This paper presents a comprehensive review of machine learning (ML) and deep learning (DL) models used for demand forecasting in supply chain management. By analyzing 119 papers from the Scopus database covering the period from 2015 to 2024, this study provides both macro- and micro-level insights into the effectiveness of AI-based methodologies. The macro-level analysis illustrates the overall trajectory and trends in ML and DL applications, while the micro-level analysis explores the specific distinctions and advantages of these models. This review aims to serve as a valuable resource for improving demand forecasting in supply chain management using ML and DL techniques.

Large Language Models (LLMs) such as GPT, BART, and Gemini stand at the forefront of Generative Artificial Intelligence, showcasing remarkable prowess in natural language comprehension and task execution. This paper proposes a novel framework developed on the foundation of Llama 2, aiming to bridge the gap between natural language queries and executable code for geospatial analyses within the PyQGIS environment. It empowers non-expert users to leverage GIS technology without requiring deep knowledge of geospatial programming or tools. Through cutting-edge Natural Language Processing (NLP) techniques, including tailored entity recognition and ontology mapping, the framework accurately interprets user intents and translates them into specific GIS operations. Integration of geospatial ontologies enriches semantic comprehension, ensuring precise alignment between user descriptions, geospatial datasets, and geospatial analysis tasks. A code generation module empowered by Llama 2 converts these interpretations into PyQGIS scripts, enabling the execution of geospatial analysis and results visualization. Rigorous testing across a spectrum of geospatial analysis tasks, with incremental complexity, evaluates the framework and the performance of such a system, with LLM at its core. The proposed system demonstrates proficiency in handling various geometries, spatial relationships, and attribute queries, enabling accurate and efficient analysis of spatial datasets. Moreover, it offers robust error-handling mechanisms and supports tasks related to map styling, visualization, and data manipulation. However, it has some limitations, such as occasional struggles with ambiguous attribute names and aliases, which leads to potential inaccuracies in the filtering and retrieval of features. Despite these limitations, the system presents a promising solution for applications integrating LLMs into GIS and offers a flexible and user-friendly approach to geospatial analysis.

The recent computerizations of cars, together with the development of sensor technologies and car communication devices have transformed the cars into wealthy sources of information. The analysis of data generated continuously by cars can contribute greatly in improving driving safety and drivers comfort. Even though different analytical solutions have emerged recently, there still exist some important issues in driving safety that we assume were poorly addressed, as well as diverse mathematical methodologies whose application in driving behavior analysis is to be investigated. In this paper, we developed a methodology to process and analyze car-generated data, with focus on two analysis goals: 1) automatic verification of drivers' behavior conformity to traffic rules; and 2) visualization and comparison of drivers' behaviors. The proposed methodology is divided into three steps. At first, the abstraction using numerical domains is used to reduce the size of the generated data. Then, the probabilistic graphical models (Probabilistic Automata, and Labeled Directed Graphs) combined with a machine-learning algorithm are used for building a formal model of the driver behavior. Finally, two indepth analyses are carried out by applying automatic model checking and graph matching techniques. Early experimental results point out that the design of numerical domains considered influences hugely the analysis results.

Map label placement is an important task in map production, which needs to be automated since it is tedious and requires a significant amount of manual work. In this paper, we identify five cartographic labeling situations that present challenges by causing intensive manual work in map production of city wayfinding maps, e.g., label placement in high density areas, utilizing true label geometries in automated methods, and creating a good relationship between text labels and icons. We evaluate these challenges in an open source map labeling tool (QGIS), provide results from a preliminary study, and discuss if there are other techniques that could be applicable to solving these challenges. These techniques are based on quantified cartographic rules or on machine learning. We focus on deep learning for which we provide several examples of techniques from other application domains that might have a potential in map label placement. The aim of the paper is to explore those techniques and to recommend future practical studies for each of the identified five challenges in map production. We believe that targeting the revealed challenges using the proposed solutions will significantly raise the automation level for producing city wayfinding maps, thus, having a real, measurable impact on production time and costs.