This study provides a comprehensive analysis of the combination of Genetic Algorithms (GA) and XGBoost, a well-known machine-learning model. The primary emphasis lies in hyperparameter optimization for fraud detection in smart grid applications. The empirical findings demonstrate a noteworthy enhancement in the model’s performance metrics following optimization, particularly emphasizing a substantial increase in accuracy from 0.82 to 0.978. The precision, recall, and AUROC metrics demonstrate a clear improvement, indicating the effectiveness of optimizing the XGBoost model for fraud detection. The findings from our study significantly contribute to the expanding field of smart grid fraud detection. These results emphasize the potential uses of advanced metaheuristic algorithms to optimize complex machine-learning models. This work showcases significant progress in enhancing the accuracy and efficiency of fraud detection systems in smart grids.

Recently, we have observed a noticeable evolution and growing use and incorporation of chatbots on websites, mobile and social networking apps. A chatbot is a computer program that exhibits a capacity to converse quite naturally with users in a way that resembles a human dialogue. Examples of chatbots can be found in several areas, including education, commerce, and tourism. The use of Artificial Intelligence (AI) and its sub-fields, such as Machine Learning, Deep Learning, and Natural Language Processing (NLP), is increasing across all business sectors. One of the most advanced applications of this technology is the chatbot, which is particularly beneficial due to the quick response times and its simplicity. Nevertheless, although studies on chatbots exist in tourism, academic research covering their adoption, technological evolution, and impact on this sector is still relatively sparse. Therefore, this study aims to provide a comprehensive overview of chatbots and their effect on tourism. First, we provide a new classification of chatbots based on specific criteria. Second, we explore the conceptual architecture of chatbots and their key components. Third, this study aims to assess and contrast the main existing tools for developing chatbots, classifying them and highlighting their key advantages and disadvantages. Fourth, this study aims to examine the integration of chatbots in the tourism sector by identifying their key applications in the industry over the past decade. Additionally, it seeks to analyze the impact of chatbots on the various functionalities outlined in the 6A framework for tourism. To achieve this, a thorough search will be conducted using five prominent databases - Scopus, ACM, IEEE Xplore, Springer Link, and Web of Science - covering the period from 2013 to 2023. For this study, 1155 academic publications were reviewed after applying a systematic review protocol including purpose, research questions, keywords, digital libraries, search strings, and inclusion and exclusion criteria. Only 31 were identified to be primary studies.

Recommender systems have become fundamental computational tools deployed across diverse domains, including e-commerce, tourism, and streaming platforms to facilitate personalized content delivery through the analysis of user preferences, behavioral patterns, and interaction data. With the use of Machine Learning (ML) techniques, these Recommender Systems (RS) can be implemented either through established frameworks such as NReco and Apache Mahout, or through custom development. Despite the availability of multiple implementation approaches, RS development remains computationally complex, necessitating substantial expertise in both software engineering principles and artificial intelligence methodologies. To address these technical challenges, this paper presents a Domain-Specific Language (DSL) framework that streamlines and accelerates RS development for smart tourism applications through a Model-Driven Engineering (MDE) methodology. The proposed framework minimizes the requirement for extensive programming or ML expertise and enables developers to efficiently generate customized RS implementations while maintaining system quality. The DSL framework automates the generation of code that leverages the services provided by the Apache Mahout framework for implementing recommendation algorithms, abstracting the underlying complexity and enabling developers to focus on high-level system design rather than the technical details of algorithm implementation. In addition, the framework integrates a code generator and a modeling tool, allowing developers to design and implement recommendation systems with reduced complexity and enhanced productivity. Unlike generic or low-code platforms, this approach combines domain-specific abstractions with automation tools to address the unique challenges of the smart tourism sector, ensuring adaptability, efficiency, and high-quality results.

In the last few years, the evolution of new Information and Communication Technologies (ICT) and networks has enabled the appearance and development of several platforms and tools that serve to operate and distribute the learning content. In some particular domains, especially the scientific one, learners need to work on practical activities, using specific products and equipment to complete, consolidate, or verify their conceptual acquisitions. However, facing the increasing number of learners in Moroccan institutions, it becomes hard and expensive for developing countries, like Morocco, to ensure the appropriate conditions for each learner to perform such activities. The majority of the suggested platforms and tools cannot solve this issue, because of their inefficiency regarding offering students good interactive practical activities. Virtual Reality (VR) and the Internet of Things (IoT), as the two most incredible technologies of the last few decades, can be used as an alternative to create a virtual environment where the learner can carry out practical activities like in the real world. In such an environment, learners interact with both virtual and physical objects. In this research paper, we propose a new approach based on VR and IoT to enhance learning by providing learners with an educational space where they can perform some practical activities. The hybrid proposed approach has been used to create a virtual environment where learners (the final year of high school) can measure ultrasonic velocity in the air. The evaluation results show that the manipulation and coupling of real objects with virtual 3D objects increases in a striking way the learning outcomes of learners, as this allows them to feel linked to the real context.

Nowadays, the fast evolution of artificial intelligence (AI) technologies and the increasing amount of data are posing a challenge for the tourism sector, paving the way for the massive use of smart software applications; one of these applications is chatbots, which are computer programs designed to simulate conversation with human users using natural language. They are most beneficial due to the fast response times and facility-to-use. However, there has been a lack of academic research on the impact of chatbots on the tourism sector so far. and also, building chatbots is a tricky task for software developers, requiring a wide array of AI skills. Therefore, this work aims to explore the impact of AI-based chatbots on tourism through an in-depth analysis of the six As components for analyzing tourism destinations. Then, the tools for developing chatbots are examined.