Buckling and postbuckling characteristics of laminated graphene-enhanced composite(GEC)truncated conical shells exposed to torsion under temperature conditions using finite element method(FEM)simulation are presented ...Buckling and postbuckling characteristics of laminated graphene-enhanced composite(GEC)truncated conical shells exposed to torsion under temperature conditions using finite element method(FEM)simulation are presented in this study.In the thickness direction,the GEC layers of the conical shell are ordered in a piece-wise arrangement of functionally graded(FG)distribution,with each layer containing a variable volume fraction for graphene reinforcement.To calculate the properties of temperaturedependent material of GEC layers,the extended Halpin-Tsai micromechanical framework is used.The FEM model is verified via comparing the current results obtained with the theoretical estimates for homogeneous,laminated cylindrical,and conical shells,the FEM model is validated.The computational results show that a piece-wise FG graphene volume fraction distribution can improve the torque of critical buckling and torsional postbuckling strength.Also,the geometric parameters have a critical impact on the stability of the conical shell.However,a temperature rise can reduce the crucial torsional buckling torque as well as the GEC laminated truncated conical shell’s postbuckling strength.展开更多
As the amount of medical images transmitted over networks and kept on online servers continues to rise,the need to protect those images digitally is becoming increasingly important.However,due to the massive amounts o...As the amount of medical images transmitted over networks and kept on online servers continues to rise,the need to protect those images digitally is becoming increasingly important.However,due to the massive amounts of multimedia and medical pictures being exchanged,low computational complexity techniques have been developed.Most commonly used algorithms offer very little security and require a great deal of communication,all of which add to the high processing costs associated with using them.First,a deep learning classifier is used to classify records according to the degree of concealment they require.Medical images that aren’t needed can be saved by using this method,which cuts down on security costs.Encryption is one of the most effective methods for protecting medical images after this step.Confusion and dispersion are two fundamental encryption processes.A new encryption algorithm for very sensitive data is developed in this study.Picture splitting with image blocks is nowdeveloped by using Zigzag patterns,rotation of the image blocks,and random permutation for scrambling the blocks.After that,this research suggests a Region of Interest(ROI)technique based on selective picture encryption.For the first step,we use an active contour picture segmentation to separate the ROI from the Region of Background(ROB).Permutation and diffusion are then carried out using a Hilbert curve and a Skew Tent map.Once all of the blocks have been encrypted,they are combined to create encrypted images.The investigational analysis is carried out to test the competence of the projected ideal with existing techniques.展开更多
The growing number of decarbonization standards in the transportation sector has resulted in an increase in demand for electric cars.Renewable energy sources have the ability to bring the fossil fuel age to an end.Ele...The growing number of decarbonization standards in the transportation sector has resulted in an increase in demand for electric cars.Renewable energy sources have the ability to bring the fossil fuel age to an end.Electrochemical storage devices,particularly lithium-ion batteries,are critical for this transition’s success.This is owing to a combination of favorable characteristics such as high energy density and minimal self-discharge.Given the environmental degradation caused by hazardous wastes and the scarcity of some resources,recycling used lithium-ion batteries has significant economic and practical importance.Many efforts have been undertaken in recent years to recover cathode materials(such as high-value metals like cobalt,nickel,and lithium).Regrettably,the regeneration of lower-value-added anode materials(mostly graphite)has received little attention.However,given the widespread use of carbon-based materials and the higher concentration of lithium in the anode than in the environment,anode recycling has gotten a lot of attention.As a result,this article provides the most recent research progress in the recovery of graphite anode materials from spent lithium ion batteries,analyzing the strengths and weaknesses of various recovery routes such as direct physical recovery,heat treatment recovery,hydrometallurgy recovery,heat treatment-hydrometallurgy recovery,extraction,and electrochemical methods from the perspectives of energy,environment,and economy;additionally,the reuse of recycled anode mats is discussed.Finally,the problems and future possibilities of anode recycling are discussed.To enable the green recycling of wasted lithium ion batteries,a low energy-consuming and ecologically friendly solution should be investigated.展开更多
Parents concerns for their children who has a critical health conditions may limit the children movements and live to engage with others peers anytime and anywhere.Thus,in this study aims to propose a framework to hel...Parents concerns for their children who has a critical health conditions may limit the children movements and live to engage with others peers anytime and anywhere.Thus,in this study aims to propose a framework to help the children who has critical disease to have more activity and engagement with other peers.Additionally,reducing their parents’concerns by providing monitoring and tracking system to their parents for their children health conditions.However,this study proposed a framework include tracking and monitoring wearable(TMW)device and decision system to alert healthcare providers and parents for any failure in the children health condition and status.The framework was designed based on the IoT environments and architecture.This project is also limited to apply in health IoT environments and may can be extend in future to apply in different internet coverage area.展开更多
Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and databases.Association rule mining,a fundamental process in data mining,involves discoveri...Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and databases.Association rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within datasets.In the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information rapidly.This paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)processes.Initially,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant attributes.Inspired by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for food.Subsequently,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute relationships.We propose a COA-based feature selection approach for medical data classification using machine learning techniques.This approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature set.We evaluate the performance of our approach on various medical datasets employing diverse machine learning classifiers.Experimental results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification tasks.The study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various diseases.To provide further validation,we conduct detailed experiments on a benchmark medical dataset,revealing the superiority of the MLARMCHDMS model over other methods,with a maximum accuracy of 99.75%.Therefore,this research contributes to the advancement of feature selection techniques in medical data classification and highlights the potential for improving healthcare outcomes through accurate and efficient data analysis.The presented MLARMC-HDMS framework and COA-based feature selection approach offer valuable insights for researchers and practitioners working in the field of healthcare data mining and machine learning.展开更多
In this study,a completely different approach to optimization is introduced through the development of a novel metaheuristic algorithm called the Barber Optimization Algorithm(BaOA).Inspired by the human interactions ...In this study,a completely different approach to optimization is introduced through the development of a novel metaheuristic algorithm called the Barber Optimization Algorithm(BaOA).Inspired by the human interactions between barbers and customers,BaOA captures two key processes:the customer’s selection of a hairstyle and the detailed refinement during the haircut.These processes are translated into a mathematical framework that forms the foundation of BaOA,consisting of two critical phases:exploration,representing the creative selection process,and exploitation,which focuses on refining details for optimization.The performance of BaOA is evaluated using 52 standard benchmark functions,including unimodal,high-dimensional multimodal,fixed-dimensional multimodal,and the Congress on Evolutionary Computation(CEC)2017 test suite.This comprehensive assessment highlights BaOA’s ability to balance exploration and exploitation effectively,resulting in high-quality solutions.A comparative analysis against twelve widely known metaheuristic algorithms further demonstrates BaOA’s superior performance,as it consistently delivers better results across most benchmark functions.To validate its real-world applicability,BaOA is tested on four engineering design problems,illustrating its capability to address practical challenges with remarkable efficiency.The results confirm BaOA’s versatility and reliability as an optimization tool.This study not only introduces an innovative algorithm but also establishes its effectiveness in solving complex problems,providing a foundation for future research and applications in diverse scientific and engineering domains.展开更多
In this study,an alternative precursor for production of activated carbon was introduced using dragon fruit(Hylocereus costaricensis)peel(DFP).Moreover,KOH was used as a chemical activator in the thermal carbonization...In this study,an alternative precursor for production of activated carbon was introduced using dragon fruit(Hylocereus costaricensis)peel(DFP).Moreover,KOH was used as a chemical activator in the thermal carbonization process to convert DFP into activated carbon(DFPAC).In order to accomplish this research,several approaches were employed to examine the elemental composition,surface properties,amorphous and crystalline nature,essential active group,and surface morphology of the DFPAC.The BrunauerEmmettTeller test demonstrated a mesoporous structure of the DFPAC has a high surface area of 756.3 m2g 1.The cationic dye Methylene Blue(MB)was used as a probe to assess the efficiency of DFPAC towards the removal of MB dye from aqueous solution.The effects of adsorption input factors(e.g.DFPAC dose(A:0.040.12 g L 1),pH(B:310),and temperature(C:3050℃))were investigated and optimized using statistical analysis(i.e.BoxBehnken design(BBD)).The adsorption kinetic model can be best categorized as the pseudofirst order(PFO).Whereas,the adsorption isotherm model can be best described by Langmuir model,with maximum adsorption capacity of DFPAC for MB dye was 195.2 mg g 1 at 50℃.The adsorption mechanism of MB by DFPAC surface was attributed to the electrostatic interaction,pp interaction,and Hbonding.Finally,the results support the ability of DFP to be a promising precursor for production of highly porous activated carbon suitable for removal of cationic dyes(e.g.MB).展开更多
The development of science and technology has led to the era of Industry 4.0.The core concept is the combination of“material and informationization”.In the supply chain and manufacturing process,the“material”of th...The development of science and technology has led to the era of Industry 4.0.The core concept is the combination of“material and informationization”.In the supply chain and manufacturing process,the“material”of the physical entity world is realized by data,identity,intelligence,and information.Industry 4.0 is a disruptive transformation and upgrade of intelligent industrialization based on the Internet-of-Things and Big Data in traditional industrialization.The goal is“maximizing production efficiency,minimizing production costs,and maximizing the individual needs of human beings for products and services.”Achieving this goal will surely bring about a major leap in the history of the industry,which will lead to the“Fourth Industrial Revolution.”This paper presents a detailed discussion of industrial big data,strategic roles,architectures,characteristics,and four types of innovative business models that can generate profits for enterprises.The key revolutionary aspect of Industry 4.0 is explained,which is the equipment revolution.Six important attributes of equipment are explained under the Industry 4.0 perspective.展开更多
Recent economic growth and development have considerably raised energy consumption over the globe.Electric load prediction approaches become essential for effective planning,decision-making,and contract evaluation of ...Recent economic growth and development have considerably raised energy consumption over the globe.Electric load prediction approaches become essential for effective planning,decision-making,and contract evaluation of the power systems.In order to achieve effective forecasting outcomes with minimumcomputation time,this study develops an improved whale optimization with deep learning enabled load prediction(IWO-DLELP)scheme for energy storage systems(ESS)in smart grid platform.The major intention of the IWO-DLELP technique is to effectually forecast the electric load in SG environment for designing proficient ESS.The proposed IWO-DLELP model initially undergoes pre-processing in two stages namely min-max normalization and feature selection.Besides,partition clustering approach is applied for the decomposition of data into distinct clusters with respect to distance and objective functions.Moreover,IWO with bidirectional gated recurrent unit(BiGRU)model is applied for the prediction of load and the hyperparameters are tuned by the use of IWO algorithm.The experiment analysis reported the enhanced results of the IWO-DLELP model over the recent methods interms of distinct evaluation measures.展开更多
Flue gas heat loss accounts for a significant component of theoverall heat loss for coal-fired boilers in power plants. The flue gas absorbsmore heat as the exhaust gas temperature rises, which reduces boiler efficien...Flue gas heat loss accounts for a significant component of theoverall heat loss for coal-fired boilers in power plants. The flue gas absorbsmore heat as the exhaust gas temperature rises, which reduces boiler efficiencyand raises coal consumption. Additionally, if the exhaust gas temperatureis too high, a lot of water must be used to cool the flue gas for the wetflue gas desulfurization system to function well, which has an impact onthe power plant’s ability to operate profitably. It is consequently vital totake steps to lower exhaust gas temperatures in order to increase boilerefficiency and decrease the amount of coal and water used. Desulfurizationperformance may be enhanced and water use can be decreased by reasonableflue gas characteristics at the entry. This study analyzed the unit’s energyconsumption, investment, and coal savings while proposing four couplingstrategies for regulating flue gas temperature and waste heat recovery. Agraded flue gas conditioning and waste heat recovery plan was presentedunder the condition of ensuring high desulfurization efficiency, along withthe notion of minimizing energy loss owing to energy inflow temperaturedifference. Numerical results show that the proposed methods improved thesystem performance and reduced the water consumption and regulated theboiler temperature.展开更多
The number of mobile devices accessing wireless networks isskyrocketing due to the rapid advancement of sensors and wireless communicationtechnology. In the upcoming years, it is anticipated that mobile datatraffic wo...The number of mobile devices accessing wireless networks isskyrocketing due to the rapid advancement of sensors and wireless communicationtechnology. In the upcoming years, it is anticipated that mobile datatraffic would rise even more. The development of a new cellular networkparadigm is being driven by the Internet of Things, smart homes, and moresophisticated applications with greater data rates and latency requirements.Resources are being used up quickly due to the steady growth of smartphonedevices andmultimedia apps. Computation offloading to either several distantclouds or close mobile devices has consistently improved the performance ofmobile devices. The computation latency can also be decreased by offloadingcomputing duties to edge servers with a specific level of computing power.Device-to-device (D2D) collaboration can assist in processing small-scaleactivities that are time-sensitive in order to further reduce task delays. The taskoffloading performance is drastically reduced due to the variation of differentperformance capabilities of edge nodes. Therefore, this paper addressed thisproblem and proposed a new method for D2D communication. In thismethod, the time delay is reduced by enabling the edge nodes to exchangedata samples. Simulation results show that the proposed algorithm has betterperformance than traditional algorithm.展开更多
Wireless Sensor Networks(WSN)have revolutionized the processes involved in industrial communication.However,the most important challenge faced by WSN sensors is the presence of limited energy.Multiple research inves-t...Wireless Sensor Networks(WSN)have revolutionized the processes involved in industrial communication.However,the most important challenge faced by WSN sensors is the presence of limited energy.Multiple research inves-tigations have been conducted so far on how to prolong the energy in WSN.This phenomenon is a result of inability of the network to have battery powered-sensor terminal.Energy-efficient routing on packetflow is a parallel phenomenon to delay nature,whereas the primary energy gets wasted as a result of WSN holes.Energy holes are present in the vicinity of sink and it is an important efficient-routing protocol for WSNs.In order to solve the issues discussed above,an energy-efficient routing protocol is proposed in this study named as Adaptive Route Decision Sink Relocation Protocol using Cluster Head Chain Cycling approach(ARDSR-CHC2H).The proposed method aims at improved communica-tion at sink-inviting routes.At this point,Cluster Head Node(CHN)is selected,since it consumes low energy and permits one node to communicate with others in two groups.The main purpose of the proposed model is to reduce energy con-sumption and define new interchange technology.A comparison of simulation results demonstrates that the proposed algorithm achieved low cluster creation time,better network error and high Packet Delivery Rate with less network failure.展开更多
From raw material storage through final product distribution,a cold supply chain is a technique in which all activities are managed by temperature.The expansion in the number of imported meat and other comparable comm...From raw material storage through final product distribution,a cold supply chain is a technique in which all activities are managed by temperature.The expansion in the number of imported meat and other comparable commodities,as well as exported seafood has boosted the performance of cold chain logistics service providers.On the basis of the standard basicpursuit(BP)neural network,a rough BP particle swarm optimization(PSO)neural network model is constructed by combining rough set and particle swarm algorithms to aid cold chain food production enterprises in quickly picking the best cold chain logistics service providers.To reduce duplicate information in the original data and make the input index more compact,the model employs rough set.Instead of using gradient descent to train the weights of the neural network,particle swarm optimization is utilized to ensure that the output results are not readily caught in local minima and that the network’s generalization capacity is improved.Finally,an example is presented to demonstrate the model’s validity and viability.The findings reveal that the model’s prediction error is 40.94 percent lower than the BP neural network model,and the prediction result is more accurate and dependable,providing a new technique for cold chain food production companies to swiftly pick the best cold chain logistics service provider.展开更多
Physical sensors,intelligent sensors,and output recommenda-tions are all examples of smart health technology that can be used to monitor patients’health and change their behavior.Smart health is an Internet-of-Things...Physical sensors,intelligent sensors,and output recommenda-tions are all examples of smart health technology that can be used to monitor patients’health and change their behavior.Smart health is an Internet-of-Things(IoT)-aware network and sensing infrastructure that provides real-time,intelligent,and ubiquitous healthcare services.Because of the rapid development of cloud computing,as well as related technologies such as fog computing,smart health research is progressively moving in the right direction.Cloud,fog computing,IoT sensors,blockchain,privacy and security,and other related technologies have been the focus of smart health research in recent years.At the moment,the focus in cloud and smart health research is on how to use the cloud to solve the problem of enormous health data and enhance service performance,including cloud storage,retrieval,and calculation of health big data.This article reviews state-of-the-art edge computing methods that has shifted to the collection,transmission,and calculation of health data,which includes various sensors and wearable devices used to collect health data,various wireless sensor technologies,and how to process health data and improve edge performance,among other things.Finally,the typical smart health application cases,blockchain’s application in smart health,and related privacy and security issues were reviewed,as well as future difficulties and potential for smart health services.The comparative analysis provides a reference for the the mobile edge computing in healthcare systems.展开更多
With the rapid development of the next-generation mobile network,the number of terminal devices and applications is growing explosively.Therefore,how to obtain a higher data rate,wider network coverage and higher reso...With the rapid development of the next-generation mobile network,the number of terminal devices and applications is growing explosively.Therefore,how to obtain a higher data rate,wider network coverage and higher resource utilization in the limited spectrum resources has become the common research goal of scholars.Device-to-Device(D2D)communication technology and other frontier communication technologies have emerged.Device-to-Device communication technology is the technology that devices in proximity can communicate directly in cellular networks.It has become one of the key technologies of the fifth-generation mobile communications system(5G).D2D communication technology which is introduced into cellular networks can effectively improve spectrum utilization,enhance network coverage,reduce transmission delay and improve system throughput,but it would also bring complicated and various interferences due to reusing cellular resources at the same time.So resource management is one of the most challenging and importing issues to give full play to the advantages of D2D communication.Optimal resource allocation is an important factor that needs to be addressed in D2D communication.Therefore,this paper proposes an optimization method based on the game-matching concept.The main idea is to model the optimization problem of the quality-of-experience based on user fairness and solve it through game-matching theory.Simulation results show that the proposed algorithm effectively improved the resource allocation and utilization as compared with existing algorithms.展开更多
Computational intelligence(CI)is a group of nature-simulated computationalmodels and processes for addressing difficult real-life problems.The CI is useful in the UAV domain as it produces efficient,precise,and rapid ...Computational intelligence(CI)is a group of nature-simulated computationalmodels and processes for addressing difficult real-life problems.The CI is useful in the UAV domain as it produces efficient,precise,and rapid solutions.Besides,unmanned aerial vehicles(UAV)developed a hot research topic in the smart city environment.Despite the benefits of UAVs,security remains a major challenging issue.In addition,deep learning(DL)enabled image classification is useful for several applications such as land cover classification,smart buildings,etc.This paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification(MDLS-UAVIC)model in a smart city environment.Themajor purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class labels.The proposedMDLS-UAVIC model follows a two-stage process:encryption and image classification.The encryption technique for image encryption effectively encrypts the UAV images.Next,the image classification process involves anXception-based deep convolutional neural network for the feature extraction process.Finally,shuffled shepherd optimization(SSO)with a recurrent neural network(RNN)model is applied for UAV image classification,showing the novelty of the work.The experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset,and the outcomes are examined in various measures.It achieved a high accuracy of 98%.展开更多
There are two common types of polymers(thermoplastics and thermosets),which have been classified by various methods depending on their molecular structures.The bonding of molecular chains is the fundamenta...There are two common types of polymers(thermoplastics and thermosets),which have been classified by various methods depending on their molecular structures.The bonding of molecular chains is the fundamental physical difference between these two polymer types.The polymer types are named based on their general thermal and processing characteristics,and chemical structure,which in turn significantly influence their polymer properties[1].展开更多
This research studies the changes in flow patterns and hemodynamic parameters of diverse shapes and sizes of stenosis.Six different shapes and sizes of stenosis are constructed to investigate the variations in hemodyn...This research studies the changes in flow patterns and hemodynamic parameters of diverse shapes and sizes of stenosis.Six different shapes and sizes of stenosis are constructed to investigate the variations in hemodynamics as the morphology changes.Changes in shape(trapezoidal and bell-shaped)and sizes of stenosis change the stresses on the walls and their flow patterns.TAWSS and OSI results specify that trapezoidal stenosis exerts greater stress than bell-shaped stenosis.Also,as the length of the trapezoidal stenosis increases,the TAWSS increases,whereas the trend is the opposite for bell-shaped stenosis.Later,this paper also studies different degrees of stenosis extracted from real images.Changes in velocity flow patterns,wall shear stress(WSS),Time-averaged wall shear stress(TAWSS)and Oscillatory shear index(OSI)have been studied for these images.Results illustrate that the peak velocity rises drastically as the stenosis percentage increases.Negative velocity is seen close to the artery's walls,indicating flow separation.This flow separation region is seen throughout the cycle except in the accelerating flow region.An increase in stenosis also increases WSS and TAWSS drastically.Negative WSS is seen downstream of stenosis,indicating flow recirculation.Such negative WSS in the blood vessels also promotes endothelial dysfunction.OSI values greater than 0.2 are seen near the stenosis region,indicating atherosclerosis growth.Regions of high OSI and low TAWSS are also identified,indicating probable regions of plaque development.展开更多
A need for a prosthetic hand device has arisen based on the fact that many people lose one of their upper limbs for various reasons.Many systems are available to control prosthetic hands,such as electromyography(EMG)a...A need for a prosthetic hand device has arisen based on the fact that many people lose one of their upper limbs for various reasons.Many systems are available to control prosthetic hands,such as electromyography(EMG)and mechanomyography(MMG).These systems present many problems,including complexity,high cost,and other issues.Voice commands are among the solutions recommended to address these issues.The proliferation of the Internet,voice recognition technology built into mobile phones,and Internet of Things(IoT)technology has facilitated the use of voice commands to operate prosthetic devices.In this paper,robotic prosthetics were controlled using this technology in the context of five different movement classes.This study involved five participants and reports as accuracy rate of 97%.展开更多
文摘Buckling and postbuckling characteristics of laminated graphene-enhanced composite(GEC)truncated conical shells exposed to torsion under temperature conditions using finite element method(FEM)simulation are presented in this study.In the thickness direction,the GEC layers of the conical shell are ordered in a piece-wise arrangement of functionally graded(FG)distribution,with each layer containing a variable volume fraction for graphene reinforcement.To calculate the properties of temperaturedependent material of GEC layers,the extended Halpin-Tsai micromechanical framework is used.The FEM model is verified via comparing the current results obtained with the theoretical estimates for homogeneous,laminated cylindrical,and conical shells,the FEM model is validated.The computational results show that a piece-wise FG graphene volume fraction distribution can improve the torque of critical buckling and torsional postbuckling strength.Also,the geometric parameters have a critical impact on the stability of the conical shell.However,a temperature rise can reduce the crucial torsional buckling torque as well as the GEC laminated truncated conical shell’s postbuckling strength.
文摘As the amount of medical images transmitted over networks and kept on online servers continues to rise,the need to protect those images digitally is becoming increasingly important.However,due to the massive amounts of multimedia and medical pictures being exchanged,low computational complexity techniques have been developed.Most commonly used algorithms offer very little security and require a great deal of communication,all of which add to the high processing costs associated with using them.First,a deep learning classifier is used to classify records according to the degree of concealment they require.Medical images that aren’t needed can be saved by using this method,which cuts down on security costs.Encryption is one of the most effective methods for protecting medical images after this step.Confusion and dispersion are two fundamental encryption processes.A new encryption algorithm for very sensitive data is developed in this study.Picture splitting with image blocks is nowdeveloped by using Zigzag patterns,rotation of the image blocks,and random permutation for scrambling the blocks.After that,this research suggests a Region of Interest(ROI)technique based on selective picture encryption.For the first step,we use an active contour picture segmentation to separate the ROI from the Region of Background(ROB).Permutation and diffusion are then carried out using a Hilbert curve and a Skew Tent map.Once all of the blocks have been encrypted,they are combined to create encrypted images.The investigational analysis is carried out to test the competence of the projected ideal with existing techniques.
基金Deanship of Scientific Research at Taif University for the grant received for this research.This research was supported by Taif University with research grant(TURSP-2020/77).
文摘The growing number of decarbonization standards in the transportation sector has resulted in an increase in demand for electric cars.Renewable energy sources have the ability to bring the fossil fuel age to an end.Electrochemical storage devices,particularly lithium-ion batteries,are critical for this transition’s success.This is owing to a combination of favorable characteristics such as high energy density and minimal self-discharge.Given the environmental degradation caused by hazardous wastes and the scarcity of some resources,recycling used lithium-ion batteries has significant economic and practical importance.Many efforts have been undertaken in recent years to recover cathode materials(such as high-value metals like cobalt,nickel,and lithium).Regrettably,the regeneration of lower-value-added anode materials(mostly graphite)has received little attention.However,given the widespread use of carbon-based materials and the higher concentration of lithium in the anode than in the environment,anode recycling has gotten a lot of attention.As a result,this article provides the most recent research progress in the recovery of graphite anode materials from spent lithium ion batteries,analyzing the strengths and weaknesses of various recovery routes such as direct physical recovery,heat treatment recovery,hydrometallurgy recovery,heat treatment-hydrometallurgy recovery,extraction,and electrochemical methods from the perspectives of energy,environment,and economy;additionally,the reuse of recycled anode mats is discussed.Finally,the problems and future possibilities of anode recycling are discussed.To enable the green recycling of wasted lithium ion batteries,a low energy-consuming and ecologically friendly solution should be investigated.
文摘Parents concerns for their children who has a critical health conditions may limit the children movements and live to engage with others peers anytime and anywhere.Thus,in this study aims to propose a framework to help the children who has critical disease to have more activity and engagement with other peers.Additionally,reducing their parents’concerns by providing monitoring and tracking system to their parents for their children health conditions.However,this study proposed a framework include tracking and monitoring wearable(TMW)device and decision system to alert healthcare providers and parents for any failure in the children health condition and status.The framework was designed based on the IoT environments and architecture.This project is also limited to apply in health IoT environments and may can be extend in future to apply in different internet coverage area.
基金Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the Project Number RI-44-0444.
文摘Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and databases.Association rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within datasets.In the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information rapidly.This paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)processes.Initially,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant attributes.Inspired by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for food.Subsequently,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute relationships.We propose a COA-based feature selection approach for medical data classification using machine learning techniques.This approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature set.We evaluate the performance of our approach on various medical datasets employing diverse machine learning classifiers.Experimental results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification tasks.The study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various diseases.To provide further validation,we conduct detailed experiments on a benchmark medical dataset,revealing the superiority of the MLARMCHDMS model over other methods,with a maximum accuracy of 99.75%.Therefore,this research contributes to the advancement of feature selection techniques in medical data classification and highlights the potential for improving healthcare outcomes through accurate and efficient data analysis.The presented MLARMC-HDMS framework and COA-based feature selection approach offer valuable insights for researchers and practitioners working in the field of healthcare data mining and machine learning.
文摘In this study,a completely different approach to optimization is introduced through the development of a novel metaheuristic algorithm called the Barber Optimization Algorithm(BaOA).Inspired by the human interactions between barbers and customers,BaOA captures two key processes:the customer’s selection of a hairstyle and the detailed refinement during the haircut.These processes are translated into a mathematical framework that forms the foundation of BaOA,consisting of two critical phases:exploration,representing the creative selection process,and exploitation,which focuses on refining details for optimization.The performance of BaOA is evaluated using 52 standard benchmark functions,including unimodal,high-dimensional multimodal,fixed-dimensional multimodal,and the Congress on Evolutionary Computation(CEC)2017 test suite.This comprehensive assessment highlights BaOA’s ability to balance exploration and exploitation effectively,resulting in high-quality solutions.A comparative analysis against twelve widely known metaheuristic algorithms further demonstrates BaOA’s superior performance,as it consistently delivers better results across most benchmark functions.To validate its real-world applicability,BaOA is tested on four engineering design problems,illustrating its capability to address practical challenges with remarkable efficiency.The results confirm BaOA’s versatility and reliability as an optimization tool.This study not only introduces an innovative algorithm but also establishes its effectiveness in solving complex problems,providing a foundation for future research and applications in diverse scientific and engineering domains.
基金the Universiti Teknologi MARA,Institute of Research Management and Innovation(Institut Pengu-rusan Penyelidikan&Inovasi)for funding this project underLES-TARI grant(600-IRMI 5/3/LESTARI(037/2019)).The authors Zeid A.ALOthman and Mohammad Rizwan Khan are thankful to the Researchers Supporting Project(RSP-2020/138),King Saud University,Riyadh,Saudi Arabia.
文摘In this study,an alternative precursor for production of activated carbon was introduced using dragon fruit(Hylocereus costaricensis)peel(DFP).Moreover,KOH was used as a chemical activator in the thermal carbonization process to convert DFP into activated carbon(DFPAC).In order to accomplish this research,several approaches were employed to examine the elemental composition,surface properties,amorphous and crystalline nature,essential active group,and surface morphology of the DFPAC.The BrunauerEmmettTeller test demonstrated a mesoporous structure of the DFPAC has a high surface area of 756.3 m2g 1.The cationic dye Methylene Blue(MB)was used as a probe to assess the efficiency of DFPAC towards the removal of MB dye from aqueous solution.The effects of adsorption input factors(e.g.DFPAC dose(A:0.040.12 g L 1),pH(B:310),and temperature(C:3050℃))were investigated and optimized using statistical analysis(i.e.BoxBehnken design(BBD)).The adsorption kinetic model can be best categorized as the pseudofirst order(PFO).Whereas,the adsorption isotherm model can be best described by Langmuir model,with maximum adsorption capacity of DFPAC for MB dye was 195.2 mg g 1 at 50℃.The adsorption mechanism of MB by DFPAC surface was attributed to the electrostatic interaction,pp interaction,and Hbonding.Finally,the results support the ability of DFP to be a promising precursor for production of highly porous activated carbon suitable for removal of cationic dyes(e.g.MB).
基金The authors(Basem Alkazemi,bykazemi@uqu.edu.saAli Safaa Sadiq,ali.sadiq@wlv.ac.uk)would like to thank deanship of scientific research(DSR)at umm Al-Qura University for their partial funding the work(Grant#17-COM-1-01-0007)the National Research Foundation(NRF),Korea(2019R1C1C1007277)funded by the Ministry of Science and ICT(MSIT),Korea.
文摘The development of science and technology has led to the era of Industry 4.0.The core concept is the combination of“material and informationization”.In the supply chain and manufacturing process,the“material”of the physical entity world is realized by data,identity,intelligence,and information.Industry 4.0 is a disruptive transformation and upgrade of intelligent industrialization based on the Internet-of-Things and Big Data in traditional industrialization.The goal is“maximizing production efficiency,minimizing production costs,and maximizing the individual needs of human beings for products and services.”Achieving this goal will surely bring about a major leap in the history of the industry,which will lead to the“Fourth Industrial Revolution.”This paper presents a detailed discussion of industrial big data,strategic roles,architectures,characteristics,and four types of innovative business models that can generate profits for enterprises.The key revolutionary aspect of Industry 4.0 is explained,which is the equipment revolution.Six important attributes of equipment are explained under the Industry 4.0 perspective.
文摘Recent economic growth and development have considerably raised energy consumption over the globe.Electric load prediction approaches become essential for effective planning,decision-making,and contract evaluation of the power systems.In order to achieve effective forecasting outcomes with minimumcomputation time,this study develops an improved whale optimization with deep learning enabled load prediction(IWO-DLELP)scheme for energy storage systems(ESS)in smart grid platform.The major intention of the IWO-DLELP technique is to effectually forecast the electric load in SG environment for designing proficient ESS.The proposed IWO-DLELP model initially undergoes pre-processing in two stages namely min-max normalization and feature selection.Besides,partition clustering approach is applied for the decomposition of data into distinct clusters with respect to distance and objective functions.Moreover,IWO with bidirectional gated recurrent unit(BiGRU)model is applied for the prediction of load and the hyperparameters are tuned by the use of IWO algorithm.The experiment analysis reported the enhanced results of the IWO-DLELP model over the recent methods interms of distinct evaluation measures.
文摘Flue gas heat loss accounts for a significant component of theoverall heat loss for coal-fired boilers in power plants. The flue gas absorbsmore heat as the exhaust gas temperature rises, which reduces boiler efficiencyand raises coal consumption. Additionally, if the exhaust gas temperatureis too high, a lot of water must be used to cool the flue gas for the wetflue gas desulfurization system to function well, which has an impact onthe power plant’s ability to operate profitably. It is consequently vital totake steps to lower exhaust gas temperatures in order to increase boilerefficiency and decrease the amount of coal and water used. Desulfurizationperformance may be enhanced and water use can be decreased by reasonableflue gas characteristics at the entry. This study analyzed the unit’s energyconsumption, investment, and coal savings while proposing four couplingstrategies for regulating flue gas temperature and waste heat recovery. Agraded flue gas conditioning and waste heat recovery plan was presentedunder the condition of ensuring high desulfurization efficiency, along withthe notion of minimizing energy loss owing to energy inflow temperaturedifference. Numerical results show that the proposed methods improved thesystem performance and reduced the water consumption and regulated theboiler temperature.
文摘The number of mobile devices accessing wireless networks isskyrocketing due to the rapid advancement of sensors and wireless communicationtechnology. In the upcoming years, it is anticipated that mobile datatraffic would rise even more. The development of a new cellular networkparadigm is being driven by the Internet of Things, smart homes, and moresophisticated applications with greater data rates and latency requirements.Resources are being used up quickly due to the steady growth of smartphonedevices andmultimedia apps. Computation offloading to either several distantclouds or close mobile devices has consistently improved the performance ofmobile devices. The computation latency can also be decreased by offloadingcomputing duties to edge servers with a specific level of computing power.Device-to-device (D2D) collaboration can assist in processing small-scaleactivities that are time-sensitive in order to further reduce task delays. The taskoffloading performance is drastically reduced due to the variation of differentperformance capabilities of edge nodes. Therefore, this paper addressed thisproblem and proposed a new method for D2D communication. In thismethod, the time delay is reduced by enabling the edge nodes to exchangedata samples. Simulation results show that the proposed algorithm has betterperformance than traditional algorithm.
文摘Wireless Sensor Networks(WSN)have revolutionized the processes involved in industrial communication.However,the most important challenge faced by WSN sensors is the presence of limited energy.Multiple research inves-tigations have been conducted so far on how to prolong the energy in WSN.This phenomenon is a result of inability of the network to have battery powered-sensor terminal.Energy-efficient routing on packetflow is a parallel phenomenon to delay nature,whereas the primary energy gets wasted as a result of WSN holes.Energy holes are present in the vicinity of sink and it is an important efficient-routing protocol for WSNs.In order to solve the issues discussed above,an energy-efficient routing protocol is proposed in this study named as Adaptive Route Decision Sink Relocation Protocol using Cluster Head Chain Cycling approach(ARDSR-CHC2H).The proposed method aims at improved communica-tion at sink-inviting routes.At this point,Cluster Head Node(CHN)is selected,since it consumes low energy and permits one node to communicate with others in two groups.The main purpose of the proposed model is to reduce energy con-sumption and define new interchange technology.A comparison of simulation results demonstrates that the proposed algorithm achieved low cluster creation time,better network error and high Packet Delivery Rate with less network failure.
基金This research was supported by the MSIT(Ministry of Science and ICT),Korea,under the National Research Foundation(NRF),Korea(2022R1A2C4001270).
文摘From raw material storage through final product distribution,a cold supply chain is a technique in which all activities are managed by temperature.The expansion in the number of imported meat and other comparable commodities,as well as exported seafood has boosted the performance of cold chain logistics service providers.On the basis of the standard basicpursuit(BP)neural network,a rough BP particle swarm optimization(PSO)neural network model is constructed by combining rough set and particle swarm algorithms to aid cold chain food production enterprises in quickly picking the best cold chain logistics service providers.To reduce duplicate information in the original data and make the input index more compact,the model employs rough set.Instead of using gradient descent to train the weights of the neural network,particle swarm optimization is utilized to ensure that the output results are not readily caught in local minima and that the network’s generalization capacity is improved.Finally,an example is presented to demonstrate the model’s validity and viability.The findings reveal that the model’s prediction error is 40.94 percent lower than the BP neural network model,and the prediction result is more accurate and dependable,providing a new technique for cold chain food production companies to swiftly pick the best cold chain logistics service provider.
基金supported by the Ministry of Education,Malaysia(Grant Code:FRGS/1/2018/ICT02/UKM/02/6).
文摘Physical sensors,intelligent sensors,and output recommenda-tions are all examples of smart health technology that can be used to monitor patients’health and change their behavior.Smart health is an Internet-of-Things(IoT)-aware network and sensing infrastructure that provides real-time,intelligent,and ubiquitous healthcare services.Because of the rapid development of cloud computing,as well as related technologies such as fog computing,smart health research is progressively moving in the right direction.Cloud,fog computing,IoT sensors,blockchain,privacy and security,and other related technologies have been the focus of smart health research in recent years.At the moment,the focus in cloud and smart health research is on how to use the cloud to solve the problem of enormous health data and enhance service performance,including cloud storage,retrieval,and calculation of health big data.This article reviews state-of-the-art edge computing methods that has shifted to the collection,transmission,and calculation of health data,which includes various sensors and wearable devices used to collect health data,various wireless sensor technologies,and how to process health data and improve edge performance,among other things.Finally,the typical smart health application cases,blockchain’s application in smart health,and related privacy and security issues were reviewed,as well as future difficulties and potential for smart health services.The comparative analysis provides a reference for the the mobile edge computing in healthcare systems.
文摘With the rapid development of the next-generation mobile network,the number of terminal devices and applications is growing explosively.Therefore,how to obtain a higher data rate,wider network coverage and higher resource utilization in the limited spectrum resources has become the common research goal of scholars.Device-to-Device(D2D)communication technology and other frontier communication technologies have emerged.Device-to-Device communication technology is the technology that devices in proximity can communicate directly in cellular networks.It has become one of the key technologies of the fifth-generation mobile communications system(5G).D2D communication technology which is introduced into cellular networks can effectively improve spectrum utilization,enhance network coverage,reduce transmission delay and improve system throughput,but it would also bring complicated and various interferences due to reusing cellular resources at the same time.So resource management is one of the most challenging and importing issues to give full play to the advantages of D2D communication.Optimal resource allocation is an important factor that needs to be addressed in D2D communication.Therefore,this paper proposes an optimization method based on the game-matching concept.The main idea is to model the optimization problem of the quality-of-experience based on user fairness and solve it through game-matching theory.Simulation results show that the proposed algorithm effectively improved the resource allocation and utilization as compared with existing algorithms.
基金Deputyship for Research&Inno-vation,Ministry of Education in Saudi Arabia for funding this research work through the Project Number RI-44-0446.
文摘Computational intelligence(CI)is a group of nature-simulated computationalmodels and processes for addressing difficult real-life problems.The CI is useful in the UAV domain as it produces efficient,precise,and rapid solutions.Besides,unmanned aerial vehicles(UAV)developed a hot research topic in the smart city environment.Despite the benefits of UAVs,security remains a major challenging issue.In addition,deep learning(DL)enabled image classification is useful for several applications such as land cover classification,smart buildings,etc.This paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification(MDLS-UAVIC)model in a smart city environment.Themajor purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class labels.The proposedMDLS-UAVIC model follows a two-stage process:encryption and image classification.The encryption technique for image encryption effectively encrypts the UAV images.Next,the image classification process involves anXception-based deep convolutional neural network for the feature extraction process.Finally,shuffled shepherd optimization(SSO)with a recurrent neural network(RNN)model is applied for UAV image classification,showing the novelty of the work.The experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset,and the outcomes are examined in various measures.It achieved a high accuracy of 98%.
文摘There are two common types of polymers(thermoplastics and thermosets),which have been classified by various methods depending on their molecular structures.The bonding of molecular chains is the fundamental physical difference between these two polymer types.The polymer types are named based on their general thermal and processing characteristics,and chemical structure,which in turn significantly influence their polymer properties[1].
文摘This research studies the changes in flow patterns and hemodynamic parameters of diverse shapes and sizes of stenosis.Six different shapes and sizes of stenosis are constructed to investigate the variations in hemodynamics as the morphology changes.Changes in shape(trapezoidal and bell-shaped)and sizes of stenosis change the stresses on the walls and their flow patterns.TAWSS and OSI results specify that trapezoidal stenosis exerts greater stress than bell-shaped stenosis.Also,as the length of the trapezoidal stenosis increases,the TAWSS increases,whereas the trend is the opposite for bell-shaped stenosis.Later,this paper also studies different degrees of stenosis extracted from real images.Changes in velocity flow patterns,wall shear stress(WSS),Time-averaged wall shear stress(TAWSS)and Oscillatory shear index(OSI)have been studied for these images.Results illustrate that the peak velocity rises drastically as the stenosis percentage increases.Negative velocity is seen close to the artery's walls,indicating flow separation.This flow separation region is seen throughout the cycle except in the accelerating flow region.An increase in stenosis also increases WSS and TAWSS drastically.Negative WSS is seen downstream of stenosis,indicating flow recirculation.Such negative WSS in the blood vessels also promotes endothelial dysfunction.OSI values greater than 0.2 are seen near the stenosis region,indicating atherosclerosis growth.Regions of high OSI and low TAWSS are also identified,indicating probable regions of plaque development.
基金supported by College of Dentistry and College of medicine of Mosul University,as well as Department of Medical Instrumentation Techniques Engineering,Electrical Engineering Technical College,Middle Technical University,。
文摘A need for a prosthetic hand device has arisen based on the fact that many people lose one of their upper limbs for various reasons.Many systems are available to control prosthetic hands,such as electromyography(EMG)and mechanomyography(MMG).These systems present many problems,including complexity,high cost,and other issues.Voice commands are among the solutions recommended to address these issues.The proliferation of the Internet,voice recognition technology built into mobile phones,and Internet of Things(IoT)technology has facilitated the use of voice commands to operate prosthetic devices.In this paper,robotic prosthetics were controlled using this technology in the context of five different movement classes.This study involved five participants and reports as accuracy rate of 97%.
