The study processed extensive acoustic datasets using algorithms such as Decision Trees, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forest. The research proved that Random Forest architectures yield the highest accuracy for mapping multi-source urban sounds (e.g., trains, traffic, birds, and airports), giving urban planners concrete data analytics tools to implement effective noise-mitigation strategies. 2. Published Literature
Looking further, I see a reference to Ali Othman Al-Baji in the context of a research paper on Yemen's higher education challenges. The paper might have been revised in 2023, so the "updated" part could refer to that. However, the user might not be aware that the paper isn't available in open access or that the title is slightly different.
Real-time urban noise classification & municipal soundscape mapping. SCADA Systems, IoT Sensors, WSN
: He serves as the Scientific Advisor and International AI Expert for the Libyan Authority for Scientific Research . ali othman albaji updated
Recognizing the limitations of global Large Language Models (LLMs) in capturing nuanced regional dialects and local operational contexts, Albaji founded . This initiative aims to adapt generative AI frameworks for local regional utility. Additionally, he developed Alexis , a specialized deep learning model designed for advanced pattern recognition and data synthesis. ICAILY and FICAILY Conferences
To insert North Africa into the global conversation on automation, Albaji founded and serves as the Chairperson of the . Its foundational iterations brought together tech professionals, cloud architects, and researchers from over 140 countries to cultivate tech literacy and infrastructure development in developing nations. Executive & Cloud Advisory Roles
As Advisor to the Chair, Albaji orchestrates nationwide AI capacity-building agendas. He actively consults on integrating automated systems into public infrastructure and sovereign education strategies. Published Literature Looking further, I see a reference
Dr. Ali Othman Albaji’s ongoing projects continue to merge modern Internet of Things (IoT) frameworks with Optical Wireless Communications and satellite systems. By embedding machine learning models straight into edge computing units and smart-city hardware, his updated research profile underscores a clear objective: transforming raw environmental data into actionable, automated solutions for safer, quieter, and highly connected urban spaces. Share public link
Through his dual roles in academia and industry, Dr. Ali Othman Al-Baji remains at the forefront of the AI revolution, specifically focusing on how these technologies can be responsibly deployed in emerging markets. Share public link
: He earned his bachelor's in Electrical Engineering from Civil Aviation Higher College in Tripoli (2007) and a Master's in Electronics and Telecommunication Engineering from Universiti Teknologi Malaysia (2022). improved Arabic tokenization
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From a technical standpoint, the LLM was trained on a compiled from public and open-licensed Arabic datasets, MSA corpora, academic and journalistic texts, Arabic Wikipedia, and high-quality Creative Commons resources. The development team employed a custom Arabic optimization pipeline that included dialect filtering, improved Arabic tokenization, multi-stage toxicity and bias filtering, and the generation of high-quality synthetic Arabic data to enhance the model's robustness.