This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits ...This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly.It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function.It works for 3-tier architecture,including cloudlet and public cloud.As cloudlets have limited resources,fuzzy logic is used for cloudlet selection using capacity and waiting time as input.Fuzzy provides human-like decisions without using any mathematical model.Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed.It balances the load on cloud and cloudlet while minimizing makespan and execution time.However,it may trap in local optimum;levy flight can handle it.Hybridization of fuzzy fireflywith levy flight is a novel technique that provides reduced makespan,execution time,and Degree of imbalance while balancing the load.Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration(NASA)and Clarknet datasets.Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker(ACOQDM),Distributed Scheduling Optimization Algorithm(DSOA),andUtility-based Firefly Algorithm(UFA)when compared in terms of makespan,Degree of imbalance,and Figure of Merit.展开更多
Shape and size optimization with frequency constraints is a highly nonlinear problem withmixed design variables,non-convex search space,and multiple local optima.Therefore,a hybrid sine cosine firefly algorithm(HSCFA)...Shape and size optimization with frequency constraints is a highly nonlinear problem withmixed design variables,non-convex search space,and multiple local optima.Therefore,a hybrid sine cosine firefly algorithm(HSCFA)is proposed to acquire more accurate solutions with less finite element analysis.The full attraction model of firefly algorithm(FA)is analyzed,and the factors that affect its computational efficiency and accuracy are revealed.A modified FA with simplified attraction model and adaptive parameter of sine cosine algorithm(SCA)is proposed to reduce the computational complexity and enhance the convergence rate.Then,the population is classified,and different populations are updated by modified FA and SCA respectively.Besides,the random search strategy based on Lévy flight is adopted to update the stagnant or infeasible solutions to enhance the population diversity.Elitist selection technique is applied to save the promising solutions and further improve the convergence rate.Moreover,the adaptive penalty function is employed to deal with the constraints.Finally,the performance of HSCFA is demonstrated through the numerical examples with nonstructural masses and frequency constraints.The results show that HSCFA is an efficient and competitive tool for shape and size optimization problems with frequency constraints.展开更多
There are several ethical issues that have arisen in recent years due to the ubiquity of the Internet and the popularity of social media and community platforms.Among them is cyberbullying,which is defined as any viol...There are several ethical issues that have arisen in recent years due to the ubiquity of the Internet and the popularity of social media and community platforms.Among them is cyberbullying,which is defined as any violent intentional action that is repeatedly conducted by individuals or groups using online channels against victims who are not able to react effectively.An alarmingly high percentage of people,especially teenagers,have reported being cyberbullied in recent years.A variety of approaches have been developed to detect cyberbullying,but they require time-consuming feature extraction and selection processes.Moreover,no approach to date has examined the meanings of words and the semantics involved in cyberbullying.In past work,we proposed an algorithm called Cyberbullying Detection Based on Deep Learning(CDDL)to bridge this gap.It eliminates the need for feature engineering and generates better predictions than traditional approaches for detecting cyberbullying.This was accomplished by incorporating deep learning—specifically,a convolutional neural network(CNN)—into the detection process.Although this algorithm shows remarkable improvement in performance over traditional detection mechanisms,one problem with it persists:CDDL requires that many parameters(filters,kernels,pool size,and number of neurons)be set prior to classification.These parameters play a major role in the quality of predictions,but a method for finding a suitable combination of their values remains elusive.To address this issue,we propose an algorithm called firefly-CDDL that incorporates a firefly optimisation algorithm into CDDL to automate the hitherto-manual trial-and-error hyperparameter setting.The proposed method does not require features for its predictions and its detection of cyberbullying is fully automated.The firefly-CDDL outperformed prevalent methods for detecting cyberbullying in experiments and recorded an accuracy of 98%within acceptable polynomial time.展开更多
Watermarking of digital images is required in diversified applicationsranging from medical imaging to commercial images used over the web.Usually, the copyright information is embossed over the image in the form ofa l...Watermarking of digital images is required in diversified applicationsranging from medical imaging to commercial images used over the web.Usually, the copyright information is embossed over the image in the form ofa logo at the corner or diagonal text in the background. However, this formof visible watermarking is not suitable for a large class of applications. In allsuch cases, a hidden watermark is embedded inside the original image as proofof ownership. A large number of techniques and algorithms are proposedby researchers for invisible watermarking. In this paper, we focus on issuesthat are critical for security aspects in the most common domains like digitalphotography copyrighting, online image stores, etc. The requirements of thisclass of application include robustness (resistance to attack), blindness (directextraction without original image), high embedding capacity, high Peak Signalto Noise Ratio (PSNR), and high Structural Similarity Matrix (SSIM). Mostof these requirements are conflicting, which means that an attempt to maximizeone requirement harms the other. In this paper, a blind type of imagewatermarking scheme is proposed using Lifting Wavelet Transform (LWT)as the baseline. Using this technique, custom binary watermarks in the formof a binary string can be embedded. Hu’s Invariant moments’ coefficientsare used as a key to extract the watermark. A Stochastic variant of theFirefly algorithm (FA) is used for the optimization of the technique. Undera prespecified size of embedding data, high PSNR and SSIM are obtainedusing the Stochastic Gradient variant of the Firefly technique. The simulationis done using Matrix Laboratory (MATLAB) tool and it is shown that theproposed technique outperforms the benchmark techniques of watermarkingconsidering PSNR and SSIM as quality metrics.展开更多
Fog computing is an emergent and powerful computing paradigm to serve latency-sensitive applications by executing internet of things(IoT)appli-cations in the proximity of the network.Fog computing offers computational...Fog computing is an emergent and powerful computing paradigm to serve latency-sensitive applications by executing internet of things(IoT)appli-cations in the proximity of the network.Fog computing offers computational and storage services between cloud and terminal devices.However,an efficient resource allocation to execute the IoT applications in a fog environment is still challenging due to limited resource availability and low delay requirement of services.A large number of heterogeneous shareable resources makes fog computing a complex environment.In the sight of these issues,this paper has proposed an efficient levy flight firefly-based resource allocation technique.The levy flight algorithm is a metaheuristic algorithm.It offers high efficiency and success rate because of its longer step length and fast convergence rate.Thus,it treats global optimization problems more efficiently and naturally.A system framework for fog computing is presented,followed by the proposed resource allocation scheme in the fog computing environment.Experimental evaluation and comparison with the firefly algorithm(FA),particle swarm optimization(PSO),genetic algorithm(GA)and hybrid algorithm using GA and PSO(GAPSO)have been conducted to validate the effectiveness and efficiency of the proposed algorithm.Simulation results show that the proposed algorithm performs efficient resource allocation and improves the quality of service(QoS).The proposed algorithm reduces average waiting time,average execution time,average turnaround time,processing cost and energy consumption and increases resource utilization and task success rate compared to FA,GAPSO,PSO and GA.展开更多
The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this work.The Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of fi...The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this work.The Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of fireflies.It has already proved its competence in various optimization prob-lems,but it suffers from slow convergence issues.To improve the convergence performance of FA,a new variant named EFA is proposed.The effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions,and simulation results show its superior performance compared to biogeography-based optimization(BBO),bat algorithm,artificial bee colony,and FA.As an application of this algorithm to real-world problems,EFA is also applied to optimize the CR system.CR is a revolutionary technique that uses a dynamic spectrum allocation strategy to solve the spectrum scarcity problem.However,it requires optimization to meet specific performance objectives.The results obtained by EFA in CR system optimization are compared with results in the literature of BBO,simulated annealing,and genetic algorithm.Statistical results further prove that the proposed algorithm is highly efficient and provides superior results.展开更多
The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and ideas.Many people share their views and ideas around the world through social media like Face...The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and ideas.Many people share their views and ideas around the world through social media like Facebook and Twitter.The goal of opinion mining,commonly referred to as sentiment analysis,is to categorise and forecast a target’s opinion.Depending on if they provide a positive or negative perspective on a given topic,text documents or sentences can be classified.When compared to sentiment analysis,text categorization may appear to be a simple process,but number of challenges have prompted numerous studies in this area.A feature selection-based classification algorithm in conjunction with the firefly with levy and multilayer perceptron(MLP)techniques has been proposed as a way to automate sentiment analysis(SA).In this study,online product reviews can be enhanced by integrating classification and feature election.The firefly(FF)algorithm was used to extract features from online product reviews,and a multi-layer perceptron was used to classify sentiment(MLP).The experiment employs two datasets,and the results are assessed using a variety of criteria.On account of these tests,it is possible to conclude that the FFL-MLP algorithm has the better classification performance for Canon(98%accuracy)and iPod(99%accuracy).展开更多
CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferrin...CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.展开更多
In Mobile ad hoc Networks(MANETs),the packet scheduling process is considered the major challenge because of error-prone connectivity among mobile nodes that introduces intolerable delay and insufficient throughput wi...In Mobile ad hoc Networks(MANETs),the packet scheduling process is considered the major challenge because of error-prone connectivity among mobile nodes that introduces intolerable delay and insufficient throughput with high packet loss.In this paper,a Modified Firefly Optimization Algorithm improved Fuzzy Scheduler-based Packet Scheduling(MFPA-FSPS)Mechanism is proposed for sustaining Quality of Service(QoS)in the network.This MFPA-FSPS mechanism included a Fuzzy-based priority scheduler by inheriting the merits of the Sugeno Fuzzy inference system that potentially and adaptively estimated packets’priority for guaranteeing optimal network performance.It further used the modified Firefly Optimization Algorithm to optimize the rules uti-lized by the fuzzy inference engine to achieve the potential packet scheduling pro-cess.This adoption of a fuzzy inference engine used dynamic optimization that guaranteed excellent scheduling of the necessitated packets at an appropriate time with minimized waiting time.The statistical validation of the proposed MFPA-FSPS conducted using a one-way Analysis of Variance(ANOVA)test confirmed its predominance over the benchmarked schemes used for investigation.展开更多
Night tourism often involves a large number of lighting facilities,which consume a large amount of energy.Therefore,one of the unique low energy consumption natural ecological tourism activities—firefly night tour ha...Night tourism often involves a large number of lighting facilities,which consume a large amount of energy.Therefore,one of the unique low energy consumption natural ecological tourism activities—firefly night tour has attracted attention and become an important breakthrough point for night tourism in tourist destinations.In this paper,Guangzhou firefly night tour project is taken as the research object.Based on the comprehensive economic,environmental,and socio-cultural benefits brought by the development of firefly night tour,the resources distribution,current development status,and existing problems of firefly night tour in Guangzhou are analyzed,and its high-quality development paths are proposed from three levels:government,industry,and tourist.The aim is to explore a new model for the economic development of Guangzhou night tour,boosting the transformation and upgrading of the night tourism economy,while also providing reference ideas and value for the development of night tourism economy in other tourist destinations.展开更多
Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis...Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis of the increasing data. The Firefly Algorithm (FA) is one of the bio-inspired algorithms and it is recently used to solve the clustering problems. In this paper, Hybrid F-Firefly algorithm is developed by combining the Fuzzy C-Means (FCM) with FA to improve the clustering accuracy with global optimum solution. The Hybrid F-Firefly algorithm is developed by incorporating FCM operator at the end of each iteration in FA algorithm. This proposed algorithm is designed to utilize the goodness of existing algorithm and to enhance the original FA algorithm by solving the shortcomings in the FCM algorithm like the trapping in local optima and sensitive to initial seed points. In this research work, the Hybrid F-Firefly algorithm is implemented and experimentally tested for various performance measures under six different benchmark datasets. From the experimental results, it is observed that the Hybrid F-Firefly algorithm significantly improves the intra-cluster distance when compared with the existing algorithms like K-means, FCM and FA algorithm.展开更多
Autonomous mobile robot navigation is one of the most emerging areas of research by using swarm intelligence. Path planning and obstacle avoidance are most researched current topics like navigational challenges for mo...Autonomous mobile robot navigation is one of the most emerging areas of research by using swarm intelligence. Path planning and obstacle avoidance are most researched current topics like navigational challenges for mobile robot. The paper presents application and implementation of Firefly Algorithm(FA)for Mobile Robot Navigation(MRN) in uncertain environment. The uncertainty is defined over the changing environmental condition from static to dynamic. The attraction of one firefly towards the other firefly due to variation of their brightness is the key concept of the proposed study. The proposed controller efficiently explores the environment and improves the global search in less number of iterations and hence it can be easily implemented for real time obstacle avoidance especially for dynamic environment. It solves the challenges of navigation, minimizes the computational calculations, and avoids random moving of fireflies. The performance of proposed controller is better in terms of path optimality when compared to other intelligent navigational approaches.展开更多
This paper introduces a new approach of firefly algorithm based on opposition-based learning(OBFA) to enhance the global search ability of the original algorithm. The new algorithm employs opposition based learning co...This paper introduces a new approach of firefly algorithm based on opposition-based learning(OBFA) to enhance the global search ability of the original algorithm. The new algorithm employs opposition based learning concept to generate initial population and also updating agents' positions. The proposed OBFA is applied for minimization of the factor of safety and search for critical failure surface in slope stability analysis. The numerical experiments demonstrate the effectiveness and robustness of the new algorithm.展开更多
Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system ...Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control,automatic generation control(AGC) plays a crucial role. In this paper, multi-area(Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative(PID) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm(FFA). The experimental results demonstrated the comparison of the proposed system performance(FFA-PID)with optimized PID controller based genetic algorithm(GAPID) and particle swarm optimization(PSO) technique(PSOPID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error(ITAE) cost function with one percent step load perturbation(1 % SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.展开更多
Wireless Sensor Networks(WSNs)comprises low power devices that are randomly distributed in a geographically isolated region.The energy consumption of nodes is an essential factor to be considered.Therefore,an improved...Wireless Sensor Networks(WSNs)comprises low power devices that are randomly distributed in a geographically isolated region.The energy consumption of nodes is an essential factor to be considered.Therefore,an improved energy management technique is designed in this investigation to reduce its consumption and to enhance the network’s lifetime.This can be attained by balancing energy clusters using a meta-heuristic Firefly algorithm model for network communication.This improved technique is based on the cluster head selection technique with measurement of the tour length of fireflies.Time Division Multiple Access(TDMA)scheduler is also improved with the characteristics/behavior of fireflies and also executed.At last,the development approach shows the progression of the network lifetime,the total number of selected Cluster Heads(CH),the energy consumed by nodes,and the number of packets transmitted.This approach is compared with Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR)and Low Energy Adaptive Clustering Hierarchy(LEAH)protocols.Simulation is performed in MATLAB with the numerical outcomes showing the efficiency of the proposed approach.The energy consumption of sensor nodes is reduced by about 50%and increases the lifetime of nodes by 78%more than AODV,DSR and LEACH protocols.The parameters such as cluster formation,end to end delay,percentage of nodes alive and packet delivery ratio,are also evaluated...The anticipated method shows better trade-off in contrast to existing techniques.展开更多
To achieve optimal configuration of switching devices in a power distribution system,this paper proposes a repulsive firefly algorithm-based optimal switching device placement method.In this method,the influence of te...To achieve optimal configuration of switching devices in a power distribution system,this paper proposes a repulsive firefly algorithm-based optimal switching device placement method.In this method,the influence of territorial repulsion during firefly courtship is considered.The algorithm is practically applied to optimize the position and quantity of switching devices,while avoiding its convergence to the local optimal solution.The experimental simulation results have showed that the proposed repulsive firefly algorithm is feasible and effective,with satisfying global search capability and convergence speed,holding potential applications in setting value calculation of relay protection and distribution network automation control.展开更多
Software development effort estimation is considered a fundamental task for software development life cycle as well as for managing project cost, time and quality. Therefore, accurate estimation is a substantial facto...Software development effort estimation is considered a fundamental task for software development life cycle as well as for managing project cost, time and quality. Therefore, accurate estimation is a substantial factor in projects success and reducing the risks. In recent years, software effort estimation has received a considerable amount of attention from researchers?and became a challenge for software industry. In the last two decades, many researchers and practitioners proposed statistical and machine learning-based models for software effort estimation. In this work, Firefly Algorithm is proposed as a metaheuristic optimization method for optimizing the parameters of three COCOMO-based models. These models include the basic COCOMO model and other two models proposed in the literature as extensions of the basic COCOMO model. The developed estimation models are evaluated using different evaluation metrics. Experimental results show high accuracy and significant error minimization of Firefly Algorithm over other metaheuristic optimization algorithms including Genetic Algorithms and Particle Swarm Optimization.展开更多
A simple and rapid expression and purification method of recombinant firefly luciferase was developed for bacteria detection. A modified luciferase gene from North American firefly Photinus pyralis was cloned into pET...A simple and rapid expression and purification method of recombinant firefly luciferase was developed for bacteria detection. A modified luciferase gene from North American firefly Photinus pyralis was cloned into pET28a expression vector and the recombinant protein was produced in Escherichia coli BL21. The recombinant luciferase,equipped with a polyhistidine affinity tag,was purified by immobilized metal ion affinity chromatography (IMAC). The approach generated an abundant expression and an efficient purification of a recombinant luciferase with final yield 1.995mg/L of cell culture. Experiments on the recombinant luciferase also showed that the relative light units (RUL) of the enzyme were 5.8×108,and the specific activity was 2.9×1010 RLU/mg. By applying adenosine triphosphate (ATP) bioluminescence to detection of the coin bacteria using the recombinant protein,the ATP content of bacteria was 9.48×10-16mol/mL,and was identical to the bacteria counts (4500CFU/mL) in order of magnitude. Taken together,our results provided a simple and efficacious method of the preparation of recombinant luciferase,which could be applied in the determination of bacteria via ATP bioluminescence.展开更多
基金funded by University Grant Commission with UGC-Ref.No.:3364/(NET-JUNE 2015).
文摘This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly.It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function.It works for 3-tier architecture,including cloudlet and public cloud.As cloudlets have limited resources,fuzzy logic is used for cloudlet selection using capacity and waiting time as input.Fuzzy provides human-like decisions without using any mathematical model.Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed.It balances the load on cloud and cloudlet while minimizing makespan and execution time.However,it may trap in local optimum;levy flight can handle it.Hybridization of fuzzy fireflywith levy flight is a novel technique that provides reduced makespan,execution time,and Degree of imbalance while balancing the load.Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration(NASA)and Clarknet datasets.Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker(ACOQDM),Distributed Scheduling Optimization Algorithm(DSOA),andUtility-based Firefly Algorithm(UFA)when compared in terms of makespan,Degree of imbalance,and Figure of Merit.
基金supported by the NationalNatural Science Foundation of China(No.11672098).
文摘Shape and size optimization with frequency constraints is a highly nonlinear problem withmixed design variables,non-convex search space,and multiple local optima.Therefore,a hybrid sine cosine firefly algorithm(HSCFA)is proposed to acquire more accurate solutions with less finite element analysis.The full attraction model of firefly algorithm(FA)is analyzed,and the factors that affect its computational efficiency and accuracy are revealed.A modified FA with simplified attraction model and adaptive parameter of sine cosine algorithm(SCA)is proposed to reduce the computational complexity and enhance the convergence rate.Then,the population is classified,and different populations are updated by modified FA and SCA respectively.Besides,the random search strategy based on Lévy flight is adopted to update the stagnant or infeasible solutions to enhance the population diversity.Elitist selection technique is applied to save the promising solutions and further improve the convergence rate.Moreover,the adaptive penalty function is employed to deal with the constraints.Finally,the performance of HSCFA is demonstrated through the numerical examples with nonstructural masses and frequency constraints.The results show that HSCFA is an efficient and competitive tool for shape and size optimization problems with frequency constraints.
文摘There are several ethical issues that have arisen in recent years due to the ubiquity of the Internet and the popularity of social media and community platforms.Among them is cyberbullying,which is defined as any violent intentional action that is repeatedly conducted by individuals or groups using online channels against victims who are not able to react effectively.An alarmingly high percentage of people,especially teenagers,have reported being cyberbullied in recent years.A variety of approaches have been developed to detect cyberbullying,but they require time-consuming feature extraction and selection processes.Moreover,no approach to date has examined the meanings of words and the semantics involved in cyberbullying.In past work,we proposed an algorithm called Cyberbullying Detection Based on Deep Learning(CDDL)to bridge this gap.It eliminates the need for feature engineering and generates better predictions than traditional approaches for detecting cyberbullying.This was accomplished by incorporating deep learning—specifically,a convolutional neural network(CNN)—into the detection process.Although this algorithm shows remarkable improvement in performance over traditional detection mechanisms,one problem with it persists:CDDL requires that many parameters(filters,kernels,pool size,and number of neurons)be set prior to classification.These parameters play a major role in the quality of predictions,but a method for finding a suitable combination of their values remains elusive.To address this issue,we propose an algorithm called firefly-CDDL that incorporates a firefly optimisation algorithm into CDDL to automate the hitherto-manual trial-and-error hyperparameter setting.The proposed method does not require features for its predictions and its detection of cyberbullying is fully automated.The firefly-CDDL outperformed prevalent methods for detecting cyberbullying in experiments and recorded an accuracy of 98%within acceptable polynomial time.
基金funded by Princess Nourah Bint Abdulrahman University Researchers Supporting Project Number (PNURSP2022R235)Princess Nourah Bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Watermarking of digital images is required in diversified applicationsranging from medical imaging to commercial images used over the web.Usually, the copyright information is embossed over the image in the form ofa logo at the corner or diagonal text in the background. However, this formof visible watermarking is not suitable for a large class of applications. In allsuch cases, a hidden watermark is embedded inside the original image as proofof ownership. A large number of techniques and algorithms are proposedby researchers for invisible watermarking. In this paper, we focus on issuesthat are critical for security aspects in the most common domains like digitalphotography copyrighting, online image stores, etc. The requirements of thisclass of application include robustness (resistance to attack), blindness (directextraction without original image), high embedding capacity, high Peak Signalto Noise Ratio (PSNR), and high Structural Similarity Matrix (SSIM). Mostof these requirements are conflicting, which means that an attempt to maximizeone requirement harms the other. In this paper, a blind type of imagewatermarking scheme is proposed using Lifting Wavelet Transform (LWT)as the baseline. Using this technique, custom binary watermarks in the formof a binary string can be embedded. Hu’s Invariant moments’ coefficientsare used as a key to extract the watermark. A Stochastic variant of theFirefly algorithm (FA) is used for the optimization of the technique. Undera prespecified size of embedding data, high PSNR and SSIM are obtainedusing the Stochastic Gradient variant of the Firefly technique. The simulationis done using Matrix Laboratory (MATLAB) tool and it is shown that theproposed technique outperforms the benchmark techniques of watermarkingconsidering PSNR and SSIM as quality metrics.
文摘Fog computing is an emergent and powerful computing paradigm to serve latency-sensitive applications by executing internet of things(IoT)appli-cations in the proximity of the network.Fog computing offers computational and storage services between cloud and terminal devices.However,an efficient resource allocation to execute the IoT applications in a fog environment is still challenging due to limited resource availability and low delay requirement of services.A large number of heterogeneous shareable resources makes fog computing a complex environment.In the sight of these issues,this paper has proposed an efficient levy flight firefly-based resource allocation technique.The levy flight algorithm is a metaheuristic algorithm.It offers high efficiency and success rate because of its longer step length and fast convergence rate.Thus,it treats global optimization problems more efficiently and naturally.A system framework for fog computing is presented,followed by the proposed resource allocation scheme in the fog computing environment.Experimental evaluation and comparison with the firefly algorithm(FA),particle swarm optimization(PSO),genetic algorithm(GA)and hybrid algorithm using GA and PSO(GAPSO)have been conducted to validate the effectiveness and efficiency of the proposed algorithm.Simulation results show that the proposed algorithm performs efficient resource allocation and improves the quality of service(QoS).The proposed algorithm reduces average waiting time,average execution time,average turnaround time,processing cost and energy consumption and increases resource utilization and task success rate compared to FA,GAPSO,PSO and GA.
基金funded by King Saud University,Riyadh,Saudi Arabia.Researchers Supporting Proiect Number(RSP2023R167)King Saud University,Riyadh,Saudi Arabia.
文摘The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this work.The Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of fireflies.It has already proved its competence in various optimization prob-lems,but it suffers from slow convergence issues.To improve the convergence performance of FA,a new variant named EFA is proposed.The effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions,and simulation results show its superior performance compared to biogeography-based optimization(BBO),bat algorithm,artificial bee colony,and FA.As an application of this algorithm to real-world problems,EFA is also applied to optimize the CR system.CR is a revolutionary technique that uses a dynamic spectrum allocation strategy to solve the spectrum scarcity problem.However,it requires optimization to meet specific performance objectives.The results obtained by EFA in CR system optimization are compared with results in the literature of BBO,simulated annealing,and genetic algorithm.Statistical results further prove that the proposed algorithm is highly efficient and provides superior results.
文摘The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and ideas.Many people share their views and ideas around the world through social media like Facebook and Twitter.The goal of opinion mining,commonly referred to as sentiment analysis,is to categorise and forecast a target’s opinion.Depending on if they provide a positive or negative perspective on a given topic,text documents or sentences can be classified.When compared to sentiment analysis,text categorization may appear to be a simple process,but number of challenges have prompted numerous studies in this area.A feature selection-based classification algorithm in conjunction with the firefly with levy and multilayer perceptron(MLP)techniques has been proposed as a way to automate sentiment analysis(SA).In this study,online product reviews can be enhanced by integrating classification and feature election.The firefly(FF)algorithm was used to extract features from online product reviews,and a multi-layer perceptron was used to classify sentiment(MLP).The experiment employs two datasets,and the results are assessed using a variety of criteria.On account of these tests,it is possible to conclude that the FFL-MLP algorithm has the better classification performance for Canon(98%accuracy)and iPod(99%accuracy).
文摘CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.
文摘In Mobile ad hoc Networks(MANETs),the packet scheduling process is considered the major challenge because of error-prone connectivity among mobile nodes that introduces intolerable delay and insufficient throughput with high packet loss.In this paper,a Modified Firefly Optimization Algorithm improved Fuzzy Scheduler-based Packet Scheduling(MFPA-FSPS)Mechanism is proposed for sustaining Quality of Service(QoS)in the network.This MFPA-FSPS mechanism included a Fuzzy-based priority scheduler by inheriting the merits of the Sugeno Fuzzy inference system that potentially and adaptively estimated packets’priority for guaranteeing optimal network performance.It further used the modified Firefly Optimization Algorithm to optimize the rules uti-lized by the fuzzy inference engine to achieve the potential packet scheduling pro-cess.This adoption of a fuzzy inference engine used dynamic optimization that guaranteed excellent scheduling of the necessitated packets at an appropriate time with minimized waiting time.The statistical validation of the proposed MFPA-FSPS conducted using a one-way Analysis of Variance(ANOVA)test confirmed its predominance over the benchmarked schemes used for investigation.
基金the 14th Five-year Plan Project for the Development of Philosophy and Social Sciences of Guangzhou(2023GZGJ83)the 2021 General University Key Scientific Research Project of Department of Education of Guangdong Province(2021ZDZX4104)+1 种基金Project of Guangdong Provincial Department of Education(2021GDJG600,2021ZQXY45)the Guangdong Ploytechnic of Industry and Commerce Project(2023-SKJ-20).
文摘Night tourism often involves a large number of lighting facilities,which consume a large amount of energy.Therefore,one of the unique low energy consumption natural ecological tourism activities—firefly night tour has attracted attention and become an important breakthrough point for night tourism in tourist destinations.In this paper,Guangzhou firefly night tour project is taken as the research object.Based on the comprehensive economic,environmental,and socio-cultural benefits brought by the development of firefly night tour,the resources distribution,current development status,and existing problems of firefly night tour in Guangzhou are analyzed,and its high-quality development paths are proposed from three levels:government,industry,and tourist.The aim is to explore a new model for the economic development of Guangzhou night tour,boosting the transformation and upgrading of the night tourism economy,while also providing reference ideas and value for the development of night tourism economy in other tourist destinations.
文摘Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis of the increasing data. The Firefly Algorithm (FA) is one of the bio-inspired algorithms and it is recently used to solve the clustering problems. In this paper, Hybrid F-Firefly algorithm is developed by combining the Fuzzy C-Means (FCM) with FA to improve the clustering accuracy with global optimum solution. The Hybrid F-Firefly algorithm is developed by incorporating FCM operator at the end of each iteration in FA algorithm. This proposed algorithm is designed to utilize the goodness of existing algorithm and to enhance the original FA algorithm by solving the shortcomings in the FCM algorithm like the trapping in local optima and sensitive to initial seed points. In this research work, the Hybrid F-Firefly algorithm is implemented and experimentally tested for various performance measures under six different benchmark datasets. From the experimental results, it is observed that the Hybrid F-Firefly algorithm significantly improves the intra-cluster distance when compared with the existing algorithms like K-means, FCM and FA algorithm.
文摘Autonomous mobile robot navigation is one of the most emerging areas of research by using swarm intelligence. Path planning and obstacle avoidance are most researched current topics like navigational challenges for mobile robot. The paper presents application and implementation of Firefly Algorithm(FA)for Mobile Robot Navigation(MRN) in uncertain environment. The uncertainty is defined over the changing environmental condition from static to dynamic. The attraction of one firefly towards the other firefly due to variation of their brightness is the key concept of the proposed study. The proposed controller efficiently explores the environment and improves the global search in less number of iterations and hence it can be easily implemented for real time obstacle avoidance especially for dynamic environment. It solves the challenges of navigation, minimizes the computational calculations, and avoids random moving of fireflies. The performance of proposed controller is better in terms of path optimality when compared to other intelligent navigational approaches.
文摘This paper introduces a new approach of firefly algorithm based on opposition-based learning(OBFA) to enhance the global search ability of the original algorithm. The new algorithm employs opposition based learning concept to generate initial population and also updating agents' positions. The proposed OBFA is applied for minimization of the factor of safety and search for critical failure surface in slope stability analysis. The numerical experiments demonstrate the effectiveness and robustness of the new algorithm.
文摘Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control,automatic generation control(AGC) plays a crucial role. In this paper, multi-area(Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative(PID) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm(FFA). The experimental results demonstrated the comparison of the proposed system performance(FFA-PID)with optimized PID controller based genetic algorithm(GAPID) and particle swarm optimization(PSO) technique(PSOPID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error(ITAE) cost function with one percent step load perturbation(1 % SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.
文摘Wireless Sensor Networks(WSNs)comprises low power devices that are randomly distributed in a geographically isolated region.The energy consumption of nodes is an essential factor to be considered.Therefore,an improved energy management technique is designed in this investigation to reduce its consumption and to enhance the network’s lifetime.This can be attained by balancing energy clusters using a meta-heuristic Firefly algorithm model for network communication.This improved technique is based on the cluster head selection technique with measurement of the tour length of fireflies.Time Division Multiple Access(TDMA)scheduler is also improved with the characteristics/behavior of fireflies and also executed.At last,the development approach shows the progression of the network lifetime,the total number of selected Cluster Heads(CH),the energy consumed by nodes,and the number of packets transmitted.This approach is compared with Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR)and Low Energy Adaptive Clustering Hierarchy(LEAH)protocols.Simulation is performed in MATLAB with the numerical outcomes showing the efficiency of the proposed approach.The energy consumption of sensor nodes is reduced by about 50%and increases the lifetime of nodes by 78%more than AODV,DSR and LEACH protocols.The parameters such as cluster formation,end to end delay,percentage of nodes alive and packet delivery ratio,are also evaluated...The anticipated method shows better trade-off in contrast to existing techniques.
基金supported by the State Grid Science and Technology Project “Research on Technology System and Applications Scenarios of Artificial Intelligence in Power System” (No. SGZJ0000KXJS1800435)Key Technology Project of State Grid Shanghai Municipal Electric Power Company “Research and demonstration of Shanghai power grid reliability analysis platform”Key Technology Project of China Electric Power Research Institute “Research on setting calculation technology of power grid phase protection based on Artificial Intelligence” (JB83-19-007)
文摘To achieve optimal configuration of switching devices in a power distribution system,this paper proposes a repulsive firefly algorithm-based optimal switching device placement method.In this method,the influence of territorial repulsion during firefly courtship is considered.The algorithm is practically applied to optimize the position and quantity of switching devices,while avoiding its convergence to the local optimal solution.The experimental simulation results have showed that the proposed repulsive firefly algorithm is feasible and effective,with satisfying global search capability and convergence speed,holding potential applications in setting value calculation of relay protection and distribution network automation control.
文摘Software development effort estimation is considered a fundamental task for software development life cycle as well as for managing project cost, time and quality. Therefore, accurate estimation is a substantial factor in projects success and reducing the risks. In recent years, software effort estimation has received a considerable amount of attention from researchers?and became a challenge for software industry. In the last two decades, many researchers and practitioners proposed statistical and machine learning-based models for software effort estimation. In this work, Firefly Algorithm is proposed as a metaheuristic optimization method for optimizing the parameters of three COCOMO-based models. These models include the basic COCOMO model and other two models proposed in the literature as extensions of the basic COCOMO model. The developed estimation models are evaluated using different evaluation metrics. Experimental results show high accuracy and significant error minimization of Firefly Algorithm over other metaheuristic optimization algorithms including Genetic Algorithms and Particle Swarm Optimization.
基金supported by the National Key Technology R & D Program (2006BAK02A13)the National Basic Research Program of China (973Program, No.2007CB714507)
文摘A simple and rapid expression and purification method of recombinant firefly luciferase was developed for bacteria detection. A modified luciferase gene from North American firefly Photinus pyralis was cloned into pET28a expression vector and the recombinant protein was produced in Escherichia coli BL21. The recombinant luciferase,equipped with a polyhistidine affinity tag,was purified by immobilized metal ion affinity chromatography (IMAC). The approach generated an abundant expression and an efficient purification of a recombinant luciferase with final yield 1.995mg/L of cell culture. Experiments on the recombinant luciferase also showed that the relative light units (RUL) of the enzyme were 5.8×108,and the specific activity was 2.9×1010 RLU/mg. By applying adenosine triphosphate (ATP) bioluminescence to detection of the coin bacteria using the recombinant protein,the ATP content of bacteria was 9.48×10-16mol/mL,and was identical to the bacteria counts (4500CFU/mL) in order of magnitude. Taken together,our results provided a simple and efficacious method of the preparation of recombinant luciferase,which could be applied in the determination of bacteria via ATP bioluminescence.
