.226:8889/handle/3KE4DYBR/19841
专题	中国科学院新疆生态与地理研究所
作者单位	BHARATH HIGHER EDUCATION & RES INST (BHAR-Non-standard)
推荐引用方式 GB/T 7714	KALAISELVI B,UMAMAHESHWARI M,SUBBULAKSHM S,et al. Multimodal depressive dictionary learning method for performing prediction of social media mental disorder, such as cyber relationship addiction, involves using benchmark depression and non-depression datasets that applying Social Network Mental Disorder Detection on large-scale datasets. IN202341069608-A[P]. 2023.

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