This systematic review maps how artificial intelligence and machine learning are applied to threat awareness in aerial, missile, and hypersonic contexts from 2010 to 2025. In sensing and recognition, research progressed from interpretable radar micromotion features to convolutional–recurrent and attention architectures that learn spatiotemporal structure from range–Doppler, stepped-frequency, High-Resolution Range Profile (HRRP), point clouds, and kinematic telemetry. Trajectory prediction for hypersonic glide vehicles increasingly couples signal decomposition with hybrid neural models, while reinforcement learning reframes weapon–target assignment as sequential control. Evaluation practices remain heterogeneous and fragile for comparison, with varying metrics, a lack of strong baselines, limited propagation of uncertainty across the pipeline, and scarce reproducibility artefacts. Stress testing under realism, degraded Signal-to-Noise Ratio (SNR), track dropouts, out-of-family manoeuvres, contested electromagnetic conditions, is limited. Operationalisation is constrained by security architecture. Cross-Domain Solutions govern data movement, and verifiable human–autonomy teaming emphasises auditable behaviour and operator trust. Robustness to distribution shift, sensor degradation, and adversarial manipulation is the least developed dimension, while federated or edge inference appears sporadically. We identify three priorities that will move the field forward, namely shared benchmarks and metrics, fused ISR that operates under connectivity constraints, and assurance by design encompassing CDS-aware data flows, operator-centred teaming, and robustness as a first-class requirement.

Abstract Predictive Mutation Testing (PMT) has emerged as a promising technique for reducing the high computational cost of traditional mutation testing. This is achieved by using predictive models to estimate mutant behavior without actually requiring their execution. However, despite its growing interest, PMT is still a young and evolving field and, therefore, its experimental studies remain particularly vulnerable to various methodological threats that may compromise the validity, comparability, and reproducibility of results. This paper aims to identify and examine key methodological threats in prior PMT studies that not only affect their validity, but may also limit the technique’s ability to reach its full predictive potential. For each of the eight identified threats, spanning the main stages of the PMT workflow, we describe its nature, analyze how it has been addressed (or overlooked) in previous work, and offer a validation list of recommended practices. We further conduct an empirical validation to substantiate the impact of these threats. In addition, based on the gaps identified in this analysis, we outline several open challenges that remain unexplored in the field, such as the lack of standardization in dataset sharing, the prevalence of Java-centric studies with method-level operators, and the challenges posed by class imbalance and project heterogeneity. This work contributes to strengthening the methodological soundness of PMT research, promoting more meaningful cross-study comparisons, and encouraging the adoption of practices that foster reproducibility, scalability, and real-world applicability.

Abstract Computational intelligence focuses on intelligent computer systems that mimic human nature and linguistic reasoning. Sentiment analysis is an area of considerable relevance within computational intelligence. Multimodal sentiment analysis is an extension of textual sentiment analysis, where the sentiments of people’s opinions are analysed by including multimedia content in addition to textual content. This mode of sentiment analysis faces multiple problems, as the sentiments of text and multimedia content may be contradictory. In addition, another added factor is the imbalance of the data that these problems suffer from in certain topics, which causes a problem when generating intelligent models. In this paper, we design a novel approach for multimodal sentiment analysis, proposing a new way of labelling tweets, not always prioritising polarized classes but using annotator confidence. Moreover, during this design, an information integration and fusion methodology is proposed for the construction of a metamodel that includes fuzzy logic to perform information weighting according to the confidence of the annotator. This proposal has been applied a public unbalanced dataset of tweets with text and images, with a large unbalance towards the negative class label. Applying the proposed fuzzy methodology, we reached a macro-F1 score of 0.493 for the negative class, 0.681 for the neutral class, and 0.832 for the positive class. The model obtains satisfactory performance since the individual image and text sentiment analysis results are worse, especially the negative class, which in initial image classification achieves an F1 score of 0.08.

With the increasing adoption of AI in safety-critical applications within urban environments, the interpretability of these systems is paramount. This study explores the application of Explainable Artificial Intelligence (XAI) techniques to enhance transparency in audio-based detection of emergency vehicle sirens, a crucial component in urban sound management. Adopting methods such as SHAP (SHapley Additive exPlanations) values, Permutation Feature Importance, and model-specific feature scores, this research identifies key audio features, including mid-frequency spectral contrasts and targeted chroma components, which significantly help in distinguishing siren sounds among urban noise. The study examines various machine learning models, identifying K-Nearest Neighbors (KNN) and XGBoost as top performers; KNN excelled in class-specific precision, while XGBoost demonstrated strong cross-class discrimination. The findings highlight the potential of XAI in improving both accuracy and accountability for sound detection systems in safety-critical urban applications, advancing the deployment of transparent AI within smart city infrastructures.

Road safety is a critical global issue, with traffic accidents causing substantial human and economic losses annually. Advanced Driver Assistance Systems (ADAS) have emerged as an effective solution, leveraging technologies such as computer vision and artificial intelligence to enhance driving safety. However, the high costs and complexity of these systems limit their widespread adoption. This paper presents a cost-effective ADAS prototype utilizing vision-based and deep learning technologies to monitor and maintain safe following distances between vehicles. The performance of the system is evaluated using standard datasets and real-world scenarios, demonstrating adaptability across diverse driving conditions. Additionally, the feasibility of deploying the system on low-resource devices, such as Raspberry Pi and Radxa Zero, is analyzed, highlighting its potential for practical applications.

Countries all around the world recognise the impact of air quality on public health, advocating for city centre decarbonisation and pollutant monitoring via Internet of Things technologies. Using data collected from these systems, it is possible to generate models that predict pollution based on regular patterns where mobility data can enhance the accuracy and robustness of these advanced machine learning models. This paper follows this approach, utilising vehicle traffic data from image recognition, on-site vehicle detectors, and synthetic data to maximise prediction accuracy in various urban environments. The results reveal that this proposal improves prediction for traffic-related pollutants, such as ${\text{SO}}_{2}$ and ${\text{PM}}_{2.5}$, which are linked to severe respiratory diseases. These results also highlight the role of synthetic data in enhancing prediction performance under limited datasets.

Sentiment analysis has gained prominence in the digital era, particularly within social media contexts. This study explores the integration of metadata, such as reposts, favorites, or followers, alongside textual information for sentiment analysis. A dataset of posts in Spanish is created, filtering by a set of keywords (a total of 17) of interest to us. A total of 3,962 posts were collected along with their metadata. Then, they have been manually classified as positive, negative, and neutral, and processed. The research studies and compares different models like LightGBM, BERT, and a hybrid model combining BERT outputs with metadata. Evaluation metrics, such as F1-score and precision, were employed, revealing that the hybrid model achieved the highest F1-score of 69.4%, showcasing the effectiveness of merging text comprehension capabilities with contextual metadata. While the incorporation of metadata enhanced overall sentiment classification, precision varied across sentiment classes, with notable improvements in neutral and positive sentiments, over 16% and 8% respectively.

The management of road traffic incidents is a problem faced by governments in many countries. Normally, road operators have the infrastructure in place to monitor such incidents, albeit in a reactive manner. In Spain, there are traffic cameras on major roads to check for possible incidents, however, incident notification is slow and not automated. As an alternative, this paper proposes a system for automatic real-time traffic alerts. Thus, 1,500 camera images from the Dirección General de Tráfico (DGT) deployed on the main Spanish roads are analyzed in real time every 4 minutes. These images are not preprocessed, they have different qualities and are also affected by weather conditions such as fog, rain, sun reflections, etc. The system uses several Deep Learning classification models trained on a well-known dataset of traffic images including flowing traffic, dense traffic, accidents and fires. These models are used to classify the DGT images in real time, with satisfactory initial results, detecting both flowing traffic and dense traffic.

Mobile systems are gaining more and more importance, and new promising paradigms like Mobile Cloud Computing are emerging. Mobile Cloud Computing provides an infrastructure where data storage and processing could happen outside the mobile node. Specifically, there is a major interest in the use of the services obtained by taking advantage of the distributed resource pooling provided by nearby mobile nodes in a transparent way. This kind of systems is useful in application domains such as emergencies, education and tourism. However, these systems are commonly based on dynamic network topologies, in which disconnections and network partitions can occur frequently, and thus the availability of the services is usually compromised. Techniques and methods from Autonomic Computing can be applied to Mobile Cloud Computing to build dependable service models taking into account changes in the context. In this work, a context-aware software architecture is proposed to support the availability of the services deployed in mobile and dynamic network environments. The proposal is based on a service replication scheme together with a self-configuration approach for the activation/hibernation of the replicas of the service depending on relevant context information from the mobile system. To that end, an election algorithm has been designed and implemented.