What is Computational Intelligence and where is it going?

What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed

BrainGene: computational creativity algorithm that invents novel interesting names

Human-level intelligence implies creativity, not only on the grand scale, but primarily in the everyday activity, such as understanding intentions, behavior, and invention of new words. Psychological models of creativity have some support in experimental cognitive psychology, but computational models of creative processes are quite rare. This paper presents a model of creative processes behind invention of novel words related to description of products and services

Learning Innovation of Qibla Direction with Mobile-Based App by Adapting Computational Thinking

This research was conducted to obtain the results of the development of Learning to Determine Qibla Direction with Computational Thinking Adaptation using Mobile-Based Applications. The researcher used mixed methods with a sequential exploratory research design that takes two stages (qualitative-quantitative). The R & D stage used 5 (five) steps of the ADDIE method, namely Analysis, Design, Development, Implementation and Evaluation. This research was conducted at the office of the Ministry of Religion of the Thousand Islands Regency, with the result that the learning innovations carried out were appropriate and met expectations. The learning process for training participants and in practice its application was easier and more affordable. The Qibla direction training used in this mobile-based application was applied with a learning pattern that adapts computational thinking skills, which were arranged in four phases, namely: Elaboration, Determination, Calculation, and Evaluation. This learning innovation shows that it can improve participants' understanding of the concept of calculating Qibla Direction which was applied integratively between religion, science and technology as a coherent knowledge. Keywords: Mobile-Based App, Learning Innovation, Adapting  Computational Thinkin

Artificial intelligence research community and associations in Poland

In last years Artificial Intelligence presented a tremendous progress by offering a variety of novel methods, tools and their spectacular applications. Besides showing scientific breakthroughs it attracted interest both of the general public and industry. It also opened heated debates on the impact of Artificial Intelligence on changing the economy and society. Having in mind this international landscape, in this short paper we discuss the Polish AI research community, some of its main achievements, opportunities and limitations. We put this discussion in the context of the current developments in the international AI community. Moreover, we refer to activities of Polish scientific associations and their initiative of founding Polish Alliance for the Development of Artificial Intelligence (PP-RAI). Finally two last editions of PP-RAI joint conferences are summarized

Forecasting The Italian Day-Ahead Electricity Price Using Bootstrap Aggregation Method

Electricity price forecasting has become a crucial element for both private and public decision-making. This importance has been growing since the wave of deregulation and liberalization of the energy sector on a global scale since the late 1990s. Given these facts, this paper is an attempt to establish and demonstrate a precision based applicable forecasting model for wholesale electricity prices with respect to the Italian power market on an hourly basis. Artificial intelligence models such as neural networks and bagged regression trees are utilized, although they are rarely used to forecast electricity prices. After model calibration, bagged regression trees with exogenous variables comprised the final model. The selected model outperformed neural network and bagged regression with a single price used in this paper, it also outperformed other statistical and non-statistical models used in other studies. We also confirm certain theoretical specifications of the model. As a policy tool, this model could be used by energy traders, transmission system operators and energy regulators for an enhanced decision-making process

Bio-Inspired Mechanism for Aircraft Assessment Under Upset Conditions

Based on the artificial immune systems paradigm and a hierarchical multi-self strategy, a set of algorithms for aircraft sub-systems failure detection, identification, evaluation and flight envelope estimation has been developed and implemented. Data from a six degrees-of-freedom flight simulator were used to define a large set of 2-dimensional self/non-self projections as well as for the generation of antibodies and identifiers designated for health assessment of an aircraft under upset conditions. The methodology presented in this paper classifies and quantifies the type and severity of a broad number of aircraft actuators, sensors, engine and structural component failures. In addition, the impact of these upset conditions on the flight envelope is estimated using nominal test data. Based on immune negative and positive selection mechanisms, a heuristic selection of sub-selves and the formulation of a mapping- based algorithm capable of selectively capturing the dynamic fingerprint of upset conditions is implemented. The performance of the approach is assessed in terms of detection and identification rates, false alarms, and correct prediction of flight envelope reduction with respect to specific states. Furthermore, this methodology is implemented in flight test by using an unmanned aerial vehicle subjected to nominal and four different abnormal flight conditions instrumented with a low cost microcontroller

Adapting to artificial intelligence through workforce re-skilling within the banking sector in South Africa

Abstract: This research paper intends to inspire the banking sector to re-skill the workforces and present the opportunities in re-skilling the banking institutions workforces in South Africa to adapt to the roll out of Artificial Intelligence technologies. The research addresses the factors that will contribute to the workers re-skilling and the skills that are needed in order for the banking workforce to survive in the competitive labor market of the fourth industrial revolution which may result in the obsolete of many job skills. This research also considers the relevant skills and competencies that will be on-demand by the future banking workforces to enable them to successfully adapt to the aspects of the 4IR technological innovations inclusive of the AI toolset such as machine learning, blockchain, nanotechnology, robotics, Internet of Things, biotechnology, cloud computing and so forth, which may ultimately impact the workforce’s performance and productivity in the banking institutions. The research uses descriptive statistics and inferential statistics. The research has achieved results based on the assessment of the relationship between workforces’ capabilities and the components that make up Artificial Intelligence toolset. The findings show that the adaptation of AI strongly depends on most of the stated skills, therefore banks are required to re-skill their workforces in order to adapt to AI advanced technologies so as to make them relevant in the future. Re-skilling the banking workforce to cooperate and collaborate effectively with Artificial Intelligence will enable not only efficiency, but innovation and growth

The use of computational intelligence techniques for mid-term electricity price forecasting

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementWe currently live in a world ruled by large amounts of data. Organizations’ success is highly determined by the way they foresee and assess changes occurring in the future. Predictive data analytics is the art of building and using models that create forecasts based on patterns extracted from historical data. So, it is a process of making projections about a specific event which the outcome is still unknown in the present. One of the main applications is price prediction (Kelleher, Namee, & D’Arcy, 2015). Price prediction can be applied in innumerous types of business, including the energy sector. Additionally, Big Data has created opportunities for development of new energy services and bears a promise of better energy management and conservation (Grolinger, L’Heureux, Capretz, & Seewald, 2016). Whenever prediction deals with time-series data, it can be designated as forecasting. The electricity spot prices (ESP) represent the result of the market bidding prices outcome, in the electric wholesale market. Predicting these prices is an important and impactful task for market participants, like producers, consumers and retailers, since the principal objective for such players is to achieve the lowest cost in comparison with competitors. ESP play a huge role in energy market’s decision making. It is important both for developing proper bidding strategies as well as for making conscient and sustainable investment decisions (Keynia & Heydari, 2019). Additionally, it impacts the decision of the technologies to use, for example, choosing between renewable energy generators or classic gas turbines. Furthermore, the topic of electricity prices forecasting is extremely relevant for both developed and developing countries. Developed countries search for their economic prospect’s improvement. Electric energy efficiency is a crucial metric for that improvement. Electric energy efficiency can decrease the electricity prices thanks to the reduction of consumption, thus decreasing the need of having new expensive power generation and diminishing the pressure on energy resources. Therefore, ESP behavior is an important factor in their economy. Regarding developing economies, which have faced problems to take the populations out of poverty, the electricity sector restructuring has been fundamental for helping increase the levels of economic development (Ebrahimian, Barmayoon, Mohammadi, & Ghadimi, 2018)