Showing papers in "Arabian Journal for Science and Engineering in 2019"

Atomistic Simulation: A Unique and Powerful Computational Tool for Corrosion Inhibition Research

Abstract: It is difficult to understand the atomistic information on the interaction at the metal/corrosion inhibitor interface experimentally which is a key to understanding the mechanism by which inhibitors prevent the corrosion of metals. Atomistic simulations (molecular dynamics and Monte Carlo) are mostly performed in corrosion inhibition research to give deeper insights into the mechanism of inhibition of corrosion inhibitors on metal surfaces at the atomic and molecular time scales. A lot of works on the use of molecular dynamics and Monte Carlo simulation to investigate corrosion inhibition phenomenon have appeared in the literature in recent times. However, there is still a lack of comprehensive review on the understanding of corrosion inhibition mechanism using these atomistic simulation methodologies. In this review paper, we first of all introduce briefly some important molecular modeling simulations methods. Thereafter, the basic theories of molecular dynamics and Monte Carlo simulations are highlighted. Several studies on the use of atomistic simulations as a modern tool in corrosion inhibition research are presented. Some mechanistic and energetic information on how organic corrosion inhibitors interact with iron and copper metals are provided. This atomic and molecular level information could aid in the design, synthesis and development of new and novel corrosion inhibitors for industrial applications.

Some Generalized Complex Intuitionistic Fuzzy Aggregation Operators and Their Application to Multicriteria Decision-Making Process

Abstract: The objective of this manuscript is to present some generalized weighted averaging aggregation operators for aggregating the different complex intuitionistic fuzzy sets using t-norm operations In the existing studies of fuzzy and its extension, the uncertainties present in the data are handled with the help of degrees of membership which are the subset of real numbers, which may loose some useful information and hence consequently affect on the decision results As a modification to these, complex intuitionistic fuzzy set handles the uncertainties with the degrees whose ranges are extended from real subset to the complex subset with the unit disk and hence handle the two-dimensional information in a single set Thus, motivated by this, we developed some new averaging aggregation operators, namely complex intuitionistic fuzzy (CIF) weighted averaging, CIF ordered weighted averaging and CIF hybrid averaging in conjunction with their desirable properties Then, we utilized these operators to propose a multicriteria decision-making approach and illustrated a numerical example to demonstrate the working of the proposed approach Finally, the proposed results are compared with existing approaches results

Natural Convection Analysis in a Cavity with an Inclined Elliptical Heater Subject to Shape Factor of Nanoparticles and Magnetic Field

Abstract: The objective of the present study is to peruse the natural convection in the cavity containing inclined elliptical heater under shape factor of nanoparticles and magnetic field. The control volume-based finite element method is used for solving conservation equations. Numerical results show very good grid independency and very good compromise with other works. The result shows the heat transfer grows via mounting nanofluid volume fraction. The increment of Ra number also leads the heat transfer to ascend. Heat transfer of nanofluid with three different shapes of nanoparticles is studied, and results show the platelet nanoparticle is better than the other ones. The influence of magnetic field on heat transfer is also investigated and discussed. The obtained outcomes represent that at a certain Rayleigh number, the average Nusselt number descends with the ascendant of Hartmann number. Finally, the new correlation is reported for calculating the Nu number in these geometries.

Brain Tumor Detection and Segmentation in MR Images Using Deep Learning

Abstract: Gliomas are the most infiltrative and life-threatening brain tumors with exceptionally quick development. Gliomas segmentation using computer-aided diagnosis is a challenging task, due to irregular shape and diffused boundaries of tumor with the surrounding area. Magnetic resonance imaging (MRI) is the most widely used method for imaging structures of interest in human brain. In this study, a deep learning-based method that uses different modalities of MRI is presented for the segmentation of brain tumor. The proposed hybrid convolutional neural network architecture uses patch-based approach and takes both local and contextual information into account, while predicting output label. The proposed network deals with over-fitting problem by utilizing dropout regularizer alongside batch normalization, whereas data imbalance problem is dealt with by using two-phase training procedure. The proposed method contains a preprocessing step, in which images are normalized and bias field corrected, a feed-forward pass through a CNN and a post-processing step, which is used to remove small false positives around the skull portion. The proposed method is validated on BRATS 2013 dataset, where it achieves scores of 0.86, 0.86 and 0.91 in terms of dice score, sensitivity and specificity for whole tumor region, improving results compared to the state-of-the-art techniques.

A Survey on Parallel Particle Swarm Optimization Algorithms

Abstract: Most of the complex research problems can be formulated as optimization problems. Emergence of big data technologies have also commenced the generation of complex optimization problems with large size. The high computational cost of these problems has rendered the development of optimization algorithms with parallelization. Particle swarm optimization (PSO) algorithm is one of the most popular swarm intelligence-based algorithm, which is enriched with robustness, simplicity and global search capabilities. However, one of the major hindrance with PSO is its susceptibility of getting entrapped in local optima and; alike other evolutionary algorithms the performance of PSO gets deteriorated as soon as the dimension of the problem increases. Hence, several efforts are made to enhance its performance that includes the parallelization of PSO. The basic architecture of PSO inherits a natural parallelism, and receptiveness of fast processing machines has made this task pretty convenient. Therefore, parallelized PSO (PPSO) has emerged as a well-accepted algorithm by the research community. Several studies have been performed on parallelizing PSO algorithm so far. Proposed work presents a comprehensive and systematic survey of the studies on PPSO algorithms and variants along with their parallelization strategies and applications.

Ripeness Classification of Bananas Using an Artificial Neural Network

Abstract: The quality of fresh banana fruit is a main concern for consumers and fruit industrial companies. The effectiveness and fast classification of banana’s maturity stage are the most decisive factors in determining its quality. It is necessary to design and implement image processing tools for correct ripening stage classification of the different fresh incoming banana bunches. Ripeness in banana fruit generally affects the eating quality and the market price of the fruit. In this paper, an automatic computer vision system is proposed to identify the ripening stages of bananas. First, a four-class homemade database is prepared. Second, an artificial neural network-based framework which uses color, development of brown spots, and Tamura statistical texture features is employed to classify and grade banana fruit ripening stage. Results and the performance of the proposed system are compared with various techniques such as the SVM, the naive Bayes, the KNN, the decision tree, and discriminant analysis classifiers. Results reveal that the proposed system has the highest overall recognition rate, which is 97.75%, among other techniques.

Generalized Cubic Intuitionistic Fuzzy Aggregation Operators Using t-Norm Operations and Their Applications to Group Decision-Making Process

Abstract: Cubic intuitionistic fuzzy (CIF) set (CIFS) is one of the newly developed extension of the intuitionistic fuzzy set (IFS) in which data are represented in terms of their interval numbers membership and non-membership degrees and further the degree of agreeness, as well as disagreeness corresponding to these intervals, are given in the form of an IFS Its fundamental characteristic lies in the fact that it is a combined version of both interval-valued IFS and IFS rather than being confined to any single fuzzy environment Under this environment, the present work focused on exploring the structural characteristics of the CIFS by defining operational laws between them Further, based on these operational laws, we propose some new generalized CIF averaging aggregation operators and group decision-making methods Finally, an illustrative example is provided to discuss the reliability of the proposed operators

Deep Learning Based Sentiment Analysis Using Convolution Neural Network

Abstract: Sentiment analysis (SA) of natural language text is an important and challenging task for many applications of Natural Language Processing. Till now, researchers have used different types of SA techniques such as lexicon based and machine learning to perform SA for different languages such as English, Chinese. Inspired by the gain in popularity of deep learning models, we conducted experiments using different configuration settings of convolutional neural network (CNN) and performed SA of Hindi movie reviews collected from online newspapers and Web sites. The dataset has been manually annotated by three native speakers of Hindi to prepare it for training of the model. The experiments are conducted using different numbers of convolution layers with varying number and size of filters. The CNN models are trained on 50% of the dataset and tested on remaining 50% of the dataset. For the movie reviews dataset, the results given by our CNN model are compared with traditional ML algorithms and state-of-the-art results. It has been observed that our model is able to achieve better performance than traditional ML approaches and it has achieved an accuracy of 95%.

Data-Driven Short-Term Forecasting for Urban Road Network Traffic Based on Data Processing and LSTM-RNN

Abstract: A short-term traffic flow prediction framework is proposed for urban road networks based on data-driven methods that mainly include two modules. The first module contains a set of algorithms to process traffic flow data. After analysis and repair, a complete data set without outliers is provided as well as a data set containing pairs of road segments that are the most similar to each other in regard to their trends. The second module focuses on multiple time-step short-term forecasting. With a good understanding of the periodicity and randomness of traffic flow, the time series is first decomposed into a trend series and residual series. After reconstructing the two time series, model training and prediction based on a long short-term memory-recurrent neural network (LSTM-RNN) are performed. Finally, the two results are combined together to form the final prediction. A model evaluation is performed using two urban road networks. The results show that the data processing module can effectively improve the data quality, reduce the training time and enhance the model robustness. The LSTM-RNN correctly identifies the time trend and spatial similarity of traffic flow and obtains a more accurate multiple time-step prediction. The proposed framework outperforms other deep learning algorithms and has better accuracy and stability.

A Linguistic Neutrosophic Multi-criteria Group Decision-Making Approach with EDAS Method

Abstract: This study develops an approach that incorporates power aggregation operators with the evaluation based on distance from average solution (EDAS) method under linguistic neutrosophic situations to solve fuzzy multi-criteria group decision-making problems. Firstly, the existing operational laws and comparison methods of linguistic neutrosophic numbers (LNNs) are analysed. Secondly, the distance measurement between two LNNs is defined. Thirdly, the power-weighted averaging operator and the power-weighted geometric operator with LNNs are developed to support the decision makers’ evaluation information. The models to derive the criteria weights are also constructed based on the proposed distance measurements. Finally, the EDAS method is extended to resolve group decision-making problems in the linguistic neutrosophic environment. An illustrative example of the property management company selection is given to verify the effectiveness and practicality of the proposed approach.

A Novel Liver Image Classification Method Using Perceptual Hash-Based Convolutional Neural Network

Abstract: Classification of liver masses plays an important role in early diagnosis of patients. This paper proposes a method to reduce the liver computed tomography (CT) images classification time and maintain the classification performance above an acceptable threshold by using convolutional neural network (CNN). A hybrid model called fused perceptual hash-based CNN (F-PH-CNN) is proposed by using a perceptual hash function together with the CNN. The proposed method has been designed for differential diagnosis between benign and malignant masses using CT images. The most important feature of the perceptual hash functions is to obtain the salient features of images. In the proposed F-PH-CNN method, DWT–SVD-based perceptual hash functions are used. The study uses CT images of 41 benign and 34 malign samples obtained from Elazig Education and Research Hospital. These samples were augmented up to 112 samples. The experimental results show that the CNN features achieved a better classification performance in which the ANN simulation results validate that the all output data with 98.2% success. The proposed method might also address the clinical computer-aided diagnosis of liver masses.

Feature Selection Using Chaotic Salp Swarm Algorithm for Data Classification

Abstract: Salp swarm algorithm (SSA) is a recently created bio-inspired optimization algorithm presented in 2017 which is based on the swarming mechanism of salps. Despite high performance of SSA, slow convergence speed and getting stuck in local optima are two disadvantages of SSA. This paper introduces a novel chaotic SSA algorithm (CSSA) to avoid these weaknesses, where chaotic maps are used to enhance the performance of SSA algorithm. The CSSA algorithm is incorporated with the K-nearest neighbor classifier to solve the feature selection problem, in which twenty-seven datasets are used to assess the performance of CSSA algorithm. The results confirmed that the proposed chaotic SSA (especially Tent map) produced superior results compared to standard SSA and other optimization algorithms.

Very High Capacity Image Steganography Technique Using Quotient Value Differencing and LSB Substitution

Abstract: This article proposes a very high capacity steganography technique using differencing and substitution mechanisms. It divides the image into non-overlapped $$3{\times }3$$ pixel blocks. For every pixel of a block, least significant bit (LSB) substitution is applied on two LSBs and quotient value differencing (QVD) is applied on the remaining six bits. Thus, there are two levels of embedding: (i) LSB substitution at lower bit planes and (ii) QVD at higher bit planes. If a block after embedding in this fashion suffers with fall off boundary problem, then that block is undone from the above hybrid embedding and modified 4-bit LSB substitution is applied. Experimentally, it is evidenced that the hiding capacity is improved to a greater extent. It is also experimentally proved that pixel difference histogram and RS analysis techniques cannot detect the proposed steganography technique.

Experimental Investigation on the Tribo-Thermal Properties of Brake Friction Materials Containing Various Forms of Graphite: A Comparative Study

Abstract: The objective of this research work is to study the impact of graphite forms used as solid lubricant additives on brake friction materials performance. Three composites were fabricated using the conventional process and were characterised for its physical, chemical, mechanical and thermal properties conforming to industrial standards. The thermal stability of the graphite particles and developed composites was measured in an air atmosphere using the thermogravimetric analyser. The tribological performances were studied using the Chase friction test machine as per IS-2742 Part-4 standard. The results indicated that the friction composite containing expandable graphite exhibited better thermal stability with good fade and recovery performances. This led to enhance wear resistance and stable friction due to its better heat dissipation and lubricity comparing with the other two composites. An empirical relationship for the friction and wear was developed based on Chase test results. The worn surface morphologies of the Chase tested composites were analysed using scanning electron microscopy to study the sensitivity of the friction–wear mechanisms to the graphite-type effect.

Cutting Forces, Surface Roughness and Tool Wear Quality Assessment Using ANN and PSO Approach During Machining of MDN431 with TiN/AlN-Coated Cutting Tool

Abstract: The aim of this study was to improve the life and performance of tungsten carbide turning tool inserts coated with TiN/AlN multilayer thin films using physical vapor deposition technique. Quality characteristics of the coating are evaluated using Calo and VDI 3198 tests. Thickness of the coating is found to be $$3.651 \,\upmu \hbox {m}$$ with adhesion quality of HF1. The performance of coated tool inserts is evaluated using cutting speed (59–118 m/min), feed rate (0.062–0.125 mm/rev) and depth of cut (0.2–0.4 mm) as process parameters in turning MDN431 steel. Experimental investigation has been carried out based on full factorial design, and regression analysis was used to analyze and build the mathematical models for cutting force and surface roughness. Multi-objective optimization of the process parameters has been done with the combination of desirability approach and MOPSO technique. Optimum machining condition for least cutting force and optimum surface roughness is found to be $${V}_{\mathrm{c}} =59\, \hbox {m/min}$$, $${f}=0.063\,\hbox {mm/rev}$$ and $${a}_{{p}} =0.2\,\hbox {mm}$$. Cutting force and surface roughness are reduced by 9% in TiN/AlN-coated tools compared with the uncoated tool. To improve the CoD and capability of predictive regression models, ANN modeling has been adopted. ANN trained model and mathematical regression models are used to predict the results and predict the responses, which follow the experimental data with minimum absolute error. The predicted results are validated using ANN and regression analysis found with minimum error, and developed models are adequate for further usage. Tool wear was reduced by 105% in TiN/AlN-coated tools compared with the uncoated tool.

Novel Automatic Food Trading System Using Consortium Blockchain

Abstract: Conventional food trading platforms face several issues, such as quickly to find trading objects and protect the reliability of transaction information With e-commerce developing rapidly, food trading has also recently shifted to the online domain Blockchain has changed many industries owing to its robustness, decentralization and end-to-end credibility This paper proposes a novel Food Trading System with COnsortium blockchaiN (FTSCON) to improve trust and security issues in transactions It uses consortium blockchain technology to set permission and authentication for different roles in food transaction, which meet the challenge of the privacy protection of multi-stakeholders The algorithm of optimized transaction combination is designed for the purpose of helping users find suitable transaction objects It can choose the optimized trading portfolio for buyers The online double auction mechanism is used to eliminate competition And the improved PBFT (iPBFT) is used to enhance efficiency of system Moreover, a smart-contract life- cycle management method is introduced, and security analysis shows that FTSCON improves transaction security and privacy protection Experiment results based on a series of data indicate that the proposed algorithm can achieve profit improvement of merchants

Integrated Intrusion Detection Model Using Chi-Square Feature Selection and Ensemble of Classifiers

Abstract: Intrusion detection system is a device or software application that monitors a network of systems to identify any malicious activity or policy violations. In order to identify intrusions or normal activity, IDS would consider different network-related features such as source address, protocol and flag. The major challenge for any intrusion detection model is to achieve maximum accuracy with minimal false alarms. The aim of this paper is to identify the critical features required in the construction of intrusion detection model, thereby achieving the maximum accuracy. The model utilizes an ensemble approach of classifiers with minimum complexity to overcome the issues in the existing ensemble-based intrusion detection models. In this paper, Chi-square feature selection and the ensemble of classifiers such as support vector machine (SVM), modified Naive Bayes (MNB) and LPBoost are utilized to develop an intrusion detection model. The motivation for selecting Chi-square feature selection is that they rank the features based on the statistical significance test and consider only those features that are dependent on the class label. Supervised classifiers are highly consistent and produce precise results as the use of training data improves the ability to distinguish between classes with similar features. Experimental results indicate high accuracy in comparison with base classifiers by the ensemble of LPBoost. As there is a huge class imbalance present in the network traffic, the prediction of the class label by a majority voting of SVM, MNB and LPBoost is an optimal solution in preference to reliance on a single classifier.

Airflow-Dust Migration Law and Control Technology Under the Simultaneous Operations of Shotcreting and Drilling in Roadways

Abstract: The simultaneous operations of drilling (cutting rock) and shotcreting are occasionally carried out in order to advance the tunneling speed in underground roadways. Under these circumstances, the dust pollution from multiple dust sources is serious, especially threatening worker health. In this paper, the main dust sources were divided; a new ventilation system under the simultaneous operations was proposed by adding one turbulator (T) and the second exhaust air outlet (SEAO). The airflow-dust migration law after improving ventilation system was analyzed to determine the optimum combination and corresponding working parameters of T and SEAO based on computational fluid dynamics. Results showed that the combinatorial arrangements of A-3 pattern, T installed upstream while SEAO laid downstream of shotcreting area, were the best ventilation pattern in terms of dust suppression. The more air volume from SEAO is, the smaller the diffusion distance of high concentration dust is. The dust concentration at the interface between cutting rock and shotcrete declined and then increased with increasing total forced air volume. The bigger the forced-exhaust air ratio is, the larger the dust concentration at interface is. According to the on-site test, the new method prevented the high concentration dust from diffusing at tunneling district and reduces the dust concentration at shotcreting district.

Influence of Variable Transport Properties on Nonlinear Radioactive Jeffrey Fluid Flow Over a Disk: Utilization of Generalized Differential Quadrature Method

Abstract: In the present research work, two-dimensional boundary layer flow of Jeffrey fluid over a radially stretching disk in the presence nonlinear Rosseland thermal radiation is investigated. The thermal conductivity and viscosity of Jeffrey fluid are considered variable and assumed to be a function of temperature. The self-similar equations are obtained by employing appropriate transformations. These transformed highly nonlinear equations are solved numerically by a powerful technique generalized differential quadrature method. Chebyshev–Gauss–Lobatto spectral method and midpoint method with Richardson extrapolation (RMM) are also adopted to establish the validity and reliability of the numerical solution. The variations of velocity and temperature profiles with the governing parameters such as Prandtl number, variable viscosity and thermal conductivity parameters, heating parameter, radiation parameter, Deborah number and ratio of relaxation to retardation time are presented and discussed graphically. Reduction in momentum boundary layer thickness and velocity of fluid is observed by increasing variable viscosity ratio parameter $$\delta $$ . Velocity of fluid increases, whereas the temperature of fluid decreases with an increase in Deborah number De. The rise in temperature is observed with increasing values of variable thermal conductivity parameter $$\varepsilon $$ , ratio of relaxation to retardation time $$\lambda _1 $$ and heating parameter $$\theta _{r} $$ .

Response Surface Methodology Integrated with Desirability Function and Genetic Algorithm Approach for the Optimization of CNC Machining Parameters

Abstract: In this study, response surface method (RSM), desirability function (DF) and genetic algorithm (GA) techniques were integrated to estimate optimal machining parameters that lead to minimum surface roughness value of beech (Fagus orientalis Lipsky) species. Design of experiment was used to determine the effect of computer numerical control machining parameters such as spindle speed, feed rate, tool radius and depth of cut on arithmetic average roughness ( $$R_{\mathrm{a}}$$ ). Average surface roughness values of the samples were measured by employing a stylus type equipment. The second-order mathematical model was developed by using response surface methodology with experimental design results. Optimum machining condition for minimizing the surface roughness was carried out in three stages. Firstly, the DF was used to optimize the mathematical model. Secondly, the results obtained from the desirability function were selected as the initial point for the GA. Finally, the optimum parameter values were obtained by using genetic algorithm. Experimental results showed that the proposed approach presented an efficient methodology for minimizing the surface roughness.

3D MHD Free Convective Stretched Flow of a Radiative Nanofluid Inspired by Variable Magnetic Field

Abstract: This paper carries on investigation to study the effects of variable magnetic field and thermal radiation on free convective flow of an electrically conducting incompressible nanofluid over an exponential stretching sheet. The model implemented in the present study significantly enriches the thermal conductivity and hence more heat transfer capability of nanofluids. The transformed governing equations have been solved numerically using fourth-order Runge–Kutta method along with shooting technique. The influence of variable magnetic field and thermal radiation associated with thermal buoyancy on the dimensionless velocity, temperature, skin friction and Nusselt number have been analyzed. The obtained numerical results in the present study are validated and found to be in excellent agreement with some previous results seen in the literature. The present study contributes to the result that augmented Hartmann number belittles the fluid flow and enhances the fluid temperature and the related thermal boundary layer thickness.

On Some Improved Versions of Whale Optimization Algorithm

Abstract: Whale optimization algorithm (WOA) is a recently developed swarm intelligence-based algorithm which is inspired from the social behavior of humpback whale This algorithm mimics the bubble-net hunting strategy of whales and has been applied to optimization problems But the algorithm suffers from the problem of poor exploration and local optima stagnation In this paper, three different modified algorithms of WOA have been proposed to improve its explorative ability The modified versions are based on the concepts of opposition-based learning, exponentially decreasing parameters and elimination or re-initialization of worst particles These properties have been added to improve the explorative properties of WOA by maintaining diversity among the search agents The proposed algorithms have been tested on CEC2005 benchmark problems for variable population and dimension sizes Statistical testing and scalability testing of the best algorithm have been carried out to prove its significance over other algorithms such as with well-known algorithms such as bat algorithm, bat flower pollinator, differential evolution, firefly algorithm, flower pollination algorithm It has been found from the experimental results that the performance of all the proposed versions is better than the original WOA Here, opposition- and exponential-based WOA is the best among all the proposed variants Statistical testing and convergence profiles further validate the results

Multi-objective-Oriented Cuckoo Search Optimization-Based Resource Scheduling Algorithm for Clouds

Abstract: Scheduling problems in cloud computing environment are mostly influenced by multi-objective optimization but frequently deal with using single-objective algorithms. The algorithms need to resolve multi-objective problems which are significantly different from the procedure or techniques used for single-objective optimizations. For this purpose, meta-heuristic algorithms always show their strength to deal with multi-objective optimization problems. In this research article, we present an innovative Multi-objective Cuckoo Search Optimization (MOCSO) algorithm for dealing with the resource scheduling problem in cloud computing. The main objective of resource scheduling problem is to reduce the cloud user cost and enhance the performance by minimizing makespan time, which helps to increase the revenue or profit for cloud providers with maximum resource utilization. Therefore, the proposed MOCSO algorithm is a new method for solving multi-objective resource scheduling problems in IaaS cloud computing environment. Moreover, the effects of the proposed algorithm are analyzed and evaluated by comparison with state-of-the-art multi-objective resource scheduling algorithms using simulation framework. Results obtained from simulation show that the proposed MOSCO algorithm performs better than MOACO, MOGA, MOMM and MOPSO, and balance multiple objectives in terms of expected time to completion and expected cost to completion matrices for resource scheduling in IaaS cloud computing environment.

Prediction of Uniaxial Compressive Strength of Different Quarried Rocks Using Metaheuristic Algorithm

Abstract: The direct measurement of uniaxial compressive strength (UCS) as one of the main important rock engineering parameters is destructive, cumbersome, difficult and costly. Therefore, the prediction of this parameter using simpler, cheaper indirect methods is of interest. In this paper, the UCS was predicted using a developed hybrid intelligent model including generalized feedforward neural network (GFFN) incorporated with imperialist competitive algorithm (ICA). To find the optimum model, 197 sets including rock class, density, porosity, P-wave velocity, point load index and water absorption from almost all over quarries of Iran were compiled. The efficiency and performance of GFFN and hybrid ICA-GFFN models subjected to different error criteria and established confusion matrixes were compared to multilayer perceptron (MLP) and radial basis function (RBF) neural network models as well as conducted multivariate regression. The hybrid ICA-GFFN with 11.37%, 14.27% and 22.74% improvement in correct classification rate over than GFFN, RBF and MLP demonstrated superior predictability level. The results indicated that the developed ICA-GFFN model as a feasible and accurate enough tool can effectively be applied for UCS prediction purposes. Using the sensitivity analyses, the P-wave velocity and rock class were identified as the most and least influences factors on predicted UCS.

A Concept for Automated Construction Progress Monitoring: Technologies Adoption for Benchmarking Project Performance Control

Abstract: Despite recent advances in technologies and equipment for automated progress monitoring, most construction companies worldwide do not utilize them for their projects. This can be due to many reasons, such as the high cost of technologies and equipment, need for skilled staff, and lack of sufficient information about the impact of automated progress monitoring on project performance control. The aim of the present research is to investigate the impact of automated progress monitoring on key project performance indicators: time, cost, and quality. This study prepared based on a survey of contracting and engineering consulting firms in North America, Europe, and the Middle East. In the first part of this study, structural equation modeling is used to identify the relations of different factors of project progress monitoring (both conventional and automated) with project performance control. In the second part of the study, a benefit analysis is evaluated based on the sixteen (16) journal and international conference papers and also twenty-four (24) international construction projects for which automated progress monitoring was employed. The research findings validate the positive impact of real-time, accurate, and cost-effective automated progress monitoring environments and reveal how automated progress monitoring affects construction project success.

Convective Heat and Mass Transfer in Magneto Walter’s B Nanofluid Flow Induced by a Rotating Cone

Abstract: This work explores theoretical analysis for time-dependent flow of a Nano Walter’s B fluid on a rotational cone under magnetic regime. Angular velocity is taken as a reverse linear function of time near and away from the cone to obtain self-similar solutions. The analytical result of reduced nonlinear system has been achieved via optimal homotopy analysis method to simulate the flow performance graphically. Comparison with the published material is also a salient feature of the present exploration that validates the obtained results. It is observed that heat and mass transfer rates have contradictory effects on Brownian motion and thermophoresis parameters, respectively.

Robust Invisible Digital Image Watermarking Using Hybrid Scheme

Abstract: Digital image watermarking is a technique to protect copyright of the image owner in the world of digital communication, and robustness is the major property to be addressed effectively. We propose an invisible hybrid watermarking scheme which is composed of blind and non-blind watermarking techniques. First, blind scheme is used as inner watermarking scheme and then non-blind watermarking scheme as outer watermarking scheme. A secret binary image is taken as a watermark and is embedded in an inner cover image using discrete wavelet transformation (DWT) with the help of the blind watermarking scheme in association with predefined binary digit sequence block and gain factor $${\varvec{\upalpha }}$$ to get inner watermarked image. Then, this inner watermarked image is embedded into an outer cover image using DWT and singular value decomposition by non-blind watermarking technique to get hybrid watermarked image. On the contrary, to extract the secret binary image, first non-blind watermark extraction and then blind watermark extraction techniques are used. From the experimental results, it is shown that this hybrid watermarking approach is robust against—rotation, JPEG compression, salt and pepper noise, Gaussian noise, speckle noise and Poisson noise.

Service-Level Agreement—Energy Cooperative Quickest Ambulance Routing for Critical Healthcare Services

Abstract: In this study, the problem of critical ambulance routing scheme, which is a significant variant of the quickest path problem (QPP), was investigated. The proposed QPP incorporates additional factors, such as service-level agreement (SLA) and energy cooperation, to compute the SLA-energy cooperative quickest route (SEQR) for a real-time critical healthcare service vehicle (e.g., ambulance). The continuity of critical healthcare services depends on the performance of the transport system. Therefore, in this research, SLA and energy were proposed as important measures for quantifying the performance. The developed algorithm (SEQR) evaluates the SLA-energy cooperative quickest ambulance route according to the user’s service requirements. The SEQR algorithm was tested with various transport networks. The SLAs and energy variation were quantified through the mean candidate s–t qualifying service set (QSS) routes for the service, average hop count, and average energy efficiency.

An Improved PSO-Based Multilevel Image Segmentation Technique Using Minimum Cross-Entropy Thresholding

Abstract: Entropy-based thresholding techniques are quite popular and effective for image segmentation. Among different entropy-based techniques, minimum cross-entropy thresholding (MCET) has received wide attention in the field of image segmentation. Considering the high time complexity of MCET technique for multilevel thresholding, recursive approach to reducing its computational cost is highly desired. To reduce the complexity, further optimization techniques can be applied to find optimal multilevel threshold values. In this paper, a novel improved particle swarm optimization (IPSO)-based multilevel thresholding algorithm is proposed to search the near-optimal MCET thresholds. The general PSO algorithm often suffers from premature convergence problem which has been addressed in the IPSO by decomposing a high-dimensional swarm into several one-dimensional swarms, and then premature convergence is removed from each one-dimensional swarm. The proposed technique is applied to the set of grayscale images, and the experimental results infer that it produces better MCET optimal threshold values at a higher and faster convergence rate. The qualitative and quantitative results are compared with existing optimization techniques like modified artificial bee colony, Cuckoo search, Firefly, particle swarm optimization, and genetic algorithm. It has been observed that the proposed technique performs better in terms of producing better fitness value, less CPU time as quantitative measurements, and effective misclassification error, peak signal-to-noise ratio, feature similarity index measurement, complex wavelet structural similarity index measurement values as qualitative measurements compared to other considered state-of-the-art methods.

Surface Defects Classification of Hot-Rolled Steel Strips Using Multi-directional Shearlet Features

Abstract: In this paper, a method combining the use of discrete shearlet transform (DST) and the gray-level co-occurrence matrix (GLCM) is presented to classify surface defects of hot-rolled steel strips into the six classes of rolled-in scale, patches, crazing, pitted surface, inclusion and scratches. Feature extraction involves the extraction of multi-directional shearlet features from each input image followed by GLCM calculations from all extracted sub-bands, from which a set of statistical features is extracted. The resultant high-dimensional feature vectors are then reduced using principal component analysis. A supervised support vector machine classifier is finally trained to classify the surface defects. The proposed feature set is compared against the Gabor, wavelets and the original GLCM in order to evaluate and validate its robustness. Experiments were conducted on a database of hot-rolled steel strips consisting of 1800 grayscale images whose defects exhibit high inter-class similarity as well as high intra-class appearance variations. Results indicate that the proposed DST–GLCM method is superior to other methods and achieves classification rates of 96.00%.