Constructing a Global and Integral Model of Business Management Using a CBR System

Knowledge has become the most strategic resource in the new business environment. A case-based reasoning system, which incorporates a novel clustering and retrieval method, has been developed for identifying critical situations in business processes. The proposed method is based on a Cooperative Maximum Likelihood Hebbian Learning model, which can be used to categorize the necessities for the Acquisition, Transfer and Updating of Knowledge of the different departments of a firm. This technique is used as a tool to develop a part of a Global and Integral Model of business Management, which brings about a global improvement in the firm, adding value, flexibility and competitiveness. From this perspective, the model tries to generalise the hypothesis of organizational survival and competitiveness, so that the organisation that is able to identify, strengthen, and use key knowledge will reach a pole position

Smart Buildings

This talk presents an efficient cyberphysical platform for the smart management of smart buildings http://www.deepint.net. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart building is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study at Salamanca - Ecocasa. This platform could enable smart building to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques

Smart territories

The concept of smart cities is relatively new in research. Thanks to the colossal advances in Artificial Intelligence that took place over the last decade we are able to do all that that we once thought impossible; we build cities driven by information and technologies. In this keynote, we are going to look at the success stories of smart city-related projects and analyse the factors that led them to success. The development of interactive, reliable and secure systems, both connectionist and symbolic, is often a time-consuming process in which numerous experts are involved. However, intuitive and automated tools like “Deep Intelligence” developed by DCSc and BISITE, facilitate this process. Furthermore, in this talk we will analyse the importance of complementary technologies such as IoT and Blockchain in the development of intelligent systems, as well as the use of edge platforms or fog computing

MOVICAB-IDS: Visual Analysis of Network Traffic Data Streams for Intrusion Detection

MOVICAB-IDS enables the more interesting projections of a massive traffic data set to be analysed, thereby providing an overview of any possible anomalous situations taking place on a computer network. This IDS responds to the challenges presented by traffic volume and diversity. It is a connectionist agent-based model extended by means of a functional and mobile visualization interface. The IDS is designed to be more flexible, accessible and portable by running on a great variety of applications, including small mobile ones such as PDA’s, mobile phones or embedded devices. Furthermore, its effectiveness has been demonstrated in different tests

Testing CAB-IDS Through Mutations: On the Identification of Network Scans

This study demonstrates the ability of powerful visualization tools (based on the use of connectionist models) to identify network intrusion attempts in an effective and reliable manner. It presents a novel technique to test and evaluate a previously developed network-based intrusion detection system (IDS). This technique applies mutant operators and is intended to test IDSs using numerical data sets. It should be made clear that some mutations were discarded as they did not all provide real life situations. As an application example of the proposed testing model, it has been specially applied to the identification of network scans and mutations of these. The tested Connectionist Agent-Based IDS (CAB-IDS) is used as a method to investigate the traffic which travels along the analysed network, detecting anomalous traffic patterns. The specific tests performed in this study were based on the mutation of one or several variables analysed by CAB-IDS

Efficiency and Reliability in Bringing AI into Transport and Smart Cities Solutions

capacity and the low cost of the Cloud have facilitated the development of new, powerful algorithms. The efficiency of these algorithms in Big Data processing, Deep Learning and Convolutional Networks is transforming the way we work and is opening new horizons. Thanks to them, we can now analyse data and obtain unimaginable solutions to today’s problems. Nevertheless, our success is not entirely based on algorithms, it also comes from our ability to follow our “gut” when choosing the best combination of algorithms for an intelligent artefact. Their development involves the use of both connectionist and symbolic systems, that is to say data and knowledge. Moreover, it is necessary to work with both historical and real-time data. It is also important to consider development time, costs and the ability to create systems that will interact with their environment, will connect with the objects that surround them and will manage the data they obtain in a reliable manner. In this keynote, the evolution of intelligent computer systems will be examined, especially that of convolutional networks. The need for human capital will be discussed, as well as the need to follow one’s “gut instinct” in problem-solving. Furthermore, the importance of IoT and Blockchain in the development of intelligent systems will be analysed and it will be shown how tools like "Deep Intelligence" make it possible to create computer systems efficiently and effectively. "Smart" infrastructures need to incorporate all added-value resources so they can offer useful services to the society, while reducing costs, ensuring reliability and improving the quality of life of the citizens. The combination of AI with IoT and with blockchain offers a world of possibilities and opportunities. The development of transport, smart cities, urbanizations and leisure areas can be improved through the use of distributed intelligent computer systems. In this regard, edge platforms or fog computing help increase efficiency, reduce network latency, improve security and bring intelligence to the edge of the network, the sensors, users and the environment. Several use cases of intelligent systems will be presented, and it will be analysed how the processes of implementation and use have been optimized by means of different tools