This paper (in the absence of exogenous disturbances) proposes an approach that stabilizes nonlinear multiple time scale systems. First, a general class of nonlinear systems containing a finite number of subsystems is...
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Detecting driver drowsiness to prevent car accidents is extremely important, leading to the demand for dependable monitoring systems. Recent studies are concentrating on utilizing cutting-edge technologies, particular...
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Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)*** plays a vital role in infl...
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Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)*** plays a vital role in influencing crop *** wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are *** the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision *** proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as *** proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed *** parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.
Processing big data poses a significant challenge when transitioning from sequential to distributed code, primarily due to the extensive scale at which data is handled. This complexity arises from both syntax and sema...
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computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(DL)methods are more successful than other tra...
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computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(DL)methods are more successful than other traditional machine learning(ML)methods *** techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face *** this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is *** sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and *** review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.
This paper proposes a replacement algorithm for file caching in mobile edge computing (MEC) networks. While there are numerous schemes for file replacement, it remains a challenge to achieve good, robust, and predicta...
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This paper considers the task of estimating the principal eigenvector of a positive semi-definite matrix using the power method subjected to random row erasures at each iteration. This can be used to model application...
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Thyroid nodules are common and multiple diseases of the head and neck. Ultrasound examination is an important imaging method for the diagnosis of benign and malignant thyroid nodules. The extraction of TI -RADS standa...
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In the rapidly evolving cosmetics market, it is imperative to prioritize customer safety while offering personalized recommendations. This study introduces a hybrid recommendation system for cosmetics, using deep lear...
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The enormous volume of heterogeneous data fromvarious smart device-based applications has growingly increased a deeply interlaced cyber-physical *** order to deliver smart cloud services that require low latency with ...
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The enormous volume of heterogeneous data fromvarious smart device-based applications has growingly increased a deeply interlaced cyber-physical *** order to deliver smart cloud services that require low latency with strong computational processing capabilities,the Edge Intelligence System(EIS)idea is now being employed,which takes advantage of Artificial Intelligence(AI)and Edge Computing Technology(ECT).Thus,EIS presents a potential approach to enforcing future Intelligent Transportation Systems(ITS),particularly within a context of a Vehicular Network(VNets).However,the current EIS framework meets some issues and is conceivably vulnerable tomultiple adversarial attacks because the central aggregator server handles the entire ***,this paper introduces the concept of distributed edge intelligence,combining the advantages of Federated Learning(FL),Differential Privacy(DP),and blockchain to address the issues raised *** performing decentralized data management and storing transactions in immutable distributed ledger networks,the blockchain-assisted FL method improves user privacy and boosts traffic prediction ***,DP is utilized in defending the user’s private data from various threats and is given the authority to bolster the confidentiality of data-sharing *** model has been deployed in two strategies:First,DP-based FL to strengthen user privacy by masking the intermediate data during model ***,blockchain-based FL to effectively construct secure and decentralized traffic management in vehicular *** simulation results demonstrated that our framework yields several benefits for VNets privacy protection by forming a distributed EIS with privacy budget(ε)of 4.03,1.18,and 0.522,achieving model accuracy of 95.8%,93.78%,and 89.31%,respectively.
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