Computational Intelligence Paradigms Research Articles

Computational intelligence paradigm with Levenberg-Marquardt networks for dynamics of Reynolds nanofluid model for Casson fluid flow

The work in hand examines the dynamics of Reynolds nanofluid model for Casson fluid flow (RNFM-CFF) through a slandering sheet by using computational intelligence of Levenberg-Marquardt networks (CILMNs). For two-dimensional and two-directional flows, mathematical formulations of Reynolds nanofluid model are developed. The partial differential equations are reduced to ordinary differential equations by using appropriate transformations. The evaluation of the Prandtl number, Casson fluid parameter, velocity power index parameter, and Surface thickness parameter over velocity profiles and temperature profiles is accomplished on synthetic dataset for various variations of RNFM-CFF by exploiting CILMNs. For the investigation and examination of the estimated solution of the validation, training and testing process are utilized, and the performance is verified through error histogram, regression index, mean square error-based fitness in the range of 10−09 to 10−13.

MPDM: A Multi-Paradigm Deployment Model for Large-Scale Edge-Cloud Intelligence

The development of cloud and edge computing has enabled the easy access of artificial intelligence (AI) services for massive heterogeneous and resource-constrained devices. Particularly, computation-intensive AI services can be orchestrated and deployed in the cloud or edge according to varying performance and cost requirements. Nonetheless, the improved accessibility of deep learning (DL) model variants and the evolving of computational intelligence paradigms pose great challenges for orchestrating large-scale DL inference services in the cloud-edge continuum. Focusing on cloud or edge-based deployment, existing work on multi-variant service orchestration often has a limited solution space of deployment plans. To address this limitation, we first propose a novel Multi-Paradigm Deployment Model (MPDM) for service orchestration, which not only considers the model variants but also allows the co-existence of multiple paradigms for large-scale inference service deployment. The service deployment in the MPDM model is then formulated as a multi-objective optimization problem of seeking a better trade-off among the system accuracy, service scale and deployment cost. To solve the multi-objective optimization, we further propose a weighted metric based constructive heuristic algorithm (WCH), which can efficiently obtain an approximately optimal Pareto frontier. Extensive experimental results have validated the effectiveness and efficiency of WCH, and revealed the impacts of both multi-paradigm deployment and edge-cloud collaborative intelligence (ECCI) paradigm on large-scale DL serving systems.

Computational intelligence paradigms for automata based secured energy efficient routing in wireless sensor network - A technical survey

Wireless Sensor Network (WSN) has been deployed in several areas of interest for controlling the region, automation of mundane tasks due to features such as smart sensor nodes, low cost, and small-scale factors. Earlier, the sensory units were costly and deficient in the computational and communicational capabilities which were overcome by sensing, processing, storing, and forwarding the data powered by a battery. There predominates diverse applications which affects the low-cost solutions of WSNs in numerous specialties such as observing patients in health care, target exposure, and tracking purposes, examining the atmosphere and climate, home applications, contributing protection for vehicular networks Owing to the diversity of the applications of Wireless Sensor Network, it needs to impose varying design, implementation, and performance requirements on the Wireless Sensor Network to have deep knowledge about the characteristics of WSNs. The pervasiveness of designing and optimizing WSN appeared to be a surplus to several application fields which influenced countless researchers to pay attention to several issues such as routing, mobility of nodes, coverage, and security. In recent years, designing of WSN becomes the leading domain for many researchers. A WSN is bounded with a collection of devices that are associated in the network to communicate the information collected from the field through the wireless links that have been established in the network. The data thus collected is transmitted through multiple nodes and also, the data is communicated to other networks through the gateway. During the design and deployment of WSNs, more attention is required at various levels like routing, coverage, and security. A complete system could be designed so that it could resolve the problems such as energy exhaustion, network lifetime, throughput, end-to-end time, routing, and intrusion detection could also monitor and keep control of the network environment. Therefore, this proposed technical research survey work has been carried out by devising an automaton system that can learn, monitor and control the dynamic behaviour of the network environment as well as can obtain an optimal route using Computational Intelligence Techniques. Besides, it detects the malicious activities that occur in the network using Hybrid Intrusion Detection System (IDS) model to enhance the throughput, lifetime of the network, utilization of the energy, end-to-end delay, accuracy, detection rate, computational time and recall rate.

Review on Deep Learning-Based CAD Systems for Breast Cancer Diagnosis.

Breast Cancer (BC) is a major health issue in women of the age group above 45. Identification of BC at an earlier stage is important to reduce the mortality rate. Image-based noninvasive methods are used for early detection and for providing appropriate treatment. Computer-Aided Diagnosis (CAD) schemes can support radiologists in making correct decisions. Computational intelligence paradigms such as Machine Learning (ML) and Deep Learning (DL) have been used in the recent past in CAD systems to accelerate diagnosis. ML techniques are feature driven and require a high amount of domain expertise. However, DL approaches make decisions directly from the image. The current advancement in DL approaches for early diagnosis of BC is the motivation behind this review. This article throws light on various types of CAD approaches used in BC detection and diagnosis. A survey on DL, Transfer Learning, and DL-based CAD approaches for the diagnosis of BC is presented in detail. A comparative study on techniques, datasets, and performance metrics used in state-of-the-art literature in BC diagnosis is also summarized. The proposed work provides a review of recent advancements in DL techniques for enhancing BC diagnosis.

Expert systems and computational intelligence paradigms for ubiquitous computing in E‐health systems

Expert systems and computational intelligence paradigms for ubiquitous computing in <scp>E‐health</scp> systems

Fractional order Lorenz based physics informed SARFIMA-NARX model to monitor and mitigate megacities air pollution

Air Pollution is an emerging disaster and considered one of the biggest challenges of the world to effectively control, mitigate and forecast due to abrupt variability, stochastic, and chaotic pattern of particulate matter (PM) in terms of time and space of the pollutants. Composition of ambient PM not only causes serious damage to public health but also emerging as a global hazard particularly for urban environment with negative impact on human health including morbidity. Mortality and ultimately towards unstable economy. In this study, hourly short-term trends of PM2.5 and air quality index (AQI) of Lahore city of Pakistan is monitored and mitigated by the design of fractional order Lorenz based physics informed hybrid computing paradigm SARFIMA-NARX for forecasting hourly pattern of next two days. The complex dynamics of earth system and its weather forecast are characterized by combination of biological, physical, and chemical processes governed by the different laws of science that provides additional information for the climate variation in terms of physics inform intelligence. The performance index based on statistical indicator of RMSE confirmed the high accuracy and efficiency of designed model to predict the pattern. The early predictions based on computational intelligence paradigm may serve as a surveillance system to reduce the air pollution through cost-effectiveness planning by environmental monitoring agencies.

Metaheuristic algorithms in optimization and its application: a review

Metaheuristic algorithms are computational intelligence paradigms especially used for solving different optimization issues. Metaheuristics examine a collection of solutions otherwise really be wide to be thoroughly addressed or discussed in any other way. Metaheuristics can be applied to a wide range of problems because they make accurate predictions in any optimization situation. Natural processes such as the fact of evolution in Natural selection behavioral genetics, ant behaviors in genetics, swarm behaviors of certain animals, annealing in metallurgy, and others motivate metaheuristics algorithms. The big cluster search algorithm is by far the most commonly used metaheuristic algorithm. The principle behind this algorithm is that it begins with an optimal state and then uses heuristic methods from the community search algorithm to try to refine it. Many metaheuristic algorithms in diverse environments and areas are examined, compared, and described in this article. Such as Genetic Algorithm (GA), ant Colony Optimization Algorithm (ACO), Simulated Annealing (SA), Particle Swarm Optimization (PSO) algorithm, Differential Evolution (DE) algorithm and etc. Finally, show the results of each algorithm in various environments were addressed.

Design of Mayer Wavelet Neural Networks for Solving Functional Nonlinear Singular Differential Equation

In the present work, an advance computational intelligence paradigm based on functional Mayer artificial neural network (FM-ANN) is accessible for solving the singular nonlinear functional differential equation (NFDE) numerically. The solution of singular NFDE is performed by using the artificial neural networks (ANNs) optimized with global search genetic algorithm (GA) enhanced by local refinements of sequential quadratic (SQ) programming and the hybrid of GASQ programming. The proposed scheme is applied for solving three types of second-order singular NFDEs. In order to validate the correctness of the designed scheme, the comparison of the proposed and exact solutions has been performed. Moreover, the statistical interpretations are used to prove the worth, convergence, accuracy, stability, and robustness of FM-ANN-GASQP for the solution of singular NFDEs.

Computational intelligence paradigms for UWB antennas: a comprehensive review of analysis, synthesis and optimization

Computational intelligence paradigms for UWB antennas: a comprehensive review of analysis, synthesis and optimization

Supervised Kohonen Self-Organizing Maps of Acute Asthma from Air Pollution Exposure.

There are unanswered questions with regards to acute respiratory outcomes, particularly asthma, due to environmental exposures. In contribution to asthma research, the current study explored a computational intelligence paradigm of artificial neural networks (ANNs) called self-organizing maps (SOM). To train the SOM, air quality data (nitrogen dioxide, sulphur dioxide and particulate matter), interpolated to geocoded addresses of asthmatics, were used with clinical data to classify asthma outcomes. Socio-demographic data such as age, gender and race were also used to perform the classification by the SOM. All pollutants and demographic traits appeared to be important for the correct classification of asthma outcomes. Age was more important: older patients were more likely to have asthma. The resultant SOM model had low quantization error. The study concluded that Kohonen self-organizing maps provide effective classification models to study asthma outcomes, particularly when using multidimensional data. SO2 was concluded to be an important pollutant that requires strict regulation, particularly where frail subpopulations such as the elderly may be at risk.

Adaptation of metaheuristic algorithms to improve training performance of an ESZSL model

Zero-shot learning (ZSL) is a recent promising learning approach that is similar to human vision systems. ZSL essentially allows machines to categorize objects without requiring labeled training data. In principle, ZSL proposes a novel recognition model by specifying merely the attributes of the category. Recently, several sophisticated approaches have been introduced to address the challenges regarding this problem. Embarrassingly simple approach to zeroshot learning (ESZSL) is one of the critical of those approaches that basically proposes a simple but efficient linear code solution. However, the performance of the ESZSL model mainly depends on parameter selection. Metaheuristic algorithms are considered as one the most sophisticated computational intelligence paradigms that allows to approximate optimization problems with high success. This paper addresses this problem by adapting leading metaheuristic algorithms to automatically train the parameters of a linear ESZSL model. The model is statistically validated by performing a series of experiments with benchmark datasets.

Computational intelligence paradigm for job shop scheduling and routing in an uncertain environment

ABSTRACT Computational Intelligence (CI) is a more efficient paradigm for solving real-world problems in uncertain conditions. The traditional CI approaches are not capable to provide the complete and sufficient solutions for problems. Therefore, new techniques are necessary to efficiently solve these issues seriously. New techniques, such as Emergent Intelligence (EI), Multi-Agent System (MAS), etc., provide robust, generic, flexible, and self-organised to solve complex real-world problems. In this paper, we discuss Emergent Intelligence (EI) and its uniqueness in solving problems in an uncertain environment. We also discuss EI, Swarm Intelligence (SI) and MultiAgent System (MAS)-based problem-solving in an uncertain environment and compared their performance. We have considered two different problems: job shop scheduling using EI and MAS and route establishment for routing using MAS, SI and EI in an uncertain environment. Each problem is categorically analysed and solved step by step using MAS, SI and EI in a dynamic environment. We measure the performance of these three methods by varying the number of agents, tasks and time. Performance measures are compared and shown to demonstrate the importance of EI over MAS and SI for solving problems in an uncertain environment.

Toward the Development of a Universal Choke Correlation – Global Optimization and Rigorous Computational Techniques

Wellhead choke is an integral part of production systems. Wellhead chokes act as regulators to protect surface facilities from high rate slugs, formation damage, and water coning. These chokes also control the decline rate of reservoir pressure. Accordingly, a diverse data bank of critical flow across wellhead chokes has been compiled to develop a universal choke performance model. Three models developed to predict liquid flow rate in two-phase flow through chokes. The first two models are measured by computational intelligence paradigms such as the Artificial Neural Network and the Least Square Support Vector Machine. The third model was developed using a global optimization simplex algorithm. The three models outperformed all existing models, and the results clearly highlight the accuracy and superiority of the intelligence models over the existing empirical correlations.

Intelligence computing approach for solving second order system of Emden–Fowler model

In this research study, an advance computational intelligence paradigm is used for solving second order Emden-Fowler system (EFS) based on artificial neural network, genetic algorithm (GA) which is a famous global search method, sequential quadratic programming (SQP) known as rapid local refinement and the hybrid of GA-SQP. The proficiency of the designed scheme is inspected by solving the three examples of EFS to check the efficiency, consistency, precision and exactness of the technique. The numerical outcomes of the purposed scheme are compared with the exact solution that shows the significance of the scheme based on accuracy, correctness and convergence. Moreover, statistical explorations have been executed to verify the precision and accuracy of the outcomes based on performance measures of mean absolute deviation, root mean squared error and variance account for.

Applying the Computational Intelligence Paradigm to Nuclear Power Plant Operation

In the guise of artificial neural networks (ANNs), genetic/evolutionary computation algorithms (GAs/ECAs), fuzzy logic (FL) inference systems (FLIS) and their variants as well as combinations, the computational intelligence (CI) paradigm has been applied to nuclear energy (NE) since the late 1980s as a set of efficient and accurate, non-parametric, robust-to-noise as well as to-missing-information, non-invasive on-line tools for monitoring, predicting and overall controlling nuclear (power) plant (N(P)P) operation. Since then, the resulting CI-based implementations have afforded increasingly reliable as well as robust performance, demonstrating their potential as either stand-alone tools, or - whenever more advantageous - combined with each other as well as with traditional signal processing techniques. The present review is focused upon the application of CI methodologies to the - generally acknowledged as - key-issues of N(P)P operation, namely: control, diagnostics and fault detection, monitoring, N(P)P operations, proliferation and resistance applications, sensor and component reliability, spectroscopy, fusion supporting operations, as these have been reported in the relevant primary literature for the period 1990-2015. At one end, 1990 constitutes the beginning of the actual implementation of innovative, and – at the same time – robust as well as practical, directly implementable in H/W, CI-based solutions/tools which have proved to be significantly superior to the traditional as well as the artificial-intelligence-(AI)derived methodologies in terms of operation efficiency as well as robustness-to-noise and/or otherwise distorted/missing information. At the other end, 2015 marks a paradigm shift in terms of the emergent (and, swiftly, ubiquitous) use of deep neural networks (DNNs) over existing ANN architectures and FL problem representations, thus dovetailing the increasing requirements of the era of complex - as well as Big - Data and forever changing the means of ANN/neuro-fuzzy construction and application/performance. By exposing the prevalent CI-based tools for each key-issue of N(P)P operation, overall as well as over time for the given 1990-2015 period, the applicability and optimal use of CI tools to NE problems is revealed, thus providing the necessary know-how concerning crucial decisions that need to be made for the increasingly efficient as well as safe exploitation of NE.

A Computational Intelligence Paradigm with Human Computer Interface Learning

The cognitive Science is the leading technology which works on the principle of Neuroscience. Human Computer Interface is a challenging approach in neurosciences, which is the leading method to handle the brain activities to control external communications with the electronic devices for physically challenged human beings. The various HCI applications are developed with this advance technology. This helps in various patients which are physically challenged or facing the lock in syndrome, a condition where limbs are not functioning to full extent. Therefore, this paper is the review paper to the various EEG signal classification techniques using different taxonomy with techniques like linear, nonlinear, stableubstable, static-discriminant to design various HCI applications.

Chaotic Quantum Double Delta Swarm Algorithm Using Chebyshev Maps: Theoretical Foundations, Performance Analyses and Convergence Issues

The Quantum Double Delta Swarm (QDDS) Algorithm is a networked, fully-connected novel metaheuristic optimization algorithm inspired by the convergence mechanism to the center of potential generated within a single well of a spatially colocated double–delta well setup. It mimics the wave nature of candidate positions in solution spaces and draws upon quantum mechanical interpretations much like other quantum-inspired computational intelligence paradigms. In this work, we introduce a Chebyshev map driven chaotic perturbation in the optimization phase of the algorithm to diversify weights placed on contemporary and historical, socially-optimal agents’ solutions. We follow this up with a characterization of solution quality on a suite of 23 single–objective functions and carry out a comparative analysis with eight other related nature–inspired approaches. By comparing solution quality and successful runs over dynamic solution ranges, insights about the nature of convergence are obtained. A two-tailed t-test establishes the statistical significance of the solution data whereas Cohen’s d and Hedge’s g values provide a measure of effect sizes. We trace the trajectory of the fittest pseudo-agent over all iterations to comment on the dynamics of the system and prove that the proposed algorithm is theoretically globally convergent under the assumptions adopted for proofs of other closely-related random search algorithms.

IEEE Access Special Section Editorial: Recent Advances in Computational Intelligence Paradigms for Security and Privacy for Fog and Mobile Edge Computing

Today, cloud computing services are undeniably becoming parts of modern information and communication systems in our daily lives. Cloud computing has proven to be an incredible technology for provisioning quickly deployed and scalable information technology (IT) solutions at reduced infrastructure costs. Cloud computing is unable to meet the requirements of low latency, location awareness, and mobility support. To solve this problem, researchers have introduced a trusted and dependable solution through fog and mobile edge computing (FMEC) to put the services and resources of the cloud closer to users, which facilitates the leveraging of available services and resources in the edge networks. Organizations are beginning to look at FMEC as the answer to the cloud computing problem. FMEC consists of putting micro data centers or even small, purpose-built high-performance data analytics machines in remote offices and locations in order to gain real-time insights from the data collected, or to promote data thinning at the edge, by dramatically reducing the amount of data that needs to be transmitted to a central data center. Without having to move unnecessary data to a central data center, analytics at the edge can simplify and drastically speed analysis while also cutting costs. FMEC pushes applications, data, and computing power (services) away from centralized points to the logical extremes of a network. FMEC replicates fragments of information across distributed networks of web servers, which may be vast. As a topological paradigm, edge computing is also referred to as mesh computing, peer-to-peer computing, autonomic (self-healing) computing, grid computing, and other names implying non-centralized, node-less availability.

Development of air–soil temperature model using computational intelligence paradigms: artificial neural network versus multiple linear regression

A multilayer perceptron artificial neural network (MLPNN) model is proposed to estimate the soil temperature from air temperature. The results obtained using MLPNN are compared with those obtained using the multiple linear regression (MLR) using data measured at hourly time step. Firstly, the two models were developed and compared using only air temperature as input variable, and results obtained reveals that soil temperature can be estimated reliably with acceptable accuracy and the MLPNN was slightly more accurate than the MLR model. During the testing phase, the coefficient of correlation R, RMSE and MAE were calculated as 0.834, 7.476 °C, and 5.757 °C for the MLPNN model, while the MLR model provided an R, RMSE and MAE of 0.827, 7.646 °C and 6.039 °C. Secondly, in order to improve the accuracy of the models, the periodicity represented by the hours of the day number, the day of the month, and the month number, were included as input variable in addition to the air temperature. Results obtained demonstrated that the performances of the MLPNN model were significantly improved and the performances of the MLR model were marginally increased. Results demonstrated that MLPNN model is an excellent alternative to the direct measurement of soil temperature.

An introduction to brain emotional learning inspired models (BELiMs) with an example of BELiMs’ applications

Brain emotional learning-inspired models (BELiMs) is a new category of computational intelligence (CI) paradigms. The general structure of BELiMs is based on the neural structure of the emotion system which processes and evaluates fear-induced stimuli, to produce emotional responses. The function of a BELiM is implemented by assigning adaptive networks to different parts of its structure. The primary motivation for developing BELiMs is to address model and time complexity issues associated with supervised machine learning artificial neural networks and neuro-fuzzy methods. One of the applications of BELiMs is chaotic time series prediction problems. A BEliM can be used as a time series prediction model. This paper introduces BELiMs as a new CI paradigm and explains historical, theoretical, structural and functional aspects of BELiMs. I also validate and evaluate the performance of BELiMs as a time series prediction model by examining different variations of BELiMs on benchmark time series data sets and comparing obtained results with different CI models.