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Approximately 186 thousand pairs of images generated by Midjourney and prompts used to generated these images. Dataset for image to text transformation. The size of images 224 x 224. Prompts contain only text without links on base image/images.

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The dataset includes images representing three different types of brain-related conditions: brain glioma, brain meningioma, and brain tumor. Brain glioma refers to a type of tumor that originates in the glial cells of the brain, which provide support and protection to neurons. Gliomas are often aggressive and can occur in various parts of the brain, leading to symptoms like headaches, seizures, and cognitive impairments. Brain meningioma is a tumor that develops from the meninges, the layers of tissue covering the brain and spinal cord. Meningiomas are typically benign but can cause problems depending on their size and location, leading to symptoms such as vision problems, headaches, and neurological deficits. Lastly, the term brain tumor is a broad classification that includes various types of tumors that can develop in the brain, including both malignant and benign forms. Brain tumors can be primary (originating in the brain) or secondary (spreading from other parts of the body), and they can result in diverse symptoms based on their type and location, such as motor dysfunction, memory issues, or seizures.

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Context Airline data holds immense importance as it offers insights into the functioning and efficiency of the aviation industry. It provides valuable information about flight routes, schedules, passenger demographics, and preferences, which airlines can leverage to optimize their operations and enhance customer experiences. By analyzing data on delays, cancellations, and on-time performance, airlines can identify trends and implement strategies to improve punctuality and mitigate disruptions. Moreover, regulatory bodies and policymakers rely on this data to ensure safety standards, enforce regulations, and make informed decisions regarding aviation policies. Researchers and analysts use airline data to study market trends, assess environmental impacts, and develop strategies for sustainable growth within the industry. In essence, airline data serves as a foundation for informed decision-making, operational efficiency, and the overall advancement of the aviation sector. Content This dataset comprises diverse parameters relating to airline operations on a global scale. The dataset prominently incorporates fields such as Passenger ID, First Name, Last Name, Gender, Age, Nationality, Airport Name, Airport Country Code, Country Name, Airport Continent, Continents, Departure Date, Arrival Airport, Pilot Name, and Flight Status. These columns collectively provide comprehensive insights into passenger demographics, travel details, flight routes, crew information, and flight statuses. Researchers and industry experts can leverage this dataset to analyze trends in passenger behavior, optimize travel experiences, evaluate pilot performance, and enhance overall flight operations. Dataset Glossary (Column-wise) Passenger ID - Unique identifier for each passenger First Name - First name of the passenger Last Name - Last name of the passenger Gender - Gender of the passenger Age - Age of the passenger Nationality - Nationality of the passenger Airport Name - Name of the airport where the passenger boarded Airport Country Code - Country code of the airport's location Country Name - Name of the country the airport is located in Airport Continent - Continent where the airport is situated Continents - Continents involved in the flight route Departure Date - Date when the flight departed Arrival Airport - Destination airport of the flight Pilot Name - Name of the pilot operating the flight Flight Status - Current status of the flight (e.g., on-time, delayed, canceled)