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- W3136996037 abstract "AbstractAs increasing lot of social network organization mental clutters (SNMDs), for example, Cyber-Relationship Addiction, Information Overload, and Net Compulsion. Signs of these intellectually disturbed are by and large observed idly these advanced days. Right now, utilizing the substance of information mining through Web administrations it is anything but difficult to recognize through SNMDS at beginning time. It is attempting to recognize SNMD considering the way that the mental variables considered in standard demonstrative criteria (survey) cannot be seen from social development logs. Our strategy, new and imaginative to the demonstration of snmd revelation, does not rely upon self-revealing of those mental components by methods for reviews. Or maybe, we propose an AI structure, in particular, Informal community Mental Disorder Detection (SNMDD), that adventures highlights extricated from informal community information to precisely distinguish potential cases of SNMDs. We also abuse multi-source learning and propose another SNMD-based Tensor Model (STM) to improve the show. Our structure is assessed by means of a client concentrate with 3126 informal community customers. We lead a component assessment, and moreover apply for huge extension datasets and separate the characteristics of the three SNMD types. The results show that SNMDD is promising for recognizing on the Web casual network customers with potential.KeywordsOnline social networkMental disorder detectionFeature extractionSocial network servicesTensor factorization acceleration" @default.
- W3136996037 created "2021-03-29" @default.
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- W3136996037 date "2021-01-01" @default.
- W3136996037 modified "2023-10-14" @default.
- W3136996037 title "Social Network Mental Disorders Detection Using Machine Learning" @default.
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- W3136996037 doi "https://doi.org/10.1007/978-981-15-8685-9_37" @default.
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