Mohammad Arif Payenda

• Payenda, Mohammad Arif; Wang, Shuaishuai; Prinz, Andreas; Hussiny, Mohammad Ali & Jiang, Zhiyu (2025). Predicting Mooring Tensions of a Floating Wind Turbine Using Motion Sensors and Neural Networks. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 3131. doi: 10.1088/1742-6596/3131/1/012032. Full text in Research Archive Show summary

  In floating wind turbines, mooring systems are vital for station-keeping and operational safety. Ensuring the safe and cost-effective maintenance of these systems is of great importance. In this research, we have investigated the performance of nacelle motion accelerometers in predicting mooring line tensions under various operational conditions using deep learning algorithms. An accelerometer with varying degrees of freedom (DOFs) is assumed to be placed inside the nacelle to measure platform vibrations. To predict dynamic mooring tensions, we propose a hybrid model (CNN-BiLSTM) that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks, leveraging the strengths of both algorithms to improve prediction accuracy. Our findings show that including the surge motion accelerometer has the greatest effect in predicting mooring tensions, with an R 2 value of 0.9941, compared to an R 2 of 0.9907 for the 6-DOF model without accelerometers, representing a 0.34% increase. However, the sway motion shows a 0.15% increase with an R 2 value of 0.9956, and the heave motion shows a 0.17% increase with an R 2 value of 0.9956, both relative to the surge motion accelerometer. The robustness of the model under varying dynamic conditions suggests its potential utility in other predictive maintenance tasks in the marine energy sector.

• Hussiny, Mohammad Ali; Payenda, Mohammad Arif & Øvrelid, Lilja (2024). PersianEmo: Enhancing Farsi-Dari Emotion Analysis with a Hybrid Transformer and Recurrent Neural Network Model. Proceedings of LREC. p. 257–263. Full text in Research Archive Show summary

  Emotion analysis is a critical research domain within the field of natural language processing (NLP). While substantial progress has been made in this area for the Persian language, there is still a need for more precise models and larger datasets specifically focusing on the Farsi and Dari dialects. In this research, we introduce “LearnArmanEmo” as a new dataset and a superior ensemble approach for Persian text emotion classification. Our proposed model, which combines XLM-RoBERTa-large and BiGRU, undergoes evaluation on LetHerLearn for the Dari dialect, ARMANEMO for the Farsi dialect, and LearnArmanEmo for both Dari and Farsi dialects. The empirical results substantiate the efficacy of our approach with the combined model demonstrating superior performance. Specifically, our model achieves an F1 score of 72.9% on LetHerLearn, an F1 score of 77.1% on ARMANEMO, and an F1 score of 78.8% on the LearnArmanEmo dataset, establishing it as a better ensemble model for these datasets. These findings underscore the potential of this hybrid model as a useful tool for enhancing the performance of emotion analysis in Persian language processing.

• Payenda, Mohammad Arif; Wang, Shuaishuai; Jiang, Zhiyu & Prinz, Andreas (2024). Prediction of mooring dynamics for a semi-submersible floating wind turbine with recurrent neural network models. Ocean Engineering. ISSN 0029-8018. 313(3). doi: 10.1016/j.oceaneng.2024.119490. Full text in Research Archive Show summary

  Offshore wind technology emerges as a promising solution to tap into wind resources in shallow and deep waters. For floating wind turbines, mooring systems provide station-keeping functionalities. Efficient monitoring of mooring loads is critical for safe and cost-effective operation and maintenance. As direct measurement of the dynamic mooring line tension is expensive, alternative means are needed. To this end, this article focuses on predicting mooring tensions based on accessible motion data of the floating platform and proposes utilizing deep learning algorithms, i.e., recurrent neural networks (RNNs). We selected three RNN algorithms: Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM). These algorithms were trained with various load cases of a 10-MW floating wind turbine and their efficacy was assessed using unseen data. Among the three algorithms, the BiLSTM algorithm performed best, achieving R2 of more than 0.97 under various load conditions. Further, a statistical assessment of the tension time series demonstrates an excellent agreement between the predicted and the measured mooring line tensions with a percentage difference in the peak tension of less than 0.5%. The outcomes of this paper contribute to real-time prediction and structural health monitoring of floating wind turbines.

• Payenda, Mohammad Arif; Vahidi, Abdul Razaq; Hussiny, Mohammad Ali; Prinz, Andreas & Øvrelid, Lilja (2024). PashtoEmo: Enhancing Text-Based Emotion Analysis in the Pashto Language Through Dataset Creation. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14763, p. 225–236. doi: 10.1007/978-3-031-70242-6_22. Full text in Research Archive Show summary

  This paper presents the comprehensive PashtoEmo dataset for emotion analysis in the Pashto language. PashtoEmo contains 8016 text instances from X social media platform covering several topics, including politics, women’s rights, social justice, culture, sports, and education. The texts are annotated with six emotional categories and an additional ‘Other’ class. We tested PashtoEmo for emotion analysis with several machine learning, deep learning, and transformer models. Even the best model, XLM-RoBERTa-large, achieved only a 76.85% F1 score and a 78.11% accuracy. This indicates that the data set must be extended with adequate resources in Pashto language text-based applications.

• Hussiny, Mohammad Ali; Payenda, Mohammad Arif & Vahidi, Abdul Razaq (2023). SentiDariPers: Sentiment Analysis of Dari-Persian Tweets Based on People’s Views and Opinion. In Valencia-Garcia, Rafael; Bucaram-Leverone, Martha; Cioppo-Morstadt, Javier Del; Vera-Lucio, Nestor & Centanaro-Quiroz, Pablo Humberto (Ed.), International Conference on Technologies and Innovation (CITI 2023). Springer. ISSN 9783031456817. p. 138–156. doi: 10.1007/978-3-031-45682-4_11. Full text in Research Archive Show summary

  In the research area of sentiment analysis, there is a noticeable gap when it comes to the Dari-Persian dialect. To bridge this gap, our research aimed to curate a comprehensive dataset encompassing people’s opinions in this specific language variant. This paper presents the development of a benchmark sentiment annotated dataset for the Dari dialect of Persian, which serves as an official language of Afghanistan. The dataset, named “SentiDariPers”, comprises 43,089 tweets posted between August 2021 and April 2023. It has been manually annotated with four sentiment classes: Negative, Positive, Neutral, and Mixed. We applied a range of models, such as Support Vector Machine (SVM), Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN). Additionally, we develop an ensemble model that combines different sets of sentiment classes for each system. We present a detailed comparative analysis of the results obtained from these models. Experimental findings demonstrate that the ensemble model achieves the highest accuracy 91%. We provide insights into the data collection and annotation process, offer relevant dataset statistics, discuss the experimental results, and provide further analysis of the data.

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