Innovative Research Projects in Signal Processing, Tourism, and Language Translation

This text introduces several cutting-edge research projects in different domains, including signal processing, tourism recommendation systems, price prediction, hotel rating anticipation, and natural language processing for translations. Each project aims to solve unique challenges using advanced techniques in machine learning and data analysis, showcasing the diverse applications of these fields.

• Research Projects
• Signal Processing
• Tourism Recommendation
• Price Prediction
• Hotel Rating Prediction

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1. 1stEURASIP-GAIPDM Seasonal School on Learning from Signals, Images, and Video HACKATHON-I Organizers: C. Kotropoulos M. Ntemi G. Karantaidis I. Sarridis E. Gionanidis

2. Tourism Recommendation Problem formulation: Recommend Places of Interest (POIs) using adaptive hypergraph learning. Input: POIs, which are represented by geo-clusters. Output: optimal ranking vector f. The sorted f contains the recommended POIs in descending order. Recall-Precision curve. Research ideas: Fine tuning of weight matrix W parameters. Fine tuning of incidence matrix H parameters. Combine W and H matrix updates. More ideas and detailed descriptions can be found in README.txt file.

3. PricePrediction Problem: Given the past airfare prices predict the next price Dataset: 27 routes. 1 route departure from New York, 26 from European cities 14 arrival to Athens, Greece, 13 arrival to Thessaloniki, Greece Input: " days_until_flight, departure, arrival, ticket_price " Output: airfare price prediction Approaches: Collaborative Kalman filter, Particle filter, LSTM NN, etc.

4. Hotel Rating Prediction Problem: anticipate the ratings that will be given to hotels from users with similar taste Dataset: 196,536 ratings from 170,187 users and 1,097 hotels in Santorini, Greece Input: "hotel_id, user_id, rating, date Output: Hotel rating Approaches: Matrix factorization techniques, Collaborative Kalman filter, Collaborative Deep Learning etc.

5. Natural Language Processing Problem: given a sentence (or text) in one language, find the translated one in another language. Dataset: pairs of sentences (192,881) in English and in German e.g., I am safe / ich bin sicher Input: the text in the input language Output: the translated text Approaches: Statistical Machine Translation, Neural Machine Learning, Rule-based and corpus-based machine translations