Computational Intelligence Methodologies Research Articles

WITHDRAWN: Investigating the Polymer Structure Effects on SBS-Modified Asphalt Binders under Short-Term Aging through the Artificial Neural Networks, Genetic Algorithms, and Fuzzy Logic

SBS-modified asphalt (SBSMA) exhibits complicated aging behavior in the weathering environment due to oxidation of the matrix asphalt, degradation of the SBS, and disruption of the SBS-asphalt relationship. The weathering process of SBS-modified asphalt has been studied extensively. However, most of this research has taken a broad, environmental approach. This research delved into the nuanced effects of aging on low polymer-modified asphalt (LPMA) compositions integrated with a rejuvenating agent (RA), leveraging advanced computational intelligence methodologies. The study uniquely employed fuzzy logic, Artificial Neural Networks (ANN), and genetic algorithms to forecast and analyze the performance dynamics of distinct asphalt mixtures. Five asphalt mixtures were examined: a PG 58-28 binder served as the foundational benchmark, while the subsequent four used modified PG 58-28 with different ratios of Ethylene Vinyl Acetate (EVA) and sesame oil (RA). Assessment methodologies comprised the Resilient Modulus Test, Wheel Track Test, Flow Time, Direct Tension, and Quarter-Circle Bend tests. The ANN framework was instrumental in predicting rutting and crack trajectories, while fuzzy logic facilitated understanding the intricate interplays between component ratios and resultant performance metrics. Genetic algorithms optimized the EVA and RA ratios for enhanced efficacy. Initial results demonstrated that the amalgamation of EVA and RA strengthened the overall lifespan and rutting resilience of the asphalt composites. Enhanced blends, optimized using the genetic algorithm, exhibited improved resistance to initial crack formations relative to the baseline. As the aging process advanced, the modified blend's resilience to recurrent stress-induced cracks experienced minor reductions but paralleled the foundational benchmark. A harmonized combination, pinpointed via the genetic algorithm containing 6.5% EVA and 6% RA, emerged as the most proficient in amplifying durability, resistance against deformations, and immediate crack resistance.

Unraveling Arrhythmias with Graph-Based Analysis: A Survey of the MIT-BIH Database

Cardiac arrhythmias, characterized by deviations from the normal rhythmic contractions of the heart, pose a formidable diagnostic challenge. Early and accurate detection remains an integral component of effective diagnosis, informing critical decisions made by cardiologists. This review paper surveys diverse computational intelligence methodologies employed for arrhythmia analysis within the context of the widely utilized MIT-BIH dataset. The paucity of adequately annotated medical datasets significantly impedes advancements in various healthcare domains. Publicly accessible resources such as the MIT-BIH Arrhythmia Database serve as invaluable tools for evaluating and refining computer-assisted diagnosis (CAD) techniques specifically targeted toward arrhythmia detection. However, even this established dataset grapples with the challenge of class imbalance, further complicating its effective analysis. This review explores the current research landscape surrounding the application of graph-based approaches for both anomaly detection and classification within the MIT-BIH database. By analyzing diverse methodologies and their respective accuracies, this investigation aims to empower researchers and practitioners in the field of ECG signal analysis. The ultimate objective is to refine and optimize CAD algorithms, ultimately culminating in improved patient care outcomes.

Computational Intelligence Methodology based on Neutrosophic Set with Multi-Criteria Decision Making for Evaluating Natural Gas Automobiles

Computational Intelligence Methodology based on Neutrosophic Set with Multi-Criteria Decision Making for Evaluating Natural Gas Automobiles

Detection and classification of abnormal red blood cells with computational intelligence techniques: a review

<p class="LO-normal"><span lang="EN-US">Red blood cells (RBCs), or erythrocytes, are usually disc-shaped. However, pathological conditions can change their shape. The complete blood count (CBC) is a test that can detect abnormal RBCs. Yet it is a manual test susceptible to errors, so there are efforts to automate the detection and classification of abnormal cells. A total of 31 papers were reviewed, and all the selected studies focus on the detection or classification of abnormal RBCs. Different approaches were applied to tackle this issue, including image processing techniques, classification using machine learning, and convolutional neural networks for detection, among other methods. Furthermore, machine learning techniques are recently presenting promising results for abnormal RBCs detection and classification. This review also brings a discussion on the computational intelligence methodologies applied.</span></p>

State-of-the-Art Review on Permanent Deformation Characterization of Asphalt Concrete Pavements

Rutting is one of the significant distresses in flexible pavements. Examining the methods to decrease permanent deformation is of considerable importance to provide long service life and safe highways. The main objective of this paper is to undertake a state-of-the-art review to combine the existing work on the permanent deformation of asphalt concrete pavements. For this purpose, the review synthesizes the evolution of the permanent deformation models, the tests methods used to evaluate and quantify the rutting potential of asphalt mixtures with a particular focus provided on the stress sweep rutting test which is gaining popularity as it tackles the shortcomings of its predecessor for the exact characterization and prediction of permanent deformation. Additionally, some advanced computational intelligence methodologies such as finite element model and soft computing are reviewed. Furthermore, the most common permanent deformation solutions are reviewed. It was found that efforts are put towards improving either the rheological properties of base asphalt by using modifiers or asphalt mixture by using selected aggregates to enhance the aggregate interlock and by implementing semi-flexible asphalt pavements which is expected to be a promising method against permanent deformation. This state-of-the-art work is expected to supply a comprehensive perception of the available models, rutting test, and solutions, and to suggest future studying areas related to the rutting of asphalt pavements.

Private blockchain-based encryption framework using computational intelligence approach

Electronic Health monitoring system has performed an essential role in managing healthcare monitoring. E-health can provide effective and valuable facilities for the patients to monitor. Though, there are protection disputes in the current E-Health system. The current e-health system, on the other hand, has security issues. Malevolent doctors may work together with cloud Storage Service Providers (CSPs) to interfere with patients' electronic health records (EHRs) or promptly leak EHR matter to other enemies for income. (EHRs). The malevolent doctors may conspire with the Patient Healthcare Monitoring Service Provider (PHMSP) to manipulate with the patients'. For profit, EHRs or directly divulge the EHR content of EHRs to other opponents. Block-chain has recently appeared as one of the most powerful methods in the protection and secrecy fields. It is assumed to be the promised security approach that will eventually replace the security challenges in existing e-health monitoring systems. Encryption in blockchain refers to technical methods that make accessing encrypted data difficult for unauthorized resources. This research proposed a blockchain-based encryption framework to provide security-based solutions using a computational intelligence methodology. The proposed approach provides better results in terms of 0.93 in the training phase and 0.91 in the validation accuracy.

A framework of genetic algorithm-based CNN on multi-access edge computing for automated detection of COVID-19.

This paper designs and develops a computational intelligence-based framework using convolutional neural network (CNN) and genetic algorithm (GA) to detect COVID-19 cases. The framework utilizes a multi-access edge computing technology such that end-user can access available resources as well the CNN on the cloud. Early detection of COVID-19 can improve treatment and mitigate transmission. During peaks of infection, hospitals worldwide have suffered from heavy patient loads, bed shortages, inadequate testing kits and short-staffing problems. Due to the time-consuming nature of the standard RT-PCR test, the lack of expert radiologists, and evaluation issues relating to poor quality images, patients with severe conditions are sometimes unable to receive timely treatment. It is thus recommended to incorporate computational intelligence methodologies, which provides highly accurate detection in a matter of minutes, alongside traditional testing as an emergency measure. CNN has achieved extraordinary performance in numerous computational intelligence tasks. However, finding a systematic, automatic and optimal set of hyperparameters for building an efficient CNN for complex tasks remains challenging. Moreover, due to advancement of technology, data are collected at sparse location and hence accumulation of data from such a diverse sparse location poses a challenge. In this article, we propose a framework of computational intelligence-based algorithm that utilize the recent 5G mobile technology of multi-access edge computing along with a new CNN-model for automatic COVID-19 detection using raw chest X-ray images. This algorithm suggests that anyone having a 5G device (e.g., 5G mobile phone) should be able to use the CNN-based automatic COVID-19 detection tool. As part of the proposed automated model, the model introduces a novel CNN structure with the genetic algorithm (GA) for hyperparameter tuning. One such combination of GA and CNN is new in the application of COVID-19 detection/classification. The experimental results show that the developed framework could classify COVID-19 X-ray images with 98.48% accuracy which is higher than any of the performances achieved by other studies.

A comparative analysis of Statistical and Computational Intelligence methodologies for the prediction of traffic-induced fine particulate matter and NO2

With the urbanization increase, urban mobility and transportation induce higher traffic volumes causing environmental, economic and social impacts. This is due to continuous usage of fossil fuel energy resources generating air pollutants, such as nitrogen oxides (NOx), sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3) and particulate matter (PM10 and PM2.5), which impact on climate and air quality and adversely affect the human health. The present paper aims at training an ensemble of forecasting methodologies for traffic-induced pollutant emissions and implementing it for predicting PM10, PM2.5 and NO2 for the case study of Cambridge, UK inner-city region. Such an ensemble enables decision makers to evaluate the impact of various transportation policies and measures on human health and the ecosystem, and subsequently contribute towards urban resilience and sustainability. Since the chemical synthesis of air pollution is triggered by meteorological factors, the forecasting incorporates them along with the traffic volumes. We opted to combine Statistical and Computational Intelligence learning methods including Adaptive Neuro Fuzzy Inference Systems (ANFIS), Long Short-Term Memory (LSTM) recurrent neural networks and Extreme Learning Machines (ELM). Initially, Multivariate Imputation by Chained Equation (MICE) and trend and seasonality removal was performed at data preprocessing and then Principal Component Analysis (PCA) highlighted the principal parameters for ANFIS to predict next day's PM10, PM2.5 and NO2 values. LSTM and ELM methods estimated next day values and compared with the ANFIS model results for hourly time series data of length 2703. The performance of the embedded models was quantified by the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), and Coefficient of Determination (R2) indices. The ensemble was found to be superior in predicting PM10, PM2.5 and NO2 emissions when compared with existing traditional models.

Lightweight Computational Intelligence for IoT Health Monitoring of Off-Road Vehicles: Enhanced Selection Log-Scaled Mutation GA Structured ANN

Smart monitoring of off-road vehicles is cursed by their complex and expensive IoT sensors technologies. High dependence on the cloud/fog computation, availability of the network, and expert knowledge make it handicap in the rural off-network areas. Use of edge devices, such as smartphones, attributed by computation capabilities is the solution that is yet to be developed at commercial level (Fawwaz and Chung 2020) and (Zhengwei <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2021). Additionally, the user's growing demand for economic and user-friendly technology motivates to shift from costly and complex sensors to economic. In this article, we present the hybridized computational intelligence methodology to develop an edge-device-enabled AI technology for health monitoring and diagnosis (HM&D) of the off-road vehicles, taking use of super economic microphones as sensors. Smartphones are benefited by integrated microphones, and thus, the App-based developed technology is generalized for all vehicles from old to new. Enhanced selection and log-scaled mutation genetic algorithms is used to evolve the structure of the artificial neural network toward an optimally lightweight structure. Each evolved lightweight ANN structure is trained by scaled conjugate gradient back-propagation training algorithm to optimize corresponding weights and biases. The comparative results with currently reported genetic algorithms for edge computation prove it a breakthrough technology for edge-device-enabled HM&D of off-road vehicles (Yan <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2020).

Special Issue on Behavior Adaptation, Interaction, and Artificial Perception for Assistive Robotics

This special issue aims at examining and promoting recent developments in the Assistive/Social Robotics field and future directions including the related challenges and how these can be overcome with particular focus on computational intelligence methodologies. With robots being integrated more and more in our environments and daily activities, the effectiveness of robotics applications has to rely on robots' ability to perceive, reason, and adapt on-the-fly to the users' behavior and needs. In particular, the development of personal robots, as assistive technological tools, challenges researchers to develop socially intelligent and adaptive robots that can collaborate with people in real environment. This special issue is comprised of 14 papers, fostering discussions and investigations in robot behavior adaptation and learning, in socially assistive robotics, and in developing reliable systems for natural interactions between humans and robots. Several aspects and perspectives are taken into consideration by the authors covering various topics such as adaptability during real-world human-robot interactions ; new reinforcement learning and learning by teaching techniques especially used in the context of child-robot interaction ; social robot responses, perception and adaptation by means of a semantic representation; robots acceptability and security. Furthermore, several assistive applications for the elderly, children, and people are presented. B Mariacarla Staffa

WITHDRAWN: Forecasting of nonlinear time series using artificial neural network

Abstract When forecasting time series, it is important to classify them according to linearity behavior; the linear time series remains at the forefront of academic and applied research. It has often been found that simple linear time series models usually leave certain aspects of economic and financial data unexplained. The dynamic behavior of most of the time series in our real life, with its autoregressive and inherited moving average terms, pose the challenge to forecast nonlinear times series that contain inherited moving average terms using computational intelligence methodologies such as neural networks. It is rare to find studies that concentrate on forecasting nonlinear times series that contain moving average terms. In this study, we demonstrate that the common neural networks are not efficient for recognizing the behavior of nonlinear or dynamic time series which has moving average terms and hence low forecasting capability. This leads to the importance of formulating new models of neural networks such as Deep Learning neural networks with or without hybrid methodologies such as Fuzzy Logic.

Forecasting of nonlinear time series using ANN

When forecasting time series, it is important to classify them according linearity behavior that the linear time series remains at the forefront of academic and applied research, it has often been found that simple linear time series models usually leave certain aspects of economic and financial data unexplained. The dynamic behavior of most of the time series in our real life with its autoregressive and inherited moving average terms issue the challenge to forecast nonlinear times series that contain inherited moving average terms using computational intelligence methodologies such as neural networks. It is rare to find studies that concentrate on forecasting nonlinear times series that contain moving average terms. In this study, we demonstrate that the common neural networks are not efficient for recognizing the behavior of nonlinear or dynamic time series which has moving average terms and hence low forecasting capability. This leads to the importance of formulating new models of neural networks such as Deep Learning neural networks with or without hybrid methodologies such as Fuzzy Logic.

Prediction of laser welding quality by computational intelligence approaches

In this investigation was established a model of laser welding quality prediction based on different input parameters. As the quality factors for the laser welding process lap-shear strength and weld-seam width were used. Laser power, welding speed, stand-off distance and clamping pressure were used as input parameters. Experimental test were used to acquire the training data for the computational intelligence methodologies. In this article support vector regression (SVR) was applied. The results from this study could be used as benchmark results in order to improve the laser welding process.

Economic development evaluation based on science and patents

Economic development could be achieved through many factors. Science and technology factors could influence economic development drastically. Therefore the main aim in this study was to apply computational intelligence methodology, artificial neural network approach, for economic development estimation based on different science and technology factors. Since economic analyzing could be very challenging task because of high nonlinearity, in this study was applied computational intelligence methodology, artificial neural network approach, to estimate the economic development based on different science and technology factors. As economic development measure, gross domestic product (GDP) was used. As the science and technology factors, patents in different field were used. It was found that the patents in electrical engineering field have the highest influence on the economic development or the GDP.

An Evolutionary Method for Financial Forecasting in Microscopic High-Speed Trading Environment.

The advancement of information technology in financial applications nowadays have led to fast market-driven events that prompt flash decision-making and actions issued by computer algorithms. As a result, today's markets experience intense activity in the highly dynamic environment where trading systems respond to others at a much faster pace than before. This new breed of technology involves the implementation of high-speed trading strategies which generate significant portion of activity in the financial markets and present researchers with a wealth of information not available in traditional low-speed trading environments. In this study, we aim at developing feasible computational intelligence methodologies, particularly genetic algorithms (GA), to shed light on high-speed trading research using price data of stocks on the microscopic level. Our empirical results show that the proposed GA-based system is able to improve the accuracy of the prediction significantly for price movement, and we expect this GA-based methodology to advance the current state of research for high-speed trading and other relevant financial applications.

Computational intelligence methodology for the analysis of RC circuit modelled with nonlinear differential order system

In this study, we solve nonlinear initial value problems arising in circuit analysis by applying bio-inspired computational intelligence technique using feed-forward artificial neural networks (ANNs) optimized with genetic algorithms (GAs), sequential quadratic programming (SQP), and their combined scheme. The system of resister–capacitor (RC) circuit having nonlinear capacitance is mathematically modelled with unsupervised ANNs by defining an energy function in mean-square error (MSE) sense. The objectives are to minimize the MSE for which the parameters of the networks are estimated initially with GA-based global search and in steady state with SQP algorithm for efficient local search. We consider a set of scenarios to evaluate the performance of the proposed scheme for different resistance and capacitance values along with current variations in the nonlinear RC circuit system. The results are compared with well-established fully explicit Runge–Kutta numerical solver in order to verify the accuracy of the applied bio-inspired heuristics. To prove the worth of the scheme, a comprehensive statistical analysis is provided for the performance metrics based on root MSE, mean absolute error, Theil’s inequality coefficient, Nash–Sutcliffe efficiency, variance account for, and the coefficient of determination (R 2).

Survey of Uses of Evolutionary Computation Algorithms and Swarm Intelligence for Network Intrusion Detection

Many infrastructures, such as those of finance and banking, transportation, military and telecommunications, are highly dependent on the Internet. However, as the Internet’s underlying structural protocols and governance can be disturbed by intruders, for its smooth operation, it is important to minimize such disturbances. Of the available techniques for achieving this, computational intelligence methodologies, such as evolutionary algorithms and swarm intelligence approaches, are popular and have been successfully applied to detect intrusions. In this paper, we present an overview of these techniques and related literature on intrusion detection, analyze their research contributions, compare their approaches and discuss new research directions which will provide useful insights for intrusion detection researchers and practitioners.

Computational intelligence in software cost estimation

One of the key features for the failure of project estimation techniques is the selection of inappropriate estimation models. Further, noisy data poses a challenge to build accurate estimation models. Therefore, the software cost estimation (SCE) is a challenging problem that has attracted many researchers over the past few decades. In the recent times,the use of computational intelligence methodologies for software cost estimation have gained prominence. This paper reviews some of the commonly used computational intelligence (CI) techniques and analyzes their application in software cost estimation and outlines the emerging trends in this area

Large-Scale Computational Experiments for Complex Enterprise Systems Behaviour Prediction

Abstract This work comes as a contribution to the efforts that are undergoing within engineering systems community to account for the increased complexity of today's manufacturing or service systems. These systems are becoming more and more complicated due to the increase in the number of elements, interconnections within the system, and necessary integration with other systems. Moreover, through the emphasis on self-organization and considering the multi-stakeholders context and objectives, these systems are crossing the line towards complexity. There is a need for developing a framework to be used in modeling, analysis, and integration of systems that operate in uncertain environments, in which characteristics such as adaptation, self-organization and evolution, or in other words behavior prediction, need to be addressed. The proposed complex enterprise systems framework combines knowledge coming from complex systems science and systems engineering domains, and uses computational intelligence and agent-based systems simulation methodologies. The approach requires computational experience in manipulating large amounts of data and building large-scale simulation models. A significant result to be made possible by this research is that systems may no longer have a fixed, life-cycle long, design based on identified requirements; systems will be engineered to evolve and adapt as needed during the operational phase, while respecting their operational environment constraints.

Air quality estimation by computational intelligence methodologies

The subject of this study is to compare different computational intelligence methodologies based on artificial neural networks used for forecasting an air quality parameter - the emission of CO2, in the city of Nis. Firstly, inputs of the CO2 emission estimator are analyzed and their measurement is explained. It is known that the traffic is the single largest emitter of CO2 in Europe. Therefore, a proper treatment of this component of pollution is very important for precise estimation of emission levels. With this in mind, measurements of traffic frequency and CO2 concentration were carried out at critical intersections in the city, as well as the monitoring of a vehicle direction at the crossroad. Finally, based on experimental data, different soft computing estimators were developed, such as feed forward neural network, recurrent neural network, and hybrid neuro-fuzzy estimator of CO2 emission levels. Test data for some characteristic cases presented at the end of the paper shows good agreement of developed estimator outputs with experimental data. Presented results are a true indicator of the implemented method usability.