The used car market has a high global economic importance, with more than 35 million cars sold yearly. Accurately predicting prices is a crucial task for both buyers and sellers to facilitate informed decisions in terms of opportunities or potential problems. Although various machine learning techniques have been applied to create robust prediction models, a comprehensive approach has yet to be studied. This research introduced two datasets from different markets, one with over 300,000 entries from Germany to serve as a training basis for deep prediction models and a second dataset from Romania containing more than 15,000 car quotes used mainly to observe local traits. As such, we included extensive cross-market analyses by comparing the emerging Romanian market versus one of the world’s largest and most developed car markets, Germany. Our study used several neural network architectures that captured complex relationships between car model features, individual add-ons, and visual features to predict used car prices accurately. Our models achieved a high R² score exceeding 0.95 on both datasets, indicating their effectiveness in estimating used car prices. Moreover, we experimented with advanced convolutional architectures to predict car prices based solely on visual features extracted from car images. This approach exhibited transfer-learning capabilities, leading to improved prediction accuracy, especially since the Romanian training dataset was limited. Our experiments highlighted the most important factors influencing the price, while our findings have practical implications for buyers and sellers in assessing the value of vehicles. At the same time, the insights gained from this study enable informed decision making and provide valuable guidance in the used car market.

Email classification systems are essential for handling and organizing the massive flow of communication, especially in a business context. Although many solutions exist, the lack of standardized classification categories limits their applicability. Furthermore, the lack of Romanian language business-oriented public datasets makes the development of such solutions difficult. To this end, we introduce a versatile automated email classification system based on a novel public dataset of 1447 manually annotated Romanian business-oriented emails. Our corpus is annotated with 5 token-related labels, as well as 5 sequence-related classes. We establish a strong baseline using pre-trained Transformer models for token classification and multi-task classification, achieving an F1-score of 0.752 and 0.764, respectively. We publicly release our code together with the dataset of labeled emails.

The used car market has a high global economic importance, with more than 35 million cars sold yearly. Accurately predicting prices is a crucial task for both buyers and sellers to facilitate informed decisions in terms of opportunities or potential problems. Although various Machine Learning techniques have been applied to create robust prediction models, a comprehensive approach has yet to be studied. This research introduces two datasets from different markets, one with over 300,000 entries from Germany to serve as a training base for deep prediction models and a second dataset from Romania containing more than 15,000 car quotes used mainly to observe local traits. As such, we include extensive cross-market analyses by comparing the emerging Romanian market versus one of the world’s largest and most developed car markets, Germany. Our study uses several neural network architectures that capture complex relationships between car model features, individual add-ons, and visual features to predict used car prices accurately. Our models achieved a high R2 score exceeding 0.95 on both datasets, indicating their effectiveness in estimating used car prices. Moreover, we experimented with advanced convolutional architectures to predict car prices based solely on visual features extracted from car images. This approach exhibited transfer-learning capabilities, leading to improved prediction accuracy, especially since the Romanian training dataset is limited. Our experiments highlight the most important factors influencing the price, while our findings have practical implications for buyers and sellers in assessing the value of vehicles. At the same time, the insights gained from this study enable informed decision-making and provide valuable guidance in the used car market.

REZUMAT Sintagma „Inteligență Artificială (IA)” apare din ce în ce mai des în discuțiile despre tehnologie și societate. În consecință, comunitatea sociologică are nevoie de un cadru conceptual pentru studierea fenomenului și a simbolurilor sale. Deși e REZUMAT

The impact of the COVID-19 pandemic on the economic environment had and continues to have long-lasting consequences. Also, the COVID-19 pandemic induced changes in business strategies and partnerships. As identifying new trustworthy organizational partners is crucial for enhancing the business resilience during the COVID-19 crisis, this chapter presents some results of the authors’ work in this respect: the aim is to investigate the business partnership networks before and during the COVID-19 pandemic. The authors opted for an opinion mining approach: they analyzed a pipeline of Romanian online news, between 2018 and 2022, using adapted Natural Language Processing (NLP) techniques and Social Network Analysis (SNA) metrics. As a result, their novel approach provides business leaders with a tool to compute an initial level of trust in a potential business partner as a centrality measure of the latter in business co-occurrence networks, automatically extracted from online news. A direct implication is this: the research results demonstrate that business press – when explored with deep learning means – can be considered a source of information needed to reasonably estimate the level of organizational business trust. <!----> <!----> <!----> <!---->