In recent years, academics have placed a high value on multi-modal emotion identification, as well as extensive research has been conducted in the areas of video, text, voice, and physical signal emotion detection. Th...
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Bot detection is considered a crucial security issue that is extensively analysed in various existingapproaches. Machine Learning is an efficient way of botnet attack detection. Bot detectionis the major issue faced b...
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Bot detection is considered a crucial security issue that is extensively analysed in various existingapproaches. Machine Learning is an efficient way of botnet attack detection. Bot detectionis the major issue faced by the existing system. This research concentrates on adopting a graphbasedfeature learning process to reduce feature dimensionality. The incoming samples arecorrectly classified and optimised using an Adaboost classifier with an improved grey wolfoptimiser (g-AGWO). The proposed IGWO optimisation approach is adopted to fulfil the multiconstraintissues related to bot detection and provide better local and global solutions (to satisfyexploration and exploitation). The extensive results show that the proposed g-AGWO model outperformsexisting approaches to reduce feature dimensionality, under-fitting/over-fitting andexecution time. The error rate prediction shows the feasibility of the given model to work over thechallenging environment. This model also works efficiently towards the unseen data to achievebetter generalization.
In recent decades, Cellular Networks (CN) have been used broadly in communication technologies. The most critical challenge in the CN was congestion control due to the distributed mobile environment. Some approaches, ...
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Sign language recognition is an important social issue to be addressed which can benefit the deaf and hard of hearing community by providing easier and faster communication. Some previous studies on sign language reco...
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Purpose: The rapid spread of COVID-19 has resulted in significant harm and impacted tens of millions of people globally. In order to prevent the transmission of the virus, individuals often wear masks as a protective ...
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Aflood is a significant damaging natural calamity that causes loss of life and *** work on the construction offlood prediction models intended to reduce risks,suggest policies,reduce mortality,and limit property damage c...
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Aflood is a significant damaging natural calamity that causes loss of life and *** work on the construction offlood prediction models intended to reduce risks,suggest policies,reduce mortality,and limit property damage caused byfl*** massive amount of data generated by social media platforms such as Twitter opens the door toflood *** of the real-time nature of Twitter data,some government agencies and authorities have used it to track natural catastrophe events in order to build a more rapid rescue ***,due to the shorter duration of Tweets,it is difficult to construct a perfect prediction model for determiningfl*** learning(ML)and deep learning(DL)approaches can be used to statistically developflood prediction *** the same time,the vast amount of Tweets necessitates the use of a big data analytics(BDA)tool forflood *** this regard,this work provides an optimal deep learning-basedflood forecasting model with big data analytics(ODLFF-BDA)based on Twitter *** suggested ODLFF-BDA technique intends to anticipate the existence offloods using tweets in a big data *** ODLFF-BDA technique comprises data pre-processing to convert the input tweets into a usable *** addition,a Bidirectional Encoder Representations from Transformers(BERT)model is used to generate emotive contextual embed-ding from ***,a gated recurrent unit(GRU)with a Multilayer Convolutional Neural Network(MLCNN)is used to extract local data and predict thefl***,an Equilibrium Optimizer(EO)is used tofine-tune the hyper-parameters of the GRU and MLCNN models in order to increase prediction *** memory usage is pull down lesser than 3.5 MB,if its compared with the other algorithm *** ODLFF-BDA technique’s performance was validated using a benchmark Kaggle dataset,and thefindings showed that it outperformed other recent approaches significantly.
When the ground communication base stations in the target area are severely destroyed,the deployment of Unmanned Aerial Vehicle(UAV)ad hoc networks can provide people with temporary communication ***,it is necessary t...
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Dear Editor,The distributed constraint optimization problems(DCOPs) [1]-[3]provide an efficient model for solving the cooperative problems of multi-agent systems, which has been successfully applied to model the real-...
Dear Editor,The distributed constraint optimization problems(DCOPs) [1]-[3]provide an efficient model for solving the cooperative problems of multi-agent systems, which has been successfully applied to model the real-world problems like the distributed scheduling [4], sensor network management [5], [6], multi-robot coordination [7], and smart grid [8]. However, DCOPs were not well suited to solve the problems with continuous variables and constraint cost in functional form, such as the target tracking sensor orientation [9], the air and ground cooperative surveillance [10], and the sensor network coverage [11].
Spiking Neural Networks (SNN) are biologically inspired networks working on the principle of communication triggered while crossing of threshold potentials. During the COVID-19 pandemic, immunity has been acquired by ...
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This paper proposes a Poor and Rich Squirrel Algorithm (PRSA)-based Deep Maxout network to find fraud data transactions in the credit card system. Initially, input transaction data is passed to the data transformation...
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