Prediction of pressure gradient and hold-up in horizontal liquid-liquid pipe flow

This paper aims to propose correlations to predict pressure gradient,friction factor and fluid phase hold-up in liquid-liquid horizontal pipe flow.To develop the correlations,experiments are conducted using high viscous oils(202 and 630 mPa⋅s)in a steel pipe of length 11.25 m and length-to-diameter ratio of 708.In addition,the experimental data from the literature comprising wide range of flow and fluid properties is analyzed.For the analysis,the liquid-liquid pipe flow data is categorized into two as:stratified and dispersed.The existing friction factor correlations are modified to incorporate the effects of viscosity of the oil phase,interfacial curvature(contact/wetting angle-in lieu of material of the pipe)and fluid phase fraction.In the two-fluid model of stratified flow,the wall stress and interfacial stress correlations are substituted with superficial velocities of fluids and superficial Reynolds numbers of fluid phases replacing fluid phase velocities and fluid Reynolds numbers.Similarly,for dispersed flow,an effective Reynolds number is described as the sum of superficial Reynolds number of oil and water phases.Substituting the generally employed mean or mixture Reynolds number with the effective Reynolds number into the existing single-phase turbulent flow friction factor correlation,an effective friction factor for oil-water flow is proposed.Employing the proposed correlations,the pressure gradient across the oil-water flow and hold-up volume fraction are predicted with significant reduction in error compared with that of conventionally employed correlations.The average error and standard deviation values of−7.06%,20.72%and 0.31%,18.79%are found for stratified flow and dispersed flow respectively.

An Energy-Efficient Protocol for Internet of Things Based Wireless Sensor Networks

The performance of Wireless Sensor Networks(WSNs)is an important fragment of the Internet of Things(IoT),where the current WSNbuilt IoT network’s sensor hubs are enticing due to their critical resources.By grouping hubs,a clustering convention offers a useful solution for ensuring energy-saving of hubs andHybridMedia Access Control(HMAC)during the course of the organization.Nevertheless,current grouping standards suffer from issues with the grouping structure that impacts the exhibition of these conventions negatively.In this investigation,we recommend an Improved Energy-Proficient Algorithm(IEPA)for HMAC throughout the lifetime of the WSN-based IoT.Three consecutive segments are suggested.For the covering of adjusted clusters,an ideal number of clusters is determined first.Then,fair static clusters are shaped,based on an updated calculation for fluffy cluster heads,to reduce and adapt the energy use of the sensor hubs.Cluster heads(CHs)are,ultimately,selected in optimal locations,with the pivot of the cluster heads working among cluster members.Specifically,the proposed convention diminishes and balances the energy utilization of hubs by improving the grouping structure,where the IEPAis reasonable for systems that need a long time.The assessment results demonstrate that the IEPA performs better than existing conventions.

Investigation of Various Laminating Materials for Interior Permanent Magnet Brushless DC Motor for Cooling Fan Application

Permanent magnet brushless DC motors are used for various low-power applications,namely domestic fans,washing machines,mixer grinders and cooling fan applications.This paper focuses on selecting the best laminating material for the interior permanent magnet brushless DC(IPM BLDC)motor used in the cooling fan of automobiles.Various laminating materials,namely M19-29GA,M800-65A and M43,are tested using finite element analysis.The machine's vital performance metrics,namely the stator current,torque ripple,and hysteresis loss were analyzed in selecting the laminating material.The designed motor is also modelled as a mathematical model from the computed lumped parameters.The performance of the machines was validated through electromagnetic and thermal analysis.

Intelligent Networked Control of Vasoactive Drug Infusion for Patients with Uncertain Sensitivity

Abnormal high blood pressure or hypertension is still the leading risk factor for death and disability worldwide.This paper presents a new intelligent networked control of medical drug infusion system to regulate the mean arterial blood pressure for hypertensive patients with different health status conditions.The infusion of vasoactive drugs to patients endures various issues,such as variation of sensitivity and noise,which require effective and powerful systems to ensure robustness and good performance.The developed intelligent networked system is composed of a hybrid control scheme of interval type-2 fuzzy(IT2F)logic and teaching-learning-based optimization(TLBO)algorithm.This networked IT2F control is capable of managing the uncertain sensitivity of the patient to anti-hypertensive drugs successfully.To avoid the manual selection of control parameter values,the TLBO algorithm is mainly used to automatically find the best parameter values of the networked IT2F controller.The simulation results showed that the optimized networked IT2F achieved a good performance under external disturbances.A comparative study has also been conducted to emphasize the outperformance of the developed controller against traditional PID and type-1 fuzzy controllers.Moreover,the comparative evaluation demonstrated that the performance of the developed networked IT2F controller is superior to other control strategies in previous studies to handle unknown patients’sensitivity to infused vasoactive drugs in a noisy environment.

Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the client.Clients can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and value.One of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’projects.The goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO clients.The projects belong to OSMO vendors,having offices in developing countries while providing services to developed countries.In the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed model.The proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden layers.The results express that the suggested model has gained a notable recognition rate in comparison to any previous studies.The current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

Pattern Recognition of Modulation Signal Classification Using Deep Neural Networks

In recent times,pattern recognition of communication modulation signals has gained significant attention in several application areas such as military,civilian field,etc.It becomes essential to design a safe and robust feature extraction(FE)approach to efficiently identify the various signal modulation types in a complex platform.Several works have derived new techniques to extract the feature parameters namely instant features,fractal features,and so on.In addition,machine learning(ML)and deep learning(DL)approaches can be commonly employed for modulation signal classification.In this view,this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks(CSM-FFDNN).The goal of the CSM-FFDNN model is to classify the different types of digitally modulated signals.The proposed CSM-FFDNN model involves two major processes namely FE and classification.The proposed model uses Sevcik Fractal Dimension(SFD)technique to extract the fractal features from the digital modulated signals.Besides,the extracted features are fed into the DNN model for modulation signal classification.To improve the classification performance of the DNN model,a barnacles mating optimizer(BMO)is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised.A wide range of simulations takes place to highlight the enhanced performance of the CSM-FFDNN model.The experimental outcomes pointed out the superior recognition rate of the CSM-FFDNN model over the recent state of art methods interms of different evaluation parameters.

A Visual pH Indicator through Purple Cabbage Dye for Freshness Test of Venison

A visual pH indicator through purple cabbage dye is selected to test the freshness of venison.Chitosan and cassava starch of an equal weight were used to prepare the film-forming matrix of the indicator.The crystallization of natural purple cabbage dyes with a weight ratio of 5%,10%,20%and 40%were added to the matrix,respectively.The pH color test showed that the natural purple cabbage lyophilized powder with a weight ratio of 40%was the best for the pH indicator,which was used to test the freshness of venison stored at 4℃.The total bacterial count,volatile basic nitrogen(TVB-N)and thiobarbituric acid(TBA)of venison were tested through an experiment.When stored at under 4℃,a significant color change of the indicator from pink to blue-green was shown when the total amount of volatile basic nitrogen(TVB-N)(22.78 mg/100 g)exceeded the critical value(20 mg/100 g).The test result showed that the indicator was very sensitive to pH value and it would help customers identify the freshness of venison.

Realtime Object Detection Through M-ResNet in Video Surveillance System

Object detection plays a vital role in the video surveillance systems.To enhance security,surveillance cameras are now installed in public areas such as traffic signals,roadways,retail malls,train stations,and banks.However,monitor-ing the video continually at a quicker pace is a challenging job.As a consequence,security cameras are useless and need human monitoring.The primary difficulty with video surveillance is identifying abnormalities such as thefts,accidents,crimes,or other unlawful actions.The anomalous action does not occur at a high-er rate than usual occurrences.To detect the object in a video,first we analyze the images pixel by pixel.In digital image processing,segmentation is the process of segregating the individual image parts into pixels.The performance of segmenta-tion is affected by irregular illumination and/or low illumination.These factors highly affect the real-time object detection process in the video surveillance sys-tem.In this paper,a modified ResNet model(M-Resnet)is proposed to enhance the image which is affected by insufficient light.Experimental results provide the comparison of existing method output and modification architecture of the ResNet model shows the considerable amount improvement in detection objects in the video stream.The proposed model shows better results in the metrics like preci-sion,recall,pixel accuracy,etc.,andfinds a reasonable improvement in the object detection.

Hybrid Deep Learning-Improved BAT Optimization Algorithm for Soil Classification Using Hyperspectral Features

Now a days,Remote Sensing(RS)techniques are used for earth observation and for detection of soil types with high accuracy and better reliability.This technique provides perspective view of spatial resolution and aids in instantaneous measurement of soil’s minerals and its characteristics.There are a few challenges that is present in soil classification using image enhancement such as,locating and plotting soil boundaries,slopes,hazardous areas,drainage condition,land use,vegetation etc.There are some traditional approaches which involves few drawbacks such as,manual involvement which results in inaccuracy due to human interference,time consuming,inconsistent prediction etc.To overcome these draw backs and to improve the predictive analysis of soil characteristics,we propose a Hybrid Deep Learning improved BAT optimization algorithm(HDIB)for soil classification using remote sensing hyperspectral features.In HDIB,we propose a spontaneous BAT optimization algorithm for feature extraction of both spectral-spatial features by choosing pure pixels from the Hyper Spectral(HS)image.Spectral-spatial vector as training illustrations is attained by merging spatial and spectral vector by means of priority stacking methodology.Then,a recurring Deep Learning(DL)Neural Network(NN)is used for classifying the HS images,considering the datasets of Pavia University,Salinas and Tamil Nadu Hill Scene,which in turn improves the reliability of classification.Finally,the performance of the proposed HDIB based soil classifier is compared and analyzed with existing methodologies like Single Layer Perceptron(SLP),Convolutional Neural Networks(CNN)and Deep Metric Learning(DML)and it shows an improved classification accuracy of 99.87%,98.34%and 99.9%for Tamil Nadu Hills dataset,Pavia University and Salinas scene datasets respectively.

Defected Ground Structure Multiple Input-Output Antenna For Wireless Applications

In this paper,the investigation of a novel compact 2×2,2×1,and 1×1 Ultra-Wide Band(UWB)based Multiple-Input Multiple-Output(MIMO)antenna with Defected Ground Structure(DGS)is employed.The proposed Electromagnetic Radiation Structures(ERS)is composed of multiple radiating elements.These MIMO antennas are designed and analyzed with and without DGS.The feeding is introduced by a microstrip-fed line to significantly moderate the radiating structure’s overall size,which is 60×40×1 mm.The high directivity and divergence characteristics are attained by introducing the microstripfed lines perpendicular to each other.And the projected MIMO antenna structures are compared with others by using parameters like Return Loss(RL),Voltage Standing Wave Ratio(VSWR),Radiation Pattern(RP),radiation efficiency,and directivity.The same MIMO set-up is redesigned with DGS,and the resultant parameters are compared.Finally,the Multiple Input and Multiple Output Radiating Structures with and without DGS are compared for result considerations like RL,VSWR,RP,radiation efficiency,and directivity.This projected antenna displays an omnidirectional RP with moderate gain,which is highly recommended for human healthcare applications.By introducing the defected ground structure in bottom layer the lower cut-off frequencies of 2.3,4.5 and 6.0 GHz are achieved with few biological effects on radio propagation in human body communications.The proposed design covers numerous well-known wireless standards,along with dual-function DGS slots,and it can be easily integrated into Wireless Body Area Networks(WBAN)in medical applications.This WBAN links the autonomous nodes that may be situated either in the clothes,on-body or beneath the skin of a person.This system typically advances the complete human body and the inter-connected nodes through a wireless communication channel.

Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

Active Learning Strategies for Textual Dataset-Automatic Labelling

The Internet revolution has resulted in abundant data from various sources,including social media,traditional media,etcetera.Although the availability of data is no longer an issue,data labelling for exploiting it in supervised machine learning is still an expensive process and involves tedious human efforts.The overall purpose of this study is to propose a strategy to automatically label the unlabeled textual data with the support of active learning in combination with deep learning.More specifically,this study assesses the performance of different active learning strategies in automatic labelling of the textual dataset at sentence and document levels.To achieve this objective,different experiments have been performed on the publicly available dataset.In first set of experiments,we randomly choose a subset of instances from training dataset and train a deep neural network to assess performance on test set.In the second set of experiments,we replace the random selection with different active learning strategies to choose a subset of the training dataset to train the same model and reassess its performance on test set.The experimental results suggest that different active learning strategies yield performance improvement of 7% on document level datasets and 3%on sentence level datasets for auto labelling.

An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial benefits.There is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced tools.The main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information security.The original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI mechanisms.Here,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting security.Then,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more effectively.The Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis function.Moreover,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security framework.The results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

This study forecasts the return and volatility dynamics of S&P BSE Sensex and S&P BSE IT indices of the Bombay Stock Exchange.To achieve the objectives,the study uses descriptive statistics;tests including variance ratio,Augmented Dickey-Fuller,Phillips-Perron,and Kwiatkowski Phillips Schmidt and Shin;and Autoregressive Integrated Moving Average(ARIMA).The analysis forecasts daily stock returns for the S&P BSE Sensex and S&P BSE IT time series,using the ARIMA model.The results reveal that the mean returns of both indices are positive but near zero.This is indicative of a regressive tendency in the longterm.The forecasted values of S&P BSE Sensex and S&P BSE IT are almost equal to their actual values,with few deviations.Hence,the ARIMA model is capable of predicting medium-or long-term horizons using historical values of S&P BSE Sensex and S&P BSE IT.

Estimating stock closing indices using a GA-weighted condensed polynomial neural network

Accurate forecasting of changes in stock market indices can provide financial managers and individual investors with strategically valuable information.However,predicting the closing prices of stock indices remains a challenging task because stock price movements are characterized by high volatility and nonlinearity.This paper proposes a novel condensed polynomial neural network(CPNN)for the task of forecasting stock closing price indices.We developed a model that uses partial descriptions(PDs)and is limited to only two layers for the PNN architecture.The outputs of these PDs along with the original features are fed to a single output neuron,and the synaptic weight values and biases of the CPNN are optimized by a genetic algorithm.The proposed model was evaluated by predicting the next day’s closing price of five fast-growing stock indices:the BSE,DJIA,NASDAQ,FTSE,and TAIEX.In comparative testing,the proposed model proved its ability to provide closing price predictions with superior accuracy.Further,the Deibold-Mariano test justified the statistical significance of the model,establishing that this approach can be adopted as a competent financial forecasting tool.

Extreme learning with chemical reaction optimization for stock volatility prediction

Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability.Further,choosing the optimal number of hidden nodes for a network usually requires intensive human intervention,which may lead to an ill-conditioned situation.In this context,chemical reaction optimization(CRO)is a meta-heuristic paradigm with increased success in a large number of application areas.It is characterized by faster convergence capability and requires fewer tunable parameters.This study develops a learning framework combining the advantages of ELM and CRO,called extreme learning with chemical reaction optimization(ELCRO).ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy.We evaluate its performance by predicting the daily volatility and closing prices of BSE indices.Additionally,its performance is compared with three other similarly developed models—ELM based on particle swarm optimization,genetic algorithm,and gradient descent—and find the performance of the proposed algorithm superior.Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model.Hence,this model can be used as a promising tool for financial forecasting.

A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction

Accurate prediction of stock market behavior is a challenging issue for financial forecasting.Artificial neural networks,such as multilayer perceptron have been established as better approximation and classification models for this domain.This study proposes a chemical reaction optimization(CRO)based neuro-fuzzy network model for prediction of stock indices.The input vectors to the model are fuzzified by applying a Gaussian membership function,and each input is associated with a degree of membership to different classes.A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model.CRO was chosen because it requires fewer control parameters and has a faster convergence rate.Five statistical parameters are used to evaluate the performance of the model,and the model is validated by forecasting the daily closing indices for five major stock markets.The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior.We conducted the Deibold-Mariano test to check the statistical significance of the proposed model,and it was found to be significant.This model can be used as a promising tool for financial forecasting.

Imperative Dynamic Routing Between Capsules Network for Malaria Classification

Malaria is a severe epidemic disease caused by Plasmodium falciparum.The parasite causes critical illness if persisted for longer durations and delay in precise treatment can lead to further complications.The automatic diagnostic model provides aid for medical practitioners to avail a fast and efficient diagnosis.Most of the existing work either utilizes a fully connected convolution neural network with successive pooling layers which causes loss of information in pixels.Further,convolutions can capture spatial invariances but,cannot capture rotational invariances.Hence to overcome these limitations,this research,develops an Imperative Dynamic routing mechanism with fully trained capsule networks for malaria classification.This model identifies the presence of malaria parasites by classifying thin blood smears containing samples of parasitized and healthy erythrocytes.The proposed model is compared and evaluated with novel machine vision models evolved over a decade such as VGG,ResNet,DenseNet,MobileNet.The problems in previous research are cautiously addressed and overhauled using the proposed capsule network by attaining the highest Area under the curve(AUC)and Specificity of 99.03%and 99.43%respectively for 20%test samples.To understand the underlying behavior of the proposed network various tests are conducted for variant shuffle patterns.The model is analyzed and assessed in distinct environments to depict its resilience and versatility.To provide greater generalization,the proposed network has been tested on thick blood smear images which surpassed with greater performance.

Sm^(3+)induced structural phase transition,spectral,morphological,magnetic,dielectric and impedance properties of Ni_(0.1)Cu_(0.9)Sm_(x)Fe_(2-x)O_(4) nanoferrites

In the present study,rare earth samarium(Sm^(3+))substituted Ni-Cu spinel ferrites with the composition of Ni_(0.1)Cu_(0.9)Sm_(x)Fe_(2-x)O_(4)(0≤x≤0.05 in steps of 0.01)were synthesized by using the citrate induced sol-gel auto combustion technique.These ferrites'structural,optical,magnetic,and dielectric studies were carried out using X-ray diffraction(XRD),Fourier transform infrared spectroscopy(FTIR),field emission scanning electron microscopy(FESEM),ultraviolet-visible(UV-vis),a vibrational sample magnetometer(VSM),and an LCR meter.The pure Ni-Cu ferrite exhibits a tetragonal structure owing to the presence of the John Tellar ion(Cu^(2+)).XRD patterns confirm that the tetragonal structure gradually transforms into the cubic spinel structure with samarium substitution.The nano-scale structures of these ferrites were confirmed by the average crystallite size(10.11-20.99 nm)derived from the X-ray diffraction patterns,and grain size(42.60-83.36 nm)assessed from FESEM photographs.The existence of elements according to their chemical composition was verified by using energy dispersive X-ray(EDX)spectra.The absorption bands(v_(1) and v_(2))detected in FTIR transmission spectra below the wavenumber of 600 cm^(-1)reveal the stretching vibrations of M-O bonds in the spinel structure at tetrahedral and octahedral locations.The band gap ene rgy obtained from UV absorption reveals the semiconducting nature of the samples.The high saturation magnetization(M_(s))is noticed at 15 K temperature for x=0.02 composition as 32.98 emu/g,while at 300 K for x=0.01composition as 27.61 emu/g.The suggested cation distribution is in good agreement with observed and predicted magnetic moment values at 300 K.The expected behavior of ferrites reveals the observed dielectric constant,loss tangent,and ac-conductivity values in the frequency range of 20 Hz-20 MHz.Cole-Cole plots confirm that the impedance contribution is attributed to grain boundaries.

Devanagari Handwriting Grading System Based on Curvature Features

Grading of writers in perspective of their handwriting is a challenging task owing to various writing styles of different individuals.This paper presents a framework for grading of Devanagari writers in perspective of their handwriting.This framework of grading can be useful in conducting the handwriting competitions and then deciding the winners on the basis of an automated process.Selecting the set of features is a challenging task for implementing a handwriting grading system of particular language.In this paper,curvature features,namely,parabola curve fitting and power curve fitting have been considered for extracting the vital information of writers,which can be helpful in grading the writers.For obtaining the classification score,k-NN classifier has been considered in the present work.Four printed Devanagari font styles,namely,Devlys,Krishna,Krutidev,and Utsaah have been considered for train the proposed model of handwriting evaluation.For evaluating the effectiveness of the proposed framework,we have conducted a mock test of 75 Devanagari writers(Left handed and Right handed)and noticed that the proposed framework performing well suitable for conducting the handwriting competition of Devanagari text writers.This work is also newly in the ground of Devanagari text recognition.