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3This paper introduces and examines a novel realized volatility forecasting model that makes use of Long Short-Term Memory (LSTM) neural networks and the risk metric financial turbulence (FT). The proposed model is compared to five alternative models, of which two incorporate LSTM neural networks and the remaining three include GARCH(1,1), EGARCH(1,1), and HAR models. The results of this paper demonstrate that the proposed model yields statistically significantly more accurate and robust forecasts than all other studied models when applied to stocks with middle-to-high volatility. Yet, considering low-volatility stocks, it can only be confidently affirmed that the proposed model yields statistically significantly more robust forecasts relative to all other models considered.
ARTIKEL
From the article: Abstract: An overview of neural network architectures is presented. Some of these architectures have been created in recent years, whereas others originate from many decades ago. Apart from providing a practical tool for comparing deep learning models, the Neural Network Zoo also uncovers a taxonomy of network architectures, their chronology, and traces back lineages and inspirations for these neural information processing systems.
CONFERENTIEBIJDRAGE
We present a novel architecture for an AI system that allows a priori knowledge to combine with deep learning. In traditional neural networks, all available data is pooled at the input layer. Our alternative neural network is constructed so that partial representations (invariants) are learned in the intermediate layers, which can then be combined with a priori knowledge or with other predictive analyses of the same data. This leads to smaller training datasets due to more efficient learning. In addition, because this architecture allows inclusion of a priori knowledge and interpretable predictive models, the interpretability of the entire system increases while the data can still be used in a black box neural network. Our system makes use of networks of neurons rather than single neurons to enable the representation of approximations (invariants) of the output.
CONFERENTIEBIJDRAGE
The recent success of Machine Learning encouraged research using artificial neural networks (NNs) in computer graphics. A good example is the bidirectional texture function (BTF), a data-driven representation of surface materials that can encapsulate complex behaviors that would otherwise be too expensive to calculate for real-time applications, such as self-shadowing and interreflections. We propose two changes to the state-of-the-art using neural networks for BTFs, specifically NeuMIP. These changes, suggested by recent work in neural scene representation and rendering, aim to improve baseline quality, memory footprint, and performance. We conduct an ablation study to evaluate the impact of each change. We test both synthetic and real data, and provide a working implementation within the Mitsuba 2 rendering framework. Our results show that our method outperforms the baseline in all these metrics and that neural BTF is part of the broader field of neural scene representation. Project website: https://traverse-research.github.io/NeuBTF/.
POSTER
The tourism industry is a major economic sector worldwide, significantly contributing to job creation and GDP growth. However, the rapid expansion of this industry, along with rising environmental and social concerns, underscores the critical need for sustainable strategies. This paper presents a novel multi-objective game theory model that simultaneously optimizes profitability and sustainability in the tourism supply chain. The key innovation of this study lies in the integration of game theory with an artificial neural network (ANN) to predict customer demand, effectively capturing nonlinear consumer behaviors and enabling more accurate decision-making. The model analyzes the dynamic interactions between tour operators and local service providers, identifying Nash Equilibrium outcomes where no player can improve profitability through unilateral strategy adjustments. Additionally, the study introduces a comprehensive approach to government subsidies, evaluating their effectiveness in enhancing sustainability incentives and overall profitability. A detailed sensitivity analysis is conducted to examine how variations in pricing, sustainability efforts, and subsidy rates influence profit margins. Another distinctive contribution of this research is its emphasis on human resource management, highlighting how employee training, green organizational culture, and financial incentives can improve productivity and support sustainability initiatives. The results demonstrate that collaborative strategies, such as resource sharing and joint sustainability efforts between tour operators and local providers, significantly increase profitability. The findings further indicate that a combination of optimal pricing, maximum sustainability efforts, and full government subsidies yields the highest total profit of 6,395 units. Overall, this research offers strategic guidelines for pricing, human resource development, and subsidy policies, providing a robust framework for achieving both profitability and sustainability in the tourism supply chain.
ARTIKEL
Passenger flow management is an important issue at many airports around the world. There are high concentrations of passengers arriving and leaving the airport in waves of large volumes in short periods, particularly in big hubs. This might cause congestion in some locations depending on the layout of the terminal building. With a combination of real airport data, as well as synthetic data obtained through an airport simulator, a Long Short-Term Memory Recurrent Neural Network has been implemented to predict the possible trajectories that passengers may travel within the airport depending on user-defined passenger profiles. The aim of this research is to improve passenger flow predictability and situational awareness to make a more efficient use of the airport, that could also positively impact communication with public and private land transport operators.
PAPER
A considerable amount of literature has been published on Corporate Reputation, Branding and Brand Image. These studies are extensive and focus particularly on questionnaires and statistical analysis. Although extensive research has been carried out, no single study was found which attempted to predict corporate reputation performance based on data collected from media sources. To perform this task, a biLSTM Neural Network extended with attention mechanism was utilized. The advantages of this architecture are that it obtains excellent performance for NLP tasks. The state-of-the-art designed model achieves highly competitive results, F1 scores around 72%, accuracy of 92% and loss around 20%.
CONFERENTIEBIJDRAGE
Dit project richt zich op de ontwikkeling van een robuust en energiezuinig Automatic Meter Reading (AMR)-systeem voor analoge meters in industriële omgevingen. Het systeem maakt gebruik van beeldherkenning om meterstanden automatisch uit te lezen, zelfs onder uitdagende omstandigheden zoals slechte verlichting, vervuiling en beschadigingen.
Het AMR-systeem bestaat uit drie onderdelen:
Een detectiemodel (YOLOv11nano) voor het lokaliseren van het cijferdisplay.
Een compact neuraal netwerk voor het classificeren van cijfers.
Een reconstructiemodel gebaseerd op een Graph Neural Network (GNN) voor het herstellen van vervormde of deels verborgen cijfers.
Door gebruik te maken van TinyML-technieken is het systeem geoptimaliseerd voor resource-beperkte hardware zoals microcontrollers, waardoor lokale, energiezuinige verwerking mogelijk is zonder netwerkverbinding.
Het GNN-reconstructiemodel is op dit moment nog niet geschikt voor embedded hardware en vormt een aandachtspunt voor verdere ontwikkeling.
Hoewel het systeem onder gecontroleerde omstandigheden goed presteert, is de robuustheid in realistische, industriële situaties nog beperkt.
SOFTWARE
