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The rapid expansion of medical data poses numerous challenges for Machine Learning (ML) tasks due to their potential to include excessive noisy, irrelevant, and redundant features. As a result, it is critical to pick the most pertinent features for the classification task, which is referred to as Feature Selection (FS). Among the FS approaches, wrapper methods are designed to select the most appropriate subset of features. In this study, two intelligent wrapper FS approaches are implemented using a new meta-heuristic algorithm called Sand Cat Swarm Optimizer (SCSO). First, the binary version of SCSO, known as BSCSO, is constructed by utilizing the S-shaped transform function to effectively manage the binary nature in the FS domain. However, the BSCSO suffers from a poor search strategy because it has no internal memory to maintain the best location. Thus, it will converge very quickly to the local optimum. Therefore, the second proposed FS method is devoted to formulating an enhanced BSCSO called Binary Memory-based SCSO (BMSCSO). It has integrated a memory-based strategy into the position updating process of the SCSO to exploit and further preserve the best solutions. Twenty one benchmark disease datasets were used to implement and evaluate the two improved FS methods, BSCSO and BMSCSO. As per the results, BMSCSO acted better than BSCSO in terms of fitness values, accuracy, and number of selected features. Based on the obtained results, BMSCSO as a FS method can efficiently explore the feature domain for the optimal feature set.

Feature Selection (FS) aims to ameliorate the classification rate of dataset models by selecting only a small set of appropriate features from the initial range of features. In consequence, a reliable optimization method is needed to deal with the matters involved in this problem. Often, traditional methods fail to optimally reduce the high dimensionality of the feature space of complex datasets, which lead to the elicitation of weak classification models. Meta-heuristics can offer a favorable classification rate for high-dimensional datasets. Here, a binary version of a new human-based algorithm named Ali Baba and the Forty Thieves (AFT) was applied to tackle a pool of FS problems. Although AFT is an efficient meta-heuristic for optimizing many problems, it sometimes exhibits premature convergence and low search performance. These issues were mitigated by proposing three enhanced versions of AFT, namely: (1) A Binary Multi-layered AFT called BMAFT which uses hierarchical and distributed frameworks, (2) Binary Elitist AFT (BEAFT) which uses an elitist learning strategy, and, (3) Binary Self-adaptive AFT (BSAFT) which uses an adapted tracking distance parameter. These versions along with the basic Binary AFT (BAFT) were expansively assessed on twenty-four problems gathered from different repositories. The results showed that the proposed algorithms substantially enhance the performance of BAFT in terms of convergence speed and solution accuracy. On top of that, the overall results showed that BMAFT is the most competitive, which provided the best results with excellent performance scores compared to other competing algorithms.

Motivated by the increasing complexity and operational productivity of industrial processes, the need for efficient modeling schemes for industrial systems is highly demanded. This study presents a new simulator model for a real winding process based on a combination of Multi-gene Genetic Programming (MGP) and Capuchin Search Algorithm (CapSA), referred to as MGP-CapSA modeling approach. CapSA is a meta-heuristic algorithm used to optimize the coefficients of the regression equations of the MGP technique. The winding process has tensions in the web between reels 1 and 2 and between reels 2 and 3. On this basis, two mathematical models were developed by the MGP-CapSA method to estimate the tensions in the web for this process. The efficacy and superiority of the proposed MGP-CapSA method were verified by extensive experiments and hypothesis testing, and the proposed method was then compared with other well-known intelligent and conventional modeling methods. The proposed MGP-CapSA method can be exploited to enhance control performance and achieve robust fault-tolerant system. A comparison of the MGP-CapSA method with other promising modeling methods corroborates the performance level of MGP-CapSA over those competitors. The results demonstrate that MGP-CapSA is a suitable method for generating robust models for complex nonlinear systems.

Image reconstruction for industrial applications based on Electrical Capacitance Tomography (ECT) has been broadly applied. The goal of image reconstruction based ECT is to locate the distribution of permittivity for the dielectric substances along the cross-section based on the collected capacitance data. In the ECT-based image reconstruction process: (1) the relationship between capacitance measurements and permittivity distribution is nonlinear, (2) the capacitance measurements collected during image reconstruction are inadequate due to the limited number of electrodes, and (3) the reconstruction process is subject to noise leading to an ill-posed problem. Thence, constructing an accurate algorithm for real images is critical to overcoming such restrictions. This paper presents novel image reconstruction methods using Deep Learning for solving the forward and inverse problems of the ECT system for generating high-quality images of conductive materials in the Lost Foam Casting (LFC) process. Here, Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) models were implemented to predict the distribution of metal filling for the LFC process-based ECT. The recurrent connection and the gating mechanism of the LSTM is capable of extracting the contextual information that is repeatedly passing through the neural network while filtering out the noise caused by adverse factors. Experimental results showed that the presented ECT-LSTM-RNN model is highly reliable for industrial applications and can be utilized for other manufacturing processes.

This research provides a study on how the weights of artificial neural networks (ANNs) can be automatically updated by applying bio-inspired algorithms, particularly using the particle swarm optimisation (PSO) algorithm, grasshopper optimisation algorithm (GOA) and grey wolf optimisation (GWO). These evolutionary computation algorithms were used to evolve the synaptic weights of ANNs to find a particular architecture of ANNs. The developed nonlinear models were targeted to the identification of a particular nonlinear prediction system, an industrial winding process, as a case study. These new models were referred, respectively, to as ANN-PSO, ANN-GOA and ANN-GWO. The proposed models were compared with other linear and nonlinear conventional models including least square error and multiple nonlinear regression methods, respectively, as well as other state-of-the-art models including multilayer perceptron-type NNs, radial basis function and recurrent local linear neuro-fuzzy. The performance of the developed models was assessed using several metric criteria. Comparison of the proposed ANN-PSO, ANN-GOA and ANN-GWO models with other traditional and state-of-the-art models asserts the efficacy of the proposed modelling approaches.

The performance of any meta-heuristic algorithm depends highly on the setting of dependent parameters of the algorithm. Different parameter settings for an algorithm may lead to different outcomes. An optimal parameter setting should support the algorithm to achieve a convincing level of performance or optimality in solving a range of optimization problems. This paper presents a novel enhancement method for the salp swarm algorithm (SSA), referred to as enhanced SSA (ESSA). In this ESSA, the following enhancements are proposed: First, a new position updating process was proposed. Second, a new dominant parameter different from that used in SSA was presented in ESSA. Third, a novel lifetime convergence method for tuning the dominant parameter of ESSA using ESSA itself was presented to enhance the convergence performance of ESSA. These enhancements to SSA were proposed in ESSA to augment its exploration and exploitation capabilities to achieve optimal global solutions, in which the dominant parameter of ESSA is updated iteratively through the evolutionary process of ESSA so that the positions of the search agents of ESSA are updated accordingly. These improvements on SSA through ESSA support it to avoid premature convergence and efficiently find the global optimum solution for many real-world optimization problems. The efficiency of ESSA was verified by testing it on several basic benchmark test functions. A comparative performance analysis between ESSA and other meta-heuristic algorithms was performed. Statistical test methods have evidenced the significance of the results obtained by ESSA. The efficacy of ESSA in solving real-world problems and applications is also demonstrated with five well-known engineering design problems and two real industrial problems. The comparative results show that ESSA imparts better performance and convergence than SSA and other meta-heuristic algorithms.

Meta-heuristic search algorithms were successfully used to solve a variety of problems in engineering, science, business, and finance. Meta-heuristic algorithms share common features since they are population-based approaches that use a set of tuning parameters to evolve new solutions based on the natural behavior of creatures. In this paper, we present a novel nature-inspired search optimization algorithm called the capuchin search algorithm (CapSA) for solving constrained and global optimization problems. The key inspiration of CapSA is the dynamic behavior of capuchin monkeys. The basic optimization characteristics of this new algorithm are designed by modeling the social actions of capuchins during wandering and foraging over trees and riverbanks in forests while searching for food sources. Some of the common behaviors of capuchins during foraging that are implemented in this algorithm are leaping, swinging, and climbing. Jumping is an effective mechanism used by capuchins to jump from tree to tree. The other foraging mechanisms exercised by capuchins, known as swinging and climbing, allow the capuchins to move small distances over trees, tree branches, and the extremities of the tree branches. These locomotion mechanisms eventually lead to feasible solutions of global optimization problems. The proposed algorithm is benchmarked on 23 well-known benchmark functions, as well as solving several challenging and computationally costly engineering problems. A broad comparative study is conducted to demonstrate the efficacy of CapSA over several prominent meta-heuristic algorithms in terms of optimization precision and statistical test analysis. Overall results show that CapSA renders more precise solutions with a high convergence rate compared to competitive meta-heuristic methods.

Ozone is a toxic gas with massive distinct chemical components from oxygen. Breathing ozone in the air can cause severe effects on human health, especially people who have asthma. It can cause long-lasting damage to the lungs and heart attacks and might lead to death. Forecasting the ozone concentration levels and related pollutant attribute is critical for developing sophisticated environment safety policies. In this paper, we present three artificial neural network (ANN) models to forecast the daily ozone (O3), coarse particulate matter (PM10), and particulate matter (PM2.5) concentrations in a highly polluted city in the Republic of China. The proposed models are (1) recurrent multilayer perceptron (RMLP), (2) recurrent fuzzy neural network (RFNN), and (3) hybridization of RFNN and grey wolf optimizer (GWO), which are referred to as RMLP-ANN, RFNN, and RFNN-GWO models, respectively. The performance of the proposed models is compared with other conventional models previously reported in the literature. The comparative results showed that the proposed models presented outstanding performance. The RFNN-GWO model revealed superior results in the modeling of O3, PM10, and PM2.5 compared with the RMLP-ANN and RFNN models.

This paper presents a reliable and real-time method to detect pedestrians in image scenes that can vary greatly in appearance. To achieve greater reliability in what can be detected, a combination of visual cues is used in conjunction with edge-based features and colour information as a basis for training a random forest (RF) classifier to detect the local contour cues for pedestrian images. To achieve a real-time detection rate, the contour cues, edge-based features and colour information are incorporated and then trained using a cascade RF classifier with a census transform histogram visual descriptor that implicitly captures the global contours of the pedestrians. The contour detector favourably exceeded previous leading contour detectors and achieved a 95% detection rate. The reliability and specificity of the pedestrian detector are demonstrated on more than 5000 positive images containing street furniture, lamp posts and trees, structures that are frequently confused with persons by computer vision systems. Evaluation with over 220 video sequences with $$640 \times 480$$ pixel resolution presented a true positive rate of 96%. The proposed pedestrian detector outperforms previous competitive pedestrian detectors on many varied person data sets. The speed of execution in a robot is about 62 ms per frame for images of $$640 \times 480$$ pixels on an Intel Core i3-2310M™ processor running at 2.10 GHz with a RAM of 4 GB.

This study explores the application of bio-inspired algorithms (BIAs) in training artificial neural networks (ANNs) in the area of the nonlinear chemical process modeling. Motivated by the increasing complexity and operational efficiency of chemical processes, the need for schemes that can improve model identification of highly nonlinear systems is demanded. As a case study, the Tennessee Eastman (TE) chemical reactor problem is considered. The key issue is to find a particular architecture for an ANN that best model the TE chemical process. We propose the use of BIAs of bat algorithm (BA), firefly algorithm (FA), and artificial bee colony (ABC) algorithm as mechanisms to automatically update the synaptic weights of ANN. These algorithms were conducted to increase the ability of ANN to adapt the dynamic aspect of the TE process and prevent trapping into a local optimum. The proposed modeling framework was devised with extensive experiments and statistical analysis to illustrate the usability and suitability of the entire modeling and identification procedures. The results were explored with a discussion using the mean square error (MSE) and the variance account for (VAF) criteria to assess the degree of identification performance of the TE case study model. The reliability of the presented models was compared with previously developed models for the same subsystems of the TE reactor using competitive, intelligent approaches. The comparative results show that the proposed approach provides superior modeling performance and outperforms its competitors.

To fulfill the national air quality standards, many countries have created emissions monitoring strategies on air quality. Nowadays, policymakers and air quality executives depend on scientific computation and prediction models to monitor that cause air pollution, especially in industrial cities. Air pollution is considered one of the primary problems that could cause many human health problems such as asthma, damage to lungs, and even death. In this study, we present investigated development forecasting models for air pollutant attributes including Particulate Matters (PM2.5, PM10), ground-level Ozone (O3), and Nitrogen Oxides (NO2). The dataset used was collected from Dubrovnik city, which is located in the east of Croatia. The collected data has missing values. Therefore, we suggested the use of a Layered Recurrent Neural Network (L-RNN) to impute the missing value(s) of air pollutant attributes then build forecasting models. We adopted four regression models to forecast air pollutant attributes, which are: Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Artificial Neural Network (ANN) and L-RNN. The obtained results show that the proposed method enhances the overall performance of other forecasting models.

Image clustering presents a hot topic that researchers have chased extensively. There is always a need to a promising clustering technique due to its vital role in further image processing steps. This paper presents a compelling clustering approach for brain tumors and breast cancer in Magnetic Resonance Imaging (MRI). Driven by the superiority of nature-inspired algorithms in providing computational tools to deal with optimization problems, we propose Flower Pollination Algorithm (FPA) and Crow Search Algorithm (CSA) to present a clustering method for brain tumors and breast cancer. Evaluation clustering results of CSA and FPA were judged using two apposite criteria and compared with results of K-means, fuzzy c-means and other metaheuristics when applied to cluster the same benchmark datasets. The clustering method-based CSA and FPA yielded encouraging results, significantly outperforming those obtained by K-means and fuzzy c-means and slightly surpassed those of other metaheuristic algorithms.

Text clustering problem (TCP) is a leading process in many key areas such as information retrieval, text mining, and natural language processing. This presents the need for a potent document clustering algorithm that can be used effectively to navigate, summarize, and arrange information to congregate large data sets. This paper encompasses an adaptation of the grey wolf optimizer (GWO) for TCP, referred to as TCP-GWO. The TCP demands a degree of accuracy beyond that which is possible with metaheuristic swarm-based algorithms. The main issue to be addressed is how to split text documents on the basis of GWO into homogeneous clusters that are sufficiently precise and functional. Specifically, TCP-GWO, or referred to as the document clustering algorithm, used the average distance of documents to the cluster centroid (ADDC) as an objective function to repeatedly optimize the distance between the clusters of the documents. The accuracy and efficiency of the proposed TCP-GWO was demonstrated on a sufficiently large number of documents of variable sizes, documents that were randomly selected from a set of six publicly available data sets. Documents of high complexity were also included in the evaluation process to assess the recall detection rate of the document clustering algorithm. The experimental results for a test set of over a part of 1300 documents showed that failure to correctly cluster a document occurred in less than 20% of cases with a recall rate of more than 65% for a highly complex data set. The high F-measure rate and ability to cluster documents in an effective manner are important advances resulting from this research. The proposed TCP-GWO method was compared to the other well-established text clustering methods using randomly selected data sets. Interestingly, TCP-GWO outperforms the comparative methods in terms of precision, recall, and F-measure rates. In a nutshell, the results illustrate that the proposed TCP-GWO is able to excel compared to the other comparative clustering methods in terms of measurement criteria, whereby more than 55% of the documents were correctly clustered with a high level of accuracy.

Regrettably, a large proportion of likely patients with sleep apnea are underdiagnosed. Obstructive sleep apnea (OSA) is one of the main causes of hypertension, type II diabetes, stroke, coronary artery disease, and heart failure. OSA affects not only adults but also children where it forms one of the sources of learning disabilities for children. This study aims to provide a classification model for one of the well-known sleep disorders known as OSA, which causes a serious malady that affects both men and women. OSA affects both genders with different scope. Men versus women diagnosed with OSA are about 8:1. In this research, logistic regression (LR) and artificial neural networks were applied successfully in several classification applications with promising results, particularly in the bio-statistics area. LR was used to derive a membership probability for a potential OSA system from a range of anthropometric features including weight, height, body mass index (BMI), hip, waist, age, neck circumference, modified Friedman, snoring, Epworth sleepiness scale (ESS), sex, and daytime sleepiness. We developed two models to predict OSA, one for men and one for women. The proposed sleep apnea diagnosis model has yielded accurate classification results and possibly a prototype software module that can be used at home. These findings shall reduce the patient’s need to spend a night at a laboratory and make the study of sleep apnea to implement at home.

This work proposes a new approach in addressing Economic Load Dispatch (ELD) optimization problem in power unit systems using nature-inspired metaheuristics search algorithms. Solving such a problem requires a degree of maximization of the economic pact of a power network system, where this is possible with some existing population-based metaheuristic search algorithms. The key issue to be handled here is how to maximize the economic benevolence of a power network under a variety of operational constraints, taking into account the reduction in the generated fuel cost as well as the aggregate power loss in the transmission power network system. Some nature-inspired metaheuristics will be explored. Meanwhile, we shall focus our attention on a newly developed nature-inspired search algorithm, referred to as the Crow Search Algorithm or CSA for short, as well as the Differential Evolution (DE) that is commonly known as a metaheuristic. The CSA emerged to light by simulating the intelligent flocking behavior of crows. The practicability of the proposed approach-based CSA was conducted to common types of power generators, including three and six buses (nodes) in addition to the IEEE 30-bus standard system. The results of the presented approaches were compared to other results developed using existing nature-inspired metaheuristic algorithms like particle swarm optimization and genetic algorithms and also compared to traditional approaches such as quadratic programming method. The results reported here support that CSA has achieved an outstanding performance in solving the problem of ELD in power systems, demonstrating their good optimization capabilities through arriving at a combination of power loads that consummate the constraints of the ELD problem while simultaneously lessening the entire fuel cost. The experimental results also showed that the CSA solutions were capable of maximizing the reliability of the power supplied to the customers, and also reducing both the generated power cost and the loss of power in the transmission power systems.

Rapid data growth presents many challenges for Machine Learning (ML) tasks as it can include lots of irrelevant, noisy, and redundant features. Thus, it is vital to select the most relevant features to the classification task, known as Feature Selection (FS). The main goal of FS techniques is to maximize the performance of a classification task while keeping the number of features to a minimum. In this study, a hybrid metaheuristic model is designed to solve FS problems based on Binary Biogeography Optimization (BBO) followed by the application of Support Vector Machine Recursive Feature Elimination (SVM-RFE), known as BBO-SVM-RFE. The SVM-RFE is embedded into the BBO to improve the quality of the obtained solutions in the mutation operator in order to enhance the exploitation capability as well as striking an adequate balance between exploitation and exploration of the original BBO. The proposed BBO-SVM-RFE method for solving FS problems was assessed on eighteen benchmark datasets. Comparative results showed that the BBO-SVM-RFE method outperforms the BBO method and other existing wrapper and filter methods in terms of accuracy and number of selected features. The obtained results reveal the high potentiality of BBO-SVM-RFE in reliably searching the feature space to obtain the optimal combination of features.

Time, effort and the estimation of number of staff desired are critical tasks for project managers and particularly for software projects. The software testing process signifies about 40–50% of the software development lifecycle. Faults are detected and corrected during software testing. Accurate prediction of the number of test workers necessary to test a software before the delivery to a customer will save time and effort. In this paper, we present two models for estimating the number of test workers required for software testing using Artificial Neural Networks (ANN) and Genetic Programming (GP). We utilize the expected time to finish testing and the rate of change of fault observation as inputs to the proposed models. The proposed models were able to predict the required team size; thus, supporting project managers in allocating the team effort to various project phases. Both models yielded promising estimation results in real-time applications.

The design and automation of a steel making process is getting more complex as a result of the advances in manufacturing and becoming more demanding in quality requirements. It is essential to have an intelligent business process model which brings consistent and outstanding product quality thus keeping the trust with the business stakeholders. Hence, schemes are highly needed for improving the nonlinear process automation. The empirical mathematical model for steel making process is usually time consuming and may require high processing power. Fuzzy neural approach has recently proved to be very beneficial in the identification of such complex nonlinear systems. In this chapter, we discuss the applicability of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to model the dynamics of the hot rolling industrial process including: roll force, roll torque and slab temperature. The proposed system was developed, tested as well as compared with other existing systems. We have conducted several simulation experiments on real data and the results confirm the effectiveness of the ANFIS based algorithms.

We propose and evaluate a framework for detection of plant leaf/stem diseases. Studies show that relying on pure naked-eye observation of experts to detect such diseases can be prohibitively expensive, especially in developing countries. Providing fast, automatic, cheap and accurate image-processing-based solutions for that task can be of great realistic significance. The proposed framework is image-processing-based and is composed of the following main steps; in the first step the images at hand are segmented using the K-Means technique, in the second step the segmented images are passed through a pre-trained neural network. As a testbed, we use a set of leaf images taken from Al-Ghor area in Jordan. Our experimental results indicate that the proposed approach can significantly support accurate and automatic detection of leaf diseases. The developed Neural Network classifier that is based on statistical classification perform well and could successfully detect and classify the tested diseases with a precision of around 93%.

This paper discusses the application of artificial neural networks (ANNs) in the area of identification and control of nonlinear dynamical systems. Since chemical processes are getting more complex and complicated, the need of schemes that can improve process operations is highly demanded. ANNs are capable of learning from examples, perform non-linear mappings, and have a special capacity to approximate the dynamics of nonlinear systems in many applications. This paper describe the application of neural network for modeling reactor level, reactor pressure, reactor cooling water temperature, and reactor temperature problems in the Tennessee Eastman (TE) chemical process reactor. The potential of neural network technology in the process industries is great. Its ability to model process dynamics makes it powerful tool for modeling and control processes. A comparison between the applications of ANNs to model the TE plant is compared with other soft computing techniques like fuzzy logic (FL) and adaptive neuro-fuzzy inference systems (ANFIS).

Particle Swarm Optimisation (PSO) algorithm represents a new approach to optimisation problems. In this paper, image enhancement is presented as an optimisation problem to which PSO is applied. This application is done within a nouvelle automatic image enhancement technique encompassing a real-coded particle swarms algorithm. The enhancement process is a non-linear optimisation problem with several constraints. Based upon a mathematical model of the social interactions of swarms, the algorithm has been shown to be effective at finding good solutions of the enhancement problem by adapting the parameters of a novel extension to a local enhancement technique similar to statistical scaling. This enhances the contrast and detail in the image according to an objective fitness criterion. The proposed algorithm has been compared with Genetic Algorithms (GAs) to a number of tested images. The obtained results using grey scale images indicate that PSO is better than GAs in terms of the computational time and both the objective evaluation and maximisation of the number of pixels in the edges of the tested images.