Over time, a myriad of applications have been generated for pattern classification algorithms. Several case studies include parametric classifiers such as the Multi-Layer Perceptron (MLP) classifier, which is one of the most widely used today. Others use non-parametric classifiers, Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Naïve Bayes (NB), Adaboost, and Random Forest (RF). However, there is still little work directed toward a new trend in Artificial Intelligence (AI), which is known as eXplainable Artificial Intelligence (X-AI). This new trend seeks to make Machine Learning (ML) algorithms increasingly simple and easy to understand for users. Therefore, following this new wave of knowledge, in this work, the authors develop a new pattern classification methodology, based on the implementation of the novel Minimalist Machine Learning (MML) paradigm and a higher relevance attribute selection algorithm, which we call dMeans. We examine and compare the performance of this methodology with MLP, NB, KNN, SVM, Adaboost, and RF classifiers to perform the task of classification of Computed Tomography (CT) brain images. These grayscale images have an area of 128 × 128 pixels, and there are two classes available in the dataset: CT without Hemorrhage and CT with Intra-Ventricular Hemorrhage (IVH), which were classified using the Leave-One-Out Cross-Validation method. Most of the models tested by Leave-One-Out Cross-Validation performed between 50% and 75% accuracy, while sensitivity and sensitivity ranged between 58% and 86%. The experiments performed using our methodology matched the best classifier observed with 86.50% accuracy, and they outperformed all state-of-the-art algorithms in specificity with 91.60%. This performance is achieved hand in hand with simple and practical methods, which go hand in hand with this trend of generating easily explainable algorithms.

Given the variability of challenges cities and urban areas face today, including most profoundly inclusion, safety, and resilience, the objective of this chapter is to examine how generative artificial intelligence (GenAI) may facilitate the process of addressing these challenges, thus fostering sustainable cities. By focusing explicitly on smart cities, in this chapter, it is argued that a great variety of GenAI-based applications exist, and these may bear great value for smart cities and their inhabitants. Indeed, by now, GenAI and GenAI-based applications have proven useful in integrating and analyzing information, thereby facilitating operations such as analysis, data management, and description. Accordingly, when utilized in the smart city and/or urban context, GenAI is of value in relation to urban planning, disaster preparedness and mitigation, traffic management, public safety, waste management, and last but not least improving the efficiency and quality of services’ provision to the city inhabitants. Clearly, the use of GenAI in this context presents challenges too, including data timeliness, different representations, and other issues related to the complexity of urban data. These considerations should be considered in the design, development, and implementation of GenAI-based applications to ensure their effectiveness, accuracy, and compliance with regulatory frameworks as well as with values and principles. None of these is possible without an explicit engagement of human beings, i.e., experts, who would provide monitoring and oversight over the GenAI-devised suggestions and recommendations. <!----> <!----> <!----> <!---->

Nowadays, big cities are facing many challenges with respect to traffic congestion, climate change, air and water pollution, among others. Thus, smart cities are intended to improve the life quality of the citizens, tackling such issues with the integration of information and communication technologies to reduce the impact and achieve a well-being state of citizens. In this work, a model to predict the traffic congestion applying a support vector machine method is proposed. In addition, a crowdsourcing approach based on mining the Twitter social networks collecting events associated with the traffic is also proposed. The main contribution of this research is focused on providing a methodology that characterizes the traffic congestion analyzing crowd-sensed data from a geospatial perspective. This approach was implemented over the Mexico City as a case study, in order to forecast possible future traffic events in the city, in which the citizens share their particular situation to discover alternatives routes for avoiding the traffic congestion. Future works are oriented towards designing mobile applications in order to introduce the proposed approach and integrate information from multiple platforms and navigation systems.

The pre-diagnosis of cancer has been approached from various perspectives, so it is imperative to continue improving classification algorithms to achieve early diagnosis of the disease and improve patient survival. In the medical field, there are data that, for various reasons, are lost. There are also datasets that mix numerical and categorical values. Very few algorithms classify datasets with such characteristics. Therefore, this study proposes the modification of an existing algorithm for the classification of cancer. The said algorithm showed excellent results compared with classical classification algorithms. The AISAC-MMD (Mixed and Missing Data) is based on the AISAC and was modified to work with datasets with missing and mixed values. It showed significantly better performance than bio-inspired or classical classification algorithms. Statistical analysis established that the AISAC-MMD significantly outperformed the Nearest Neighbor, C4.5, Naïve Bayes, ALVOT, Naïve Associative Classifier, AIRS1, Immunos1, and CLONALG algorithms in conducting breast cancer classification.

This research uses the computing of conceptual distance to measure information content in Wikipedia categories. The proposed metric, generality, relates information content to conceptual distance by determining the ratio of the information that a concept provides to others compared to the information that it receives. The DIS-C algorithm calculates generality values for each concept, considering each relationship’s conceptual distance and distance weight. The findings of this study are compared to current methods in the field and found to be comparable to results obtained using the WordNet corpus. This method offers a new approach to measuring information content applied to any relationship or topology in conceptualization.