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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.
Jalali Alenjareghi, M., Ghorbani, F., Keivanpour, S., Chinniah, Y. A., & Jocelyn, S. (2026). Proactive safety reasoning in human-robot collaboration in disassembly through LLM-augmented STPA and FMEA. Robotics and Computer-Integrated Manufacturing, 98, 103162 (25 pages). External link
Jalali Alenjareghi, M., Sekkay, F., Dadouchi, C., & Keivanpour, S. (2025). Wearable sensors in Industry 4.0: Preventing work-related musculoskeletal disorders. Sensors International, 7, 100343 (14 pages). External link
Jalali Alenjareghi, M., Keivanpour, S., & Chinniah, Y. A. (2025, September). AI-Driven Large Language Models for Real-Time Safety Assessment in Human-Cobot Disassembly [Paper]. International Conference on Computational Intelligence and Robotics (CIR 2025), Guangzhou, China. External link
Jalali Alenjareghi, M., Keivanpour, S., & Chinniah, Y. A. (2025, September). Computer Vision for Safe Human-Robot Collaboration in Disassembly: A Systematic Review and Conceptual Framework [Paper]. International Conference on Computational Intelligence and Robotics (CIR 2025), Guangzhou, China. External link
Jalali Alenjareghi, M., Keivanpour, S., Chinniah, Y. A., & Jocelyn, S. (2025, May). LLM-Driven FMEA for Safe Human-Robot Collaboration in Disassembly [Paper]. 5th International Conference on Human-Machine Systems (ICHMS 2025), Abu Dhabi, United Arab Emirates. External link
Mirzahosseininejad, M., Jalali Alenjareghi, M., Sekkay, F., & Keivanpour, S. (2025, May). WMSDsNet: A Deep Learning Framework for Real-Time Ergonomic Risk Prediction in Human-Robot Collaboration in Disassembly [Paper]. 5th International Conference on Human-Machine Systems (ICHMS 2025), Abu Dhabi, United Arab Emirates. External link
