Dehkharghani, Rahim

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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
rahim.dehkharghani@khas.edu.tr
Main Affiliation
Computer Engineering
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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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    Article
    Citation - Scopus: 2
    MOBRO: multi-objective battle royale optimizer
    (Springer, 2024) Dehkharghani, Rahim; Dehkharghani,R.; Akan,T.; Bhuiyan,M.A.N.; Computer Engineering
    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.
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    Conference Object
    Citation - Scopus: 0
    Multi-Task Learning on Mental Disorder Detection, Sentiment Analysis, and Emotion Detection Using Social Media Posts
    (Institute of Electrical and Electronics Engineers Inc., 2024) Dehkharghani, Rahim; Dehkharghani, R.; Computer Engineering
    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
    Citation - WoS: 4
    Citation - Scopus: 5
    Mental Disorder and Suicidal Ideation Detection From Social Media Using Deep Neural Networks
    (Springernature, 2024) Ezerceli, Ozay; Dehkharghani, Rahim; Dehkharghani, Rahim; Computer Engineering
    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.