Showing papers in "Arabian Journal for Science and Engineering in 2018"

Segmentation of Ischemic Stroke Lesion in Brain MRI Based on Social Group Optimization and Fuzzy-Tsallis Entropy

Abstract: Stroke is one of the widespread causes of morbidity worldwide and is also the foremost reason for attained disability in human community. Ischemic stroke can be confirmed by investigating the interior brain regions. Magnetic resonance image (MRI) is one of the noninvasive imaging techniques widely adopted in medical discipline to record brain malformations. In this paper, a hybrid semi-automated image processing methodology is proposed to inspect the ischemic stroke lesion using the MRI recorded with flair and diffusion-weighted modality. The proposed approach consists of two sections, namely the preprocessing based on the social group optimization monitored Fuzzy-Tsallis entropy and post-processing technique, which consists of a segmentation algorithm to extract the ISL from preprocessed image in order to estimate the stroke severity and also to plan for further treatment process. The proposed hybrid approach is experimentally investigated using the ischemic stroke lesion segmentation challenge database. This work also presents a detailed investigation among well-known segmentation approaches, like watershed algorithm, region growing technique, principal component analysis, Chan–Vese active contour, and level set approaches, existing in the literature. The results of the experimental work executed using ISLES 2015 challenge dataset confirm that proposed methodology offers superior average values for image similarity indices like Jaccard (78.60%), Dice (88.54%), false positive rate (3.69%), and false negative rate (11.78%). This work also helps to achieve improved value of sensitivity (99.65%), specificity (78.05%), accuracy (91.17%), precision (98.11%), BCR (90.19%), and BER (6.09%).

A Comparative Study on the Adsorption of Eriochrome Black T Dye from Aqueous Solution on Graphene and Acid-Modified Graphene

Abstract: In this study, the adsorption behavior of Eriochrome Black T (EBT) on graphene (G) and acid-modified graphene (AMG) was investigated. Surface of the graphene was modified by acid treatment. Surface and structural characterization of the adsorbents were conducted using Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and Brunauer–Emmett–Teller (BET) surface area analysis. The effect of influential adsorption parameters (pH, contact time, and initial concentration) on the adsorption of EBT onto G and AMG were examined in batch experiments. Adsorption behavior of EBT on the surfaces of G and AMG was evaluated by applying different isotherm models (Langmuir, Freundlich, and Redlich–Peterson) on equilibrium data. The adsorption kinetics was studied by using pseudo-first-order and pseudo-second-order model. Adsorption followed the pseudo-second-order rate kinetics. The maximum removal of EBT was found to be 95 and 80% by G and AMG, respectively, at pH 2, adsorbent dosage of 10 mg, contact time of 3 h, and initial dye concentration of 10 mg/L. The maximum adsorption capacities were 102.04 and 70.42 mg/g for G and AMG, respectively. It was found that acid modification of graphene has an adverse effect on the adsorption of EBT.

Carbon Dioxide Corrosion Inhibitors: A review

Abstract: This article intends to summarize the corrosion inhibition of pipeline steels in aqueous carbon dioxide ( $$\hbox {CO}_{2})$$ environments. The emphasis is on various inhibitors, especially imidazolines used to mitigate mild steel corrosion under various conditions. The most predominant and feared type of corrosion attack in oil and gas industries is caused by $$\hbox {CO}_{2}$$ . The application of corrosion inhibitors is considered the most suitable method of combating $$\hbox {CO}_{2 }$$ corrosion of steel pipelines. The prime objective of this work is to summarize carbon dioxide corrosion inhibitors so far tested and reported against this type of attack. The information presented in this article is of significance for oil and gas industries that use steel pipeline for the transportation of oil and gas products. Furthermore, this review would be helpful in designing better inhibitors for the mitigation of $$\hbox {CO}_{2}$$ corrosion in oil and gas industries.

A Review of Corrosion and Protection of Steel in Concrete

Abstract: Corrosion of reinforcement is one of the major durability challenges which leads to a reduction in the design life of reinforced concrete. Due to an increasing demand for longer service lives of infrastructure (typically 100–120 years) and the high cost involved in building and maintaining it, the repair of concrete structures has become extremely important. This paper discusses mechanism of corrosion in reinforced concrete and its thermodynamic and kinetic behaviour. It also presents and compares different corrosion prevention and protection techniques available and recommended by BS 1504-9:2008, including the use of corrosion inhibitors, alternative reinforcement, steel and concrete coating and electrochemical techniques. It is concluded that the electrochemical techniques are more effective than conventional methods.

Literature Review of Coral Concrete

Abstract: Coral concrete materials, which refer to cement-based composites mixed with coral and seawater, have aroused interest worldwide since they were first used in reef engineering. This paper reviews the raw materials, mechanical properties, durability and microstructure of coral concrete, and describes the performances of fiber reinforced coral concrete. The microhardness of coral concrete in the interfacial transition zone is significantly higher than that of normal concrete, the compressive strength of normal concrete is greater than that of coral concrete, while the normal concrete is more durable than coral concrete. The flexural and splitting tensile strengths of coral concretes are improved remarkably by adding fibers to the mixture. The coral concrete performance is hindered by the poor cementation of the materials, as a result of the porous and brittle coral aggregates. Therefore, by modifying the coral aggregates and innovating the mixing methods in the preparation of high-performance coral concrete, further developments in marine structures and underwater engineering applications can be made using the material.

Finite Element Modelling of Cutting Forces and Power Consumption in Turning of AISI 420 Martensitic Stainless Steel

Abstract: Martensitic stainless steels have high hardenability, good strength and good corrosion resistance; however, high power consumption is encountered in machining operations due to their hard machinability. The consumption should be eliminated for cleaner production in terms of sustainable machining. Therefore, this study aims modelling of cutting forces and power consumption in turning of AISI 420 martensitic stainless steel based on finite element method. Finite element modelling of cutting forces is preferred to estimate optimum cutting parameters for less power consumption. In this regard, finite element simulations are performed based on three different levels as cutting speed, depth of cut and feed rate. Depth of cut could be assessed as the most important factor with percentage contribution ratio of 49.55% in respect of the power consumption. The average of 7% difference is achieved between experimental and simulated cutting forces. The deviation of 4.5% is evaluated between experimental results and simulation outputs by means of comparing the power consumption. The finite element modelling of cutting forces and power consumption is quite compatible with the experimental results, and it can be performed by high accuracy without excessive machining experiments of difficult-to-cut materials.

A Novel Framework for Cloud Service Evaluation and Selection Using Hybrid MCDM Methods

Abstract: With the rapid growth of cloud services in recent years, it is very difficult to choose the suitable cloud services among those services that provide similar functionality. The non-functional quality of services is considered the most significant factor for appropriate service selection and user satisfaction in cloud computing. However, with a vast diversity in the cloud service, selection of a suitable cloud service is a very challenging task for a customer under an unpredictable environment. This study introduces a computational framework for determining the most suitable candidate cloud service by integrating the analytical hierarchical process (AHP) and Technique for order preference by similarity to ideal solution (TOPSIS). Using AHP, we define the architecture for selection process of cloud services and compute the criteria weights using pairwise comparison. Thereafter, using TOPSIS method, we obtained the final ranking of the cloud service based on overall performance. A real-time cloud case study proves the potential of our proposed framework and methodology, which demonstrates the efficacy by inducing better performance, when compared to other available cloud service selection methodologies. Finally, sensitivity analysis testifies the effectiveness and the correctness of our proposed methodology.

A Study on the Mechanical Behavior and Statistical Damage Constitutive Model of Sandstone

Abstract: Triaxial compression test results of sandstone indicate that the peak point strain, elastic modulus, peak deviatoric stress and residual deviatoric stress of the tested sandstone increase with increasing confining pressure, and the variations in them with the confining pressure can be described with a linear function, a logistic function, the generalized Hoek–Brown criterion and the linear Mohr–Coulomb criterion, respectively. Supposing that the rock material can be divided into an elastic part and a damaged part in the rock failure process, the deviatoric stress–strain relationship of the elastic part satisfies Hooke’s law, while the damaged part provides residual deviatoric stress. On this basis, it was assumed the rock meso-element strength follows a composite power function distribution. Then, the damage evolution equation was deduced using a statistical method, and a new damage model, which can reflect the rock residual deviatoric stress, was proposed. The reasonability of the new model was verified using the test results of the sandstone. A comparison of the predicted and test results shows that this damage model can well simulate the deviatoric stress–strain response in the failure process of the tested sandstone. In particular, it can reflect the residual deviatoric stress after rock failure.

A New Binary Variant of Sine–Cosine Algorithm: Development and Application to Solve Profit-Based Unit Commitment Problem

Abstract: The profit-based unit commitment (PBUC) procedure is a binary natured operational planning problem of generation company (GENCO) in competitive electricity market. The GENCO solves PBUC problem in day ahead market of energy and reserve markets by commitment and scheduling of thermal units with an objective of profit maximization for the set of given price and load forecasts. Therefore, the solution quality and efficacy of PBUC optimization problem play a vital role in ensuring maximum returns to the GENCO. This paper presents a mathematical model-inspired heuristic approach called binary sine–cosine algorithm (BSCA) for solving PBUC problem. The SCA algorithm is a heuristic approach that uses the fluctuating nature of individual candidates/search agents in the search space around the global solution. The fluctuating mechanism is realized by using mathematical functions, i.e. sine and cosine functions. The proposed binary approach of SCA algorithm uses modified sigmoidal transformation function for binary mapping of continuous real-valued search space to binary counterpart. The efficacy of proposed approach in terms of solution quality and convergence is demonstrated using test system with different market participation policies. The results demonstrate the effectiveness of proposed BSCA approach over existing approaches for solving PBUC.

Microstructure and Strength Properties of Mortar Containing Waste Ceramic Nanoparticles

Abstract: This study investigated the effects of waste ceramic powder on both the mechanical and microstructural properties of mortar. The study explored the utilization of $$\hbox {Al}_{2}\hbox {O}_{3}$$ – $$\hbox {SiO}_{2}$$ nanoparticles in mortar as cement replacement. Four mixes containing ceramic nanoparticles (0, 20, 40, and 60%) were prepared. The mortar specimens were tested for compressive strength. The role of ceramic waste powder was investigated through the analysis of microstructure in terms of scanning electron microscopy, X-ray diffraction, thermogravimetric analysis, and differential thermal analysis. The results revealed that the replacement of $$\hbox {Al}_{2}\hbox {O}_{3}$$ – $$\hbox {SiO}_{2}$$ nanoparticles to the mortar matrix had significantly enhanced the compressive strength. At 90 days, the compressive strength was in the range of 44.7–58.8 MPa. The results were validated through a microstructure test. The mortar with 40% of ceramic replacement showed better performance in terms of C-S-H production from active siliceous containing excessive calcium hydroxide content.

Removal of Copper and Lead using Banana Biochar in Batch Adsorption Systems: Isotherms and Kinetic Studies

Abstract: This study involved investigating the adsorption potential of biochar prepared from banana peel for the removal of copper ( $$\hbox {Cu}^{2+})$$ and lead ( $$\hbox {Pb}^{2+})$$ . Process parameters for batch adsorption including contact time, pH, adsorbent dose, and initial metal concentrations were optimized. The time at which the equilibrium adsorption was attained was recoded as 30 min with a higher removal efficiency of $$\hbox {Pb}^{2+}$$ when compared to $$\hbox {Cu}^{2+}$$ . Optimum removal was observed at a pH of approximately 5.5 and 9 for $$\hbox {Cu}^{2+}$$ and $$\hbox {Pb}^{2+}$$ , respectively. A linear increase in metal removal efficiency was achieved with increases in the adsorbent dose from 0.2 to 1.4 g. The latter was estimated as the optimum adsorbent dose. A 50–70% decrease in removal efficiency was observed when the initial $$\hbox {Cu}^{2+}$$ and $$\hbox {Pb}^{2+}$$ concentrations were increased from 50 to 300 mg $$\hbox {L}^{-1}$$ and from 200 to 1000 mg $$\hbox {L}^{-1}$$ , respectively. Among the isotherm models, the Freundlich model agreed best with the experimental data for $$\hbox {Pb}^{2+}$$ while the Langmuir model exhibited a better ability to describe the adsorption of $$\hbox {Cu}^{2+}$$ with each model providing the highest respective coefficient of determination. A pseudo-second-order kinetic model better described the kinetic behavior of both metal ions on the investigated adsorbent, namely banana biochar.

New Developments in Membrane Technologies Used in the Treatment of Produced Water: A Review

Abstract: The use of membrane technology for produced oily water treatment has become an active area of research for both academia and industry. The search for membranes with enhanced efficiency and prolonged life time during oily water treatment has been a rallying point for many scientists. The focus of this review is on the advancement of polymeric and ceramic membrane technologies, membrane modification strategies used to mitigate membrane fouling and optimization of permeate flux, particularly for oily water systems. In addition, recent methodologies used for modeling the permeate flux decline are also highlighted.

Phosphate-Solubilizing and Auxin-Producing Rhizobacteria Promote Plant Growth Under Saline Conditions

Abstract: The world loses 3 ha of arable lands every minute due to salinization. To counteract adverse effects of salinity on plants, the use of PGPR is an efficient/cheaper method that induces salt stress tolerance in plants. The aim of the present work was to study auxin production and inorganic phosphate solubilization capacities of Pseudomonas fluorescens Ms-01 (A newly isolated strain from grapevine rhizosphere) and Azosprillum brasilense DSM1690 (DSMZ strain isolated from Digitaria decumbens roots) under hypersaline conditions. The objective was to assess their synergetic action in the promotion of wheat plant growth in saline conditions. The results showed a prominent ability of the studied strains to grow in high NaCl concentrations of about 600 mM. In addition, both auxin production and phosphate solubilization activities were maintained in hypersaline conditions. In fact, with an initial IAA production of 32 $$\upmu \hbox {g}\,\hbox {ml}^{-1}$$ , A. brasilense DSM 1690 maintained a good production in hypersaline conditions ( $$22.5\pm 4.1\,\upmu \hbox {g}\,\hbox {ml}^{-1}$$ in 400 mM NaCl). Phosphate solubilization activity of P. fluorescens Ms-01 was also significantly improved with increase in salinity, reaching $$22.6\pm 1.7\, \upmu \hbox {g}\,\hbox {ml}^{-1}\,\hbox {P}_{2}\hbox {O}_{5}$$ in 600 mM NaCl. The inoculation of wheat plants with the studied bacteria increased the plant height and weight under normal and saline conditions. Results showed significant increase in proline accumulation and in the activity of POD and APX antioxidant enzymes in wheat plants inoculated with P. fluorescens Ms-01 in saline soil conditions. The correlation between proline accumulation and antioxidant enzymes activities indicated that the inoculation improved the defense pathway of plants against salt stress.

Electrochemical Discharge Drilling of SiC Reinforced Polymer Matrix Composite Using Taguchi’s Grey Relational Analysis

Abstract: Micro-drilling of nonconductive fibrous materials is an imperative factor in advancement of manufacturing industries. Optimization for increased material removal rate (MRR) along with minimum overcut and taper in drilling of these materials becomes an essential feature for improved results. For that purpose, grey relational analysis (GRA) proves to be a superior optimization technique to forecast and decision-making in various areas of manufacturing sector. This paper emphasizes on optimizing process parameters of electrochemical discharge drilling for micro-drilling of a SiC reinforced polymer matrix composite using GRA. The experimentation was planned as per Taguchi methodology, and output quality characteristics were observed as overcut, taper and MRR. The results for micro-drilling were optimized by the multi-response optimization technique GRA to attain improved material removal rate (MRR) with least overcut and taper. The results revealed that optimization through grey relational analysis provides enhanced output quality characteristics.

Correction to: Some Aggregation Operators for Linguistic Intuitionistic Fuzzy Set and its Application to Group Decision-Making Process Using the Set Pair Analysis

Abstract: Linguistic intuitionistic fuzzy set (LIFS) is the better way to deal with the uncertain and imprecise information in group decision-making problems. On the other hand, the set pair analysis (SPA) theory provides a quantitative analysis to integrate the certainty and uncertainties as a combined system by defining the connection number corresponding to it. In the present paper, we have enhanced the LIFS with the SPA theory and hence defined the linguistic connection number (LCN) and its various operational laws. Based on it, we have developed various aggregation operators, namely LCN weighted geometric, LCN ordered weighted geometric, and LCN hybrid geometric operators with LIFS environment. Also, the shortcoming of the existing operators under LIFS environment has been highlighted and overcomes by the proposed operators. Few properties of these operators have been also investigated. Further, a group decision-making approach has been presented, based on these operators, which has been illustrated by a numerical example to show the effectiveness and validity of the proposed approach.

Content-Based Image Retrieval Based on Visual Words Fusion Versus Features Fusion of Local and Global Features

Abstract: Due to the advancements in digital technologies and social networking, image collections are growing exponentially. The important aim in content-based image retrieval (CBIR) is to reduce the semantic gap issue that improves the performance of image retrieval. In this paper, the objective is achieved by introducing effective visual words fusion technique based on speeded-up robust features (SURF) and histograms of oriented gradients (HOG) feature descriptors. HOG is used to extract global features, whereas SURF is used for the extraction of local features. Global features are preferred for large-scale image retrieval, whereas local features perform better on those systems that support semantic queries with close visual appearance. Moreover, SURF is scale and rotation-invariant as compared to HOG descriptor and it works better for low illumination. On the contrary, HOG performs better for scene-recognition- or activity-recognition-based applications. In the proposed technique, visual words fusion of SURF and HOG feature descriptors is carried which performed better than features fusion of SURF and HOG feature descriptors as well as state-of-the-art CBIR techniques. The proposed technique based on visual words fusion gives classification accuracy of 98.40% using support vector machine while image retrieval accuracy of 80.61%. Qualitative and quantitative analyses performed on four standard image collections namely, Corel-1000, Corel-1500, Corel-5000, and Caltech-256 demonstrate the effectiveness of the proposed technique based on visual words fusion of SURF and HOG feature descriptors.

Design and Analysis of Tilt Integral Derivative Controller for Frequency Control in an Islanded Microgrid: A Novel Hybrid Dragonfly and Pattern Search Algorithm Approach

Abstract: In this research work, a maiden approach is made for the frequency control in an islanded AC microgrid (MG). A MG can be formed by combining the different sources like renewable energy source, wind power generation and the solar energy generation. Variation in any of the source influences the MG frequency, and thus, the frequency control issue for MG is always a challenge for the researcher industry. In light of these difficulties, this paper considers a tilt integral derivative (TID) controller for the secondary frequency control of the islanded MGs and a novel hybrid dragonfly algorithm and pattern search (hDF-PS) algorithm is used to tune the controller parameters. In the proposed control conspire, some sources like microturbine, diesel engine generator and fuel cell are used which balance the load and power can demand of the MG. The novel hybrid controller is inspected on a MG test system, and the robustness and execution are assessed by considering different disturbances and parametric variations. In order to show the effectiveness of the proposed hybrid algorithm-based TID controller, it is being compared with some conventional controllers like integral, proportional integral and proportional integral derivative-based controller. It can be demonstrated that the proposed hDF-PS-based TID controller approach (because of considering organized/parametric instabilities) gives preferably better execution over the other control techniques.

A Feature Selection Approach to Detect Spam in the Facebook Social Network

Abstract: The widespread adoption of social networks and their enormous facilities and growing opportunities has attracted many users and audience. But along with attractive and interesting messages and topics, inappropriate and sometimes criminal contents, such as spam, are also released on these networks. Malicious spammers intend to send inaccurate or irrelevant contents to distribute malformed information on online social networks. This paper is about the spam comments detection on the Facebook social network. By reviewing the posts and comments, and studying their features, an online spam filtering system has been designed in this paper. The proposed filtering system is able to exploit various exploration methods and optimization algorithms such as simulated annealing, particle swarm optimization, ant colony optimization, and differential evolution to detect and filter malicious contents and to prevent publishing spam comments to provide a secure environment for users of this popular social network. Furthermore, supervised machine learning methods, clustering techniques, and decision trees have been exploited to provide an accurate performance and appropriate speed for the proposed filtering system.

New Approach to Optimize the Rate of Penetration Using Artificial Neural Network

Abstract: Rate of penetration (ROP) is one of the most important parameters of the drilling operation. Optimizing the ROP will reduce the overall cost of the drilling process. ROP depends on many variables such as drilling parameters [flow rate (Q), RPM, torque (T), weight on bit (WOB), stand pipe pressure (P)], fluid properties (mud density and plastic viscosity), and formation strength (UCS). The objectives of this paper are (1) to evaluate the effect of the drilling parameters and the drilling fluid parameters on rate of penetration (ROP), (2) construct a new artificial neural network (ANN) technique to estimate the ROP as a function of drilling parameters and fluid properties using actual field measurements (3333 data points), and (3) converting the black box of the ANN model to a white box by developing a new ROP correlation based on the optimized weights and biases of the developed model. The optimization process of the ANN model showed that the optimum number of neurons was 20, which resulted in the lowest average absolute percentage error (AAPE) and the highest correlation coefficient (R). The developed ANN model was able to estimate ROP with high accuracy (R of 0.99 and AAPE of 5.6%). The developed empirical correlation for ROP prediction outperformed the previous models. The high accuracy of the developed correlation (AAPE of 4%) confirmed the importance of compiling the drilling parameters and the drilling fluid properties.

Modified Grey Wolf Optimization Algorithm for Transmission Network Expansion Planning Problem

Abstract: Transmission network expansion planning (TNEP) problem is a large-scale, complex mixed integer nonlinear programming problem. The solution of TNEP problem is essential to fulfill the load demand in an economical manner. A grey wolf optimization (GWO) algorithm which is a nature-inspired metaheuristic algorithm is used to solve the TNEP problem. Further, a modified GWO is developed, and to validate its result, it is tested on 20 standard benchmark test functions. The basic and modified version of GWO algorithms is applied to solve TNEP problem for Graver’s six-bus and Brazilian 46-bus systems. The obtained results are compared with other state-of-the-art algorithms. The results demonstrate the accuracy as well as proficiency of the proposed algorithm.

Effective Allocation of Resources and Task Scheduling in Cloud Environment using Social Group Optimization

Abstract: Effective resource distribution to regulate load uniformly in heterogeneous cloud environments is crucial. Resource allotment which is taken after capable task scheduling is a critical worry in cloud environment. The incoming job requests are assigned to resources equally by load balancer in such a way that resources are utilized effectively. Number of cloud clients is very great in number, degree of approaching job requests is uninformed and information is tremendous in cloud application. Resources in cloud environment are constrained. Hence, it is not easy to deploy different applications with unpredictable limits and functionalities in heterogeneous cloud environment. The proposed method has two phases such as allocation of resources and scheduling of tasks. Effective resource allocation is proposed using social group optimization algorithm and scheduling of tasks using shortest-job-first scheduling algorithm for minimizing the makespan time and maximizing throughput. Experimentations are performed for accurate simulations on artificial data for heterogeneous cloud environment. Experimental results are compared with first-in, first-out and genetic algorithm-based shortest-job-first scheduling. Validity of the proposed method noticeably gives improved performance of the system in provisions of makespan time and throughput.

RIFT: A Rule Induction Framework for Twitter Sentiment Analysis

Abstract: The rapid evolution of microblogging and the emergence of sites such as Twitter have propelled online communities to flourish by enabling people to create, share and disseminate free-flowing messages and information globally. The exponential growth of product-based user reviews has become an ever-increasing resource playing a key role in emerging Twitter-based sentiment analysis (SA) techniques and applications to collect and analyse customer trends and reviews. Existing studies on supervised black-box sentiment analysis systems do not provide adequate information, regarding rules as to why a certain review was classified to a class or classification. The accuracy in some ways is less than our personal judgement. To address these shortcomings, alternative approaches, such as supervised white-box classification algorithms, need to be developed to improve the classification of Twitter-based microblogs. The purpose of this study was to develop a supervised white-box microblogging SA system to analyse user reviews on certain products using rough set theory (RST)-based rule induction algorithms. RST classifies microblogging reviews of products into positive, negative, or neutral class using different rules extracted from training decision tables using RST-centric rule induction algorithms. The primary focus of this study is also to perform sentiment classification of microblogs (i.e. also known as tweets) of product reviews using conventional, and RST-based rule induction algorithms. The proposed RST-centric rule induction algorithm, namely Learning from Examples Module version: 2, and LEM2 $$+$$ Corpus-based rules (LEM2 $$+$$ CBR),which is an extension of the traditional LEM2 algorithm, are used. Corpus-based rules are generated from tweets, which are unclassified using other conventional LEM2 algorithm rules. Experimental results show the proposed method, when compared with baseline methods, is excellent, with regard to accuracy, coverage and the number of rules employed. The approach using this method achieves an average accuracy of 92.57% and an average coverage of 100%, with an average number of rules of 19.14.

Biparametric Information Measures-Based TODIM Technique for Interval-Valued Intuitionistic Fuzzy Environment

Abstract: This paper presents a novel decision-making approach, known as TODIM, in interval-valued intuitionistic fuzzy (IVIF) environment, which determines an optimal alternative by considering psychological behaviours of decision makers under risk. In recent years, TODIM technique has been developed by several authors in various discipline, but they are not able to deal with interdependent characteristics among the criteria. In the present manuscript, the TODIM technique is discussed on the basis of Shapley values under IVIFSs for some situations where elements in a set are correlated. To determine the Shapley values, an entropy measure for IVIFSs is proposed and compared with existing entropy measures. A similarity measure for IVIFSs is introduced to measure the dominance degree of each alternative over others. Mathematical illustration is demonstrated to show the competency of the proposed similarity measure. To deal with interdependent or interactive problem, we have developed Shapley-weighted similarity measure for IVIFSs based on proposed similarity measure and Shapley function. Application of proposed Shapley-weighted similarity measure is presented in pattern recognition problem and then compared with existing measures. Finally, an example of plant location selection is taken to demonstrate the validity and benefit of the proposed technique.

Power System Voltage Stability Assessment Using a Hybrid Approach Combining Dragonfly Optimization Algorithm and Support Vector Regression

Abstract: In this paper, an efficient approach based on the combination of dragonfly optimization (DFO) algorithm and support vector regression (SVR) has been proposed for online voltage stability assessment. As the performance of the SVR model extremely depends on careful selection of its parameters, the DFO algorithm involves SVR parameters setting, which significantly ameliorates their performance. In the proposed approach, the voltage magnitudes of the phasor measurement unit (PMU) buses are adopted as the input data for the hybrid DFO–SVR model, while the minimum values of voltage stability index (VSI) are taken as the output vector. Using the data provided by PMUs as the input variables makes the proposed model capable of assessing the voltage stability in a real-time manner, which helps the operators to adopt the required measures to avert large blackouts. The predictive ability of the proposed hybrid model was investigated and compared with the adaptive neuro-fuzzy inference system (ANFIS) through the IEEE 30-bus and the Algerian 59-bus systems. According to the obtained results, the proposed DFO–SVR model can successfully predict the VSI. Moreover, it provides a better performance than the ANFIS model.

A Laboratory Investigation of Soil Stabilization Using Enzyme and Alkali-Activated Ground Granulated Blast-Furnace Slag

Abstract: Development and use of non-traditional stabilizers such as enzyme and alkali-activated ground granulated blast-furnace slag (GGBS) for soil stabilization helps to reduce the cost and the detrimental effects on the environment. The objective of this study is to investigate the effectiveness of alkali-activated GGBS and enzyme as compared to ordinary Portland cement (OPC) on the soil collected from Tilda region of Chhattisgarh, India. Geopolymers are alkali alumino-silicates produced when combining a solid alumina-silicate with an aqueous alkali hydroxide or silicate solution. Various dosages of the selected stabilizers have been used and evaluated for the effects on optimum moisture content (OMC), maximum dry density, plasticity index, unconfined compressive strength (UCS) and shear strength parameters. Effect of curing period has also been studied. Microstructural changes of the stabilized soils show aggregation of particles. Significant improvement in properties of soil is observed with the addition of stabilizers leading to an increase in OMC, UCS and shear strength parameters. It is observed that the cohesion of soil sample increases significantly with the addition of stabilizers whereas there is a marginal change in angle of internal friction. Thus, the findings recommend the use of non-conventional stabilizer such as alkali-activated GGBS and enzyme as suitable and environmental friendly as compared to OPC for soil stabilization.

Feature Selection of Denial-of-Service Attacks Using Entropy and Granular Computing

Abstract: Recently, many researchers have paid attention toward denial of services (DoS) and its malicious handling. The Intrusion detection system is one of the most common detection techniques used to detect malicious attack which attempts to compromise the security goals. To deal with such an issue, some of the researchers have used entropy calculation recently to detect malicious attacks. However, it fails to identify the most potential feature for DoS attack which needs to be addressed on its early occurrence. Therefore, this paper focused on identifying some of the potential attributes of a DoS attack based on computed weight for each of the attributes using entropy calculation. In addition, the selection of potential attributes based on user-defined chosen granulation is also given using NSL KDD dataset.

Effects of Elevated Temperatures on Residual Properties of Concrete Reinforced with Waste Polypropylene Carpet Fibres

Abstract: In this study, the effect of waste polypropylene carpet fibres and palm oil fuel ash (POFA) on the mechanical and microstructural properties of concrete exposed to elevated temperatures was investigated. Concrete samples were exposed to high temperatures up to $$800\,{^{\circ}}\hbox {C}$$ then cooled to ambient temperature before tests. Four mixes containing carpet fibres (0 and 0.5%) and POFA (0 and 20%) were prepared. Mass loss, residual ultrasonic pulse velocity, compressive strength, scanning electron microscopy, X-ray diffraction and differential thermal analysis were performed to investigate the effects of carpet fibres and POFA on the performance of the concrete at elevated temperatures. The results showed that both carpet fibres and POFA were associated with a significant enhancement in the fire resistance and residual compressive strength and also eliminating the explosive spalling behaviour of the concrete at elevated temperatures. Furthermore, the role of carpet fibres and POFA is discussed through the microstructural analysis and fibre–matrix interactions as function of heat treatment.

Development of New Mathematical Model for Compressional and Shear Sonic Times from Wireline Log Data Using Artificial Intelligence Neural Networks (White Box)

Abstract: Compressional (P-wave) and shear (S-wave) velocities are used to estimate the dynamic geomechanical properties including: Poisson’s ratio, Young’s modulus, and Lame parameters These parameters are mainly used in estimating the static properties of the formation rocks as well as the in situ stresses The sonic logs are not always available, epically for old wellbores Also, in several occasions when the sonic logs are available, missing sections found in the well logs might affect the analysis results To the authors’ knowledge, there is no single straightforward correlation that can be used to accurately estimate both P- and S-wave travel times directly from the well log data Most of the existing correlations use the P-wave velocity to measure the S-wave velocity The main purpose of this study is to develop accurate and simple empirical models using wireline log data (bulk density, gamma ray, and neutron porosity) to predict the sonic travel times (P-wave and S-wave) These wireline logs are slandered wireline log data that are commonly recorded in most of the wells Three robust artificial intelligence techniques, namely: support vector machine (SVM), artificial neural network (ANN), and adaptive neurofuzzy interference systems (ANFIS), were employed and compared based on their prediction performance Ultimately, using the weights and biases of optimized ANN model, a simple generalized empirical correlation is derived that can be used without the need of costly commercial software’s to run the AI models The obtained results showed that ANN, ANFIS, and SVM can be used to estimate P-wave and S-wave travel times ANN outperformed the ANFIS and SVM by yielding the lowest average absolute percentage error (AAPE) and the highest coefficient of determination $$\left( R^{2}\right) $$ for predicting P-wave and S-wave travel times ANN model could predict the P-wave and S-wave travel times from wireline log data with high accuracy giving $$R^{2}$$ of 098 when compared to actual field data In addition, the developed empirical correlations prediction completely matched the ANNs prediction The AAPE of the predicted P and S-waves travel times was less than 5% The developed correlations are very accurate and can help geomechanical engineers to determine the dynamic geomechanical properties (Poisson’s ratio and Young’s modulus) and propose any operation in case where sonic logs are missing

Modeling of Surface Roughness Using RSM, FL and SA in Dry Hard Turning

Abstract: This paper presents the development of mathematical, predictive and optimization models of average surface roughness parameter ( $$R_{a}$$ ) in turning hardened AISI 1060 steel using coated carbide tool in dry condition. Herein, the mathematical model is formulated by response surface methodology (RSM), predictive model by fuzzy inference system (FIS), and optimization model by simulated annealing (SA) technique. For all these models, the cutting speed, feed rate and material hardness were considered as input factors for full factorial experimental design plan. After the experimental runs, the collected data are used for model development and its subsequent validation. It was found, by statistical analysis, that the quadratic model is suggested for $$R_{a}$$ in RSM. The adequacy of the models was checked by error analysis and validation test. Furthermore, the constructed model was compared with an analytical model. The analysis of variance revealed that the material hardness exerts the most dominant effect, followed by the feed rate and then cutting speed. Eventually, the RSM model was found with a coefficient of determination value of 99.64%; FIS model revealed 79.82% prediction accuracy; and SA model resulted in more than 70% improved surface roughness. Therefore, these models can be used in industries to effectively control the hard turning process to achieve a good surface quality.

Adaptive Differential Evolution-Based Lorenz Chaotic System for Image Encryption

Abstract: The main challenge for Lorenz chaotic system-based image encryption techniques is parameter sensitivity and resistance against attacks. To resolve these issues, a modified image encryption technique based on secure hash algorithm (SHA-3) and adaptive differential evolution (ADE) is proposed. In the proposed technique, ADE is used to optimize the input parameters of Lorenz chaotic system. SHA-3 is used to generate secret key based on the input image. The optimized parameters and external secret keys are used to generate initial values for Lorenz chaotic system that make it sensitive toward input image and provide resistance against both known-plaintext and known-ciphertext attacks. The proposed technique is compared with five well-known image encryption techniques over four color images. The experimental results reveal that the proposed technique outperforms existing techniques in terms of security and quality measures. The noise and enhancement attacks are also applied to test the robustness of proposed technique.