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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Armin, A., Shiri, A., & Bahrak, B. (2022, December). Comparison of Machine Learning Methods for Cryptocurrency Price Prediction [Paper]. 8th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS 2022), Behshahr, Iran (6 pages). External link
Rayegan, A., Shiri, A., & Bahrak, B. (2022, December). A comparative study of machine learning techniques for stock price prediction [Paper]. 8th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS 2022), Behshahr, Iran (7 pages). External link
Shiri, A. (2025). Integrated Real-Time Decision-Making in Smart Urban Freight Logistics: Modular and Adaptive Reinforcement Learning Approach [Ph.D. thesis, Polytechnique Montréal]. Available
Shiri, A., Mayeli Feridani, M., & Keivanpour, S. (2025, July). A Hybrid Lightweight LLM Chatbot for Sustainable Cryptocurrency Investment Decisions: Optimizing Small Models for Domain-Specific Performance [Paper]. 49th IEEE Annual Computers, Software, and Applications Conference (COMPSAC 2025), Toronto, ON, Canada. External link
Shiri, A., Yarahmadi, A., & Keivanpour, S. (2025). Real-Time Matching and Dispatching for Urban Freight Transportation: A Hierarchical Reinforcement Learning Through Actor-Critic and H3 Spatial Partitioning. IEEE Transactions on Intelligent Transportation Systems, 1-12. External link
Shiri, A., Yarahmadi, A., Keivanpour, S., & Lamghari, A. (2024, October). Real-time RL-based Matching with H3 Geohash Partitioning in Smart Freight Platform [Paper]. IEEE 100th Vehicular Technology Conference (VTC2024-Fall), Washington, DC, USA. External link
Shiri, A., Keivanpour, S., Yarahmadi, A., & Lamghari, A. (2023, October). Learning-Based Matching Algorithm for Smart Freight Platform and Sustainability Assessment in Montreal [Paper]. 1st International Conference on Smart Mobility and Vehicle Electrification, Southfield, Michigan, USA. External link
