7 sonuçlar
Arama Sonuçları
Listeleniyor 1 - 7 / 7
Yayın Hotel sales forecasting with LSTM and N-BEATS(IEEE, 2023-09-15) Özçelik, Şuayb Talha; Tek, Faik Boray; Şekerci, ErdalTime series forecasting aims to model the change in data points over time. It is applicable in many areas, such as energy consumption, solid waste generation, economic indicators (inflation, currency), global warming (heat, water level), and hotel sales forecasting. This paper focuses on hotel sales forecasting with machine learning and deep learning solutions. A simple forecast solution is to repeat the last observation (Naive method) or the average of the past observations (Average method). More sophisticated solutions have been developed over the years, such as machine learning methods that have linear (Linear Regression, ARIMA) and nonlinear (Polynomial Regression and Support Vector Regression) methods. Different kinds of neural networks are developed and used in time series forecasting problems, and two of the successful ones are Recurrent Neural Networks and N-BEATS. This paper presents a forecasting analysis of hotel sales from Türkiye and Cyprus. We showed that N-BEATS is a solid choice against LSTM, especially in long sequences. Moreover, N-BEATS has slightly better inference time results in long sequences, but LSTM is faster in short sequences.Yayın Forecasting and analysis of domestic solid waste generation in districts of istanbul with support vector regression(Institute of Electrical and Electronics Engineers Inc., 2020-10-12) Özçelik, Şuayb Talha; Tek, Faik BorayWaste planning is essential for large and developing cities such as Istanbul. In this report, we perform data analysis on "Waste Amount Based on District, Year and Waste Type"dataset shared by Istanbul Metropolitan Municipality. After analyzing the waste of the districts, we used support vector regression (SVR) to forecast the waste amounts for the coming years. The analysis has shown an overall increasing trend in the waste generation, although it dropped in 2019. The SVR predicts that the most waste generating district will be Küçükçekmece in the coming years.Yayın Application of ChatGPT in the tourism domain: potential structures and challenges(IEEE, 2023-12-23) Kılıçlıoğlu, Orkun Mehmet; Özçelik, Şuayb Talha; Yöndem, Meltem TurhanThe tourism industry stands out as a sector where effective customer communication significantly influences sales and customer satisfaction. The recent shift from traditional natural language processing methodologies to state-of-The-Art deep learning and transformer-based models has revolutionized the development of Conversational AI tools. These tools can provide comprehensive information about a company's product portfolio, enhancing customer engagement and decision-making. One potential Conversational AI application can be developed with ChatGPT. In this study, we explore the potential of using ChatGPT, a cutting-edge Conversational AI, in the context of Setur's products and services, focusing on two distinct scenarios: intention recognition and response generation. We incorporate Setur-specific data, including hotel information and annual catalogs. Our research aims to present potential structures and strategies for utilizing Language Model-based systems, particularly ChatGPT, in the tourism domain. We investigate the advantages and disadvantages of three different architectures and evaluate whether a restrictive or more independent model would be suitable for our application. Despite the impressive performance of Large Language Models (LLMs) in generating human-like dialogues, their end-To-end application faces limitations, such as system prompt constraints, fine-Tuning challenges, and model unavailability. Moreover, semantic search fails to deliver satisfactory performance when searching filters that require clear answers. To address these issues, we propose a hybrid approach that employs external interventions, the assignment of different GPT agents according to intent analysis, and traditional methods at specific junctures, which will facilitate the integration of domain knowledge into these systems.Yayın Leveraging transformer-based language models for enhanced service insight in tourism(IEEE, 2023-12-22) Er, Aleyna; Özçelik, Şuayb Talha; Yöndem, Meltem TurhanCustomer feedback is a valuable resource for enhancing customer experience and identifying areas that require improvement. Utilizing user insights allows a tourism company to identify and address problematic points in its service delivery, provide feedback to partner companies regarding their product offerings, and even reconsider agreements by incorporating these opinions when curating their product portfolio. Setur implemented a systematic approach to collecting customer feedback by distributing "after-stay surveys'' to its customers via email following the completion of the agency services provided. Guest answers to open-ended questions that gather opinions about travel experience are analyzed by four tasks: user intention for answering, the sentiment of the review, subjects touched upon, and whom it concerned. For these tasks, transformer-based natural language processing (NLP) models BERT, DistilBERT, RoBERTa, and Electra are fine-Tuned to classify customer reviews. Based on the test results, it is observed that best practices could be gathered using Bert. In addition, we showed that different insights can be obtained from text comments made for two hotels in Aydin, Turkiye. Some users made complaints using neutral sentences. In some cases, people gave high scores to the numerical rating questions, but their open-ended questions could have a negative meaning.Yayın Forecasting and analysis of energy consumption and waste generation in Antalya with SVR(IEEE, 2023-12-24) Özçelik, Şuayb Talha; Tek, Faik Boray; Şekerci, ErdalAntalya, a rapidly expanding coastal city in Türkiye, has experienced significant changes due to urbanization and increasing tourism activities. Comprehending tourism trends is crucial for the city's sustainable development and environmental management. Based on this perspective, this paper aims to present a comprehensive retrospective analysis of Antalya's energy consumption, domestic solid waste generation, wastewater generation, population growth, and tourist numbers over the years. Antalya faces significant challenges due to escalating trends in listed areas. Utilizing the Support Vector Regression, this study projects a need for an additional 1715 GWh of electricity production capacity, an expansion of wastewater capacity by 85639 thousand m3, and an increase in domestic solid waste disposal capacity by 597745 tons by 2028 to accommodate growing demands. We emphasize the importance of adopting effective policies and strategies to support energy efficiency, waste reduction, and wastewater management alongside sustainable urban planning and tourism management for Antalya's long-Term environmental sustainability and development. The findings presented in this study provide valuable insights for policymakers, urban planners, and stakeholders to make informed decisions, ensuring a balanced approach toward economic growth and environmental conservation.Yayın Transforming tourism experience: AI-based smart travel platform(Association for Computing Machinery, 2023) Yöndem, Meltem Turhan; Özçelik, Şuayb Talha; Caetano, Inés; Figueiredo, José; Alves, Patrícia; Marreiros, Goreti; Bahtiyar, Hüseyin; Yüksel, Eda; Perales, FernandoIn this paper, we propose the development of a novel personalized tourism platform incorporating artificial intelligence (AI) and augmented reality (AR) technologies to enhance the smart tourism experience. The platform utilizes various data sources, including travel history, user activity, and personality assessments, combined with machine learning algorithms to generate tailored travel recommendations for individual users. We implemented fundamental requirements for the platform: secure user identification using blockchain technology and provision of personalized services based on user interests and preferences. By addressing these requirements, the platform aims to increase tourist satisfaction and improve the efficiency of the tourism industry. In collaboration with various universities and companies, this multinational project aims to create a versatile platform that can seamlessly integrate new smart tourism units, providing an engaging, educational, and enjoyable experience for users.Yayın Integrating the focusing neuron model with N-BEATS and N-HiTS(Institute of Electrical and Electronics Engineers Inc., 2024) Özçelik, Şuayb Talha; Tek, Faik BorayThe N-BEATS (Neural Basis Expansion Analysis for Time Series) model is a robust deep learning architecture designed specifically for time series forecasting. Its foundational idea lies in the use of a generic, interpretable architecture that leverages backward and forward residual links to predict time series data effectively. N - BEATS influenced the development of N-HiTS (Neural Hierarchical Interpretable Time Series), which builds upon and extends the foundational ideas of N-BEATS. This paper introduces new integrations to enhance these models using the Focusing Neuron model in blocks of N-BEATS and N-HiTS instead of Fully Connected (Dense) Neurons. The integration aims to improve the forward and backward forecasting processes in the blocks by facilitating the learning of parametric local receptive fields. Preliminary results indicate that this new usage can significantly improve model performances on datasets that have longer sequences, providing a promising direction for future advancements in N-BEATS and N-HiTS.
