Computational Intelligence Techniques Research Articles

Hybrid swarm evolutionary programming for optimal distributed generation in distribution system

This paper discusses the incorporation of the Hybrid Swarm Evolutionary Programming (SEP) for optimal distributed generation (DG) in the distribution system. High load demand will result in unstable control power distribution due to power transmission loss. The compensation process can be implemented by installing a compensation device to prevent this from happening. It is required the optimal sizing and location for the devices to achieve the objective and this can be done by using an optimization technique. Thus, this project aims to develop a hybrid computational intelligence technique is called hybrid SEP for loss minimization. The proposed method embedded the element of Particle Swarm Optimization (PSO) into traditional Evolutionary Programming (EP) to improve precision of traditional EP algorithm. The purpose of this study is to investigate the maximum benefits of DG integration to be gained. The proposed techniques are validated on IEEE-69 bus radial system with multiple units of DG. The results showed that the most effective type of DG inject to IEEE-69 bus radial system is with DG Type III, with 95% of active power loss reduction and the best on voltage profile improvement. Hybrid SEP shows superior to EP in term of loss minimization.

Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization.

Background: Accurate and fast diagnosis of COVID-19 is very important to manage the medical conditions of affected persons. The task is challenging owing to shortage and ineffectiveness of clinical testing kits. However, the existing problems can be improved by employing computational intelligent techniques on radiological images like CT-Scans (Computed Tomography) of lungs. Extensive research has been reported using deep learning models to diagnose the severity of COVID-19 from CT images. This has undoubtedly minimized the manual involvement in abnormality identification but reported detection accuracy is limited. Methods: The present work proposes an expert model based on deep features and Parameter Free BAT (PF-BAT) optimized Fuzzy K-nearest neighbor (PF-FKNN) classifier to diagnose novel coronavirus. In this proposed model, features are extracted from the fully connected layer of transfer learned MobileNetv2 followed by FKNN training. The hyperparameters of FKNN are fine-tuned using PF-BAT. Results: The experimental results on the benchmark COVID CT scan data reveal that the proposed algorithm attains a validation accuracy of 99.38% which is better than the existing state-of-the-art methods proposed in past. Conclusion: The proposed model will help in timely and accurate identification of the coronavirus at the various phases. Such kind of rapid diagnosis will assist clinicians to manage the healthcare condition of patients well and will help in speedy recovery from the diseases. Clinical and Translational Impact Statement— The proposed automated system can provide accurate and fast detection of COVID-19 signature from lung radiographs. Also, the usage of lighter MobileNetv2 architecture makes it practical for deployment in real-time.

Hybrid intelligence model on the second generation neural network

In latest beyond, it has been visualised in various neural computing applications that the hybridisation of computational intelligence techniques yields better results over traditional methods. Thi...

Active Safety System for Urban Environments with Detecting Harmful Pedestrian Movement Patterns Using Computational Intelligence

This article presents a system for detecting pedestrian movement patterns in urban environments, by applying computational intelligence methods for image processing and pattern detection. The proposed system is capable of processing multiple images and video sources in real-time. Furthermore, it has a flexible design, as it is based on a pipes and filters architecture that makes it easy to evaluate different computational intelligence techniques to address the subproblems involved in each stage of the process. Two main stages are implemented in the proposed system: the first stage is in charge of extracting relevant features of the processed images, by applying image processing and object tracking, and the second stage is responsible for the patterns detection. The experimental analysis of the proposed system was performed over more than 1450 problem instances, using PETS09-S2L1 videos, and the results were compared with part of the Multiple Object Tracking Challenge benchmark results. Experiments covered the two main stages of the system. Results indicate that the proposed system is competitive yet simpler than other similar software methods. Overall, this article provides the theoretical frame and a proof of concept needed for the implementation of a real-time system that takes as input a group of image sequences, extracts relevant features, and detects a set of predefined patterns. The proposed implementation is a reliable proof of the viability of building pedestrian movement pattern detection systems.

Content-Based Image Retrieval (CBIR) in Big Histological Image Databases

Background: Automatic analysis of Histopathological Images (HIs) demands image processing and Computational Intelligence (CI) techniques. Both Computer-Aided Diagnosis (CAD) and Content-Based Image-Retrieval (CBIR) systems assist diagnosis, disease discovery, and biological decision-making. Classical tests comprise screening examinations and biopsy. Histopathology slides offer more ample diagnosis data. However, manual examination of microscopic images is labor-intensive and time-consuming and may depend on a subjective assessment by the pathologist, which can be a challenge.
 Methods: This work discusses a CBIR framework to extract and handle histological data, histological metadata, integrated patient records, specimen metadata, attributes, and similar stored files. This work presents a scalable image-retrieval framework for intelligent HI analysis with real-time retrieval. The potential applications of this framework include image-guided diagnosis, decision support, healthcare education, and efficient biological data management.
 Results: The considerable amount of biological-related data prompted the development and deployment of large-scale databases and data-driven techniques to bridge the semantic gap between images and diagnostic information. The new cloud computing technologies and the concept of cyber-physical systems have improved the CBIR architectures considerably. The proposed scalable architecture relies on CI and validates performance on several HIs acquired from microscopic tissues. Extensive assessments show improvements in terms of disease classification and retrieval tests.
 Conclusion: This research effort significant contributions are twofold. 1) Defining a comprehensive and large-scale CBIR framework to analyze HIs with high-dimensional features and CI methods successfully. 2) high-performance updating and optimization strategies improve the querying while better handling new training samples than traditional methods.

Early and accurate detection and diagnosis of heart disease using intelligent computational model

Heart disease is a fatal human disease, rapidly increases globally in both developed and undeveloped countries and consequently, causes death. Normally, in this disease, the heart fails to supply a sufficient amount of blood to other parts of the body in order to accomplish their normal functionalities. Early and on-time diagnosing of this problem is very essential for preventing patients from more damage and saving their lives. Among the conventional invasive-based techniques, angiography is considered to be the most well-known technique for diagnosing heart problems but it has some limitations. On the other hand, the non-invasive based methods, like intelligent learning-based computational techniques are found more upright and effectual for the heart disease diagnosis. Here, an intelligent computational predictive system is introduced for the identification and diagnosis of cardiac disease. In this study, various machine learning classification algorithms are investigated. In order to remove irrelevant and noisy data from extracted feature space, four distinct feature selection algorithms are applied and the results of each feature selection algorithm along with classifiers are analyzed. Several performance metrics namely: accuracy, sensitivity, specificity, AUC, F1-score, MCC, and ROC curve are used to observe the effectiveness and strength of the developed model. The classification rates of the developed system are examined on both full and optimal feature spaces, consequently, the performance of the developed model is boosted in case of high variated optimal feature space. In addition, P-value and Chi-square are also computed for the ET classifier along with each feature selection technique. It is anticipated that the proposed system will be useful and helpful for the physician to diagnose heart disease accurately and effectively.

Modeling the sound absorption behavior of carpets using artificial intelligence

The ability to provide some degree of noise attenuation is one of the most important properties of carpet. This is achieved by making the room in which the carpet is being installed less reverberant or minimizing the transmission of footstep noise through floors. This study aims to predict the sound absorption coefficient of acrylic carpet at different frequencies using three computational intelligence techniques viz., Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Interface System (ANFIS), and Genetic Algorithm (GA). To this end, carpets with different pile height and densities were produced. In order to simulate walking traffic, the carpets were exposed to 50, 100, 150, and 200 dynamic cycles. The sound absorption coefficient (SAC) of carpets was experimentally measured using a two-microphone impedance tube based on the transfer-function method. The effect of input parameters on SAC was statistically investigated, and the results showed that all parameters have a significant impact on SAC at a 95% confidence interval. To improve the prediction accuracy of the model, GA was implemented for the optimization of ANN and ANFIS parameters. The prediction accuracy of hybrid models ANN-GA and ANFIS-GA was compared with the traditional regression model by the mean absolute percentage error (MAPE). The results indicated that the prediction accuracy is considerably enhanced by using an optimized ANN and ANFIS structure. The MAPE for ANN-GA, ANFIS-GA, and regression models was found to be 11.85%, 17.68%, and 61.82%, respectively. The results demonstrated the applicability and performance of the hybrid ANN-GA model for the prediction of SAC of carpet.

Bi-heuristic ant colony optimization-based approaches for traveling salesman problem

Heuristic computational intelligence techniques are widely used in combinatorial optimization problems, essentially in large size configurations. Bio-inspired heuristics such as PSO, FA or FPA showed their capacities to solve such problems. Bi-heuristic optimization consists of using a couple of techniques and a collaboration mechanism. This paper reviews the major contributions in solving TSP with bi-heuristics and presents a new hybridization scheme based on FPA, ACO with Ls, ant supervised by flower pollination with local search, ASFPA-Ls; as well as the impact of social and cognitive PSO for the ant supervised by PSO with local search, ASPSO-Ls. AS-chaotic-PSO-Ls which stands for ant supervised by chaotic PSO local search is also investigated. The meta-heuristic algorithms (FPA, PSO or chaotic PSO) and ant colony optimization are used with a hierarchical collaboration schema in addition to a local search mechanism. In this work, the local search strategy, Ls, used is the 2-opt method; the proposals are called, respectively, ASFPA-Ls, cognitive ant supervised by PSO with local search, Co-ASPSO-Ls, social-ASPSO-Ls and AS-chaotic-PSO-Ls; where ACO is coupled with a local search heuristic mechanism called 2-opt, and a set of meta-heuristics, FA, FPA and PSO asked to adapt ACO parameters while running. Comparative experimental investigations are conducted using the TSP test bench. Proposed hybridizations attended fair solutions for the TSP problems used in the experimental investigations including berlin52, St70, eil76, rat 99, eil101, KroA100, Ch150 and kroA200. A good balance performance/time is found with the social ant supervised by PSO with local search, So-ASPSO-Ls. The cognitive ant supervised by PSO with local search, Co-ASPSO-Ls comes in the second position in terms of time effectiveness with close performances to AS-chaoticPS0-LS, all proposed approaches returned low rate errors or BKS for used test benches: berlin52, st70, eil76, rat99, kroA100 and kroA200.

SELECTION OF SUSTAINABLE SUPPLIER(S) IN A PAINT MANUFACTURING COMPANY USING HYBRID META-HEURISTIC ALGORITHM

Supplier selection in a manufacturing system is highly complex owing to the nature and structure of organisations, necessitating a paradigm shift from the rule-of-thumb and classical methods of supplier selection to a reliable technique that uses a hybrid algorithm to provide greater accuracy in the selection process. This study proposes the use of a hybrid computational intelligence technique — an adaptive neuro-fuzzy inference system — for the effective identification and selection of sustainable suppliers. This hybrid modelling configuration was applied in a paint manufacturing company to select the best possible supplier. Information obtained from the company within the period of investigation was fed into the model. The result obtained shows a faster and more reliable prediction by the creative model. Professionals and business managers will benefit greatly from the selection of sustainable suppliers in an in-bound and out-bound supply chain system.

An estimation of pressure rise and heat transfer rate for hybrid nanofluid with endoscopic effects and induced magnetic field: computational intelligence application

This article reflects on the use of computational intelligence technique based on feed-forward nonlinear input–output artificial neural network for the prediction of pressure rise and heat transfer coefficient in peristaltic pumping of Ree–Eyring hybrid nanofluid through an endoscope. Gold nanoparticles are dispersed in Al2O3/blood nanofluid for hybridization with 0.02% and 0.05% volume fraction of Al2O3 and Au, respectively. Estimation parameters include Ree–Eyring fluid parameter (0.05–0.2), magnetic parameter (1.0–3.0), radius ratio (0.7–0.9), and Brinkman number (0.2–0.6). Homotopy analysis method is employed to achieve experimental data from proposed mathematical model. Computational results for profiles of ΔP and Z are illustrated graphically, and it is found that M and α lead to amplify pumping while more pumping is needed in the case of rising χ. Moreover, heat transfer coefficient enhances with the increase in Br and χ. Experimental data are then validated using artificial neural network model. Levenberg–Marquardt backpropagation training algorithm is used in supervised learning. The degree of fitting the data is assessed on the bases of optimized decision threshold of mean square error. Network outputs are presented using error histogram, time-series responses, performance, and train-state plot which show that proposed technique is able to provide robust and very accurate output with least mean square error, strong correlation between target and network output with R = 1, trained with optimized weights at decreasing gradient, and a better convergence with small mu at each corresponding epochs. Further, comparison of analytical results with previously published literature shows a good agreement.

A Survey of Computational Intelligence Techniques for Wind Power Uncertainty Quantification in Smart Grids.

The high penetration level of renewable energy is thought to be one of the basic characteristics of future smart grids. Wind power, as one of the most increasing renewable energy, has brought a large number of uncertainties into the power systems. These uncertainties would require system operators to change their traditional ways of decision-making. This article provides a comprehensive survey of computational intelligence techniques for wind power uncertainty quantification in smart grids. First, prediction intervals (PIs) are introduced as a means to quantify the uncertainties in wind power forecasts. Various PI evaluation indices, including the latest trends in comprehensive evaluation techniques, are compared. Furthermore, computational intelligence-based PI construction methods are summarized and classified into traditional methods (parametric) and direct PI construction methods (nonparametric). In the second part of this article, methods of incorporating wind power forecast uncertainties into power system decision-making processes are investigated. Three techniques, namely, stochastic models, fuzzy logic models, and robust optimization, and different power system applications using these techniques are reviewed. Finally, future research directions, such as spatiotemporal and hierarchical forecasting, deep learning-based methods, and integration of predictive uncertainty estimates into the decision-making process, are discussed. This survey can benefit the readers by providing a complete technical summary of wind power uncertainty quantification and decision-making in smart grids.

A hybrid learning strategy for structural damage detection

Over the past decades, several methods for structural health monitoring have been developed and employed in various practical applications. Some of these techniques aimed to use raw dynamic measurements to detect damage or structural changes. Desirably, structural health monitoring systems should rely on computational tools capable of evaluating the information acquired from the structure continuously, in real time. However, most damage detection techniques fail to identify novelties automatically (e.g. damage, abnormal behaviors, and among others), rendering human decisions necessary. Recent studies have shown that the use of statistical parameters extracted directly from raw time domain data, such as acceleration measurements, could provide more sensitive responses to damage with less computational effort. In addition, machine learning techniques have never been more in trend than nowadays. In this context, this article proposes an original approach based on the combination of statistical indicators—to characterize acceleration measurements in the time domain—and computational intelligence techniques to detect damage. The methodology consists in the combined use of supervised (artificial neural networks) and unsupervised (k-means clustering) learning classification methods for the construction of a hybrid classifier. The objective is to detect not only structural states already known but also dynamic behaviors that have not been identified yet, that is, novelties. The main purpose is to allow a real-time structural integrity monitoring, providing responses in an automatic and continuous way while the structure is under operation. The robustness of the proposed approach is evaluated using data obtained from numerical simulations and experimental tests performed in laboratory and in situ. Results achieved so far attest a promising performance of the hybrid classifier.

Analítica Prescriptiva en Sistemas con Grandes Flujos de Eventos

Los procesos de negocio exigen tomar decisiones rápidas para lograr la adaptación constante a los cambios en búsqueda de mejorar el desempeño y aprovechar las oportunidades. Una posibilidad es resolver el proceso computacionalmente. Para ello es necesario producir analíticas que transformen los datos en conocimiento para la toma de decisiones. Existen varios tipos de analíticas, en este trabajo se introduce una línea de investigación enfocada en la analítica prescriptiva, como nivel más avanzado, capaz de calcular acciones a ser ejecutadas en el momento (decisiones operativas) o en el futuro (decisiones tácticas para corto y mediano plazo, decisiones estratégicas para largo plazo) para lograr un objetivo deseado. El cálculo de las acciones involucra el procesamiento del flujo de eventos del negocio en forma de datastreams, la aplicación de técnicas y algoritmos de Soft Computing e Inteligencia Computacional y, derivado de la necesidad de bajos tiempos de respuesta, el empleo de Computación de Alto Desempeño.

WITHDRAWN: Combining the OGA with IDS to improve the detection rate

Abstract Interruption Detection System is utilized to distinguish the unwanted actions over the network and to structure IDS computing Intelligence techniques are used. This paper explains the task of Artificial Neural Network, Fuzzy Logic and Genetic Algorithm in Intrusion Detection System. The ANN learning algorithms, information recovery utilizing fuzzy logic under vulnerability and intensity of Optimized Genetic algorithm to improve the decision of inference system by improving fitness and with IDS, searching the data took very less time and performance of the intrusion detection is improved and the false positive rate is decreased. Fundamental focus point of the paper is to combining the proposed Optimized Genetic Algorithm (OGA) with IDS.

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.

Special issue on new trends and challenges of bio‐inspired computational intelligence algorithms in massively complex systems

Massively complex systems, such as social networks (Camacho, Panizo-LLedot, Bello-Orgaz, Gonzalez-Pardo, & Cambria, 2020; Lara-Cabrera et al., 2017), renewable energy problems (Twidell & Weir, 2015), or Internet-of-Things problems (Lin et al., 2017), generate massive amounts of data. These massively complex systems have attracted the attention of both industrial and research communities, because the analysis of data can generate valuable knowledge about the specific domain. But at the same time, the amount of data generated and the complexity of the problems mean that classical algorithms and approaches do not provide suitable solutions. In this case, it is quite common for computational intelligence (CI) techniques to extract the knowledge. CI can be defined as a set of bio-inspired research areas focused on the study of adaptive mechanisms to enable, or facilitate, intelligent behaviour in complex and changing environments. There are several research fields that compose CI, including swarm intelligence (Gonzalez-Pardo, Jung, & Camacho, 2017), and evolutionary computation (Salcedo-Sanz, Ortiz-Garcýa, Ángel M. Pérez-Bellido, Portilla-Figueras, & Prieto, 2011). This special issue is focused on the application of bio-inspired algorithms to massively complex systems, ranging from concepts and theoretical developments to advances technologies and innovative applications. This special issue welcomed submissions of original papers introducing research results on all the aspects covering the application of CI algorithms to massively complex systems, ranging from concepts and theoretical developments to advanced technologies and innovative applications. This issue presents expanded versions of the best papers presented at the 19th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2018), which was held in Madrid (Spain). As the special issue editors, we would like to take this opportunity to thank the various authors for their papers and the reviewers for their work. We are also grateful to Jon Hall, Editor-in-Chief of the Wiley journal Expert Systems. We would like to particularly thank the IDEAL'18 programme committee members for their hard work and dedication.

Use of computational intelligence techniques to predict flooding in places adjacent to the Magdalena River

Floods are one of the worst natural disasters in the world. Colombia is a country that has been greatly affected by this disaster. For example, in the years 2010 and 2011 there was a heavy rainy season, which caused floods that affected at least two million people and there were economic losses of 6.5 million dollars, which is equivalent to 5.7% of the country's Gross Domestic Product (GDP) at that time. The Magdalena River is the most important since 128 municipalities and 43 cities with a population of 6.3 million people, which is 13% of the total population of the country, are located in its basins. For this reason, the objective of the research is to design and implement a model that helps predict flooding over the Magdalena River by examining three techniques of artificial intelligence (Artificial Neuronal Networks, Adaptive Neuro Fuzzy Inference System, Support Vector Machine), and thus determining which of these techniques are the most effective according to the case study. The research was limited only to these three types, due to limitations of time, data, human and financial resources, and technological infrastructure. In the end, it is concluded that the Artificial Neural Networks technique is a suitable option to implement the predictive system as long as it is not very complex and does not require high processing machine. However, to establish a model based on rules to achieve a better interpretability of the floods, the ANFIS model can be used.

Data-Driven Bandwidth Prediction Models and Automated Model Selection for Low Latency

Today's HTTP adaptive streaming solutions use a variety of algorithms to measure the available network bandwidth and predict its future values. Bandwidth prediction, which is already a difficult task, must be more accurate when lower latency is desired due to the shorter time available to react to bandwidth changes, and when mobile networks are involved due to their inherently more frequent and potentially larger bandwidth fluctuations. Any inaccuracy in bandwidth prediction results in flawed adaptation decisions, which will in turn translate into a diminished viewer experience. We propose an Automated Model for Prediction (AMP) that encompasses techniques for bandwidth prediction and model auto-selection specifically designed for low-latency live steaming with chunked transfer encoding. We first study statistical and computational intelligence techniques to implement a suite of bandwidth prediction models that can work accurately under a broad range of network conditions, and second, we introduce an automated prediction model selection method. We confirm the effectiveness of our solution through trace-driven live streaming experiments.

A Survey of Computational Intelligence Techniques for Air-Conditioners Energy Management

Effective design of air-conditioner (AC) management system has the potential to reduce the cost of electricity consumption and help users to participate in demand response (DR) program as interruptible loads. However, optimizing the operation of AC is complex and, as a potential solution, computational intelligence (CI) techniques based model predictive algorithms are being explored in the literature. This article aims to provide an overview of the CI techniques that are established in addressing relevant and timely open problems of AC management for residential buildings. To do so, first, we provide a brief background on different DR mechanisms and AC management systems. Second, a review of recent advances in CI-based model prediction and optimal control techniques of AC systems for DR management is presented. The discussion reveals that the interest in CI techniques with adaptive learning algorithms is increasing due to their ability to adjust in varying conditions. Then, we provide a brief description of a testbed, which is used for testing various newly developed CI-based AC management techniques in a residential setting. Finally, key issues related to the coordination of a large number of AC systems, modeling accuracy, and computational tractability are highlighted along with their challenges and future research directions.

Microgrid Energy Management Systems Design by Computational Intelligence Techniques

With the capillary spread of multi-energy systems such as microgrids, nanogrids, smart homes and hybrid electric vehicles, the design of a suitable Energy Management System (EMS) able to schedule the local energy flows in real time has a key role for the development of Renewable Energy Sources (RESs) and for reducing pollutant emissions. In the literature, most EMSs proposed are based on the implementation of energy systems prediction which enable to run a specific optimization algorithm. Such strategy, known as Rolling Time Horizon (RTH), demonstrated very effective when the supporting prediction system performs well. However, it is featured by high operational times. In this work, different lightweight EMS models synthesized through machine learning algorithms have been compared considering six different simulation scenarios. Results shows that an RTH-based EMS owns the best overall performances. However, in some case studies, also other EMSs show competitive results, especially those based on Adaptive Neuro Fuzzy Inference Systems (ANFIS) trained by clustering, which in one case outperform RTH EMSs, and in other 3 cases (out of 6) yields performances close to RTH EMSs within 5%. A second contribution concerns the RTH EMS implementation on a small micro-controller, highlighting the high computational effort which can range in the order of minutes. Conversely, the ANFIS EMS shows always almost negligible computational costs (less than one second) and therefore can be used in realistic scenarios on cheap devices at run time. The paper also proposed a novel graphic tool to better represent, observe and analyze microgrid energy flows in each time slot or along the overall considered dataset.