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All Journal International Journal of Electrical and Computer Engineering Applied Engineering and Technology
Khan, Shahnawaz
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Reddit social media text analysis for depression prediction: using logistic regression with enhanced term frequency-inverse document frequency features Ayyalasomayajula, Madan Mohan Tito; Agarwal, Akshay; Khan, Shahnawaz
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5998-6005

Abstract

Language provides significant insights into an individual’s emotional state, social status, and personality traits. This research aims to enhance depression detection through the analysis of linguistic features and various dataset attributes. The dataset, sourced from the social networking platform Reddit, comprises posts and comments from individuals diagnosed with depression. Logistic regression with term frequency-inverse document frequency (TF-IDF) is employed as the primary model for text classification. To improve model performance, a novel feature—the average time interval between consecutive posts or comments—is introduced, contributing to a marginal but noteworthy improvement in accuracy. The proposed model demonstrates superior F1 scores compared to other models applied to the same dataset. Given the increasing recognition of mental health’s significance, accurately diagnosing mental disorders is of paramount importance. This study underscores the potential of leveraging linguistic analysis and advanced machine learning techniques to identify depressive symptoms, thereby contributing to more effective mental health diagnostics and interventions.
Improving sustainability of precast concrete sandwich wall panels through stone waste aggregates and supplementary cementitious material Kumar, Pushpender; Kumar, Rajesh; Nighot, Nikhil Sanjay; Surabhi, Surabhi; Rahman, Mohd. Reyazur; Chidambaram, R. Siva; Khan, Shahnawaz
Applied Engineering and Technology Vol 3, No 2 (2024): August 2024
Publisher : ASCEE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/aet.v3i2.1399

Abstract

This study aims to enhance the sustainability of precast concrete sandwich wall panels by replacing 100% of natural aggregates with stone waste and 30% of cement with supplementary cementitious materials. The panels, consisting of two 60 mm thick concrete wythes reinforced with 1% steel fibers, were connected using basalt fiber-reinforced polymer (BFRP) connectors and separated by high-density expanded polystyrene (EPS) insulation (30 kg/m³). Full-scale panels were tested for flexural strength, showing that the inclusion of sustainable materials increased the failure load by 96% compared to conventional panels, with steel fiber-reinforced panels achieving a failure load of 110.5 kN. Panels incorporating stone waste aggregates demonstrated a 71% increase in strength compared to control samples. These results highlight that using stone waste and supplementary materials not only improves environmental sustainability but also enhances structural performance, making these panels a viable option for eco-friendly construction.
Co-Authors Agarwal, Akshay Ayyalasomayajula, Madan Mohan Tito Chidambaram, R. Siva Kumar, Pushpender Nighot, Nikhil Sanjay Rahman, Mohd. Reyazur Rajesh Kumar Surabhi, Surabhi