Data encryption which is associated with cryptography is necessary to prevent the compromise of Personally Identifying. Multi-level security is ensured by combining the Huffman code with certain cryptographic techniqu...
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This paper aims to implement an image classification system to detect Steroid and Non-steroid bodybuilders with RGB images using deep learning techniques. The purpose of creating a steroid use detection Deep Learning ...
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This paper advances temporal reasoning within dynamically changing high-dimensional noisy observations, focusing on a latent space that characterizes the nonlinear dynamics of objects in their environment. We introduc...
One of the universe's most mysterious things is still black holes, due to their enormous gravity. Black holes are cosmic giants with enormous gravitational force that have event horizons beyond which light cannot ...
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Non-intrusive load monitoring (NILM) is a key solution for the application of load identification to electrical measurements performed at the level of an aggregated input point. In recent years, a higher sampling rate...
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The term sentiment analysis deals with sentiment classification based on the review made by the user in a social *** sentiment classification accuracy is evaluated using various selection methods,especially those that d...
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The term sentiment analysis deals with sentiment classification based on the review made by the user in a social *** sentiment classification accuracy is evaluated using various selection methods,especially those that deal with algorithm *** this work,every sentiment received through user expressions is ranked in order to categorise sentiments as informative and *** order to do so,the work focus on Query Expansion Ranking(QER)algorithm that takes user text as input and process for sentiment analysis andfinally produces the results as informative or *** challenge is to convert non-informative into informative using the concepts of classifiers like Bayes multinomial,entropy modelling along with the traditional sentimental analysis algorithm like Support Vector Machine(SVM)and decision *** work also addresses simulated annealing along with QER to classify data based on sentiment *** the input volume is very fast,the work also addresses the concept of big data for information retrieval and *** result com-parison shows that the QER algorithm proved to be versatile when compared with the result of *** work uses Twitter user comments for evaluating senti-ment analysis.
Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable gro...
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Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar *** achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base *** regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)*** addition,data cleaning and data preprocessing were performed to the *** dataset used in this study includes 4 features and 50530 *** accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM *** evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.
In recent years, healthcare in smart cities is considered as significant to create a more resilient and well-informed healthcare ecosystem. The integration of cloud computing in healthcare industry has facilitated the...
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False data injection attack(FDIA)is an attack that affects the stability of grid cyber-physical system(GCPS)by evading the detecting mechanism of bad *** FDIA detection methods usually employ complex neural networkmod...
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False data injection attack(FDIA)is an attack that affects the stability of grid cyber-physical system(GCPS)by evading the detecting mechanism of bad *** FDIA detection methods usually employ complex neural networkmodels to detect FDIA ***,they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse,making it difficult for neural network models to obtain sufficient samples to construct a robust detection *** address this problem,this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge,which can effectively bypass the detectionmodel to threaten the power grid system.A generative adversarial network(GAN)framework is first constructed by combining residual networks(ResNet)with fully connected networks(FCN).Then,a sparse adversarial learning model is built by integrating the time-aligned data and normal data,which is used to learn the distribution characteristics between normal data and attack data through iterative ***,we introduce a Gaussian hybrid distributionmatrix by aggregating the network structure of attack data characteristics and normal data characteristics,which can connect and calculate FDIA data with normal ***,efficient FDIA attack samples can be sequentially generated through interactive adversarial *** simulation experiments are conducted with IEEE 14-bus and IEEE 118-bus system data,and the results demonstrate that the generated attack samples of the proposed model can present superior performance compared to state-of-the-art models in terms of attack strength,robustness,and covert capability.
In recent years, the prevalence of Age-Related Illnesses (ARL) has been increasing among older individuals, and early recognition and treatment will result in better living conditions. It is well known that Alzheimer&...
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