NUST Institutions Library Catalogue Search for 'kw,wrdl: (su-br:"Regression analysis")'

A practitioner's guide to resampling for data analysis, data mining, and modeling /

By Good, Phillip I.. Boca Raton, FL : CRC Press, 2012 . x, 214 p. : 25 cm.. 9781439855508

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Learning data analysis with Data Desk student version 5.0 /

By Velleman, Paul F.,. Reading, Mass. : Addison-Wesley, 1997 . xiv, 363 p. : 28 cm. +. 0201571153

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Statistics & probability & their applications /

By Brockett, Patrick.. Philadelphia : Saunders College Pub., 1984 . xvi, 576 p. : 24 cm.. 0030534062

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Biomedical Engineering (E-Book)

By Carlos Alexandre Barros de Mello. India In-Teh 2009 . x,658 P;

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Statistics for modern business decisions / MCSStudy guide

By Lapin, Lawrence L.. New York, 1978 Harcourt Brace . viii,318 ,p.; 0-15-583775-3 (hela verket) | 0-15-583776-1

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Numerical methods of statistics /

By Monahan, John F.. [S.l.] : Cambridge University Press, 2001 . 450 p. ; , Hardcover. 26 cm.. 0521791685 | 9780521791687

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Statistical methods for practice and research : a guide to data analysis using spss /

By Gaur, Sanjay S Gaur Ajai S.. [S.l.] : Sage Publications Pvt. Ltd, 2006 . 208 p. ; 25 cm.. 0761935029 (paperback) | 9780761935025 (paperback)

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Mathematical Models in Biology an introduction

UK Cambridge University press 2004 . 370 p. 0-521-61555-0

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Microarray data statistical analysis using R

Totowa, N.J. : Humana Press, 2007 . various pages 23 cm. 978-81-7319-850-2

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Mathematics for machine learning

By Deisenroth, Marc Peter,. UK Cambridge University Press 2020 . 371p 9781108679930

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Python data science handbook : essential tools for working with data /

By Vanderplas, Jacob T.,. Sebastopol, CA : O'Reilly Media, 2022 . xxiv, 563 pages : , Previous edition: 2016. 24 cm. 9781098121228 | 1098121228

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Algorithms in advanced artificial intelligence /

New Yprk CRC Press 2024 . xxiii,520 Pages.; 9781032867984

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Social Media Analytics for Mental Health Assessment /

By Liaquat, Umna . . 94p. , Social media has emerged as a tool for exploring the human psyche, offering exceptional access to real-time behavioral signals that are transforming the landscape of computational psychiatry. Among these, bipolar disorder is difficult to detect because of its volatility, requiring robust modeling of emotion, behavior, and timing. Prior studies have largely focused on text-based sentiment analysis and linguistic features to classify mental health conditions; however, these approaches often neglect non-verbal markers such as circadian rhythms and affective variability. Existing models primarily depend on static textual cues, limiting their ability to capture the dynamic, multimodal nature of psychiatric expression. This study addresses these limitations by integrating temporal rhythms, emotional dynamics, and behavioral signals extracted from Reddit user histories to develop predictive models of bipolar disorder and high-risk psychological states. We propose a series of interpretable multimodal architectures employing classical machine learning (Logistic regression, Random Forest, and XGBoost), deep sequence models (LSTM, GRU), and transformer-based frameworks (Roberta, GPT). Our approach incorporates temporal posting features, emotional entropy, and community-level interaction structures. Compared to benchmark studies, our models demonstrate significant improvements in both classification (F1 > 0.99) and regression (R² > 0.89), highlighting the predictive power of fused behavioral signals. This work advances the field by providing a scalable, languageindependent framework for the early detection of psychiatric risk. It also holds broader implications for public health by offering a foundation for real-time, ethically deployable digital mental health tools. 30cm.

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