The Internet of Things(IoT)has taken the interconnected world by *** to their immense applicability,IoT devices are being scaled at exponential proportions ***,very little focus has been given to securing such *** the...
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The Internet of Things(IoT)has taken the interconnected world by *** to their immense applicability,IoT devices are being scaled at exponential proportions ***,very little focus has been given to securing such *** these devices are constrained in numerous aspects,it leaves network designers and administrators with no choice but to deploy them with minimal or no security at *** have seen distributed denial-ofservice attacks being raised using such devices during the infamous Mirai botnet attack in *** we propose a lightweight authentication protocol to provide proper access to such *** have considered several aspects while designing our authentication protocol,such as scalability,movement,user registration,device registration,*** define the architecture we used a three-layered model consisting of cloud,fog,and edge *** have also proposed several pre-existing cipher suites based on post-quantum cryptography for evaluation and *** also provide a fail-safe mechanism for a situation where an authenticating server might fail,and the deployed IoT devices can self-organize to keep providing services with no human *** find that our protocol works the fastest when using ring learning with *** prove the safety of our authentication protocol using the automated validation of Internet security protocols and applications *** conclusion,we propose a safe,hybrid,and fast authentication protocol for authenticating IoT devices in a fog computing environment.
Fog computing extends cloud capabilities to the network edge, aiding IoT and users. It mitigates cloud issues like latency and reliability. However, fog’s limited resources pose security vulnerabilities like data the...
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In this paper,a robust and consistent COVID-19 emergency decision-making approach is proposed based on q-rung linear diophantine fuzzy set(q-RLDFS),differential evolutionary(DE)optimization principles,and evidential r...
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In this paper,a robust and consistent COVID-19 emergency decision-making approach is proposed based on q-rung linear diophantine fuzzy set(q-RLDFS),differential evolutionary(DE)optimization principles,and evidential reasoning(ER)*** proposed approach uses q-RLDFS in order to represent the evaluating values of the alternatives corresponding to the *** optimization is used to obtain the optimal weights of the attributes,and ER methodology is used to compute the aggregated q-rung linear diophantine fuzzy values(q-RLDFVs)of each *** the score values of alternatives are computed based on the aggregated *** alternative with the maximum score value is selected as a better *** applicability of the proposed approach has been illustrated in COVID-19 emergency decision-making system and sustainable energy planning ***,we have validated the proposed approach with a numerical ***,a comparative study is provided with the existing models,where the proposed approach is found to be robust to perform better and consistent in uncertain environments.
In recent years, deep neural networks have achieved remarkable accuracy in computer vision tasks. With inference time being a crucial factor, particularly in dense prediction tasks such as semantic segmentation, knowl...
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Rank aggregation is the combination of several ranked lists from a set of candidates to achieve a better ranking by combining information from different sources. In feature selection problem, due to the heterogeneity ...
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From a medical perspective,the 12 leads of the heart in an electrocardiogram(ECG)signal have functional dependencies with each ***,all these leads report different aspects of an *** differences lie in the level of hig...
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From a medical perspective,the 12 leads of the heart in an electrocardiogram(ECG)signal have functional dependencies with each ***,all these leads report different aspects of an *** differences lie in the level of highlighting and displaying information about that *** example,although all leads show traces of atrial excitation,this function is more evident in lead II than in any other *** this article,a new model was proposed using ECG functional and structural dependencies between heart *** the prescreening stage,the ECG signals are segmented from the QRS point so that further analyzes can be performed on these segments in a more detailed *** mutual information indices were used to assess the relationship between *** order to calculate mutual information,the correlation between the 12 ECG leads has been *** output of this step is a matrix containing all mutual ***,to calculate the structural information of ECG signals,a capsule neural network was implemented to aid physicians in the automatic classification of cardiac *** architecture of this capsule neural network has been modified to perform the classification *** the experimental results section,the proposed model was used to classify arrhythmias in ECG signals from the Chapman *** evaluations showed that this model has a precision of 97.02%,recall of 96.13%,F1-score of 96.57%and accuracy of 97.38%,indicating acceptable performance compared to other state-of-the-art *** proposed method shows an average accuracy of 2%superiority over similar works.
The droop-free control adopted in microgrids has been designed to cope with global power-sharing goals,i.e.,sharing disturbance mitigation among all controllable assets to even their ***,limited by neighboring communi...
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The droop-free control adopted in microgrids has been designed to cope with global power-sharing goals,i.e.,sharing disturbance mitigation among all controllable assets to even their ***,limited by neighboring communication,the time-consuming peer-to-peer coordination of the droopfree control slows down the nodal convergence to global consensus,reducing the power-sharing efficiency as the number of nodes *** this end,this paper first proposes a local power-sharing droop-free control scheme to contain disturbances within nearby nodes,in order to reduce the number of nodes involved in the coordination and accelerate the convergence speed.A hybrid local-global power-sharing scheme is then put forward to leverage the merits of both schemes,which also enables the autonomous switching between local and global power-sharing modes according to the system *** guidance for key control parameter designs is derived via the optimal control methods,by optimizing the power-sharing distributions at the steady-state consensus as well as along the dynamic trajectory to *** system stability of the hybrid scheme is proved by the eigenvalue analysis and Lyapunov direct ***,simulation results validate that the proposed hybrid local-global power-sharing scheme performs stably against disturbances and achieves the expected control performance in local and global power-sharing modes as well as mode ***,compared with the classical global power-sharing scheme,the proposed scheme presents promising benefits in convergence speed and scalability.
This paper presents a novel, energy-efficient routing approach for underwater sensor networks in tsunami early warning. Our system utilizes sensor nodes equipped with piezoelectric energy harvesting to extend network ...
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This paper presents a novel, energy-efficient routing approach for underwater sensor networks in tsunami early warning. Our system utilizes sensor nodes equipped with piezoelectric energy harvesting to extend network lifetime and stability. Continuous sensing is replaced with a duty-cycled approach to conserve energy, where the ocean surface is divided into regions and sensor nodes are grouped. These groups become active at designated intervals, while others remain dormant. The system leverages satellite networks to complement the underwater sensor network, enabling collected data to reach the central hub of early warning systems. A statistical analysis assigns scores to potential routes based on their energy consumption, prioritizing low-energy paths. Probability theory is employed to calculate the minimum number of transmission paths needed to achieve a predetermined level of reliability. A well-established tsunami wave prediction system is used to select the most suitable next hop for data transmission to avoid interference with tsunami wave propagation. Simulation results demonstrate significant improvements in energy efficiency, end-to-end delay, sensor and relay node lifespan, and network stability compared to recent research. These achievements highlight the effectiveness of our proposed routing approach in achieving energy efficiency and reliable data transmission within a tsunami early warning system. IEEE
A multi-secret image sharing (MSIS) scheme facilitates the secure distribution of multiple images among a group of participants. Several MSIS schemes have been proposed with a (n, n) structure that encodes secret...
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In recent years, mental health issues have profoundly impacted individuals’ well-being, necessitating prompt identification and intervention. Existing approaches grapple with the complex nature of mental health, faci...
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In recent years, mental health issues have profoundly impacted individuals’ well-being, necessitating prompt identification and intervention. Existing approaches grapple with the complex nature of mental health, facing challenges like task interference, limited adaptability, and difficulty in capturing nuanced linguistic expressions indicative of various conditions. In response to these challenges, our research presents three novel models employing multi-task learning (MTL) to understand mental health behaviors comprehensively. These models encompass soft-parameter sharing-based long short-term memory with attention mechanism (SPS-LSTM-AM), SPS-based bidirectional gated neural networks with self-head attention mechanism (SPS-BiGRU-SAM), and SPS-based bidirectional neural network with multi-head attention mechanism (SPS-BNN-MHAM). Our models address diverse tasks, including detecting disorders such as bipolar disorder, insomnia, obsessive-compulsive disorder, and panic in psychiatric texts, alongside classifying suicide or non-suicide-related texts on social media as auxiliary tasks. Emotion detection in suicide notes, covering emotions of abuse, blame, and sorrow, serves as the main task. We observe significant performance enhancement in the primary task by incorporating auxiliary tasks. Advanced encoder-building techniques, including auto-regressive-based permutation and enhanced permutation language modeling, are recommended for effectively capturing mental health contexts’ subtleties, semantic nuances, and syntactic structures. We present the shared feature extractor called shared auto-regressive for language modeling (S-ARLM) to capture high-level representations that are useful across tasks. Additionally, we recommend soft-parameter sharing (SPS) subtypes-fully sharing, partial sharing, and independent layer-to minimize tight coupling and enhance adaptability. Our models exhibit outstanding performance across various datasets, achieving accuracies of 96.9%, 97.
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