PsyHeal: An Approach to Remote Mental Health Monitoring System

Patel, Vivek; Mishra, Piyush; Patni, Jagdish Chandra

doi:10.1109/icacce.2018.8441760

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…In this work, we used the pre-trained embedding of GloVe [8], [9] with a 300-dimensional, which is paired with an n-gram embedding. For the embedding of words within a vector space, the GloVe model is used in the examination of the Xij presence matrix in a large text block representing the user's reviews on drugs.…”

Section: Research Methods 21 Global Vectors For Word Representationmentioning

confidence: 99%

Sentiment classification of user's reviews on drugs based on global vectors for word representation and bidirectional long short-term memory recurrent neural network

Obayed

Al-Turaihi

Alhussayni

2021

IJEECS

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<p>The process of product development in the health sector, especially pharmaceuticals, goes through a series of precise procedures due to its directrelevance to human life. The opinion of patients or users of a particular drugcan be relied upon in this development process, as the patients convey their experience with the drugs through their opinion. The social media field provides many datasets related to drugs through knowing the user's ratingand opinion on a drug after using it. In this work, a dataset is used that includes the user’s rating and review on the drug, for the purpose of classifying the user’s opinions (reviews) whether they are positive ornegative. The proposed method in this article includes two phases. The first phase is to use the Global vectors for word representation model for converting texts into the vector of embedded words. As for the second stage, the deep neural network (Bidirectional longshort-termmemory) is employedin the classification of reviews. The user's rating is used as a ground truth inevaluating the classification results. The proposed method present sencouraging results, as the classification results are evaluated through threecriteria, namely Precision, Recall and F-score, whose obtained values equal(0.9543, 0.9597and0.9558), respectively. The classification results of theproposed method are compared to a number of classifiers, and it was noticed that the results of the proposed method exceed those of the alternative classifiers.</p>

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Section: Research Methods 21 Global Vectors For Word Representationmentioning

confidence: 99%

Sentiment classification of user's reviews on drugs based on global vectors for word representation and bidirectional long short-term memory recurrent neural network

Obayed

Al-Turaihi

Alhussayni

2021

IJEECS

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“…Technique Source The Greedy Reranking Algorithm [46] Anytime Algorithm [47] Prediction score for nearest-neighbour algorithms [48] Collaborative filtering [49], [50], [51] Learning algorithm [43] Joint modelling of multiple related problems [52] Data mining [24] Blockchain, Algorithm [53] Support Vector Machine (SVM), Multi-view Bi-Clustering, [23] Classification Linear Bayes Normal, Rule-Based Reasoning Big data and cloud architecture [22] API [54] Case based reasoning [55] Fuzzy thesauri [18] Decision trees, Rule based, and Semantic technologies [56] Machine learning algorithm, Data mining [17], [57] Neural networks [16] Fuzzy logic, Rule based [15], [20], [58], [40] Artificial Neural Network (ANN) [59] Big data [13] Forward-search backward-induction algorithm [11] Machine learning [26] Fuzzy logic, Collaborative filtering [31] Adaptive [30], [42], [60] Fuzzy cognitive maps [29] Algorithm [28], [61], [35], [41], [62] SQL query [63] Natural language processing [32] Ontology based [34], [36] Nearest neighbour [64] Content-b...…”

Section: Table IV Technique Applied In the Selected Papersmentioning

confidence: 99%

A Systematic Literature Review of an Advisory System

Taib,

Zainuddin,

Mokhtar

et al. 2024

IJCDS

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The growth of Artificial Intelligence (AI) driven technologies has been proposed as a means to improve the standard of people's lives. The advent of the advisory system has manifested itself as a significant element in Artificial Intelligence, effectively helping people in various fields. This research presents a systematic literature review of an advisory system. This research initially presents 472 articles by examining the literature between 2015 and 2023. After a meticulous review process, the studies were filtered down to 67 articles for full analysis. This review provides significant contributions to the exploration of advisory systems, specifically in the existing framework of advisory systems, the techniques applied in advisory systems, the domain in which the Artificial Intelligence technique is being applied in the advisory system, and the validation technique used in validating the advisory system. Ultimately, this review contributes to a deeper understanding of an advisory system's role in Artificial Intelligence and in various domains for optimising an advisory system application.

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