The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accur...The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories.The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory(PPART)neural network for effectively managing the touring process of autonomous mobile robots in real-time.The proposed system is implemented using the AlphaBot platform,and the performance of the system is evaluated according to the obstacle prediction accuracy,path detection accuracy,time-lapse,tour length,and the overall accuracy of the system.The proposed system provide a very high obstacle prediction accuracy of 99.61%.Accordingly,the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.展开更多
Security is a critical issue in cloud computing(CC)because attackers can fabricate data by creating,copying,or deleting data with no user authorization.Most of the existing techniques make use of password-based authen...Security is a critical issue in cloud computing(CC)because attackers can fabricate data by creating,copying,or deleting data with no user authorization.Most of the existing techniques make use of password-based authentication for encrypting data.Password-based schemes suffer from several issues and can be easily compromised.This paper presents a new concept of hybrid metaheuristic optimization as an identity-based secure and optimal authentication(HMO-ISOA)scheme for CC environments.The HMOISOA technique makes use of iris and fingerprint biometrics.Initially,the HMO-ISOA technique involves a directional local ternary quantized extrema pattern–based feature extraction process to extract features from the iris and fingerprint.Next,the features are fed into the hybrid social spider using the dragon fly algorithm to determine the optimal solution.This optimal solution acts as a key for an advanced encryption standard to encrypt and decrypt the data.A central benefit of determining the optimal value in this way is that the intruder cannot determine this value.The attacker also cannot work out which specific part of the fingerprint and iris feature values are acted upon as a key for the AES technique.Finally,the encrypted data can be saved in the cloud using a cloud simulator.Experimental analysis was performed on five fingerprint and iris images for a man-in-the-middle attack.The simulation outcome validated that the presented HMO-ISOA model achieved better results compared with other existing methods.展开更多
The video inpainting process helps in several video editing and restoration processes like unwanted object removal,scratch or damage rebuilding,and retargeting.It intends to fill spatio-temporal holes with reasonable ...The video inpainting process helps in several video editing and restoration processes like unwanted object removal,scratch or damage rebuilding,and retargeting.It intends to fill spatio-temporal holes with reasonable content in the video.Inspite of the recent advancements of deep learning for image inpainting,it is challenging to outspread the techniques into the videos owing to the extra time dimensions.In this view,this paper presents an efficient video inpainting approach using beetle antenna search with deep belief network(VIA-BASDBN).The proposed VIA-BASDBN technique initially converts the videos into a set of frames and they are again split into a region of 5*5 blocks.In addition,the VIABASDBN technique involves the design of optimal DBN model,which receives input features from Local Binary Patterns(LBP)to categorize the blocks into smooth or structured regions.Furthermore,the weight vectors of the DBN model are optimally chosen by the use of BAS technique.Finally,the inpainting of the smooth and structured regions takes place using the mean and patch matching approaches respectively.The patch matching process depends upon the minimal Euclidean distance among the extracted SIFT features of the actual and references patches.In order to examine the effective outcome of the VIA-BASDBN technique,a series of simulations take place and the results denoted the promising performance.展开更多
文摘The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories.The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory(PPART)neural network for effectively managing the touring process of autonomous mobile robots in real-time.The proposed system is implemented using the AlphaBot platform,and the performance of the system is evaluated according to the obstacle prediction accuracy,path detection accuracy,time-lapse,tour length,and the overall accuracy of the system.The proposed system provide a very high obstacle prediction accuracy of 99.61%.Accordingly,the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.
文摘Security is a critical issue in cloud computing(CC)because attackers can fabricate data by creating,copying,or deleting data with no user authorization.Most of the existing techniques make use of password-based authentication for encrypting data.Password-based schemes suffer from several issues and can be easily compromised.This paper presents a new concept of hybrid metaheuristic optimization as an identity-based secure and optimal authentication(HMO-ISOA)scheme for CC environments.The HMOISOA technique makes use of iris and fingerprint biometrics.Initially,the HMO-ISOA technique involves a directional local ternary quantized extrema pattern–based feature extraction process to extract features from the iris and fingerprint.Next,the features are fed into the hybrid social spider using the dragon fly algorithm to determine the optimal solution.This optimal solution acts as a key for an advanced encryption standard to encrypt and decrypt the data.A central benefit of determining the optimal value in this way is that the intruder cannot determine this value.The attacker also cannot work out which specific part of the fingerprint and iris feature values are acted upon as a key for the AES technique.Finally,the encrypted data can be saved in the cloud using a cloud simulator.Experimental analysis was performed on five fingerprint and iris images for a man-in-the-middle attack.The simulation outcome validated that the presented HMO-ISOA model achieved better results compared with other existing methods.
文摘The video inpainting process helps in several video editing and restoration processes like unwanted object removal,scratch or damage rebuilding,and retargeting.It intends to fill spatio-temporal holes with reasonable content in the video.Inspite of the recent advancements of deep learning for image inpainting,it is challenging to outspread the techniques into the videos owing to the extra time dimensions.In this view,this paper presents an efficient video inpainting approach using beetle antenna search with deep belief network(VIA-BASDBN).The proposed VIA-BASDBN technique initially converts the videos into a set of frames and they are again split into a region of 5*5 blocks.In addition,the VIABASDBN technique involves the design of optimal DBN model,which receives input features from Local Binary Patterns(LBP)to categorize the blocks into smooth or structured regions.Furthermore,the weight vectors of the DBN model are optimally chosen by the use of BAS technique.Finally,the inpainting of the smooth and structured regions takes place using the mean and patch matching approaches respectively.The patch matching process depends upon the minimal Euclidean distance among the extracted SIFT features of the actual and references patches.In order to examine the effective outcome of the VIA-BASDBN technique,a series of simulations take place and the results denoted the promising performance.
