The escalating reliance on biometric systems for identity verification underscores the imperative for robust data protection mechanisms. Biometric authentication, leveraging unique biological and behavioral characteri...
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The escalating reliance on biometric systems for identity verification underscores the imperative for robust data protection mechanisms. Biometric authentication, leveraging unique biological and behavioral characteristics, offers unparalleled precision in individual identification. However, the integrity and confidentiality of biometric data remain paramount concerns, given its susceptibility to compromise. This research delineates the development and implementation of an innovative framework for cancellable biometrics, focusing on facial and fingerprint recognition. This study introduces a novel cancellable biometrics framework that integrates graph theory encryption with three-dimensional chaotic logistic mapping. The methodology encompasses a multifaceted approach: initially employing graph theory for the secure and efficient encryption of biometric data, subsequently enhanced by the complexity and unpredictability of three-dimensional chaotic logistic mapping. This dual-layered strategy ensures the robustness of the encryption, thereby significantly elevating the security of biometric data against unauthorized access and potential compromise. Thus, the resulting cancellable biometrics, characterized by the ability to transform biometric data into an adjustable representation, addresses critical challenges in biometric security. It allows for the revocation and reissuance of biometric credentials, thereby safeguarding the original biometric characteristics of individuals. This feature not only enhances user privacy and data security but also introduces a dynamic aspect to biometric authentication, facilitating adaptability across diverse systems and applications. Preliminary evaluations of the proposed framework demonstrate a marked improvement in the security of face and fingerprint recognition systems. Through the application of graph theory encryption, coupled with three-dimensional chaotic logistic mapping, our framework mitigates the risks associated with t
This systematic literature review delves into the dynamic realm of graphical passwords, focusing on the myriad security attacks they face and the diverse countermeasures devised to mitigate these threats. The core obj...
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This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest *** cascade multi-layer structure of deep forest classifi...
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This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest *** cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural ***,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated *** suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a *** includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and *** results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural *** considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.
Extracting large amounts of information and knowledge from a large database is a trivial task. Existing bulk item mining algorithms for an extensive database are systematic and mathematically expensive and cannot be u...
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This paper presents a new and efficient tree data structure for sorting and collision detection of disks in 2D based on a new tree-based data structure, called hexatree, which is introduced for the first time in this ...
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The challenge of bankruptcy prediction, critical for averting financial sector losses, is amplified by the prevalence of imbalanced datasets, which often skew prediction models. Addressing this, our study introduces t...
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Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of hete...
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Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.
In this paper, we show that applying adaptive methods directly to distributed minimax problems can result in non-convergence due to inconsistency in locally computed adaptive stepsizes. To address this challenge, we p...
The use of technology and information devices contributes to global warming. This issue has also become a concern for UN institutions, as stated in international environmental agreements, which aim to stabilize greenh...
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Video forgery is one of the most serious problems affecting the credibility and reliability of video content. Therefore, detecting video forgery presents a major challenge for researchers due to the diversity of forge...
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