Application Of Computational Intelligence Techniques Research Articles

Automation of fuzzy systems for intelligent traffic lights

Intense vehicle traffic is one of the main disorders in large cities, and in Brazil, where the responsible authorities have not trained the road networks, overcrowding in traffic causes even more obstacles. The applications of computational intelligence techniques in traffic are very broad, with emphasis on smart traffic lights. For the design of intelligent traffic lights, this work proposes the use of Fuzzy Logic, and has as main objective the automatic generation of fuzzy systems using evolutionary fuzzy models for this purpose. To achieve this objective, the traffic simulation software SUMO is used, which allows the elaboration of scenarios of intersections controlled by traffic lights. In these scenarios, the traffic performance is evaluated based on different adjustments in the membership functions and in the set of rules of the fuzzy system that controls the traffic lights, and these adjustments are performed by Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). When comparing the traffic performance with traffic lights controlled by fuzzy and fuzzy with optimized hyperparameters, there are important improvements in the analyzed traffic variables, such as waiting time and car queue size/length, in addition to reducing the emission of toxic gases and fuel consumption. Thus, this work highlights the importance of employing evolutionary fuzzy models in hyperparameters optimization.

Synapps - platform for estimating, controlling and optimizing wastewater treatment plants

Wastewater generated by the world's vast population is an important source of pollution and can accelerate the loss of biodiversity and impede the achievement of objectives set by the international community regarding the good status of water bodies. In line with the European Union's sustainable development strategy, which foresees the adoption of increasingly demanding environmental control, energy efficiency and rational management of resources measures, a project with high potential for economic valorization is being implemented, focused on design, development and validation in real conditions, to create an innovative platform for estimating, controlling and optimizing wastewater treatment plants (WWTP), called SYNAPPS. Based on the implementation of multiparametric measurement chains and the application of computational intelligence techniques (eg Big Data Analytics, Data Mining and Machine Learning), the SYNAPPS platform should be capable of providing integrated management of the various WWTP treatment processes, ensuring a high environmental, energy and operational performance, and also simplify and relieve the burden of operating this type of technical infrastructure. SYNAPPS is an R&D project approved by PO Centro, through ANI, started in January 2021 and will last for a period of 30 months, with a budget of around one million euros executed under the responsibility of the consortium formed by CTGA, a company with more than 25 years of experience in the operationalization and management of WWTP and leads the consortium, and by the non-business entities of SI&I, namely, ITeCons with relevant experience in the development of automation and dynamic control systems and in the evaluation of energy and environmental performance of processes, and ISR specializes in evaluating the energy performance of complex systems and developing control algorithms based on computational intelligence.

Applications of computational intelligence techniques to software engineering problems

Applications of computational intelligence techniques to software engineering problems

A SURVEY ON OPTIMIZATION OF TRANSMIT POWER IN COGNITIVE RADIO NETWORKS THROUGH NATURE INSPIRED COMPUTATIONAL INTELLIGENCE TECHNIQUES

Cognitive radio (CR) is a field which is gaining a lot of interest of researchers in around the whole world. It is also termed as nextgen technology as it aims to solve the problem of unutilized electromagnetic spectrum assigned to licensed users. With increasing wireless devices there is a demand of more spectrum resources and cognitive radio can help in making available the unused or underused spectrum for real time communication. When number of cognitive users communicate together at the same time there exists the problem of interference due to transmit power of all the devices to the licensed users. So there is a need of controlling power of transmission for cognitive users so that they may not interfere with Primary licensed users and maintain QoS with other cognitive users. The power control of a large number of devices together can be done by the application of computational intelligence techniques. In this paper three nature inspired computational intelligence techniques like GA, PSO and ABC for optimization of transmit power in cognitive radio networks are discussed.

Computational Intelligence Enabled Cybersecurity for the Internet of Things

The computational intelligence (CI) based technologies play key roles in campaigning cybersecurity challenges in complex systems such as the Internet of Things (IoT), cyber-physical-systems (CPS), etc. The current IoT is facing increasingly security issues, such as vulnerabilities of IoT systems, malware detection, data security concerns, personal and public physical safety risk, privacy issues, data storage management following the exponential growth of IoT devices. This work aims at investigating the applicability of computational intelligence techniques in cybersecurity for IoT, including CI-enabled cybersecurity and privacy solutions, cyber defense technologies, intrusion detection techniques, and data security in IoT. This paper also attempts to provide new research directions and trends for the increasingly IoT security issues using computational intelligence technologies.

Application of computational intelligence techniques for internet of things: an extensive survey

The application of computational intelligence (CI) techniques to internet of things (IoT) is gaining popularity due to its capability of providing human-like knowledge, such as cognition, recognition, understanding, learning, and others. This paper attempts to provide an exhaustive survey of the available literature on IoT using CI techniques. In addition, detailed categorisation has been provided on the basis of different CI tools and their hybridisations used to tackle different problems of IoT. The potential benefits and utility of CI techniques in IoT are highlighted. The possible mapping of CI techniques to the real-world IoT problems is presented. The advantages and disadvantages of CI algorithms over traditional IoT solutions are discussed. A general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for IoT. Finally, some considerations regarding the recent trends and potential research directions are presented. An extensive bibliography is also included.

Application of computational intelligence techniques for internet of things: an extensive survey

Application of computational intelligence techniques for internet of things: an extensive survey

Application of Computational Intelligence Techniques to an Environmental Flow Formula

Application of Computational Intelligence Techniques to an Environmental Flow Formula

Application of computational intelligence techniques to forecast daily PM10 exceedances in Brunei Darussalam

Particulate matter (PM10) is the pollutant causing exceedances of ambient air quality thresholds, and the key indicator of air quality index in Brunei Darussalam for haze related episodes caused by the recurrent biomass fires in Southeast Asia. The present study aims at providing suitable forecasts for PM10 exceedances to aid in health advisory during haze episodes at the four administrative districts of the country. A framework based on random forests (RFs), genetic algorithm (GA) and back propagation neural networks (BPNN) computational intelligence techniques has been proposed in which the final prediction is made by the BPNN model. A hybrid combination of GA and RFs is initially applied to determine optimal set of inputs from the initial data sets of largely available meteorological, persistency of high pollution levels, short and long term variations of emissions rates parameters. The inputs selection procedure does not depend on the back propagation training algorithm. The numerical results presented in this paper show that the proposed model not only produced satisfactory forecasts but also consistently performed better via several statistical performance indicators when compared with the standard BPNN and GA optimisation based on back propagation training algorithm. The model also showed satisfactory threshold exceedances forecasts achieving for instance best true predicted rate of 0.800, false positive rate of 0.014, false alarm rate of 0.333 and success index of 0.786 at Brunei-Muara district monitoring station. Overall, the current study has profound implications on future studies to develop a real-time air quality forecasting system to support haze management.

Variable Generation Power Forecasting as a Big Data Problem

To blend growing amounts of power from renewable resources into utility operations requires accurate forecasts. For both day ahead planning and real-time operations, the power from the wind and solar resources must be predicted based on real-time observations and a series of models that span the temporal and spatial scales of the problem, using the physical and dynamical knowledge as well as computational intelligence. Accurate prediction is a Big Data problem that requires disparate data, multiple models that are each applicable for a specific time frame, and application of computational intelligence techniques to successfully blend all of the model and observational information in real-time and deliver it to the decision makers at utilities and grid operators. This paper describes an example system that has been used for utility applications and how it has been configured to meet utility needs while addressing the Big Data issues.

Project Control and Computational Intelligence: Trends and Challenges

Project monitoring and control by using key performance indicators has become a widespread method for decision-making in project-oriented organizations. However, the current schools and IT tools created for this purpose require an upgrade in design due to imprecision, vagueness or uncertainty present in the raw data and changing conditions in management styles. Moreover, the use of proprietary technologies in developing nations represents high costs for governments and obstacles to achieving its technological sovereignty. This paper studies the trends and challenges in project control through computational intelligence methods. It also examines schools and technological tools to manage projects, as well as open source software for the application of computational intelligence techniques over the past decades. Current tendencies and improvement areas, valuing niche markets with high applicability around the thematic goal it is also analyzed. The contribution of this study is related to the predicted necessity of constructing new models and IT tools for project control which integrate machine learning-based approaches and treatment of imprecision, vagueness or uncertainty in the information, using key performance indicators linked to fundamental knowledge areas. The implementation of new libraries for learning evaluation in project control with open source software tools, opens a field of research related to increase technological integration with IT project management tools. The content under discussion provides support to improve decision-making in project-oriented organizations.

Automatic selection of molecular descriptors using random forest: Application to drug discovery

The optimal selection of chemical features (molecular descriptors) is an essential pre-processing step for the efficient application of computational intelligence techniques in virtual screening for identification of bioactive molecules in drug discovery. The selection of molecular descriptors has key influence in the accuracy of affinity prediction. In order to improve this prediction, we examined a Random Forest (RF)-based approach to automatically select molecular descriptors of training data for ligands of kinases, nuclear hormone receptors, and other enzymes. The reduction of features to use during prediction dramatically reduces the computing time over existing approaches and consequently permits the exploration of much larger sets of experimental data. To test the validity of the method, we compared the results of our approach with the ones obtained using manual feature selection in our previous study (Perez-Sanchez, Cano, and Garcia-Rodriguez, 2014).The main novelty of this work in the field of drug discovery is the use of RF in two different ways: feature ranking and dimensionality reduction, and classification using the automatically selected feature subset. Our RF-based method outperforms classification results provided by Support Vector Machine (SVM) and Neural Networks (NN) approaches.

A Multi-Objective Harmony Search Algorithm for Sustainable Design of Floating Settlements

This paper is concerned with the application of computational intelligence techniques to the conceptual design and development of a large-scale floating settlement. The settlement in question is a design for the area of Urla, which is a rural touristic region located on the west coast of Turkey, near the metropolis of Izmir. The problem at hand includes both engineering and architectural aspects that need to be addressed in a comprehensive manner. We thus adapt the view as a multi-objective constrained real-parameter optimization problem. Specifically, we consider three objectives, which are conflicting. The first one aims at maximizing accessibility of urban functions such as housing and public spaces, as well as special functions, such as a marina for yachts and a yacht club. The second one aims at ensuring the wind protection of the general areas of the settlement, by adequately placing them in between neighboring land masses. The third one aims at maximizing visibility of the settlement from external observation points, so as to maximize the exposure of the settlement. To address this complex multi-objective optimization problem and identify lucrative alternative design solutions, a multi-objective harmony search algorithm (MOHS) is developed and applied in this paper. When compared to the Differential Evolution algorithm developed for the problem in the literature, we demonstrate that MOHS achieves competitive or slightly better performance in terms of hyper volume calculation, and gives promising results when the Pareto front approximation is examined.

Contravening Esotery: Cryptanalysis of Knapsack Cipher using Genetic Algorithms

Cryptanalysis of knapsack cipher is a fascinating problem which has eluded the computing fraternity for decades. However, in most of the cases either the time complexity of the proposed algorithm is colossal or an insufficient number of samples have been taken for verification. The present work proposes a Genetic Algorithm based technique for cryptanalysis of knapsack cipher. The experiments conducted prove the validity of the technique. The results prove that the technique is better than the existing techniques. An extensive review has been carried out in order to find the gaps in the existing techniques. The work paves the way of the application of computational intelligence techniques to the discipline of cryptanalysis.

Application of computational intelligence technique for estimating superconducting transition temperature of YBCO superconductors

Yttrium barium copper oxide (YBCO) is a high temperature superconductor with excellent potential for long distance power transmission applications as well as other applications involving generation of high magnetic field such as magnetic resonance imaging machines in hospitals. Among the uniqueness of this material is its perpetual current carrying ability without loss of energy. Practical applications of YBCO superconductor depend greatly on the value of the superconducting transition temperature (TC) attained by YBCO superconductor upon doping with other external materials. The number of holes (i.e. doping) present in an atom of copper in CuO2 planes of YBCO superconductor controls its TC. Movement of the apical oxygen along CuO2 planes due to doping gives insight to the way of determining the effect of doping on TC using the bound related quantity (lattice parameter) that is easily measurable with reasonable high precision. This work employs excellent predictive and generalization ability of computational intelligence technique via support vector regression (SVR) to develop a computational intelligence-based model (CIM) that estimates the TC of thirty-one different YBCO superconductors using lattice parameters as the descriptors. The estimated superconducting transition temperatures agree with the experimental values with high degree of accuracy. The developed CIM allows quick and accurate estimation of TC of any fabricated YBCO superconductor without the need for any sophisticated equipment.

Analysis of Valuable Techniques and Algorithms Used in Automated Skin Lesion Recognition Systems

Application of computational intelligence techniques helps physicians as well as dermatologists in faster data process to give better and more reliable diagnoses. The whole system is categorized as: Pre-processing the lesion image to enhance its readability, Segmentation of the Lesion from skin, Feature extraction, selection, and finally the identification of dermoscopic images. Pros and cons of various methods are focused to provide a help for the researchers starting work in automated lesion detection system. Numerous computerized diagnostic systems have been reported in which different border detection, feature extraction, selection, and classification algorithms are used. The aim of this review is to summarize and compare advanced dermoscopic algorithms used for the classification of skin lesions and discuss important issues affecting the success of classification. This paper will be a guide that represents a comprehensive guideline for selecting suitable algorithms needed for different steps of automatic diagnostic procedure for ensuring timely diagnosis of skin cancer.

Lunch isn't free --- but cells are

The application of computational intelligence techniques within the vast domain of games has been increasing at a breath-taking speed. Over the past several years our research group has produced a plethora of results in numerous games of different natures, evidencing the success and efficiency of evolutionary algorithms in general --- and genetic programming in particular --- at producing top-notch, human-competitive game strategies. Herein, we describe our study of the game of FreeCell, which produced two Gold Humie Awards. Our top evolved FreeCell player is the best published player to date, able to convincingly beat high-ranking human players.

Recent advances in the application of computational intelligence techniques in oil and gas reservoir characterisation: a comparative study

A comparative study of the predictive capabilities of recent advances in computational intelligence (CI) is presented. This study utilised the machine learning paradigm to evaluate the CI techniques while applying them to the prediction of porosity and permeability of heterogeneous petroleum reservoirs using six diverse well data sets. Porosity and permeability are the major petroleum reservoir properties that serve as indicators of reservoir quality and quantity. The results showed that the performance of support vector machines (SVM) and functional networks (FN) is competitively better than that of Type-2 fuzzy logic system (T2FLS) in terms of correlation coefficient. With execution time, FN and SVM were faster than T2FLS, which took the most time for both training and testing. The results also demonstrated the capability of SVM to handle small data sets. This work will assist artificial intelligence practitioners to determine the most appropriate technique to use especially in conditions of limited amount of data and low processing power.

A genetic type-2 fuzzy logic based system for the generation of summarised linguistic predictive models for financial applications

The global economic meltdown of the late 2000s exposed many organisations around the world, this drove the need to build robust frameworks for predicting and assessing risks in financial applications. Such predictive frameworks helped organisations to increase the quality and quantity of their transactions hence increasing the revenues and reducing the risks. Many organisations around the World still use statistical regression techniques which are well established for many problems such as fraud detection or risk analysis. However, recent years have seen the application of computational intelligence techniques to develop predictive models for financial applications. Some of the computational intelligence techniques like neural networks provide good predictive models, nevertheless they are considered as black box models which do not provide an easy to understand reasoning about a given decision or even a summary of the generated model. However, in the current economic situation, transparency became an important factor where there is a need to fully understand and analyze a given financial model. In this paper, we will present a Genetic Type-2 Fuzzy Logic System (FLS) for the modeling and prediction of financial applications. The proposed system is capable of generating summarized models from a specified number of linguistic rules, which enables the user to understand the generated financial model. The system is able to use this summarized model for prediction within financial applications. We have performed several evaluations in two distinctive financial domains, one for the prediction of good/bad customers in a financial real-world lending application and the other domain was in the prediction of arbitrage opportunities in the stock markets. The proposed Genetic type-2 FLS has outperformed white box models like the Evolving Decision Rule procedure (which is a white based on Genetic Programming and decision trees) and gave a comparable performance to black box models like neural networks while the proposed genetic type-2 FLS provided a white box model which is easy to understand and analyse by the lay user.

Application of computational intelligence techniques for load shedding in power systems: A review

Recent blackouts around the world question the reliability of conventional and adaptive load shedding techniques in avoiding such power outages. To address this issue, reliable techniques are required to provide fast and accurate load shedding to prevent collapse in the power system. Computational intelligence techniques, due to their robustness and flexibility in dealing with complex non-linear systems, could be an option in addressing this problem. Computational intelligence includes techniques like artificial neural networks, genetic algorithms, fuzzy logic control, adaptive neuro-fuzzy inference system, and particle swarm optimization. Research in these techniques is being undertaken in order to discover means for more efficient and reliable load shedding. This paper provides an overview of these techniques as applied to load shedding in a power system. This paper also compares the advantages of computational intelligence techniques over conventional load shedding techniques. Finally, this paper discusses the limitation of computational intelligence techniques, which restricts their usage in load shedding in real time.