Dehkharghani, Rahim

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Profile URL
https://hdl.handle.net/20.500.12469/6145
Name Variants
Dehkharghani, Rahim
Dehkharghani,Rahim
Rahim, Dehkharghani
Rahim Dehkharghani
Dehkharghani, RAHIM
D., Rahim
D.,Rahim
Dehkharghani, R.
Rahim DEHKHARGHANI
RAHIM DEHKHARGHANI
DEHKHARGHANI, RAHIM
R. Dehkharghani
Dehkharghani,R.
DEHKHARGHANI, Rahim
Job Title
Dr. Öğr. Üyesi
Email Address
[email protected]
Main Affiliation
Computer Engineering
Status
Current Staff
Website
ORCID ID
0000-0002-9619-8247
Scopus Author ID
24528505600
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
JXY-0317-2024

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GOOD HEALTH AND WELL-BEING
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16

PEACE, JUSTICE AND STRONG INSTITUTIONS
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1

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19

Citations

301

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9

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2

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Scholarly Output

6

Articles

4

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43/0

Supervised MSc Theses

1

Supervised PhD Theses

0

WoS Citation Count

11

Scopus Citation Count

24

WoS h-index

1

Scopus h-index

2

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0

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0

WoS Citations per Publication

1.83

Scopus Citations per Publication

4.00

Open Access Source

1

Supervised Theses

1

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JournalCount
Journal of Supercomputing2
8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024 - Proceedings -- 8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024 -- 6 December 2024 through 7 December 2024 -- Istanbul -- 2063121
Journal of Computational Social Science1
SN Computer Science1
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Now showing 1 - 6 of 6
  • Thumbnail Image
    Article
    Citation - WoS: 11
    Citation - Scopus: 17
    Mental Disorder and Suicidal Ideation Detection From Social Media Using Deep Neural Networks
    (Springernature, 2024) Ezerceli, Ozay; Dehkharghani, Rahim
    Depression and suicidal ideation are global reasons for life-threatening injury and death. Mental disorders have increased especially among young people in recent years, and early detection of those cases can prevent suicide attempts. Social media platforms provide users with an anonymous space to interact with others, making them a secure environment to discuss their mental disorders. This paper proposes a solution to detect depression/suicidal ideation using natural language processing and deep learning techniques. We used Transformers and a unique model to train the proposed model and applied it to three different datasets: SuicideDetection, CEASEv2.0, and SWMH. The proposed model is evaluated using the accuracy, precision, recall, and ROC curve. The proposed model outperforms the state-of-the-art in the SuicideDetection and CEASEv2.0 datasets, achieving F1 scores of 0.97 and 0.75, respectively. However, in the SWMH data set, the proposed model is 4% points behind the state-of-the-art precision providing the F1 score of 0.68. In the real world, this project could help psychologists in the early detection of depression and suicidal ideation for a more efficient treatment. The proposed model achieves state-of-the-art performance in two of the three datasets, so they could be used to develop a screening tool that could be used by mental health professionals or individuals to assess their own risk of suicide. This could lead to early intervention and treatment, which could save lives.
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    Article
    Exploring Isis’ Takfir Discourse: A BERT-Based Entity Level Sentiment Analysis Approach
    (Springer, 2025) Dehkharghani, R.; Aydin, M.N.; Yıldırım, Ş.
    The Islamic State of Iraq and Syria (ISIS) significantly influenced the lives of many people during and after the Syrian civil conflict, especially civilians. Analyzing social media discussions about ISIS can provide valuable insights into the group’s beliefs and attitudes. In this paper, we examine ISIS’s takfir discourse—their practice of labeling other Muslims as unbelievers to justify exclusion or violence—in Telegram groups. We collected 14,500 Telegram messages (2015–2017) using snowball sampling, API-based crawling, language filtering, and time-window selection. We then integrated a BERT-based Named Entity Recognition (NER) model with two layers of the Span ASTE (Aspect-Based Sentiment Analysis) model. We also used the Span ASTE as an end-to-end baseline for comparison. Based on Precision, Recall, and F1-scores, our hybrid model outperformed the baselines, demonstrating its effectiveness in sentiment analysis of extracted named entities. © 2025 Elsevier B.V., All rights reserved.
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    Master Thesis
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    Conference Object
    Multi-Task Learning on Mental Disorder Detection, Sentiment Analysis, and Emotion Detection Using Social Media Posts
    (Institute of Electrical and Electronics Engineers Inc., 2024) Armah, C.; Dehkharghani, R.
    Mental disorders such as suicidal behavior, bipolar disorder, depressive disorders, and anxiety have been diagnosed among the youth recently. Social media platforms such as Reddit have become popular for anonymous posts. People are far more likely to share on these social media platforms what they really feel like in their real lives when they are anonymous. It is thus helpful to extract people's sentiments and feelings from these platforms in training models for mental disorder detection. This study uses multi-task learning techniques to examine the estimation of behaviors and mental states for early mental disease diagnosis. We propose a multi-task system trained on three related tasks: mental disorder detection as the primary task, emotion analysis, and sentiment analysis as auxiliary tasks. We took the SWMH dataset, which included four main different mental disorders already labeled (bipolar, depression, anxiety, and suicide) and offmychest. We then added labels for emotion and sentiment to the dataset. The observed results are comparable to previous studies in the field and demonstrate that deep learning multi-task frameworks can improve the accuracy of related text classification tasks when compared to training them separately as single-task systems. © 2024 IEEE.
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    Article
    Sarcasm Detection on News Headlines Using Transformers
    (Springer, 2025) Gumuscekicci, Gizem; Dehkharghani, Rahim
    Sarcasm poses a linguistic challenge due to its figurative nature, where intended meaning contradicts literal interpretation. Sarcasm is prevalent in human communication, affecting interactions in literature, social media, news, e-commerce, etc. Identifying the true intent behind sarcasm is challenging but essential for applications in sentiment analysis. Detecting sarcasm in written text, as a challenging task, has attracted many researchers in recent years. This paper attempts to detect sarcasm in news headlines. Journalists prefer using sarcastic news headlines as they seem much more interesting to the readers. In the proposed methodology, we experimented with Transformers, namely the BERT model, and several Machine and Deep Learning models with different word and sentence embedding methods. The proposed approach inherently requires high-performance resources due to the use of large-scale pre-trained language models such as BERT. We also extended an existing news headlines dataset for sarcasm detection using augmentation techniques and annotating it with hand-crafted features. The proposed methodology could outperform almost all existing sarcasm detection approaches with a 98.86% F1-score when applied to the extended news headlines dataset, which we made publicly available on GitHub.
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    Article
    Citation - Scopus: 7
    MOBRO: multi-objective battle royale optimizer
    (Springer, 2024) Alp,S.; Dehkharghani,R.; Akan,T.; Bhuiyan,M.A.N.
    Battle Royale Optimizer (BRO) is a recently proposed optimization algorithm that has added a new category named game-based optimization algorithms to the existing categorization of optimization algorithms. Both continuous and binary versions of this algorithm have already been proposed. Generally, optimization problems can be divided into single-objective and multi-objective problems. Although BRO has successfully solved single-objective optimization problems, no multi-objective version has been proposed for it yet. This gap motivated us to design and implement the multi-objective version of BRO (MOBRO). Although there are some multi-objective optimization algorithms in the literature, according to the no-free-lunch theorem, no optimization algorithm can efficiently solve all optimization problems. We applied the proposed algorithm to four benchmark datasets: CEC 2009, CEC 2018, ZDT, and DTLZ. We measured the performance of MOBRO based on three aspects: convergence, spread, and distribution, using three performance criteria: inverted generational distance, maximum spread, and spacing. We also compared its obtained results with those of three state-of-the-art optimization algorithms: the multi-objective Gray Wolf optimization algorithm (MOGWO), the multi-objective particle swarm optimization algorithm (MOPSO), the multi-objective artificial vulture’s optimization algorithm (MOAVAO), the optimization algorithm for multi-objective problems (MAOA), and the multi-objective non-dominated sorting genetic algorithm III (NSGA-III). The obtained results approve that MOBRO outperforms the existing optimization algorithms in most of the benchmark suites and operates competitively with them in the others. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.