In the field of sensors, in areas such as industrial, clinical, or environment, it is common to find one dimensional (1D) formatted data (e.g., electrocardiogram, temperature, power consumption). A very promising technique for modelling this information is the use of One Dimensional Convolutional Neural Networks (1D CNN), which introduces a new challenge, namely how to define the best architecture for a 1D CNN. This manuscript addresses the concept of One Dimensional Neural Architecture Search (1D NAS), an approach that automates the search for the best combination of Neuronal Networks hyperparameters (model architecture), including both structural and training hyperparameters, for optimising 1D CNNs. This work includes the implementation of search processes for 1D CNN architectures based on five strategies: greedy, random, Bayesian, hyperband, and genetic approaches to perform, collect, and analyse the results obtained by each strategy scenario. For the analysis, we conducted 125 experiments, followed by a thorough evaluation from multiple perspectives, including the best-performing model in terms of accuracy, consistency, variability, total running time, and computational resource consumption. Finally, by presenting the optimised 1D CNN architecture, the results for the manuscript’s research question (a real-life clinical case) were provided.

This study aims to understand airline passengers' satisfaction trends by analyzing the most influential factors on satisfaction before and during the COVID-19 pandemic. The sample consists of a dataset with 9745 passenger reviews published on airlinequality.com. The reviews were analyzed with a sentiment analysis tool calibrated for the aviation industry for accuracy. Machine learning algorithms were then implemented to predict review sentiment based on airline company, travelers' type and class, and country of origin. Findings show passengers were unhappy before the pandemic, aggravated after the COVID-19 outbreak. The staff's behavior is the main factor influencing passengers' satisfaction. Predictive modeling showed that it is possible to predict negative review sentiments with satisfactory performance rather than positive reviews. The main takeaway is that passengers, after the pandemic, are most worried about refunds and aircraft cabin cleanliness. From a managerial standpoint, airline companies can benefit from the created knowledge to adjust their strategies in agreement and meet their customers' expectations.

Health monitoring is crucial in hospitals and rehabilitation centers. Challenges can affect the reliability and accuracy of health data. Human error, patient compliance concerns, time, money, technology, and environmental factors might cause these issues. In order to improve patient care, healthcare providers must address these challenges. We propose a non-intrusive smart sensing system that uses a SensFloor smart carpet and an inertial measurement unit (IMU) wearable sensor on the user’s back to monitor position and gait characteristics. Furthermore, we implemented machine learning (ML) algorithms to analyze the data collected from the SensFloor and IMU sensors. The system generates real-time data that are stored in the cloud and are accessible to physical therapists and patients. Additionally, the system’s real-time dashboards provide a comprehensive analysis of the user’s gait and balance, enabling personalized training plans with tailored exercises and better rehabilitation outcomes. Using non-invasive smart sensing technology, our proposed solution enables healthcare facilities to monitor patients’ health and enhance their physical rehabilitation plans.

To help telecommunication operators in their network planning, namely coverage estimation and optimisation tasks, this paper presents a comparison between a semi-empirical propagation model and a propagation model generated using Artificial Intelligence (AI). These two types of propagation models are quite different in their design. The semi-empiric Automatically Calibrated Standard Propagation Model (ACSPM) is specific for an operating antenna, being calibrated every time a use case application is used and the Artificial Intelligence Propagation Model (AIPM) can be applied in different scenarios, once trained, allowing to estimate coverage for a new antenna location, using information from neighboring antennas. These models have quite different features and applicability. The ACSPM should be applied in network optimisation, when using data from the current state of the antennas. The AIPM can be used in the deployment of new antennas, as it uses data from a certain geographical area. For a better comparison of the models studied, extensive Drive Tests (DT) collection campaigns conducted by operators are used, since coverage estimations are more realistic when DTs are considered. Both models are generated using very different methodologies, but their resulting performance is very similar. The AIPM achieves a Mean Absolute Error (MAE) up to 6:1 dB with a standard deviation of 4 dB. When compared to the ACSPM we have an improvement of 0:5 dB, since this only achieves a MAE up to 6:6 dB. AIPM achieves better results and is the characterised for being completely agnostic and definition-free, when compared with known propagation models.

Industrial inspection is crucial for maintaining quality and safety in industrial processes. Deep learning models have recently demonstrated promising results in such tasks. This paper proposes YOLOX-Ray, an efficient new deep learning architecture tailored for industrial inspection. YOLOX-Ray is based on the You Only Look Once (YOLO) object detection algorithms and integrates the SimAM attention mechanism for improved feature extraction in the Feature Pyramid Network (FPN) and Path Aggregation Network (PAN). Moreover, it also employs the Alpha-IoU cost function for enhanced small-scale object detection. YOLOX-Ray’s performance was assessed in three case studies: hotspot detection, infrastructure crack detection and corrosion detection. The architecture outperforms all other configurations, achieving $mA{P}_{50}$ values of 89%, 99.6% and 87.7%, respectively. For the most challenging metric, $mA{P}_{50:95}$, the achieved values were 44.7%, 66.1% and 51.8%, respectively. A comparative analysis demonstrated the importance of combining the SimAM attention mechanism with Alpha-IoU loss function for optimal performance. In conclusion, YOLOX-Ray’s ability to detect and to locate multi-scale objects in industrial environments presents new opportunities for effective, efficient and sustainable inspection processes across various industries, revolutionizing the field of industrial inspections.

With the increase of devices connected to the Internet, currently known as Internet of Things (IoT), it is important to use algorithms in data transmissions to achieve optimal results for a given application.The paper main goal is to gather Global Navigation Satellite System (GNSS) positions at maximum rate possible, process them in a Post-processed Kinematics (PPK) environment, in order to perform GNSS corrections when compared to real earth GNSS coordinates. This method translates as micro-adjustments of the Unmanned Aerial Vehicle (UAV) traveled path, achieving centimeter-level accuracy associated with GNSS actual position. These GNSS positions are collected by the UAV GNSS receiver, and then sent to a gateway by using LoRaWAN™ communications protocol.Experimental results were included in the paper.