Machine Learning and Cloud-Based Knowledge Graphs to Recognize Suicidal Mental Tendencies

Abstract

To improve the quality of knowledge service selection in a cloud manufacturing environment, this paper proposes a cloud manufacturing knowledge service optimization decision method based on users’ psychological behavior. Based on the characteristic analysis of cloud manufacturing knowledge service, establish the optimal evaluation index system of cloud manufacturing knowledge service, use the rough set theory to assign initial weights to each evaluation index, and adjust the initial weights according to the user’s multiattribute preference to ensure that the consequences are allocated correctly. The system can help counselors acquire psychological knowledge in time and identify counselors with suicidal tendencies to prevent danger. This paper collected some psychological information data and built a knowledge graph by creating a dictionary and generating entities and relationships. The Han language processing word segmentation tool generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. This feature selection is a statistical premise test that is acceptable when the chi-square test results are distributed with the null hypothesis. It includes the Pearson chi-square test and its variations. The Chi-square test has several benefits, including its distributed processing resilience, ease of computation, broad information gained from the test, usage in research when statistical assumptions are not satisfied, and adaptability in organizing information from multiple or many more group investigations. For improving question and answer efficiency, compared with other models, the BiLSTM (bidirectional long short-term memory) model is preferred to build suicidal tendencies. The Han language processing is a method that is used for word segmentation, and the advantage of this method is that it plays a key role in the word segmentation tool and generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. Text classifier detects dangerous user utterances, question template matching, and answer generation by computing similarity scores. Finally, the system accuracy test is carried out, proving that the system can effectively answer the questions related to psychological counseling. The extensive experiments reveal that the method in this paper’s accuracy rate, recall rate, and F1 value is much superior to other standard models in detecting psychological issues.

Benefits and challenges of mobile banking in developing and developed countries

“…In one research (Pandey et al, 2022) it has been shown using advanced machine learning and Artificial Intelligence techniques, one can deter the intrusion threats. An effective Machine learning model can well simulate the customer behavior (Gunjan et al, 2022). The is sentimental issues can be a challenge especially when we are in a social media world (Gupta et al, 2022).…”

Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts

“…Specifically, in mental health, predictive models have successfully been applied [12,63]. Exemplary contributions include the detection of depression [29], suicidal mental tendencies [21], bipolar disorders [24] and other mental disorders [20]. Moreover, the models proposed by [38] can uncover patterns of anorexia in datasets obtained from social networks.…”

Retracted: Machine Learning and Cloud‐Based Knowledge Graphs to Recognize Suicidal Mental Tendencies

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”