Large Language Models (LLMs) have advanced significantly in Natural Language Processing (NLP) over the past few years. Ongoing research continues exploring their capabilities in recommendation systems, aiming to enhance user-tailored content delivery efficiency, accuracy, and personalisation. The investigation introduces a novel approach to integration possibilities of open-source Language Model (LLM) technology—FLAN-T5, Falcon, Vicuna, UL2, and LLAMA—into anime recommendation systems. The research delves into creating personalised recommendations by inputting anime titles, genres, and descriptions into these LLMs. Furthermore, it harnesses LLMs to explain these recommendations, bolstering user engagement and amplifying transparency in the recommendation process. The findings clearly show that using open-source LLMs for anime recommendations works well. It proves that these techniques have great potential to make anime suggestions better.

Forecasting aviation demand is a significant challenge in the airline industry. The design of commercial aviation networks heavily relies on reliable travel demand predictions. It enables the aviation industry to plan ahead of time, evaluate whether an existing strategy needs to be revised, and prepare for new demands and challenges. This study examines recently published aviation demand studies and evaluates them in terms of the various forecasting techniques used, as well as the advantages and disadvantages of each. This study investigates numerous forecasting techniques for passenger demand, emphasizing the multiple factors that influence aviation demand. It examined the benefits and drawbacks of various models ranging from econometric to statistical, machine learning to deep neural networks, and the most recent hybrid models. This paper discusses multiple application areas where passenger demand forecasting is used effectively. In addition to the benefits, the challenges and potential future scope of passenger demand forecasting were discussed. This study will be helpful to future aviation researchers while also inspiring young researchers to pursue careers in this industry.