Application Of Computational Intelligence Research Articles

Application of computational intelligence and statistical approaches for auto-estimating the compressive strength of plastic concrete

The use of clays, especially bentonite clays in composites, is one of the practical and cost-effective ways for engineers to absorb heavy metals. On the other hand, automatically obtained mechanical properties of these composites, especially plastic concrete (PC) made by adding bentonite clay to the ordinary concrete mixture, can be useful in reducing the cost and optimising the time of the cutoff wall construction in earth dam projects. For this purpose, four computational intelligence and statistical approaches, including support vector machine (SVM), multi-gene genetic programming (MGGP), group method of data handling (GMDH), and response surface methodology (RSM), were used in this article to auto-estimate the compressive strength (CS) of PC. To do this, a comprehensive dataset published in the research literature was used. Also, the models validity was assessed by both external validations of the proposed approaches and Monte-Carlo uncertainty analysis. Finally, each input variable in the models has been studied parametrically over wide ranges to provide practical results to engineers. Comparing the results with the existing literature indicates the acceptable accuracy of the developed models in this study due to optimisation in the speed of developed models and also the less complexity of the proposed ones.

Computational Intelligence for Information Security: A Survey

Information security is the set of processes that protect information away from unauthorized access, disclosure, replication, modification, or destruction. Recently, more and more real-world systems such as smart cities, wireless sensor networks, biometric systems, and surveillance, require the assurances of information security. Thus, many different techniques based on computational intelligence have been developed for information security in the past decades. However, there are no comprehensive surveys that summarize these techniques. Thus, this paper reviewed computational intelligence approaches published in journals and conferences for information security in the last decade. This paper is a brief, but a comprehensive survey to review numerous computational intelligence approaches and applications for information security. A discussion of the existing challenges of computational intelligence approaches and techniques for information security is also presented.

Application of Computational Intelligence in Describing the Drying Kinetics of Persimmon Fruit (Diospyros kaki) During Vacuum and Hot Air Drying Process

This study examines the potential of applying computational intelligence modelling to describe the drying kinetics of persimmon fruit slices during vacuum drying (VD) and hot-air-drying (HAD) under different drying temperatures of 50 °C, 60 °C and 70 °C and samples thicknesses of 5 mm and 8 mm. Kinetic models were developed using selected thin layer models and computational intelligence methods including multi-layer feed-forward artificial neural network (ANN), support vector machine (SVM) and k-nearest neighbors (kNN). The statistical indicators of the coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the suitability of the models. The effective moisture diffusivity and activation energy varied between 1.417 × 10−9 m2/s and 1.925 × 10−8 m2/s and 34.1560 kJ/mol to 64.2895 kJ/mol, respectively. The thin-layer models illustrated that page and logarithmic model can adequately describe the drying kinetics of persimmon sliced samples with R2 values (>0.9900) and lowest RMSE (<0.0200). The ANN, SVM and kNN models showed R2 and RMSE values of 0.9994, 1.0000, 0.9327, 0.0124, 0.0004 and 0.1271, respectively. The validation results indicated good agreement between the predicted values obtained from the computational intelligence methods and the experimental moisture ratio data. Based on the study results, computational intelligence methods can reliably be used to describe the drying kinetics of persimmon fruit.

Current Status of Computational Intelligence Applications in Dermatological Clinical Practice

Background: The yeast infections are increasingly frequent and the correct diagnosis consists of the identification of the yeast fungus, which in our case we are going to refer to the different species of Candida. The prescription of a broad-spectrum antifungal without taking into account the etiological agent, leads to an increase in the resistance to these treatments. Objective: The objective of this work is to differentiate Candida albicans from other Candida species (Candida spp.) By means of digital images obtained from the optical microscope. Material and Methods: It has reviewed about 100 photographs from patients in our consultations. In this study we will use the microscopic images of the Candida variety to be processed later with the Octave programming language and its image processing package (image-2.8.0). Results and Discussion: This system is able to differentiate Candida albicans from the other varieties of Candida such as C. parapsilosis, C. krusei, and C. kefyr with accuracy. The candida identifier application, which was designed and programmed in Octave, allows identification of candida species by locating certain geometric descriptors, such as the centroid and the surfaces of circular objects within the images. The program was highly effective for the diagnosis of Candida spp. So, we got a sensitivity and specificity above 90% with the images used. Conclusion: The results that we obtain from the Candida spp. identifier system that opens the way to be able to work with images obtained from the optical microscope.

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.

Computational intelligence for studies on genetic diversity between genotypes of biomass sorghum

Abstract: The objective of this work was to evaluate the potential of computational intelligence and canonical variables for studies on the genetic diversity between biomass sorghum (Sorghum bicolor) genotypes. The experiments were carried out in the experimental field of Embrapa Milho e Sorgo, in the municipalities of Nova Porteirinha and Sete Lagoas, in the state of Minas Gerais, Brazil. The following traits were evaluated: days to flowering, plant height, fresh biomass yield, total dry biomass, and dry biomass yield. The study of genetic diversity was performed through the analysis of canonical variables. For the recognition of the organization pattern of genetic diversity, Kohonen’s self-organizing map was used. The use of canonical variables and a self-organizing map were efficient for the study of genetic diversity. The application of computational intelligence using a self-organized map is promising and efficient for studies on the genetic diversity between biomass sorghum genotypes.

Automatic detection and characterization of Lunar Wrinkle ridges

Abstract Related computational advances, across various geoscience disciplines, have led to development of an upcoming field: Computers & Geosciences. This multidisciplinary research area is attracting interest of both computer science and planetary/space scientists since last two decades. Under this domain, the most significant area which is gaining importance is application of computational intelligence to automatically extract landforms present on planetary surfaces and determine their morphometric attributes. Wrinkle ridges are one of the common features on lunar surface that need to be explored more for further computation and analysis. This paper focuses on application of image processing approaches for automatic detection of wrinkle ridges and evaluation of morphometric parameters like length, width, orientation and area. The software developed was tested on DEMs (Data Elevation Model) of lunar wrinkle ridges utilizing SLDEM (Selene Lunar Data Elevation Model) data set. The test dataset comprises of 9 lunar wrinkle ridges having 62 segments with a total length of 1414 km. The results were verified by manual calculations using QGIS software. The software was found to perform with a mean percentage deviation varying from 3.10 to 5.97 for different parameters

Guest Editorial Special Issue on Discriminative Learning for Model Optimization and Statistical Inference

Model optimization and statistical inference have played a central role in various applications of computational intelligence, data analytics, and computer vision. Traditional approaches are usually based on model-centric learning. That is, even after model training, it is still required to design proper algorithms and to specify hand-crafted parameters for optimization and inference. Recently, discriminative learning has demonstrated its power for process-centric learning. Taking domain expertise and problem structure into account, problem-specific deep architectures can be formed by unfolding the model inference as an iterative process, and the parameters of the optimization process can then be learned from training data. These solutions are closely related with bilevel optimization, partial differential equation (PDE), as well as meta learning, and can provide new insights into the studies of versatile statistical and optimization models, such as sparse representation, structured regression, and conditional random fields. Moreover, generic deep network architectures are often referred to as “black-box” methods, while discriminative process-centric learning can provide a new perspective for the understanding and development of generic deep architectures. To sum up, connecting discriminative learning with model optimization and inference is not only helpful in analyzing convergence and generalization of deep architectures but also offers new perspectives for understanding and developing generic deep learning models.

Evolutionary-Fuzzy-Integral-Based Convolutional Neural Networks for Facial Image Classification

Various optimization methods and network architectures are used by convolutional neural networks (CNNs). Each optimization method and network architecture style have their own advantages and representation abilities. To make the most of these advantages, evolutionary-fuzzy-integral-based convolutional neural networks (EFI-CNNs) are proposed in this paper. The proposed EFI-CNNs were verified by way of face classification of age and gender. The trained CNNs’ outputs were set as inputs of a fuzzy integral. The classification results were operated using either Sugeno or Choquet output rules. The conventional fuzzy density values of the fuzzy integral were decided by heuristic experiments. In this paper, particle swarm optimization (PSO) was used to adaptively find optimal fuzzy density values. To combine the advantages of each CNN type, the evaluation of each CNN type in EFI-CNNs is necessary. Three CNN structures, AlexNet, very deep convolutional neural network (VGG16), and GoogLeNet, and three databases, computational intelligence application laboratory (CIA), Morph, and cross-age celebrity dataset (CACD2000), were used in experiments to classify age and gender. The experimental results show that the proposed method achieved 5.95% and 3.1% higher accuracy, respectively, in classifying age and gender.

Nitrogen Deficiency Mobile Application for Rice Plant through Image Processing Techniques

Driven by the opportunity that digital devices and robust information are readily available, the development and application of new techniques and tools in agriculture are challenging and rewarding processes. This includes techniques learned that is based on traditional methods, practices, experiences, environmental patterns and human capability. The most sought technique comes from human intelligence that is dynamic, adaptive and robust. Nitrogen deficiency in rice plants can be determined via the color of the leaves. It is dependent on the depth of the green pigment in the color spectrum present in the leaves. Based on these characteristics, the application of computational artificial intelligence and machine vision can be adopted to create assistive technologies for agriculture. In this paper, a mobile application is developed and implemented that can be used to assist rice farmers determine nitrogen deficiency, through the leaf color in rice plants. The application can be used alternatively or together with the traditional protocol of nitrogen fertilizer management. It is mobile, simple and it also addresses some drawbacks of the human eye to distinguish color depths brought about by other factors, like sunlight, shading, humidity, temperature, etc. It utilizes image processing techniques to digitally captured images represented in numerically transformed Red, Green, and Blue color formats. The digital images are then normalized to remove the effects of illumination and then compared using the image/pixel subtraction technique with the base color images converted and extracted from the leaf color chart standard. Eventually, the application determines nitrogen deficiency and suggests the concentration and volume of fertilizer to be applied to the rice plants. Accuracy of the technique is determined by computing the Z statistic score.

The Rényi Entropies Operate in Positive Semifields.

We set out to demonstrate that the Rényi entropies are better thought of as operating in a type of non-linear semiring called a positive semifield. We show how the Rényi’s postulates lead to Pap’s g-calculus where the functions carrying out the domain transformation are Rényi’s information function and its inverse. In its turn, Pap’s g-calculus under Rényi’s information function transforms the set of positive reals into a family of semirings where “standard” product has been transformed into sum and “standard” sum into a power-emphasized sum. Consequently, the transformed product has an inverse whence the structure is actually that of a positive semifield. Instances of this construction lead to idempotent analysis and tropical algebra as well as to less exotic structures. We conjecture that this is one of the reasons why tropical algebra procedures, like the Viterbi algorithm of dynamic programming, morphological processing, or neural networks are so successful in computational intelligence applications. But also, why there seem to exist so many computational intelligence procedures to deal with “information” at large.

Efficient parallel implementation of reservoir computing systems

Reservoir computing (RC) is a powerful machine learning methodology well suited for time-series processing. The hardware implementation of RC systems (HRC) may extend the utility of this neural approach to solve real-life problems for which software solutions are not satisfactory. Nevertheless, the implementation of massive parallel-connected reservoir networks is costly in terms of circuit area and power, mainly due to the requirement of implementing synapse multipliers that increase gate count to prohibitive values. Most HRC systems present in the literature solve this area problem by sequencializing the processes, thus loosing the expected fault-tolerance and low latency of fully parallel-connected HRCs. Therefore, the development of new methodologies to implement fully parallel HRC systems is of high interest to many computational intelligence applications requiring quick responses. In this article, we propose a compact hardware implementation for Echo-State Networks (an specific type of reservoir) that reduces the area cost by simplifying the synapses and using linear piece-wise activation functions for neurons. The proposed design is synthesized in a Field-Programmable Gate Array and evaluated for different time-series prediction tasks. Without compromising the overall accuracy, the proposed approach achieves a significant saving in terms of power and hardware when compared with recently published implementations. This technique pave the way for the low-power implementation of fully parallel reservoir networks containing thousands of neurons in a single integrated circuit.

Assessment of computational intelligence and conventional dissolved gas analysis methods for transformer fault diagnosis

Transformers are vital components of power systems as they are situated between energy generation and consumers and their failure disrupts the use of electrical energy. Therefore, diagnosing an incipient fault is essential in avoiding hazardous operating conditions and minimizes downtime cost. In transformers, faults take place due to electrical or thermal stresses that cause insulation decomposition in transformers. In oil-filled transformers, insulations are cellulose and oil, and the products of the insulation decomposition are gases which can be dissolved in the oil. Therefore, dissolved gas analysis (DGA) can be used for fault diagnosis in oil filled transformers. In this paper, DGA interpretation methods, conventional and intelligence, are investigated and compared. For evaluating consistency and accuracy of the methods, No Result cases are not considered. It can help the newcomers to this field to have access to a comprehensive comparison about the application of computational intelligence and conventional methods in transformer fault detection using DGA.

Survey on Complex Optimization and Simulation for the New Power Systems Paradigm

This survey provides a comprehensive analysis on recent research related to optimization and simulation in the new paradigm of power systems, which embraces the so-called smart grid. We start by providing an overview of the recent research related to smart grid optimization. From the variety of challenges that arise in a smart grid context, we analyze with a significance importance the energy resource management problem since it is seen as one of the most complex and challenging in recent research. The survey also provides a discussion on the application of computational intelligence, with a strong emphasis on evolutionary computation techniques, to solve complex problems where traditional approaches usually fail. The last part of this survey is devoted to research on large-scale simulation towards applications in electricity markets and smart grids. The survey concludes that the study of the integration of distributed renewable generation, demand response, electric vehicles, or even aggregators in the electricity market is still very poor. Besides, adequate models and tools to address uncertainty in energy scheduling solutions are crucial to deal with new resources such as electric vehicles or renewable generation. Computational intelligence can provide a significant advantage over traditional tools to address these complex problems. In addition, supercomputers or parallelism opens a window to refine the application of these new techniques. However, such technologies and approaches still need to mature to be the preferred choice in the power systems field. In summary, this survey provides a full perspective on the evolution and complexity of power systems as well as advanced computational tools, such as computational intelligence and simulation, while motivating new research avenues to cover gaps that need to be addressed in the coming years.

Big data analytics: Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment.

Application of Computational Intelligence to Improve Education in Smart Cities.

According to UNESCO, education is a fundamental human right and every nation’s citizens should be granted universal access with equal quality to it. Because this goal is yet to be achieved in most countries, in particular in the developing and underdeveloped countries, it is extremely important to find more effective ways to improve education. This paper presents a model based on the application of computational intelligence (data mining and data science) that leads to the development of the student’s knowledge profile and that can help educators in their decision making for best orienting their students. This model also tries to establish key performance indicators to monitor objectives’ achievement within individual strategic planning assembled for each student. The model uses random forest for classification and prediction, graph description for data structure visualization and recommendation systems to present relevant information to stakeholders. The results presented were built based on the real dataset obtained from a Brazilian private k-9 (elementary school). The obtained results include correlations among key data, a model to predict student performance and recommendations that were generated for the stakeholders.

3D localization of moving target nodes using single anchor node in anisotropic wireless sensor networks

In this paper, novel 3D node localization algorithms using applications of Computational Intelligence (CI) for moving target nodes are attained using single reference node (anchor node) in an anisotropic network. Target nodes are randomly deployed at beneath and middle layers. Whereas single anchor node is deployed at top layer. Recent applications of Computational Intelligence (CI), i.e., Particle Swarm Optimization (PSO), H-Best Particle Swarm Optimization (HPSO), Biogeography Based Optimization (BBO) and Firefly Algorithm (FA) are used respectively to estimate the optimum location of moving target nodes. In this paper, each target node has heterogeneous properties (due to different battery backup statuses). Degree of Irregularity (DOI) of 0.01 is considered for radiation pattern. In heterogeneous network, the geometric distance between two nodes is not proportional to their hop count. Once a moving target falls under the range of a deployed anchor node, three virtual anchor nodes (minimum 4 anchor nodes are required for 3D positions) in surrounding of anchor and respective moving target node are projected by using umbrella projection form to find the 3D position. The proposed algorithms are designed for rescue operations in highly hostile environment.

Laser Fluence Recognition Using Computationally Intelligent Pulsed Photoacoustics Within the Trace Gases Analysis

In this paper, the possibilities of computational intelligence applications for trace gas monitoring are discussed. For this, pulsed infrared photoacoustics is used to investigate $$\hbox {SF}_{6}$$ –Ar mixtures in a multiphoton regime, assisted by artificial neural networks. Feedforward multilayer perceptron networks are applied in order to recognize both the spatial characteristics of the laser beam and the values of laser fluence $$\Phi $$ from the given photoacoustic signal and prevent changes. Neural networks are trained in an offline batch training regime to simultaneously estimate four parameters from theoretical or experimental photoacoustic signals: the laser beam spatial profile R(r), vibrational-to-translational relaxation time $$\tau _{V-T} $$ , distance from the laser beam to the absorption molecules in the photoacoustic cell r* and laser fluence $$\Phi $$ . The results presented in this paper show that neural networks can estimate an unknown laser beam spatial profile and the parameters of photoacoustic signals in real time and with high precision. Real-time operation, high accuracy and the possibility of application for higher intensities of radiation for a wide range of laser fluencies are factors that classify the computational intelligence approach as efficient and powerful for the in situ measurement of atmospheric pollutants.

Application of Computational Intelligence for Solving Economic Load Dispatch Problem

Application of Computational Intelligence for Solving Economic Load Dispatch Problem

Search Based Software Engineering [Guest Editorial

The articles in this special section focus on search-based software engineering. Search Based Software Engineering (SBSE) consists of the application of computational intelligence (CI) algorithms to hard optimization problems in software engineering (SE). It has become an important application field for CI. The term SBSE was coined by Harman and Jones in 2001, although there was work on the application of CI algorithms to SE before this date. After more than fifteen years development, CI algorithms have been used to solve SE tasks in almost all the stages of an SE lifecycle, including requirements, designing, coding, testing and maintenance. solved by three steps.