The potential of cloud computing,an emerging concept to minimize the costs associated with computing has recently drawn the interest of a number of researchers.The fast advancements in cloud computing techniques led t...The potential of cloud computing,an emerging concept to minimize the costs associated with computing has recently drawn the interest of a number of researchers.The fast advancements in cloud computing techniques led to the amazing arrival of cloud services.But data security is a challenging issue for modern civilization.The main issues with cloud computing are cloud security as well as effective cloud distribution over the network.Increasing the privacy of data with encryption methods is the greatest approach,which has highly progressed in recent times.In this aspect,sanitization is also the process of confidentiality of data.The goal of this work is to present a deep learning-assisted data sanitization procedure for data security.The proposed data sanitization process involves the following steps:data preprocessing,optimal key generation,deep learning-assisted key fine-tuning,and Kronecker product.Here,the data preprocessing considers original data as well as the extracted statistical feature.Key generation is the subsequent process,for which,a self-adaptive Namib beetle optimization(SANBO)algorithm is developed in this research.Among the generated keys,appropriate keys are fine-tuned by the improved Deep Maxout classifier.Then,the Kronecker product is done in the sanitization process.Reversing the sanitization procedure will yield the original data during the data restoration phase.The study part notes that the suggested data sanitization technique guarantees cloud data security against malign attacks.Also,the analysis of proposed work in terms of restoration effectiveness and key sensitivity analysis is also done.展开更多
Advancements in Natural Language Processing and Deep Learning techniques have significantly pro-pelled the automation of Legal Judgment Prediction,achieving remarkable progress in legal research.Most of the existing r...Advancements in Natural Language Processing and Deep Learning techniques have significantly pro-pelled the automation of Legal Judgment Prediction,achieving remarkable progress in legal research.Most of the existing research works on Legal Judgment Prediction(LJP)use traditional optimization algorithms in deep learning techniques falling into local optimization.This research article focuses on using the modified Pelican Optimization method which mimics the collective behavior of Pelicans in the exploration and exploitation phase during cooperative food searching.Typically,the selection of search agents within a boundary is done randomly,which increases the time required to achieve global optimization.To address this,the proposed Chaotic Opposition Learning-based Pelican Optimization(COLPO)method incorporates the concept of Opposition-Based Learning combined with a chaotic cubic function,enabling deterministic selection of random numbers and reducing the number of iterations needed to reach global optimization.Also,the LJP approach in this work uses improved semantic similarity and entropy features to train a hybrid classifier combining Bi-GRU and Deep Maxout.The output scores are fused using improved score level fusion to boost prediction accuracy.The proposed COLPO method experiments with real-time Madras High Court criminal cases(Dataset 1)and the Supreme Court of India database(Dataset 2),and its performance is compared with nature-inspired algorithms such as Sparrow Search Algorithm(SSA),COOT,Spider Monkey Optimization(SMO),Pelican Optimization Algorithm(POA),as well as baseline classifier models and transformer neural networks.The results show that the proposed hybrid classifier with COLPO outperforms other cutting-edge LJP algorithms achieving 93.4%and 94.24%accuracy,respectively.展开更多
Accurate detection of small objects is critically important in high-stakes applications such as military reconnaissance and emergency rescue.However,low resolution,occlusion,and background interference make small obje...Accurate detection of small objects is critically important in high-stakes applications such as military reconnaissance and emergency rescue.However,low resolution,occlusion,and background interference make small object detection a complex and demanding task.One effective approach to overcome these issues is the integration of multimodal image data to enhance detection capabilities.This paper proposes a novel small object detection method that utilizes three types of multimodal image combinations,such as Hyperspectral-Multispectral(HSMS),Hyperspectral-Synthetic Aperture Radar(HS-SAR),and HS-SAR-Digital Surface Model(HS-SAR-DSM).The detection process is done by the proposed Jaccard Deep Q-Net(JDQN),which integrates the Jaccard similarity measure with a Deep Q-Network(DQN)using regression modeling.To produce the final output,a Deep Maxout Network(DMN)is employed to fuse the detection results obtained from each modality.The effectiveness of the proposed JDQN is validated using performance metrics,such as accuracy,Mean Squared Error(MSE),precision,and Root Mean Squared Error(RMSE).Experimental results demonstrate that the proposed JDQN method outperforms existing approaches,achieving the highest accuracy of 0.907,a precision of 0.904,the lowest normalized MSE of 0.279,and a normalized RMSE of 0.528.展开更多
文摘The potential of cloud computing,an emerging concept to minimize the costs associated with computing has recently drawn the interest of a number of researchers.The fast advancements in cloud computing techniques led to the amazing arrival of cloud services.But data security is a challenging issue for modern civilization.The main issues with cloud computing are cloud security as well as effective cloud distribution over the network.Increasing the privacy of data with encryption methods is the greatest approach,which has highly progressed in recent times.In this aspect,sanitization is also the process of confidentiality of data.The goal of this work is to present a deep learning-assisted data sanitization procedure for data security.The proposed data sanitization process involves the following steps:data preprocessing,optimal key generation,deep learning-assisted key fine-tuning,and Kronecker product.Here,the data preprocessing considers original data as well as the extracted statistical feature.Key generation is the subsequent process,for which,a self-adaptive Namib beetle optimization(SANBO)algorithm is developed in this research.Among the generated keys,appropriate keys are fine-tuned by the improved Deep Maxout classifier.Then,the Kronecker product is done in the sanitization process.Reversing the sanitization procedure will yield the original data during the data restoration phase.The study part notes that the suggested data sanitization technique guarantees cloud data security against malign attacks.Also,the analysis of proposed work in terms of restoration effectiveness and key sensitivity analysis is also done.
文摘Advancements in Natural Language Processing and Deep Learning techniques have significantly pro-pelled the automation of Legal Judgment Prediction,achieving remarkable progress in legal research.Most of the existing research works on Legal Judgment Prediction(LJP)use traditional optimization algorithms in deep learning techniques falling into local optimization.This research article focuses on using the modified Pelican Optimization method which mimics the collective behavior of Pelicans in the exploration and exploitation phase during cooperative food searching.Typically,the selection of search agents within a boundary is done randomly,which increases the time required to achieve global optimization.To address this,the proposed Chaotic Opposition Learning-based Pelican Optimization(COLPO)method incorporates the concept of Opposition-Based Learning combined with a chaotic cubic function,enabling deterministic selection of random numbers and reducing the number of iterations needed to reach global optimization.Also,the LJP approach in this work uses improved semantic similarity and entropy features to train a hybrid classifier combining Bi-GRU and Deep Maxout.The output scores are fused using improved score level fusion to boost prediction accuracy.The proposed COLPO method experiments with real-time Madras High Court criminal cases(Dataset 1)and the Supreme Court of India database(Dataset 2),and its performance is compared with nature-inspired algorithms such as Sparrow Search Algorithm(SSA),COOT,Spider Monkey Optimization(SMO),Pelican Optimization Algorithm(POA),as well as baseline classifier models and transformer neural networks.The results show that the proposed hybrid classifier with COLPO outperforms other cutting-edge LJP algorithms achieving 93.4%and 94.24%accuracy,respectively.
文摘Accurate detection of small objects is critically important in high-stakes applications such as military reconnaissance and emergency rescue.However,low resolution,occlusion,and background interference make small object detection a complex and demanding task.One effective approach to overcome these issues is the integration of multimodal image data to enhance detection capabilities.This paper proposes a novel small object detection method that utilizes three types of multimodal image combinations,such as Hyperspectral-Multispectral(HSMS),Hyperspectral-Synthetic Aperture Radar(HS-SAR),and HS-SAR-Digital Surface Model(HS-SAR-DSM).The detection process is done by the proposed Jaccard Deep Q-Net(JDQN),which integrates the Jaccard similarity measure with a Deep Q-Network(DQN)using regression modeling.To produce the final output,a Deep Maxout Network(DMN)is employed to fuse the detection results obtained from each modality.The effectiveness of the proposed JDQN is validated using performance metrics,such as accuracy,Mean Squared Error(MSE),precision,and Root Mean Squared Error(RMSE).Experimental results demonstrate that the proposed JDQN method outperforms existing approaches,achieving the highest accuracy of 0.907,a precision of 0.904,the lowest normalized MSE of 0.279,and a normalized RMSE of 0.528.
