## 2 Summary of the Contributions
Utilizing real-world data from a European OEM, the study highlights the superiority of global ML models over local ones. The paper introduces a comprehensive set of metrics for evaluating demand forecasting models, emphasizing their practicality. The research demonstrates the effectiveness of pooling product data based on historical demand magnitude to mitigate forecast errors in global models. The authors present two data pooling strategies for building global time series models. A novel approach is introduced to control forecast errors in global models. The integration of complementary data sources, such as world GDP, unemployment rates, and fuel prices, is discussed. The findings indicate that grouping products based on demand patterns and magnitude improves the performance of the ML models. The research reveals that certain models, such as SVR, voting ensemble, and random forest, outperform others, particularly when trained on product data of the same demand type. A comparison of batch and streaming ML models highlights the robustness of batch models. The potential of digital twins for accurate forecasts and scenario estimation is explored. The study identifies avenues for future research, focusing on refining error bounding strategies, addressing anomalous forecasts in global models, and enhancing model explainability for user trust. This paper concludes by providing insights into the potential applications of ML in demand forecasting for the automotive OEM sector. In [4], a novel flight path planning algorithm for unmanned aerial vehicles (UAVs), based on ellipsoidal mapping, is introduced. The paper discusses the use of spheres and ellipsoids for geometric representations and mapping, emphasizing their advantages over other methods. The research addresses the challenge of efficiently calculating distances between ellipsoidal objects by a neural network. The algorithm utilizes teaching-learning based optimization (TLBO) and takes advantage of ellipsoidal representations of obstacles for UAV navigation. The method aims to provide collision-free, smooth flight paths, accommodating various environments, including indoor and outdoor settings. The methodology used to calculate distances between ellipsoids using a neural network involves generating a training dataset and implementing a novel normalization method. The proposed fitness function for flight path planning considers several factors, including ensuring safe distances between the obstacle ellipsoids and the UAV ellipsoid to prevent collisions, minimizing the overall proximity range of the UAV throughout its entire flight path and other desired features. Results are presented, comparing the algorithm's performance with other evolutionary algorithms and rapidly exploring random tree star algorithm (RRT*). While evolutionary techniques with the proposed fitness function generally outperformed RRT*, the latter demonstrated a better performance in terms of time. Future work is outlined, including developing a cost function for comparing different flight path planning algorithms, replacing the greedy strategy with reinforcement learning, and designing an intelligent low-level control algorithm for UAV navigation. Paper [5] proposes a gesture recognition system integrated into visible light communication (VLC) systems for human-computer interaction applications. The GR technique utilizes light transitions between fingers, which are detected via a low-cost light-emitting diode (LED) and a photo-diode sensor at the receiver side. The system employs a long short-term memory (LSTM) neural network to classify finger movements based on interruptions in direct light transmission, making it suitable for high-speed communication. The accuracy of the proposed system in identifying gestures reaches 88%. The authors present a solution that involves minimal additional cost, as it is integrated into a VLC-capable system. The LSTM-based approach offers effective gesture recognition with low computational complexity compared to traditional video processing techniques.