Identifying Key Features for Establishing Sustainable Agro-Tourism Centre: A Data Driven Approach

Agro-tourism serves as a strategic economic model designed to facilitate rural development by diversifying income streams for local communities like farmers while promoting the conservation of indigenous cultural heritage and traditional agricultural practices. As a very booming subdomain of tourism, there is a need to study the strategies for the growth of Agro-tourism in detail. The current study has identified the important indicators for the growth and enhancement of agro-tourism. The study is conducted in two phases: identification of the important indicators through a comprehensive literature review and in the second phase state-of-the-art techniques were used to identify the important indicators for the growth of agro-tourism. The indicators are also called features synonymously, the machine learning models for feature selection were applied and it was observed that the Least Absolute Shrinkage and Selection Operator (LASSO) method combined with, the machine Learning Classifiers such as Logistic Regression (LR), Decision Trees (DT), Random Forest (RF) Tree, and Extreme Gradient Boosting (XGBOOST) models were used to suggest the growth of the agro-tourism. The results show that with the LASSO method, LR model gives the highest classification accuracy of 98% in 70-30% train-test data followed by RF with 95% accuracy. Similarly, in the 80-20% train-test data LR maintains the highest accuracy at 99%, while DT and XGBoost follow with 97% accuracy.

Information availability in different languages and various technological constraints related to multilinguism on the Internet

The usage of Internet has grown exponentially over the last two decades. The number of Internet users has grown from 16 Million to 1650 Million from 1995 to 2010. It has become a major repository of information catering almost every area. Since the Internet has its origin in USA which is English speaking country there is huge dominance of English on the World Wide Web. Although English is a globally acceptable language, still there is a huge population in the world which is not able to access the Internet due to language constraints. It has been estimated that only 20-25% of the world population speaks English as a native language. More and more people are accessing the Internet nowadays removing the cultural and linguistic barriers and hence there is a high growth in the number of non-English speaking users over the last few years on the Internet. Although many solutions have been provided to remove the linguistic barriers, still there is a huge gap to be filled. This paper attempts to analyze the need of information availability in different languages and the various technological constraints related to multi-linguism on the Internet.

Building a Llama2-finetuned LLM for Odia Language Utilizing Domain Knowledge Instruction Set

Building LLMs for languages other than English is in great demand due to the unavailability and performance of multilingual LLMs, such as understanding the local context. The problem is critical for low-resource languages due to the need for instruction sets. In a multilingual country like India, there is a need for LLMs supporting Indic languages to provide generative AI and LLM-based technologies and services to its citizens. This paper presents our approach of i) generating a large Odia instruction set, including domain knowledge data suitable for LLM fine-tuning, and ii) building a Llama2-finetuned model tailored for enhanced performance in the Odia domain. The proposed work will help researchers build an instruction set and LLM, particularly for Indic languages. We will release the model and instruction set for the public for research and noncommercial purposes.