An enhanced cosine-based visual technique for the robust tweets data clustering

Purpose-The purpose of the paper is to study multiple viewpoints which are required to access the more informative similarity features among the tweets documents,which is useful for achieving the robust tweets data clustering results.Design/methodology/approach-Let“N”be the number of tweets documents for the topics extraction.Unwanted texts,punctuations and other symbols are removed,tokenization and stemming operations are performed in the initial tweets pre-processing step.Bag-of-features are determined for the tweets;later tweets are modelled with the obtained bag-of-features during the process of topics extraction.Approximation of topics features are extracted for every tweet document.These set of topics features of N documents are treated as multi-viewpoints.The key idea of the proposed work is to use multi-viewpoints in the similarity features computation.The following figure illustrates multi-viewpoints based cosine similarity computation of the five tweets documents(here N 55)and corresponding documents are defined in projected space with five viewpoints,say,v_(1),v_(2),v_(3),v4,and v5.For example,similarity features between two documents(viewpoints v_(1),and v_(2))are computed concerning the other three multi-viewpoints(v_(3),v4,and v5),unlike a single viewpoint in traditional cosine metric.Findings-Healthcare problems with tweets data.Topic models play a crucial role in the classification of health-related tweets with finding topics(or health clusters)instead of finding term frequency and inverse document frequency(TF-IDF)for unlabelled tweets.Originality/value-Topic models play a crucial role in the classification of health-related tweets with finding topics(or health clusters)instead of finding TF-IDF for unlabelled tweets.

A Quantum-behaved Pigeon-Inspired Optimization approach to Explicit Nonlinear Model Predictive Controller for quadrotor

Purpose–The purpose of this paper is to simplify the Explicit Nonlinear Model Predictive Controller(ENMPC)by linearizing the trajectory with Quantum-behaved Pigeon-Inspired Optimization(QPIO).Design/methodology/approach–The paper deduces the nonlinear model of the quadrotor and uses the ENMPC to track the trajectory.Since the ENMPC has high demand for the state equation,the trajectory needed to be differentiated many times.When the trajectory is complicate or discontinuous,QPIO is proposed to linearize the trajectory.Then the linearized trajectory will be used in the ENMPC.Findings–Applying the QPIO algorithm allows the unequal distance sample points to be acquired to linearize the trajectory.Comparing with the equidistant linear interpolation,the linear interpolation error will be smaller.Practical implications–Small-sized quadrotors were adopted in this research to simplify the model.The model is supposed to be accurate and differentiable to meet the requirements of ENMPC.Originality/value–Traditionally,the quadrotor model was usually linearized in the research.In this paper,the quadrotormodel waskept nonlinear and the trajectorywill be linearizedinstead.Unequaldistance sample points were utilized to linearize the trajectory.In this way,the authors can get a smaller interpolation error.This method can also be applied to discrete systems to construct the interpolation for trajectory tracking.

An optimized Parkinson’s disorder identification through evolutionary fast learning network

Purpose-Parkinson’s disease(PD)is a well-known complex neurodegenerative disease.Typically,its identification is based on motor disorders,while the computer estimation of its main symptoms with computational machine learning(ML)has a high exposure which is supported by researches conducted.Nevertheless,ML approaches required first to refine their parameters and then to work with the best model generated.This process often requires an expert user to oversee the performance of the algorithm.Therefore,an attention is required towards new approaches for better forecasting accuracy.Design/methodology/approach-To provide an available identification model for Parkinson disease as an auxiliary function for clinicians,the authors suggest a new evolutionary classification model.The core of the prediction model is a fast learning network(FLN)optimized by a genetic algorithm(GA).To get a better subset of features and parameters,a new coding architecture is introduced to improve GA for obtaining an optimal FLN model.Findings-The proposed model is intensively evaluated through a series of experiments based on Speech and HandPD benchmark datasets.The very popular wrappers induction models such as support vector machine(SVM),K-nearest neighbors(KNN)have been tested in the same condition.The results support that the proposed model can achieve the best performances in terms of accuracy and g-mean.Originality/value-A novel efficient PD detectionmodel is proposed,which is called A-W-FLN.The A-W-FLN utilizes FLN as the base classifier;in order to take its higher generalization ability,and identification capability is alsoembedded to discover themost suitable featuremodel in the detection process.Moreover,the proposedmethod automatically optimizes the FLN’s architecture to a smaller number of hidden nodes and solid connecting weights.This helps the network to train on complex PD datasets with non-linear features and yields superior result.

Multi-objective vendor managed inventory system with interval type-2 fuzzy demand and order quantities

Purpose-The proposed IT2FMOVMI model intends to concurrently minimize total cost and warehouse space for the single vendor-retailer,multi-item and a consolidated vendor store.Regarding demand and order quantities with the deterministic and type-1 fuzzy numbers,we have also formulated the classic/crisp MOVMI model and type-1 fuzzy MOVMI(T1FMOVMI)model.The suggested solution technique can solve both crisp MOVMIand T1FMOVMIproblems.By finding the optimal ordered quantities and backorder levels,the Paretofronts are constructed to form the solution sets for the three models.Design/methodology/approach-A multi-objective vendor managed inventory(MOVMI)is the most recognized marketing and delivery technique for the service provider and the retail in the supply chain in Industry 4.0.Due to the evolving market conditions,the characteristics of the individual product,the delivery period and the manufacturing costs,the demand rate and order quantity of the MOVMI device are highly unpredictable.In such a scenario,a MOVMI system with a deterministic demand rate and order quantity cannot be designed to estimate the highly unforeseen cost of the problem.This paper introduces a novel interval type-2 fuzzy multi-objective vendor managed inventory(IT2FMOVMI)system,which uses interval type-2 fuzzy numbers(IT2FNs)to represent demand rate and order quantities.As the model is an NP-hard,the well-known meta-heuristic algorithm named NSGA-II(Non-dominated sorted genetic algorithm-II)with EKM(Enhanced Karnink-Mendel)algorithm based solution method has been established.Findings-The experimental simulations for the five test problems that demonstrated distinct conditions are considered from the real-datasets of SAPCO company.Experimental study concludes that T1FMOVMI and crisp MOVMI schemes are outclassed by IT2FMOVMI model,offering more accurate Pareto-Fronts and efficiency measurement values.Originality/value-Using fuzzy sets theory,a significant amount of work has been already done in past decades from various points of views to model the MOVMI.However,this is the very first attempt to introduce type-2 fuzzy modelling for the problem to address the realistic implementation of the imprecise parameters.

Facial expression recognition based on bidirectional gated recurrent units within deep residual network

Purpose-recent years,facial expression recognition has been widely used in human machine interaction,clinical medicine and safe driving.However,there is a limitation that conventional recurrent neural networks can only learn the time-series characteristics of expressions based on one-way propagation information.Design/methodology/approach-To solve such limitation,this paper proposes a novel model based on bidirectional gated recurrent unit networks(Bi-GRUs)with two-way propagations,and the theory of identity mapping residuals is adopted to effectively prevent the problem of gradient disappearance caused by the depth of the introduced network.Since the Inception-V3 network model for spatial feature extraction has too many parameters,it is prone to overfitting during training.This paper proposes a novel facial expression recognition model to add two reduction modules to reduce parameters,so as to obtain an Inception-W network with better generalization.Findings-Finally,the proposed model is pretrained to determine the best settings and selections.Then,the pretrained model is experimented on two facial expression data sets of CKþand Oulu-CASIA,and the recognition performance and efficiency are compared with the existing methods.The highest recognition rate is 99.6%,which shows that the method has good recognition accuracy in a certain range.Originality/value-By using the proposed model for the applications of facial expression,the high recognition accuracy and robust recognition results with lower time consumption will help to build more sophisticated applications in real world.

An improved evaporation rate water cycle algorithm for energy-efficient routing protocol in WSNs

Purpose-The paper aims to introduce an efficient routing algorithm for wireless sensor networks(WSNs).It proposes an improved evaporation rate water cycle(improved ER-WC)algorithm and outlining the systems performance in improving the energy efficiency of WSNs.The proposed technique mainly analyzes the clustering problem of WSNs when huge tasks are performed.Design/methodology/approach-This proposed improved ER-WC algorithm is used for analyzing various factors such as network cluster-head(CH)energy,CH location and CH density in improved ER-WCA.The proposed study will solve the energy efficiency and improve network throughput in WSNs.Findings-This proposed work provides optimal clustering method for Fuzzy C-means(FCM)where efficiency is improved in WSNs.Empirical evaluations are conducted to find network lifespan,network throughput,total network residual energy and network stabilization.Research limitations/implications-The proposed improved ER-WC algorithm has some implications when different energy levels of node are used in WSNs.Practical implications-This research work analyzes the nodes’energy and throughput by selecting correct CHs in intra-cluster communication.It can possibly analyze the factors such as CH location,network CH energy and CH density.Originality/value-This proposed research work proves to be performing better for improving the network throughput and increases energy efficiency for WSNs.

An IoT-based agriculture maintenance using pervasive computing with machine learning technique

Purpose-In cultivation,early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates,ensuring that the economy remains balanced.The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification.In this investigation,the accurate prior phase of crop imagery has been collected from different datasets like cropscience,yesmodes and nelsonwisc.In the current study,the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science,yes_modes,nelson_wisc dataset.Design/methodology/approach-In this research work,random forest machine learning-based persuasive plants healthcare computing is provided.If proper ecological care is not applied to early harvesting,it can cause diseases in plants,decrease the cropping rate and less production.Until now different methods have been developed for crop analysis at an earlier stage,but it is necessary to implement methods to advanced techniques.So,the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation.This implemented design is verified on Python 3.7.8 software for simulation analysis.Findings-In this work,different methods are developed for crops at an earlier stage,but more methods are needed to implement methods with prior stage crop harvesting.Because of this,a disease-finding system has been implemented.The methodologies like“Threshold segmentation”and RFO classifier lends 97.8% identification precision with 99.3%real optimistic rate,and 59.823 peak signal-to-noise(PSNR),0.99894 structure similarity index(SSIM),0.00812 machine squared error(MSE)values are attained.Originality/value-The implemented machine learning design is outperformance methodology,and they are proving good application detection rate.

Critical links detection in stochastic networks: application to the transport networks

Purpose–The purpose of this paper is to study a multiple-origin-multiple-destination variant of dynamic critical nodes detection problem(DCNDP)and dynamic critical links detection problem(DCLDP)in stochastic networks.DCNDP and DCLDP consist of identifying the subset of nodes and links,respectively,whose deletion maximizes the stochastic shortest paths between all origins–destinations pairs,in the graph modeling the transport network.The identification of such nodes(or links)helps to better control the road traffic and predict the necessary measures to avoid congestion.Design/methodology/approach–A Markovian decision process is used to model the shortest path problem underdynamic trafficconditions.Effectivealgorithmstodeterminethe criticalnodes(links)whileconsideringthe dynamicity of the traffic network are provided.Also,sensitivity analysis toward capacity reduction for critical links is studied.Moreover,the complexity of the underlying algorithms is analyzed and the computational efficiency resulting from the decomposition operation of the network into communities is highlighted.Findings–The numerical results demonstrate that the use of dynamic shortest path(time dependency)as a metric has a significant impact on the identification of critical nodes/links and the experiments conducted on real world networks highlight the importance of sensitive links to dynamically detect critical links and elaborate smart transport plans.Research limitations/implications–The research in this paper also revealed several challenges,which call for future investigations.First,the authors have restricted our experimentation to a small network where the only focus is on the model behavior,in the absence of historical data.The authors intend to extend this study to very large network using real data.Second,the authors have considered only congestion to assess network’s criticality;future research on this topic may include other factors,mainly vulnerability.Practical implications–Taking into consideration the dynamic and stochastic nature in problem modeling enables to be effective tools for real-time control of transportation networks.This leads to design optimized smart transport plans particularly in disaster management,to improve the emergency evacuation effeciency.Originality/value–The paper provides a novel approach to solve critical nodes/links detection problems.In contrast to the majority of research works in the literature,the proposed model considers dynamicity and betweennesswhiletakingintoaccount the stochasticaspectof transportnetworks.Thisenables theapproach to guide the traffic and analyze transport networks mainly under disaster conditions in which networks become highly dynamic.

Evaluation of employee profiles using a hybrid clustering and optimization model Practical study

Purpose-The purpose of this paper is to introduce an evaluation methodology for employee profiles that will provide feedback to the training decision makers.Employee profiles play a crucial role in the evaluation process to improve the training process performance.This paper focuses on the clustering of the employees based on their profiles into specific categories that represent the employees’characteristics.The employees are classified into following categories:necessary training,required training,and no training.The work may answer the question of how to spend the budget of training for the employees.This investigation presents the use of fuzzy optimization and clustering hybrid model(data mining approaches)as a fuzzy imperialistic competitive algorithm(FICA)and k-means to find the employees’categories and predict their training requirements.Design/methodology/approach-Prior research that served as an impetus for this paper is discussed.The approach is to apply evolutionary algorithms and clustering hybrid model to improve the training decision system directions.Findings-This paper focuses on how to find a good model for the evaluation of employee profiles.The paper introduces the use of artificial intelligence methods(fuzzy optimization(FICA)and clustering techniques(K-means))in management.The suggestion and the recommendations were constructed based on the clustering results that represent the employee profiles and reflect their requirements during the training courses.Finally,the paper proved the ability of fuzzy optimization technique and clustering hybrid model in predicting the employee’s training requirements.Originality/value-This paper evaluates employee profiles based on new directions and expands the implication of clustering view in solving organizational challenges(in TCT for the first time).

A new pose estimation method for non-cooperative spacecraft based on point cloud

Purpose–On-orbitservice technologyis one of the key technologies of space manipulation activities such as spacecraft life extension,fault spacecraft capture,on-orbit debris removal and so on.It is known that the failure satellites,space debris and enemy spacecrafts in space are almost all non-cooperative targets.Relatively accurate pose estimation is critical to spatial operations,but also a recognized technical difficulty because of the undefined prior information of non-cooperative targets.With the rapid development of laser radar,the application of laser scanning equipment is increasing in the measurement of non-cooperative targets.It is necessary to research a new pose estimation method for non-cooperative targets based on 3D point cloud.The paper aims to discuss these issues.Design/methodology/approach–In this paper,a method based on the inherent characteristics of a spacecraft is proposed for estimating the pose(position and attitude)of the spatial non-cooperative target.First,we need to preprocess the obtained point cloud to reduce noise and improve the quality of data.Second,according to the features of the satellite,a recognition system used for non-cooperative measurement is designed.The components which are common in the configuration of satellite are chosen as the recognized object.Finally,based on the identified object,the ICP algorithm is used to calculate the pose between two frames of point cloud in different times to finish pose estimation.Findings–The new method enhances the matching speed and improves the accuracy of pose estimation compared with traditional methods by reducing the number of matching points.The recognition of components on non-cooperative spacecraft directly contributes to the space docking,on-orbit capture and relative navigation.Research limitations/implications–Limited to the measurement distance of the laser radar,this paper considers the pose estimation for non-cooperative spacecraft in the close range.Practical implications–The pose estimation method for non-cooperative spacecraft in this paper is mainly applied to close proximity space operations such as final rendezvous phase of spacecraft or ultra-close approaching phase of target capture.Thesystem can recognizecomponents needed to be captureand provide the relative pose of non-cooperative spacecraft.The method in this paper is more robust compared with the traditional single component recognition method and overall matching method when scanning of laser radar is not complete or the components are blocked.Originality/value–This paper introduces a new pose estimation method for non-cooperative spacecraft based on point cloud.The experimental results show that the proposed method can effectively identify the features of non-cooperative targets and track their position and attitude.The method is robust to the noise and greatly improves the speed of pose estimation while guarantee the accuracy.

Motion control design for unmanned ground vehicle in dynamic environment using intelligent controller

Purpose–The motion control of unmanned ground vehicles(UGV)is a challenge in the industry of automation.The purpose of this paper is to propose a fuzzy inference system(FIS)based on sensory information for solving the navigation challenge of UGV in cluttered and dynamic environments.Design/methodology/approach–The representation of the dynamic environment is a key element for the operational field and for the testing of the robotic navigation system.If dynamic obstacles move randomly in the operation field,the navigation problem becomes more complicated due to the coordination of the elements for accurate navigation and collision-free path within the environmental representations.This paper considers the construction of the FIS,which consists of two controllers.The first controller uses three sensors based on the obstacles distances from the front,right and left.The second controller employs the angle difference between the heading of the vehicle and the targeted angle to obtain the optimal route based on the environment and reach the desired destination with minimal running power and delay.The proposed design shows an efficient navigation strategy that overcomes the current navigation challenges in dynamic environments.Findings–Experimental analyses are conducted for three different scenarios to investigate the validation and effectiveness of the introduced controllers based on the FIS.The reported simulation results are obtained using MATLAB software package.The results show that the controllers of the FIS consistently perform the manoeuvring task and manage the route plan efficiently,even in a complex environment that is populated with dynamic obstacles.The paper demonstrates that the destination was reached optimally using the shortest free route.Research limitations/implications–The paper represents efforts toward building a dynamic environment filled with dynamic obstacles that move at various speeds and directions.The methodology of designing the FIS is accomplished to guide the UGV to the desired destination while avoiding collisions with obstacles.However,the methodology is approached using two-dimensional analyses.Hence,the paper suggests several extensions and variations to develop a three-dimensional strategy for further improvement.Originality/value–This paper presents the design of a FIS and its characterizations in dynamic environments,specifically for obstacles that move at different velocities.This facilitates an improved functionality of the operation of UGV.

Station-keeping control for a stratosphere airship via wind speed prediction approach

Purpose–The purpose of this paper is to improve the control precision of the station-keeping control for a stratosphere airship through the feedforward-feedback PID controller which is designed by the wind speed prediction based on the incremental extreme learning machine(I-ELM).Design/methodology/approach–First of all,the online prediction of wind speed is implemented by the I-ELM with rolling time.Second,the feedforward-feedback PID controller is designed through the position information of the airship and the predicted wind speed.In the end,the one-dimensional dynamic model of the stratosphere airship is built,and the controller is applied in the numerical simulation.Findings–Based on the conducted numerical simulations,some valuable conclusions are obtained.First,through the comparison between the predicted value and true value of the wind speed,the wind speed prediction based on I-ELM is very accurate.Second,the feedforward-feedback PID controller designed in this paper is very effective.Originality/value–This paper is very valuable to the research of a high-accuracy station-keeping control of stratosphere airship.

Two efficient methods for solving Schlömilch’s integral equation

Purpose–In this paper,the exact solutions of the Schlömilch’s integral equation and its linear and non-linear generalized formulas with application are solved by using two efficient iterative methods.The Schlömilch’s integral equations have many applications in atmospheric,terrestrial physics and ionospheric problems.They describe the density profile of electrons from the ionospheric for awry occurrence of the quasi-transverse approximations.The paper aims to discuss these issues.Design/methodology/approach–First,the authors apply a regularization method combined with the standard homotopy analysis method to find the exact solutions for all forms of the Schlömilch’s integral equation.Second,the authors implement the regularization method with the variational iteration method for the same purpose.The effectiveness of the regularization-Homotopy method and the regularizationvariational method is shown by using them for several illustrative examples,which have been solved by other authors using the so-called regularization-Adomian method.Findings–The implementation of the two methods demonstrates the usefulness in finding exact solutions.Practical implications–The authors have applied the developed methodology to the solution of the Rayleigh equation,which is an important equation in fluid dynamics and has a variety of applications in different fields of science and engineering.These include the analysis of batch distillation in chemistry,scattering of electromagnetic waves in physics,isotopic data in contaminant hydrogeology and others.Originality/value–In this paper,two reliable methods have been implemented to solve several examples,where those examples represent the main types of the Schlömilch’s integral models.Each method has been accompanied with the use of the regularization method.This process constructs an efficient dealing to get the exact solutions of the linear and non-linear Schlömilch’s integral equation which is easy to implement.In addition to that,the accompanied regularization method with each of the two used methods proved its efficiency in handling many problems especially ill-posed problems,such as the Fredholm integral equation of the first kind.

Risk management research in East Asia: a bibliometric analysis

Purpose–The aim of this paper is to present a comprehensive analysis of risk management in East Asia from 1998 to 2021 by using bibliometric methods and tools to explore research trends,hotspots,and directions for future research.Design/methodology/approach–The data source for this paper is the Web of Science Core Collection,and 7,154 publications and related information have been derived.We use recognized bibliometric indicators to evaluate publications and visually analyze them through scientific mapping tools(VOS Viewer and CiteSpace).Findings–The analysis results show that China is the most productive and influential country/region.East Asia countries have strong cooperation with each other and also have cooperation with other countries.The study shows that risk management has been involved in various fields such as credit,supply chain,health emergency and disaster especially in the background of COVID-19.We also found that machine learning,especially deep learning,has been playing an increasingly important role in risk management due to its excellent performance.Originality/value–This paper focuses on studying risk management in East Asia,exploring its publication’s fundamental information,citation and cooperation networks,hotspots,and research trends.It provides some reference value for scholars who are interested or further research in this field.

Assuring enhanced privacy violation detection model for social networks

Purpose-Virtually unlimited amounts of data collection by cybersecurity systems put people at risk of having their privacy violated.Social networks like Facebook on the Internet provide an overplus of knowledge concerning their users.Although users relish exchanging data online,only some data are meant to be interpreted by those who see value in it.It is now essential for online social network(OSN)to regulate the privacy of their users on the Internet.This paper aims to propose an efficient privacy violation detection model(EPVDM)for OSN.Design/methodology/approach-In recent months,the prominent position of both industry and academia has been dominated by privateness,its breaches and strategies to dodge privacy violations.Corporations around the world have become aware of the effects of violating privacy and its effect on them and other stakeholders.Once privacy violations are detected,they must be reported to those affected and it’s supposed to be mandatory to make them to take the next action.Although there are different approaches to detecting breaches of privacy,most strategies do not have a functioning tool that can show the values of its subject heading.An EPVDM for Facebook,based on a deep neural network,is proposed in this research paper.Findings-The main aim of EPVDM is to identify and avoid potential privacy breaches on Facebook in the future.Experimental analyses in comparison with major intrusion detection system(IDS)to detect privacy violation show that the proposed methodology is robust,precise and scalable.The chances of breaches or possibilities of privacy violations can be identified very accurately.Originality/value-All the resultant is compared with well popular methodologies like adaboost(AB),decision tree(DT),linear regression(LR),random forest(RF)and support vector machine(SVM).It’s been identified from the analysis that the proposed model outperformed the existing techniques in terms of accuracy(94%),precision(99.1%),recall(92.43%),f-score(95.43%)and violation detection rate(>98.5%).

Feature reduction with PCA/ KPCA for gait classification with different assistive devices

Purpose–The purpose of this paper is to propose a gait analysis technique that aims to identify differences and similarities in gait performance between three different assistive devices(ADs).Design/methodology/approach–Two feature reduction techniques,linear principal component analysis(PCA)and nonlinear kernel-PCA(KPCA),are expanded to provide a comparison of the spatio-temporal,symmetrical indexes and postural control parameters among the three different ADs.Then,a multiclass support vector machine(MSVM)with different approaches is designed to evaluate the potential of PCA and KPCA to extract relevant gait features that can differentiate between ADs.Findings–Results demonstrated that symmetrical indexes and postural control parameters are better suited to provide useful information about the different gait patterns that total knee arthroplasty(TKA)patients present when walking with different ADs.The combination of KPCA and MSVM with discriminant functions(MSVM DF)resulted in a noticeably improved performance.Such combination demonstrated that,with symmetric indexes and postural control parameters,it is possible to extract with high-accuracy nonlinear gait features for automatic classification of gait patterns with ADs.Originality/value–The information obtained with the proposed technique could be used to identify benefits and limitations of ADs on the rehabilitation process and to evaluate the benefit of their use in TKA patients.

Integrated vision-based system for efficient,semi-automated control of a robotic manipulator

Purpose–The purpose of this paper is to develop an integrated,computer vision-based system to operate a commercial wheelchair-mounted robotic manipulator(WMRM).In addition,a gesture recognition interface system was developed specially for individuals with upper-level spinal cord injuries including object tracking and face recognition to function as an efficient,hands-free WMRM controller.Design/methodology/approach–Two Kinects cameras were used synergistically to perform a variety of simple object retrieval tasks.One camera was used to interpret the hand gestures and locate the operator’s face for object positioning,and then send those as commands to control the WMRM.The other sensor was used to automatically recognize different daily living objects selected by the subjects.An object recognition module employing the Speeded Up Robust Features algorithm was implemented and recognition results were sent as a commands for“coarse positioning”of the robotic arm near the selected object.Automatic face detection was provided as a shortcut enabling the positing of the objects close by the subject’s face.Findings–The gesture recognition interface incorporated hand detection,tracking and recognition algorithms,and yielded a recognition accuracy of 97.5 percent for an eight-gesture lexicon.Tasks’completion time were conducted to compare manual(gestures only)and semi-manual(gestures,automatic face detection,and object recognition)WMRM control modes.The use of automatic face and object detection significantly reduced the completion times for retrieving a variety of daily living objects.Originality/value–Integration of three computer vision modules were used to construct an effective and hand-free interface for individuals with upper-limb mobility impairments to control a WMRM.

Solving vehicle routing problem with time windows using metaheuristic approaches

Purpose-Drugs are strategic products with essential functions in human health.An optimum design of the pharmaceutical supply chain is critical to avoid economic damage and adverse effects on human health.The vehicle-routing problem,focused on finding the lowest-cost routes with available vehicles and constraints,such as time constraints and road length,is an important aspect of this.In this paper,the vehicle routing problem(VRP)for a pharmaceutical company in Turkey is discussed.Design/methodology/approach-A mixed-integer programming(MIP)model based on the vehicle routing problem with time windows(VRPTW)is presented,aiming to minimize the total route cost with certain constraints.As the model provides an optimum solution for small problem sizes with the GUROBI®solver,for large problem sizes,metaheuristic methods that simulate annealing and adaptive large neighborhood search algorithms are proposed.A real dataset was used to analyze the effectiveness of the metaheuristic algorithms.The proposed simulated annealing(SA)and adaptive large neighborhood search(ALNS)were evaluated and compared against GUROBI®and each other through a set of real problem instances.Findings-The model is solved optimally for a small-sized dataset with exact algorithms;for solving a larger dataset,however,metaheuristic algorithms require significantly lesser time.For the problem addressed in this study,while the metaheuristic algorithms obtained the optimum solution in less than one minute,the solution in the GUROBI®solver was limited to one hour and three hours,and no solution could be obtained in this time interval.Originality/value-The VRPTW problem presented in this paper is a real-life problem.The vehicle fleet owned by the factory cannot be transported between certain suppliers,which complicates the solution of the problem.

Exploring compression and parallelization techniques for distribution of deep neural networks over Edge-Fog continuum-a review

Purpose-The trend of“Deep Learning for Internet of Things(IoT)”has gained fresh momentum with enormous upcoming applications employing these models as their processing engine and Cloud as their resource giant.But this picture leads to underutilization of ever-increasing device pool of IoT that has already passed 15 billion mark in 2015.Thus,it is high time to explore a different approach to tackle this issue,keeping in view the characteristics and needs of the two fields.Processing at the Edge can boost applications with realtime deadlines while complementing security.Design/methodology/approach-This review paper contributes towards three cardinal directions of research in the field of DL for IoT.The first section covers the categories of IoT devices and how Fog can aid in overcoming the underutilization of millions of devices,forming the realm of the things for IoT.The second direction handles the issue of immense computational requirements of DL models by uncovering specific compression techniques.An appropriate combination of these techniques,including regularization,quantization,and pruning,can aid in building an effective compression pipeline for establishing DL models for IoT use-cases.The third direction incorporates both these views and introduces a novel approach of parallelization for setting up a distributed systems view of DL for IoT.Findings-DL models are growing deeper with every passing year.Well-coordinated distributed execution of such models using Fog displays a promising future for the IoT application realm.It is realized that a vertically partitioned compressed deep model can handle the trade-off between size,accuracy,communication overhead,bandwidth utilization,and latency but at the expense of an additionally considerable memory footprint.To reduce the memory budget,we propose to exploit Hashed Nets as potentially favorable candidates for distributed frameworks.However,the critical point between accuracy and size for such models needs further investigation.Originality/value-To the best of our knowledge,no study has explored the inherent parallelism in deep neural network architectures for their efficient distribution over the Edge-Fog continuum.Besides covering techniques and frameworks that have tried to bring inference to the Edge,the review uncovers significant issues and possible future directions for endorsing deep models as processing engines for real-time IoT.The study is directed to both researchers and industrialists to take on various applications to the Edge for better user experience.

UAV rendezvous based on Nash bargain

Purpose–The purpose of this paper is to find a solution for the unmanned aerial vehicle(UAV)rendezvous problem,which should be feasible,optimal and not time consuming.In the existing literatures,the UAV rendezvous problem is always presented as a matter of simultaneous arrival.They focus only on the time consistency.However,the arrival time of UAVs can vary according to the rendezvous position.The authors should determine the best rendezvous position with considering UAVs’maneuver constraint,so that UAVs can construct a formation in a short time.Design/methodology/approach–The authors present a decentralized method in which UAVs negotiate with each other for the best rendezvous positions by using Nash bargain.The authors analyzed the constraints of the rendezvous time and the UAV maneuver,and proposed an objective function that allows UAVs to get to their rendezvous positions as fast as possible.Bezier curve is adopted to generate smooth and feasible flight trajectories.During the rendezvous process,UAVs adjust their speed so that they can arrive at the rendezvous positions simultaneously.Findings–The effectiveness of the proposed method is verified by simulation experiments.The proposed method can successfully and efficiently solve the UAV rendezvous problem.Originality/value–As far as the authors know,it is the first time Nash bargain is used in the UAV rendezvous problem.The authors modified the Nash bargain method and make it distributed,so that it can be computed easily.The proposed method is much less consuming than ordinary Nash bargain method and ordinary swarm intelligence based methods.It also considers the UAV maneuver constraint,and can be applied online for its fast calculation speed.Simulations demonstrate the effectiveness of the proposed method.