Residential sector is one of the energy-consuming districts of countries that causes CO_(2)emission in large extent.In this regard,this sector must be considered in energy policy making related to the reduction of emi...Residential sector is one of the energy-consuming districts of countries that causes CO_(2)emission in large extent.In this regard,this sector must be considered in energy policy making related to the reduction of emission of CO_(2)and other greenhouse gases.In the present work,CO_(2)emission related to the residential sector of three countries,including Indonesia,Thailand,and Vietnam in Southeast Asia,are discussed and modeled by employing Group Method of Data Handling(GMDH)and Multilayer Perceptron(MLP)neural networks as powerful intelligent methods.Prior to modeling,data related to the energy consumption of these countries are represented,discussed,and analyzed.Subsequently,to propose a model,electricity,natural gas,coal,and oil products consumptions are applied as inputs,and CO_(2)emission is considered as the model’s output.The obtained R^(2) values for the generated models based on MLP and GMDH are 0.9987 and 0.9985,respectively.Furthermore,values of the Average Absolute Relative Deviation(AARD)of the regressions using the mentioned techniques are around 4.56%and 5.53%,respectively.These values reveal significant exactness of the models proposed in this article;however,making use of MLP with the optimal architecture would lead to higher accuracy.展开更多
Energy conservation is a crucial issue to extend the lifetime of wireless sensor networks(WSNs)where the battery capacity and energy sources are very restricted.Intelligent energy-saving techniques can help designers ...Energy conservation is a crucial issue to extend the lifetime of wireless sensor networks(WSNs)where the battery capacity and energy sources are very restricted.Intelligent energy-saving techniques can help designers overcome this issue by reducing the number of selected sensors that report environmental measurements by eliminating all replicated and unrelated features.This paper suggests a Hybrid Sensor Selection(HSS)technique that combines filter-wrappermethod to acquire a rich-informational subset of sensors in a reasonable time.HSS aims to increase the lifetime of WSNs by using the optimal number of sensors.At the same time,HSS maintains the desired level of accuracy and manages sensor failures with the most suitable number of sensors without compromising the accuracy.The evaluation of the HSS technique has adopted four experiments by using four different datasets.These experiments show that HSS can extend the WSNs lifetime and increase the accuracy using a sufficient number of sensors without affecting theWSNfunctionality.Furthermore,to ensure HSS credibility and reliability,the proposed HSS technique has been compared to other corresponding methodologies and shows its superiority in energy conservation at premium accuracy measures.展开更多
In the event of a fire breaking out or in other complicated situations,a mobile computing solution combining the Internet of Things and wearable devices can actually assist tracking solutions for rescuing and evacuati...In the event of a fire breaking out or in other complicated situations,a mobile computing solution combining the Internet of Things and wearable devices can actually assist tracking solutions for rescuing and evacuating people in multistory structures.Thus,it is crucial to increase the positioning technology's accuracy.The sequential Monte Carlo(SMC)approach is used in various applications such as target tracking and intelligent surveillance,which rely on smartphone‐based inertial data sequences.However,the SMC method has intrinsic flaws,such as sample impoverishment and particle degeneracy.A novel SMC approach is presented,which is built on the weighted differential evolution(WDE)algorithm.Sequential Monte Carlo approaches start with random particle placements and arrives at the desired distribution with a slower variance reduction,like in a high‐dimensional space,such as a multistory structure.Weighted differential evolution is included before the resampling procedure to guarantee the appropriate variety of the particle set,prevent the usage of an inadequate number of valid samples,and preserve smartphone user position accuracy.The values of the smartphone‐based sensors and BLE‐beacons are set as input to the SMC,which aids in fast approximating the posterior distributions,to speed up the particle congregation process in the proposed SMC‐based WDE approach.Lastly,the robustness and efficacy of the suggested technique more accurately reflect the actual situation of smartphone users.According to simulation findings,the suggested approach provides improved location estimation with reduced localization error and quick convergence.The results confirm that the proposed optimal fusion‐based SMC‐WDE scheme performs 9.92%better in terms of MAPE,15.24%for the case of MAE,and 0.031%when evaluating based on the R2 Score.展开更多
The demand for autonomous motion control of unmanned aerial vehicles in air combat is boosted as taking the initiative in combat appears more and more crucial.Unmanned aerial vehicles inability to manoeuvre autonomous...The demand for autonomous motion control of unmanned aerial vehicles in air combat is boosted as taking the initiative in combat appears more and more crucial.Unmanned aerial vehicles inability to manoeuvre autonomously during air combat that features highly dynamic and uncertain manoeuvres of the enemy;however,limits their combat capabilities,which proves to be very challenging.To meet the challenge,this article proposes an autonomous manoeuvre decision model using an expert actor-based soft actor critic algorithm that reconstructs empirical replay buffer with expert experience.Specifically,the algorithm uses a small amount of expert experience to increase the diversity of the samples,which can largely improve the exploration and utilisation efficiency of deep reinforcement learning.And to simulate the complex battlefield environment,a one-toone air combat model is established and the concept of missile's attack region is introduced.The model enables the one-to-one air combat to be simulated under different initial battlefield situations.Simulation results show that the expert actor-based soft actor critic algorithm can find the most favourable policy for unmanned aerial vehicles to defeat the opponent faster,and converge more quickly,compared with the soft actor critic algorithm.展开更多
Epilepsy is considered as a serious brain disorder in which patients frequently experience seizures.The seizures are defined as the unexpected electrical changes in brain neural activity,which leads to unconsciousness...Epilepsy is considered as a serious brain disorder in which patients frequently experience seizures.The seizures are defined as the unexpected electrical changes in brain neural activity,which leads to unconsciousness.Existing researches made an intense effort for predicting the epileptic seizures using brain signal data.However,they faced difficulty in obtaining the patients'characteristics because the model's distribution turned to fake predictions,affecting the model's reliability.In addition,the existing prediction models have severe issues,such as overfitting and false positive rates.To overcome these existing issues,we propose a deep learning approach known as Deep dual‐patch attention mechanism(D^(2)PAM)for classifying the pre‐ictal signals of people with Epilepsy based on the brain signals.Deep neural network is integrated with D^(2)PAM,and it lowers the effect of differences between patients to predict ES.The multi‐network design enhances the trained model's generalisability and stability efficiently.Also,the proposed model for processing the brain signal is designed to transform the signals into data blocks,which is appropriate for pre‐ictal classification.The earlier warning of epilepsy with the proposed model obtains the auxiliary diagnosis.The data of real patients for the experiments provides the improved accuracy by D2PAM approximation compared to the existing techniques.To be more distinctive,the authors have analysed the performance of their work with five patients,and the accuracy comes out to be 95%,97%,99%,99%,and 99%respectively.Overall,the numerical results unveil that the proposed work outperforms the existing models.展开更多
This paper describes an efficient solution to parallelize softwareprogram instructions, regardless of the programming language in which theyare written. We solve the problem of the optimal distribution of a set ofinst...This paper describes an efficient solution to parallelize softwareprogram instructions, regardless of the programming language in which theyare written. We solve the problem of the optimal distribution of a set ofinstructions on available processors. We propose a genetic algorithm to parallelize computations, using evolution to search the solution space. The stagesof our proposed genetic algorithm are: The choice of the initial populationand its representation in chromosomes, the crossover, and the mutation operations customized to the problem being dealt with. In this paper, geneticalgorithms are applied to the entire search space of the parallelization ofthe program instructions problem. This problem is NP-complete, so thereare no polynomial algorithms that can scan the solution space and solve theproblem. The genetic algorithm-based method is general and it is simple andefficient to implement because it can be scaled to a larger or smaller number ofinstructions that must be parallelized. The parallelization technique proposedin this paper was developed in the C# programming language, and our resultsconfirm the effectiveness of our parallelization method. Experimental resultsobtained and presented for different working scenarios confirm the theoreticalresults, and they provide insight on how to improve the exploration of a searchspace that is too large to be searched exhaustively.展开更多
Detecting the anomalous entity in real-time network traffic is a popular area of research in recent times.Very few researches have focused on creating malware that fools the intrusion detection system and this paper f...Detecting the anomalous entity in real-time network traffic is a popular area of research in recent times.Very few researches have focused on creating malware that fools the intrusion detection system and this paper focuses on this topic.We are using Deep Convolutional Generative Adversarial Networks(DCGAN)to trick the malware classifier to believe it is a normal entity.In this work,a new dataset is created to fool the Artificial Intelligence(AI)based malware detectors,and it consists of different types of attacks such as Denial of Service(DoS),scan 11,scan 44,botnet,spam,User Datagram Portal(UDP)scan,and ssh scan.The discriminator used in the DCGAN discriminates two different attack classes(anomaly and synthetic)and one normal class.The model collapse,instability,and vanishing gradient issues associated with the DCGAN are overcome using the proposed hybrid Aquila optimizer-based Mine blast harmony search algorithm(AO-MBHS).This algorithm helps the generator to create realistic malware samples to be undetected by the discriminator.The performance of the proposed methodology is evaluated using different performance metrics such as training time,detection rate,F-Score,loss function,Accuracy,False alarm rate,etc.The superiority of the hybrid AO-MBHS based DCGAN model is noticed when the detection rate is changed to 0 after the retraining method to make the defensive technique hard to be noticed by the malware detection system.The support vector machines(SVM)is used as the malicious traffic detection application and its True positive rate(TPR)goes from 80%to 0%after retraining the proposed model which shows the efficiency of the proposed model in hiding the samples.展开更多
The identification of an effective network which can efficiently meet the service requirements of the target,while maintaining ultimate performance at an increased level is significant and challenging in a fully inter...The identification of an effective network which can efficiently meet the service requirements of the target,while maintaining ultimate performance at an increased level is significant and challenging in a fully interconnected wireless medium.The wrong selection can contribute to unwanted situations like frustrated users,slow service,traffic congestion issues,missed and/or interrupted calls,and wastefulness of precious network components.Conventional schemes estimate the handoff need and cause the network screening process by a single metric.The strategies are not effective enough because traffic characteristics,user expectations,network terminology and other essential device metrics are not taken into account.This article describes an intelligent computing technique based on Multiple-Criteria Decision-Making(MCDM)approach developed based on integrated Fuzzy AHP-TOPSIS which ensures flexible usability and maximizes the experience of end-users in miscellaneous wireless settings.In different components the handover need is assessed and the desired network is chosen.Further,fuzzy sets provide effective solutions to address decision making problems where experts counter uncertainty to make a decision.The proposed research endeavor will support designers and developers to identify,select and prioritize best attributes for ensuring flexible usability in miscellaneous wireless settings.The results of this research endeavor depict that this proposed computational procedure would be the most conversant mechanism for determining the usability and experience of end-users.展开更多
The vigorous expansion of renewable energy as a substitute for fossil energy is the predominant route of action to achieve worldwide carbon neutrality. However, clean energy supplies in multi-energy building districts...The vigorous expansion of renewable energy as a substitute for fossil energy is the predominant route of action to achieve worldwide carbon neutrality. However, clean energy supplies in multi-energy building districts are still at the preliminary stages for energy paradigm transitions. In particular, technologies and methodologies for large-scale renewable energy integrations are still not sufficiently sophisticated, in terms of intelligent control management. Artificial intelligent (AI) techniques powered renewable energy systems can learn from bioinspired lessons and provide power systems with intelligence. However, there are few in-depth dissections and deliberations on the roles of AI techniques for large-scale integrations of renewable energy and decarbonisation in multi-energy systems. This study summarizes the commonly used AI-related approaches and discusses their functional advantages when being applied in various renewable energy sectors, as well as their functional contribution to optimizing the operational control modalities of renewable energy and improving the overall operational effectiveness. This study also presents practical applications of various AI techniques in large-scale renewable energy integration systems, and analyzes their effectiveness through theoretical explanations and diverse case studies. In addition, this study introduces limitations and challenges associated with the large-scale renewable energy integrations for carbon neutrality transition using relevant AI techniques, and proposes further promising research perspectives and recommendations. This comprehensive review ignites advanced AI techniques for large-scale renewable integrations and provides valuable informational instructions and guidelines to different stakeholders (e.g., engineers, designers and scientists) for carbon neutrality transition.展开更多
基金This work was funded by the Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme No.FRGS/1/2020/TK0/UNIMAS/03/4.
文摘Residential sector is one of the energy-consuming districts of countries that causes CO_(2)emission in large extent.In this regard,this sector must be considered in energy policy making related to the reduction of emission of CO_(2)and other greenhouse gases.In the present work,CO_(2)emission related to the residential sector of three countries,including Indonesia,Thailand,and Vietnam in Southeast Asia,are discussed and modeled by employing Group Method of Data Handling(GMDH)and Multilayer Perceptron(MLP)neural networks as powerful intelligent methods.Prior to modeling,data related to the energy consumption of these countries are represented,discussed,and analyzed.Subsequently,to propose a model,electricity,natural gas,coal,and oil products consumptions are applied as inputs,and CO_(2)emission is considered as the model’s output.The obtained R^(2) values for the generated models based on MLP and GMDH are 0.9987 and 0.9985,respectively.Furthermore,values of the Average Absolute Relative Deviation(AARD)of the regressions using the mentioned techniques are around 4.56%and 5.53%,respectively.These values reveal significant exactness of the models proposed in this article;however,making use of MLP with the optimal architecture would lead to higher accuracy.
文摘Energy conservation is a crucial issue to extend the lifetime of wireless sensor networks(WSNs)where the battery capacity and energy sources are very restricted.Intelligent energy-saving techniques can help designers overcome this issue by reducing the number of selected sensors that report environmental measurements by eliminating all replicated and unrelated features.This paper suggests a Hybrid Sensor Selection(HSS)technique that combines filter-wrappermethod to acquire a rich-informational subset of sensors in a reasonable time.HSS aims to increase the lifetime of WSNs by using the optimal number of sensors.At the same time,HSS maintains the desired level of accuracy and manages sensor failures with the most suitable number of sensors without compromising the accuracy.The evaluation of the HSS technique has adopted four experiments by using four different datasets.These experiments show that HSS can extend the WSNs lifetime and increase the accuracy using a sufficient number of sensors without affecting theWSNfunctionality.Furthermore,to ensure HSS credibility and reliability,the proposed HSS technique has been compared to other corresponding methodologies and shows its superiority in energy conservation at premium accuracy measures.
基金supported this research through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(2019M3F2A1073387)supported by the Institute for Information Communications Technology Promotion(IITP)(NO.2022‐0‐00,980,Cooperative Intelligence Framework of Scene Perception for Autonomous IoT Device).
文摘In the event of a fire breaking out or in other complicated situations,a mobile computing solution combining the Internet of Things and wearable devices can actually assist tracking solutions for rescuing and evacuating people in multistory structures.Thus,it is crucial to increase the positioning technology's accuracy.The sequential Monte Carlo(SMC)approach is used in various applications such as target tracking and intelligent surveillance,which rely on smartphone‐based inertial data sequences.However,the SMC method has intrinsic flaws,such as sample impoverishment and particle degeneracy.A novel SMC approach is presented,which is built on the weighted differential evolution(WDE)algorithm.Sequential Monte Carlo approaches start with random particle placements and arrives at the desired distribution with a slower variance reduction,like in a high‐dimensional space,such as a multistory structure.Weighted differential evolution is included before the resampling procedure to guarantee the appropriate variety of the particle set,prevent the usage of an inadequate number of valid samples,and preserve smartphone user position accuracy.The values of the smartphone‐based sensors and BLE‐beacons are set as input to the SMC,which aids in fast approximating the posterior distributions,to speed up the particle congregation process in the proposed SMC‐based WDE approach.Lastly,the robustness and efficacy of the suggested technique more accurately reflect the actual situation of smartphone users.According to simulation findings,the suggested approach provides improved location estimation with reduced localization error and quick convergence.The results confirm that the proposed optimal fusion‐based SMC‐WDE scheme performs 9.92%better in terms of MAPE,15.24%for the case of MAE,and 0.031%when evaluating based on the R2 Score.
基金acknowledge the National Nature Science Foundation of China(Grant No.62003267)Fundamental Research Funds for the Central Universities(Grant No.G2022KY0602)+1 种基金Technology on Electromagnetic Space Operations and Applications Laboratory(Grant No.2022ZX0090)key core technology research plan of Xi'an(Grant No.21RGZN0016)to provide fund for conducting experiments.
文摘The demand for autonomous motion control of unmanned aerial vehicles in air combat is boosted as taking the initiative in combat appears more and more crucial.Unmanned aerial vehicles inability to manoeuvre autonomously during air combat that features highly dynamic and uncertain manoeuvres of the enemy;however,limits their combat capabilities,which proves to be very challenging.To meet the challenge,this article proposes an autonomous manoeuvre decision model using an expert actor-based soft actor critic algorithm that reconstructs empirical replay buffer with expert experience.Specifically,the algorithm uses a small amount of expert experience to increase the diversity of the samples,which can largely improve the exploration and utilisation efficiency of deep reinforcement learning.And to simulate the complex battlefield environment,a one-toone air combat model is established and the concept of missile's attack region is introduced.The model enables the one-to-one air combat to be simulated under different initial battlefield situations.Simulation results show that the expert actor-based soft actor critic algorithm can find the most favourable policy for unmanned aerial vehicles to defeat the opponent faster,and converge more quickly,compared with the soft actor critic algorithm.
文摘Epilepsy is considered as a serious brain disorder in which patients frequently experience seizures.The seizures are defined as the unexpected electrical changes in brain neural activity,which leads to unconsciousness.Existing researches made an intense effort for predicting the epileptic seizures using brain signal data.However,they faced difficulty in obtaining the patients'characteristics because the model's distribution turned to fake predictions,affecting the model's reliability.In addition,the existing prediction models have severe issues,such as overfitting and false positive rates.To overcome these existing issues,we propose a deep learning approach known as Deep dual‐patch attention mechanism(D^(2)PAM)for classifying the pre‐ictal signals of people with Epilepsy based on the brain signals.Deep neural network is integrated with D^(2)PAM,and it lowers the effect of differences between patients to predict ES.The multi‐network design enhances the trained model's generalisability and stability efficiently.Also,the proposed model for processing the brain signal is designed to transform the signals into data blocks,which is appropriate for pre‐ictal classification.The earlier warning of epilepsy with the proposed model obtains the auxiliary diagnosis.The data of real patients for the experiments provides the improved accuracy by D2PAM approximation compared to the existing techniques.To be more distinctive,the authors have analysed the performance of their work with five patients,and the accuracy comes out to be 95%,97%,99%,99%,and 99%respectively.Overall,the numerical results unveil that the proposed work outperforms the existing models.
文摘This paper describes an efficient solution to parallelize softwareprogram instructions, regardless of the programming language in which theyare written. We solve the problem of the optimal distribution of a set ofinstructions on available processors. We propose a genetic algorithm to parallelize computations, using evolution to search the solution space. The stagesof our proposed genetic algorithm are: The choice of the initial populationand its representation in chromosomes, the crossover, and the mutation operations customized to the problem being dealt with. In this paper, geneticalgorithms are applied to the entire search space of the parallelization ofthe program instructions problem. This problem is NP-complete, so thereare no polynomial algorithms that can scan the solution space and solve theproblem. The genetic algorithm-based method is general and it is simple andefficient to implement because it can be scaled to a larger or smaller number ofinstructions that must be parallelized. The parallelization technique proposedin this paper was developed in the C# programming language, and our resultsconfirm the effectiveness of our parallelization method. Experimental resultsobtained and presented for different working scenarios confirm the theoreticalresults, and they provide insight on how to improve the exploration of a searchspace that is too large to be searched exhaustively.
基金This project was funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under Grant No.RG-91-611-42.
文摘Detecting the anomalous entity in real-time network traffic is a popular area of research in recent times.Very few researches have focused on creating malware that fools the intrusion detection system and this paper focuses on this topic.We are using Deep Convolutional Generative Adversarial Networks(DCGAN)to trick the malware classifier to believe it is a normal entity.In this work,a new dataset is created to fool the Artificial Intelligence(AI)based malware detectors,and it consists of different types of attacks such as Denial of Service(DoS),scan 11,scan 44,botnet,spam,User Datagram Portal(UDP)scan,and ssh scan.The discriminator used in the DCGAN discriminates two different attack classes(anomaly and synthetic)and one normal class.The model collapse,instability,and vanishing gradient issues associated with the DCGAN are overcome using the proposed hybrid Aquila optimizer-based Mine blast harmony search algorithm(AO-MBHS).This algorithm helps the generator to create realistic malware samples to be undetected by the discriminator.The performance of the proposed methodology is evaluated using different performance metrics such as training time,detection rate,F-Score,loss function,Accuracy,False alarm rate,etc.The superiority of the hybrid AO-MBHS based DCGAN model is noticed when the detection rate is changed to 0 after the retraining method to make the defensive technique hard to be noticed by the malware detection system.The support vector machines(SVM)is used as the malicious traffic detection application and its True positive rate(TPR)goes from 80%to 0%after retraining the proposed model which shows the efficiency of the proposed model in hiding the samples.
基金This work was supported by the King Abdul Aziz City for Science and Technology(KACST),under Grant No.(14-INF727-10).
文摘The identification of an effective network which can efficiently meet the service requirements of the target,while maintaining ultimate performance at an increased level is significant and challenging in a fully interconnected wireless medium.The wrong selection can contribute to unwanted situations like frustrated users,slow service,traffic congestion issues,missed and/or interrupted calls,and wastefulness of precious network components.Conventional schemes estimate the handoff need and cause the network screening process by a single metric.The strategies are not effective enough because traffic characteristics,user expectations,network terminology and other essential device metrics are not taken into account.This article describes an intelligent computing technique based on Multiple-Criteria Decision-Making(MCDM)approach developed based on integrated Fuzzy AHP-TOPSIS which ensures flexible usability and maximizes the experience of end-users in miscellaneous wireless settings.In different components the handover need is assessed and the desired network is chosen.Further,fuzzy sets provide effective solutions to address decision making problems where experts counter uncertainty to make a decision.The proposed research endeavor will support designers and developers to identify,select and prioritize best attributes for ensuring flexible usability in miscellaneous wireless settings.The results of this research endeavor depict that this proposed computational procedure would be the most conversant mechanism for determining the usability and experience of end-users.
文摘The vigorous expansion of renewable energy as a substitute for fossil energy is the predominant route of action to achieve worldwide carbon neutrality. However, clean energy supplies in multi-energy building districts are still at the preliminary stages for energy paradigm transitions. In particular, technologies and methodologies for large-scale renewable energy integrations are still not sufficiently sophisticated, in terms of intelligent control management. Artificial intelligent (AI) techniques powered renewable energy systems can learn from bioinspired lessons and provide power systems with intelligence. However, there are few in-depth dissections and deliberations on the roles of AI techniques for large-scale integrations of renewable energy and decarbonisation in multi-energy systems. This study summarizes the commonly used AI-related approaches and discusses their functional advantages when being applied in various renewable energy sectors, as well as their functional contribution to optimizing the operational control modalities of renewable energy and improving the overall operational effectiveness. This study also presents practical applications of various AI techniques in large-scale renewable energy integration systems, and analyzes their effectiveness through theoretical explanations and diverse case studies. In addition, this study introduces limitations and challenges associated with the large-scale renewable energy integrations for carbon neutrality transition using relevant AI techniques, and proposes further promising research perspectives and recommendations. This comprehensive review ignites advanced AI techniques for large-scale renewable integrations and provides valuable informational instructions and guidelines to different stakeholders (e.g., engineers, designers and scientists) for carbon neutrality transition.
