Deep Learning-Based Detection of Depression and Suicidal Tendencies in Social Media Data with Feature Selection

Behavioral Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 352 - 352

Published: March 12, 2025

Social media has become an essential platform for understanding human behavior, particularly in relation to mental health conditions such as depression and suicidal tendencies. Given the increasing reliance on digital communication, ability automatically detect individuals at risk through their social activity holds significant potential early intervention support. This study proposes a machine learning-based framework that integrates pre-trained language models advanced feature selection techniques improve detection of tendencies from data. We utilize six diverse datasets, collected platforms Twitter Reddit, ensuring broad evaluation model robustness. The proposed methodology incorporates Class-Weighted Iterative Neighborhood Component Analysis (CWINCA) Support Vector Machines (SVMs) classification. results indicate achieves high accuracy across multiple ranging 80.74% 99.96%, demonstrating its effectiveness identifying factors associated with issues. These findings highlight media-based automated methods complementary tools professionals. Future work will focus real-time capabilities multilingual adaptation enhance practical applicability approach.

Language: Английский

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Editorial: Neuroimaging in psychiatry 2023: schizophrenia

Frontiers in Psychiatry, Journal Year: 2025, Volume and Issue: 16

Published: April 21, 2025

Language: Английский

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Case Report: The intersection of psychiatry and medicine: diagnostic and ethical insights from case studies

Frontiers in Psychiatry, Journal Year: 2025, Volume and Issue: 16

Published: April 22, 2025

The intersection of psychiatry and medicine presents unique diagnostic ethical challenges, particularly for conditions involving significant brain-body interactions, such as psychosomatic, somatopsychic, complex systemic disorders. This article explores the historical contemporary issues in diagnosing conditions, emphasizing fragmentation medical psychiatric knowledge, biases clinical guidelines, mismanagement illnesses. Diagnostic errors often arise from insufficient integration between general psychiatry, compounded by reliance on population-based guidelines that neglect individual patient needs. Misclassification like myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), Lyme disease, fibromyalgia psychosomatic or psychogenic has led to stigmatization delayed care. While these are referenced emblematic examples misclassified poorly understood disorders, five cases discussed this do not directly illustrate diseases. Instead, they exemplify shared dilemmas at medicine-psychiatry interface, including uncertainty, fragmentation, risk epistemic injustice. critically examines terms medically unexplained symptoms functional highlighting their limitations potential misuse. Case underscore consequences inaccuracies urgent need improved approaches. Ethical considerations also explored, respecting experiences, promoting individualized care, acknowledging inherent uncertainties diagnosis. Advances technologies brain imaging molecular diagnostics offer hope bridging gap medicine, enabling more accurate assessments better outcomes. concludes advocating comprehensive training interface a patient-centered approach integrates observation, research insights, nuanced understanding mind-body dynamics.

Language: Английский

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The use of deep learning and artificial intelligence-based digital technologies in art education

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 6, 2025

In order to explore the application of deep learning (DL) and artificial intelligence (AI) technologies in art education, this work proposes optimizes an innovative creation system-Creative Intelligence Cloud (CIC). The system combines a generative adversarial network convolutional neural network, aiming enhance automation level, consistency artistic styles, efficiency creation. This first analyzes existing methods. It points out shortcomings traditional systems terms image quality, style transfer, computational performance, especially limitations real teaching scenarios. Therefore, designs model optimized by DL validates evaluates it through extensive experiments. experimental results show that CIC outperforms mainstream models multiple dimensions, including user experience, For example, quality evaluation, achieves high scores clarity (0.89), detail performance (0.85), (0.87), color accuracy (0.91). resource consumption, shows its superiority, with training time only 1500 s, memory consumption 4.9GB, Graphics Processing Unit usage rate 70%. Compared such as Visual Perception Generative Adversarial Network Artistic Recognition Transfer Style Convolutional Neural Network, is more efficient consumes fewer resources. Furthermore, CIC's experience transfer capability are significantly higher than those other models, providing smoother creatively rich tools for education. offers new ideas methods AI promotes practical use has certain academic contributions value.

Language: Английский

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The Impact of Artificial Intelligence on Neuroscience and Mental Health: A Perspective Review

AI in neuroscience., Journal Year: 2025, Volume and Issue: unknown

Published: May 26, 2025

Language: Английский

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