Search Results - (( developing between depression algorithm ) OR ( java implication based algorithm ))

Sentiment mining in twitter for early depression detection / Najihah Salsabila Ishak by Ishak, Najihah Salsabila

“…A classifier model is developed using Naive Bayes characteristics. A comparison between built-in Scikit Learn Naive Bayes algorithm, and the scratch Naive Bayes algorithm is used to measure its effectiveness in terms of accuracy. …”
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Identification of Freeform Depression Feature in a Part Using Vertex Attributes From Feature Volume / Pramod S Kataraki and Mohd Salman Abu Mansor by Kataraki, Pramod S, Abu Mansor, Mohd Salman

“…In this paper an effort is made to develop an algorithm that can recognize freeform depression feature of a part of any form by using vertex attributes of feature volume. …”
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Depression prediction system from Twitter’s tweet by using sentiment analysis / Nur Amalina Kamaruddin by Kamaruddin, Nur Amalina

“…The main function of this system is to classify tweet into “depressed” and “not depressed”. The classification model was built using Naïve Bayes algorithm. …”
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Depression Detection in Tweets from Urban Cities of Malaysia using Deep Learning by Priya Sri, E.K., Savita, K.S., Zaffar, M.

“…Therefore, the research entails on how the problem statement of this project on developing an algorithm that can predict text- based depression symptoms using deep learning and Natural Language Processing (NLP) can be achieved. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Development of an explainable machine learning model for predicting depression in adults with type 2 diabetes mellitus: a cross-sectional SHAP-based analysis of NHANES 2009-2023 by Tang, Yan, Jia, Lei, Zhou, Junjun, Dou, Jin, Qian, Jingjuan, Yi, Xin, Soh, Kim Lam

“…A streamlined version incorporating the top 10 predictors was further developed and implemented as a user-friendly web-based risk estimation tool. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

Identification of autism subtypes based on wavelet coherence of BOLD FMRI signals using convolutional neural network by Al-Hiyali, M.I., Yahya, N., Faye, I., Hussein, A.F.

“…The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. …”
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Identification of autism subtypes based on wavelet coherence of BOLD FMRI signals using convolutional neural network by Al-Hiyali, M.I., Yahya, N., Faye, I., Hussein, A.F.

“…The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. …”
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