I am a Computer Science (Cyber Security) student at Modern College of Business and Science (MCBS) in Muscat, Oman, currently working on my capstone project titled "FakeOut" — an AI-based forgery detection system for Commercial Registration (CR) documents in Oman. CR certificate fraud is a growing problem in Oman, with the Royal Oman Police recording 5,311 fraud cases in 2024. Scammers forge CR documents to impersonate legitimate businesses, particularly on social media platforms, to deceive buyers. To train and evaluate my AI model, I am requesting a dataset of Commercial Registration certificate records issued through the Oman Business Platform. This data will be used strictly for academic research under the supervision of MCBS faculty and will not be shared or used commercially
This dataset is requested for academic research purposes for a Master’s dissertation on engineering an Agentic AI and Explainable AI (XAI) decision-support system. The objective of the research is to model how academic milestones, regional demographics, and specific non-academic student behavioural metrics align with higher education specialisation matching and ultimate employment success across different sectors of the economy. To achieve this, the requested dataset should contain entirely anonymised historical student records with the following detailed fields: 1. Demographics & Foundation: - City/Region of origin. - High school graduation track and academic grades. - Academic Year and foundational college/university semester details. 2. Academic Progression & Setbacks: - Foundational and core semester GPAs. - Specific metrics on academic setbacks, including module failure rates, failed modules, or retaken modules. 3. Student Interests & Activities: - Documented extracurricular profiles, hobbies, technical club memberships, or structured activities (e.g., strategic gaming, team sports like football, hardware prototyping). This data will be used to analyse how non-cognitive behavioural traits correlate with or support technical engineering specialisations. 4. Academic Pathway: - Enrolled in an engineering or technological specialisation/major (e.g., Aeronautical, Civil, Marine, Systems Engineering). 5. Program Completion Status: - A clear graduation indicator showing whether the student successfully completed their study or withdrew. 6. Employment Outcomes: - Post-graduation employment status indicating whether they found a job or remain unemployed. - Ultimate destination sector categorised precisely by: Government Sector, Private Sector, or Community work. Additional Technical Requirements for AI Modelling: * Time-to-Employment: Where applicable, the approximate duration (in months) between graduation and securing first employment. * Standardised Labour Classifications: Alignment with the official Ministry of Labour (MOL) frameworks for economic sectors and skill levels to allow for automated cross-referencing. * Format: Provided in a structured, machine-readable format (e.g., CSV or XLSX) to facilitate programmatic analysis via Python/Pandas. Note on Privacy: To comply with national data privacy standards, all data must be fully anonymised and contain no Personally Identifiable Information (PII), such as names, civil IDs, or phone numbers.
Require dataset regarding energy consumed in residential homes across oman overall and also appliance based
Law firms registered in the Sultanate
Request for prayer times and Adhan data in the Sultanate of Oman, in addition to the Omani Hijri calendar.
List of Scheduled Transport Operators
I am a PhD researcher and I need statistics on the number of employees working in the National Artificial Intelligence Program.
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