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.

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.

Abstract We are witnessing the digitalization era, where artificial intelligence (AI)/machine learning (ML) models are mandatory to transform this data deluge into actionable information. However, these models require large, high-quality datasets to predict high reliability/accuracy. Even with the maturity of Internet of Things (IoT) systems, there are still numerous scenarios where there is not enough quantity and quality of data to successfully develop AI/ML-based applications that can meet market expectations. One such scenario is precision agriculture, where operational data generation is costly and unreliable due to the extreme and remote conditions of numerous crops. In this paper, we investigated the generation of synthetic data as a method to improve predictions of AI/ML models in precision agriculture. We used generative adversarial networks (GANs) to generate synthetic temperature data for a greenhouse located in Murcia (Spain). The results reveal that the use of synthetic data significantly improves the accuracy of the AI/ML models targeted compared to using only ground truth data.

MOTIVATION: Molecular docking methods are extensively used to predict the interaction between protein-ligand systems in terms of structure and binding affinity, through the optimization of a physics-based scoring function. However, the computational requirements of these simulations grow exponentially with: (i) the global optimization procedure, (ii) the number and degrees of freedom of molecular conformations generated and (iii) the mathematical complexity of the scoring function. RESULTS: In this work, we introduce a novel molecular docking method named METADOCK 2, which incorporates several novel features, such as (i) a ligand-dependent blind docking approach that exhaustively scans the whole protein surface to detect novel allosteric sites, (ii) an optimization method to enable the use of a wide branch of metaheuristics and (iii) a heterogeneous implementation based on multicore CPUs and multiple graphics processing units. Two representative scoring functions implemented in METADOCK 2 are extensively evaluated in terms of computational performance and accuracy using several benchmarks (such as the well-known DUD) against AutoDock 4.2 and AutoDock Vina. Results place METADOCK 2 as an efficient and accurate docking methodology able to deal with complex systems where computational demands are staggering and which outperforms both AutoDock Vina and AutoDock 4. AVAILABILITY AND IMPLEMENTATION: https://[email protected]/Baldoimbernon/metadock_2.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The numerical value discretization is an important task of the data preprocessing phase within the intelligent data analysis. This process allows us to reduce the number of values (among other advantages) with which techniques work, reducing the computational cost when it comes to working with large amounts of data. In this paper a numerical value discretization technique is proposed. Specifically, we discretize numerical values using a type of Cauchy distribution obtained from fuzzy clustering technique, being this technique a modification of the well-known Fuzzy C-Means clustering technique. Finally, to test the quality of the membership function we use a neural network technique over several datasets. The results obtained are compared and validated by means of statistical tests, obtaining satisfactory results.