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.
India with its linguistic diversity consists of 22 officially recognized languages. The multilingual nation is shifting towards digitization which has brought an upsurge in identification of handwritten digits in regi...
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As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static *** from the camera is sent to energy efficient sink to extract key-inform...
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As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static *** from the camera is sent to energy efficient sink to extract key-information out of *** applications range from health care monitoring to military *** a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O *** this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as *** innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the ***,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into *** propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual *** architecture is designed based on Mamdani’s fuzzy *** parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH *** inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of *** proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive *** algorithmbased machine learning optimizes the interpretability aspect of fuzzy *** results are obtained using *** result shows that the classification acc
The widespread adoption of autonomous vehicles has generated considerable interest in their autonomous operation,with path planning emerging as a critical ***,existing road infrastructure confronts challenges due to p...
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The widespread adoption of autonomous vehicles has generated considerable interest in their autonomous operation,with path planning emerging as a critical ***,existing road infrastructure confronts challenges due to prolonged use and insufficient *** research on autonomous vehicle navigation has focused on determining the trajectory with the shortest distance,while neglecting road construction information,leading to potential time and energy inefficiencies in real-world scenarios involving infrastructure *** address this issue,a digital twin-embedded multi-objective autonomous vehicle navigation is proposed under the condition of infrastructure *** authors propose an image processing algorithm that leverages captured images of the road construction environment to enable road extrac-tion and modelling of the autonomous vehicle ***,a wavelet neural network is developed to predict real-time traffic flow,considering its inherent ***,a multi-objective brainstorm optimisation(BSO)-based method for path planning is introduced,which optimises total time-cost and energy consumption objective *** ensure optimal trajectory planning during infrastructure con-struction,the algorithm incorporates a real-time updated digital twin throughout autonomous vehicle *** effectiveness and robustness of the proposed model are validated through simulation and comparative studies conducted in diverse scenarios involving road *** results highlight the improved performance and reli-ability of the autonomous vehicle system when equipped with the authors’approach,demonstrating its potential for enhancing efficiency and minimising disruptions caused by road infrastructure development.
The correction of Light Detection and Ranging(LiDAR)intensity data is of great significance for enhancing its application ***,traditional intensity correction methods based on Terrestrial Laser Scanning(TLS)technology...
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The correction of Light Detection and Ranging(LiDAR)intensity data is of great significance for enhancing its application ***,traditional intensity correction methods based on Terrestrial Laser Scanning(TLS)technology rely on manual site setup to collect intensity training data at different distances and incidence angles,which is noisy and limited in sample quantity,restricting the improvement of model *** overcome this limitation,this study proposes a fine-grained intensity correction modeling method based on Mobile Laser Scanning(MLS)*** method utilizes the continuous scanning characteristics of MLS technology to obtain dense point cloud intensity data at various distances and incidence ***,a fine-grained screening strategy is employed to accurately select distance-intensity and incidence angle-intensity modeling ***,based on these samples,a high-precision intensity correction model is established through polynomial fitting *** verify the effectiveness of the proposed method,comparative experiments were designed,and the MLS modeling method was validated against the traditional TLS modeling method on the same test *** results show that on Test Set 1,where the distance values vary widely(i.e.,0.1–3 m),the intensity consistency after correction using the MLS modeling method reached 7.692 times the original intensity,while the traditional TLS modeling method only increased to 4.630 times the original *** Test Set 2,where the incidence angle values vary widely(i.e.,0○–80○),the MLS modeling method,although with a relatively smaller advantage,still improved the intensity consistency to 3.937 times the original intensity,slightly better than the TLS modeling method’s 3.413 *** results demonstrate the significant advantage of the modeling method proposed in this study in enhancing the accuracy of intensity correction models.
Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several *** all autistic children remain undiagnosed before the age of *** problems affecting face features are oft...
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Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several *** all autistic children remain undiagnosed before the age of *** problems affecting face features are often associated with fundamental brain *** facial evolution of newborns with ASD is quite different from that of typically developing *** recognition is very significant to aid families and parents in superstition and *** facial features from typically developing children is an evident manner to detect children analyzed with ***,artificial intelligence(AI)significantly contributes to the emerging computer-aided diagnosis(CAD)of autism and to the evolving interactivemethods that aid in the treatment and reintegration of autistic *** study introduces an Ensemble of deep learning models based on the autism spectrum disorder detection in facial images(EDLM-ASDDFI)*** overarching goal of the EDLM-ASDDFI model is to recognize the difference between facial images of individuals with ASD and normal *** the EDLM-ASDDFI method,the primary level of data pre-processing is involved by Gabor filtering(GF).Besides,the EDLM-ASDDFI technique applies the MobileNetV2 model to learn complex features from the pre-processed *** the ASD detection process,the EDLM-ASDDFI method uses ensemble techniques for classification procedure that encompasses long short-term memory(LSTM),deep belief network(DBN),and hybrid kernel extreme learning machine(HKELM).Finally,the hyperparameter selection of the three deep learning(DL)models can be implemented by the design of the crested porcupine optimizer(CPO)*** extensive experiment was conducted to emphasize the improved ASD detection performance of the EDLM-ASDDFI *** simulation outcomes indicated that the EDLM-ASDDFI technique highlighted betterment over other existing models in terms of numerous performance measures.
Chronic renal disease is the term used to describe kidney function that gradually declines. The kidneys’ final byproduct of eliminating waste and surplus fluid from the bloodstream is urine. Abnormal accumulations of...
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Delay-sensitive applications are becoming more and more in demand as a result of the development of information systems and the expansion of communication in cloud computing technologies. Some of these requests will b...
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In today's era, the convergence of modern technology and healthcare has paved the path for novel diseases prediction and prevention technologies. Brain strokes, a major public health concern around the world, nece...
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This research work explores the effects of dry, liquid N2-based cryogenic cooling and cryogenic plus MQL hybrid strategy on surface roughness, rake surface temperature, principal cutting-edge temperature, auxiliary cu...
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