Early Detection of Multilingual Mental Health Depression Using Pretrained Transformers and Machine Learning Ali Sami Azeez, Osama Abduljaleel Ali, Nawar Abbood Fadhil, Dr. Ali Mohammed Sahan Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications, 2026 The social media is producing vast amounts of user-generated text, which can serve as a great indicator of initial mental health diagnosis. This paper develops a scalable, multilingual depression classifier based on classical machine learning (ML) methods and state-of-the-art, pretrained transformer-based models to overcome the weaknesses of language-specific and binary-only methods in previous studies. In a contrast to the majority of the studies, the work is a systematic exploration of bilingual and multilingual depression recognition in the context of Arabic, English, Russian, and Spanish data in a single pipeline. TF-IDF is used to represent textual information to conventional ML classifiers, such as SVM, Random Forest, Naive Bayes and AdaBoost, and transformers, such as XLM-RoBERTa and XLNet are used to train contextual semantic representations. Decades of experiments demonstrate that models using transformers always perform better in comparison to traditional models of machine learning. XLM-RoBERTa provided 94.33% accuracy, 0.94 F1-score, and 0.99 AUC, which outperforms SVM (93% accuracy) and means a lot in terms of preforming XLNet (72.36% accuracy). XLM-RoBERTa achieved 99.5% accuracy in Russian, 98% in English, 96% in Arabic, and 85.9% in Spanish in single-language tests, which shows that it is strong in various languages. The findings reveal the usefulness of pretrained multilingual transformers to identify subtle cases of depression, which offers a dependable, language-independent approach to screening early cases of digital depression in mental-health monitoring systems in the real world.
An Intelligent Iris Recognition Technique Salam Muhsin Arnoos, Ali Mohammed Sahan, Alla Hussein Omran Ansaf, Ali Sami Al-Itbi Lecture Notes in Networks and Systems, 2023
Detection of Hate Speech on Twitter for Arabic Iraqi Dialect using Stochastic Gradient Classifier Ahmed Bahaaulddin, Vian Sabeeh, Ali Sami Azeez 4th International Conference on Current Research in Engineering and Science Applications Iccresa 2022, 2022 People increasingly use social media platforms to communicate and share information worldwide. However, challenges such as verbal misbehaviors and hate messages have been raised and widely disseminated via social media. In the Arabic region, especially in Iraq, Twitter is one of the most prominent social media platforms that has gained popularity. Consequently, an ethical reason behind this research is to build a system that can distinguish Iraqi hate tweets. This work achieved three contributions. First, a dataset of Iraqi tweets has been collected and annotated; this is the first dataset for hate speech in the Iraqi dialect. Second, an algorithm for distinguishing Arabic tweets from other oriental tweets that use the same Arabic glyph was proposed to represent a new addition to the preprocessing steps for the Arabic NLP area. This algorithm was tested and showed efficiency in detecting Urdu about (0.91), Persian (0.73), and Arabic (0.98). Third, the Iraqi hate speech classifier was built using two types of text features depending on the stochastic gradient classifier. The classifier was compared with various machine learning algorithms like SVM logistic regression. The proposed system with stochastic gradient classifier was more efficient than other classifiers achieving precision and recall of 0.80.
X-ray covid-19 detection based on scatterwavelet transform and dense deep neural network Ali Sami Al-Itbi, Ahmed Bahaaulddin A. Alwahhab, Ali Mohammed Sahan Computer Systems Science and Engineering, 2022 Notwithstanding the discovery of vaccines for Covid-19, the virus's rapid spread continues due to the limited availability of vaccines, especially in poor and emerging countries. Therefore, the key issues in the present COVID-19 pandemic are the early identification of COVID-19, the cautious separation of infected cases at the lowest cost and curing the disease in the early stages. For that reason, the methodology adopted for this study is imaging tools, particularly computed tomography, which have been critical in diagnosing and treating the disease. A new method for detecting Covid-19 in X-rays and CT images has been presented based on the Scatter Wavelet Transform and Dense Deep Neural Network. The Scatter Wavelet Transform has been employed as a feature extractor, while the Dense Deep Neural Network is utilized as a binary classifier. An extensive experiment was carried out to evaluate the accuracy of the proposed method over three datasets: IEEE 80200, Kaggle, and Covid-19 X-ray image data Sets. The dataset used in the experimental part consists of 14142. The numbers of training and testing images are 8290 and 2810, respectively. The analysis of the result refers that the proposed methods achieved high accuracy of 98%. The proposed model results show an excellent outcome compared to other methods in the same domain, such as (DeTraC) CNN, which achieved only 93.1%, CNN, which achieved 94%, and stacked Multi-Resolution CovXNet, which achieved 97.4%. The accuracy of CapsNet reached 97.24%.
An intelligent ear recognition technique Yahya Hussein, ALI Sahan International Journal of Advances in Soft Computing and Its Applications, 2021 The human ear has unique and attractive details; therefore, human ear recognition is one of the most important fields in the biometric domains. In this work, we proposed an efficient and intelligent ear recognition technique based on particle swarm optimization, discrete wavelet transform, and fuzzy neural network. Discrete wavelet transform is used to provide comprise and effective features about the ear image, while the particle swarm optimization utilized to select more effective and attractive features. Furthermore, using particle swarm optimization leads to reduce the complexity of the classification stage since it reduces the number of the features. Fuzzy neural network used in the classification stage in order to provide strong distinguishing between the testing and training ear images. many experiments performed using two ear databases to examine the accuracy of the proposed technique. The analysis of the results refers that the presented technique gained high recognition accuracy using various data sets with less complexity. Keywords: Ear recognition; bio-metric; discrete wavelet transform, particle swarm optimization, fuzzy neural network.
COVID-19 detection based on deep learning and artificial bee colony Ali Mohammed Sahan, Ali Sami Al-Itbi, Jawad Sami Hameed Periodicals of Engineering and Natural Sciences, 2021 COVID-19 has become a great challenge to the whole world, as it has infected and killed millions of people and affected the different fields of our life due to its rapid ability to spread. In this paper, the COVID-19 patient's recognition technique utilized the deep learning, and An artificial bee colony is intended to be applied. Deep learning was implemented to provides the features from X-ray images, while the artificial bee colony algorithm used to refine these features by selecting the best features. The multilayer perceptron classifier has been utilized in the classification stage. The experiments carried out on the standard dataset with/without different other daises such as MERS, SARS, and ARDS as well as COVID-19(+) referred that the proposed work provided high recognition rates with high reduction in the number of deep learning features.
Rotation invariant face recognition using jacobi –fourier moments Journal of Theoretical and Applied Information Technology, 2019
RECENT SCHOLAR PUBLICATIONS
Rotation Invariant Technique for Sign Language Recognition MTD Al-Obaidi, AM Sahan, AS Al-Itbi InfoTech Spectrum: Iraqi Journal of Data Science 1 (1), 16-27 , 2024 2024.0 Citations: 6
Detection of Hate Speech on Twitter for Arabic Iraqi Dialect Using Stochastic Gradient Classifier A Bahaaulddin, V Sabeeh, AS Azeez 2022 4th International Conference on Current Research in Engineering and … , 2022 2022.0 Citations: 1
An intelligent iris recognition technique SM Arnoos, AM Sahan, AHO Ansaf, AS Al-Itbi Next Generation of Internet of Things: Proceedings of ICNGIoT 2022, 207-217 , 2022 2022.0 Citations: 1
Human identification using finger knuckle features. AM Sahan, NAA Jabr, ASA Al-Itbi International Journal of Advances in Soft Computing & Its Applications 14 (1) , 2022 2022.0 Citations: 1
X-Ray Covid-19 Detection Based on Scatter Wavelet Transform and Dense Deep Neural Network AMS Ali Sami Al-Itbi, Ahmed Bahaaulddin A. Alwahhab Computer Systems Science and Engineering 41 (3), 1255–1271 , 2022 2022.0 Citations: 8
An Intelligent Ear Recognition Technique. YA Hussein, ALI Sahan, ASA Al-Itbi International Journal of Advances in Soft Computing & Its Applications 13 (3) , 2021 2021.0 Citations: 1
COVID-19 detection based on deep learning and artificial bee colony AM Sahan, AS Al-Itbi, JS Hameed Periodicals of Engineering and Natural Sciences 9 (1), 29-36 , 2021 2021.0 Citations: 6
The fusion of local and global descriptors in face recognition application AM Sahan, AS Al-Itbi International Conference on Advanced Communication and Computational … , 2019 2019.0 Citations: 5
Rotation invariant face recognition using jacobi –fourier moments AM Sahan, AS Azeez, MF Ibrahim Journal of Theoretical and Applied Information Technology 97 (5), 1444-1456 , 2019 2019.0
PROPOSED AN ARCHITECTURE FOR BOTTELNECK NETWORK MA Jassim, AS Al-Itbi Journal of the College of Basic Education 24 (100), 109-120 , 2018 2018.0
Arabic (Indian) Numeral Handwritten Recognition Using Angular Radial Transform AS Azeez, AM Sahan Diyala Journal for Pure Sciences 13 (2), 48-64 , 2017 2017.0
Rotation Invariant Face Recognition Using Radial Harmonic Fourier Moments AS Azeez Journal of The College of Basic Education 23 (99), 87-98 , 2017 2017.0 Citations: 2
Public Auditing In Secure Cloud Storage AS Azeez International Journal of Computer Engineering & Technology 5 (3) , 2014 2014.0 Citations: 2
Early Detection of Multilingual Mental Health Depression Using Pretrained Transformers and Machine Learning AS Azeez, OA Ali, NA Fadhil, AM Sahan
MOST CITED SCHOLAR PUBLICATIONS
X-Ray Covid-19 Detection Based on Scatter Wavelet Transform and Dense Deep Neural Network AMS Ali Sami Al-Itbi, Ahmed Bahaaulddin A. Alwahhab Computer Systems Science and Engineering 41 (3), 1255–1271 , 2022 2022.0 Citations: 8
Rotation Invariant Technique for Sign Language Recognition MTD Al-Obaidi, AM Sahan, AS Al-Itbi InfoTech Spectrum: Iraqi Journal of Data Science 1 (1), 16-27 , 2024 2024.0 Citations: 6
COVID-19 detection based on deep learning and artificial bee colony AM Sahan, AS Al-Itbi, JS Hameed Periodicals of Engineering and Natural Sciences 9 (1), 29-36 , 2021 2021.0 Citations: 6
The fusion of local and global descriptors in face recognition application AM Sahan, AS Al-Itbi International Conference on Advanced Communication and Computational … , 2019 2019.0 Citations: 5
Rotation Invariant Face Recognition Using Radial Harmonic Fourier Moments AS Azeez Journal of The College of Basic Education 23 (99), 87-98 , 2017 2017.0 Citations: 2
Public Auditing In Secure Cloud Storage AS Azeez International Journal of Computer Engineering & Technology 5 (3) , 2014 2014.0 Citations: 2
Detection of Hate Speech on Twitter for Arabic Iraqi Dialect Using Stochastic Gradient Classifier A Bahaaulddin, V Sabeeh, AS Azeez 2022 4th International Conference on Current Research in Engineering and … , 2022 2022.0 Citations: 1
An intelligent iris recognition technique SM Arnoos, AM Sahan, AHO Ansaf, AS Al-Itbi Next Generation of Internet of Things: Proceedings of ICNGIoT 2022, 207-217 , 2022 2022.0 Citations: 1
Human identification using finger knuckle features. AM Sahan, NAA Jabr, ASA Al-Itbi International Journal of Advances in Soft Computing & Its Applications 14 (1) , 2022 2022.0 Citations: 1
An Intelligent Ear Recognition Technique. YA Hussein, ALI Sahan, ASA Al-Itbi International Journal of Advances in Soft Computing & Its Applications 13 (3) , 2021 2021.0 Citations: 1
Rotation invariant face recognition using jacobi –fourier moments AM Sahan, AS Azeez, MF Ibrahim Journal of Theoretical and Applied Information Technology 97 (5), 1444-1456 , 2019 2019.0
PROPOSED AN ARCHITECTURE FOR BOTTELNECK NETWORK MA Jassim, AS Al-Itbi Journal of the College of Basic Education 24 (100), 109-120 , 2018 2018.0
Arabic (Indian) Numeral Handwritten Recognition Using Angular Radial Transform AS Azeez, AM Sahan Diyala Journal for Pure Sciences 13 (2), 48-64 , 2017 2017.0
Early Detection of Multilingual Mental Health Depression Using Pretrained Transformers and Machine Learning AS Azeez, OA Ali, NA Fadhil, AM Sahan
