An operative constellation rate for smart safety units using Internet of Things

Manoharan, Hariprasath

doi:10.1002/cpe.6085

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…The early detection of demoralisation for medical therapy may profit greatly from simple measures to detect undesirable side effects in daily life. A reliable predictor of psychological health and alexithymia is the presence or absence of grief’s side effects in individuals with various mental illnesses [ 21 , 22 ]. It is particularly fascinating to look at people with PD, the most common mental illness in the US.…”

Section: Literature Surveymentioning

confidence: 99%

Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm

et al. 2022

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The majority of people in the modern biosphere struggle with depression as a result of the coronavirus pandemic’s impact, which has adversely impacted mental health without warning. Even though the majority of individuals are still protected, it is crucial to check for post-corona virus symptoms if someone is feeling a little lethargic. In order to identify the post-coronavirus symptoms and attacks that are present in the human body, the recommended approach is included. When a harmful virus spreads inside a human body, the post-diagnosis symptoms are considerably more dangerous, and if they are not recognised at an early stage, the risks will be increased. Additionally, if the post-symptoms are severe and go untreated, it might harm one’s mental health. In order to prevent someone from succumbing to depression, the technology of audio prediction is employed to recognise all the symptoms and potentially dangerous signs. Different choral characters are used to combine machine-learning algorithms to determine each person’s mental state. Design considerations are made for a separate device that detects audio attribute outputs in order to evaluate the effectiveness of the suggested technique; compared to the previous method, the performance metric is substantially better by roughly 67%.

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Section: Literature Surveymentioning

confidence: 99%

Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm

et al. 2022

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“…The integration of Internet of Things (IoT) systems with heterogeneous vehicular ad hoc networks (VANETs) has revitalized new capabilities in intelligent transportation systems (ITSs) [ 1 ]. Through machine-to-machine (M2M) communication protocols and standards, facilities such as RFID, WSN, GPS, etc., the IoT complements the evolution of ITS applications [ 2 ]. In fact, the future ITS is revolutionizing the transport system and autonomous mobility by the introduction of connected and autonomous vehicles (CAVs) with each vehicle having enormous capabilities in terms of storing, computing and networking (SCN).…”

Section: Introductionmentioning

confidence: 99%

SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks

Hussain

Azar

Ahmed

et al. 2023

Sensors

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With the emergence of delay- and energy-critical vehicular applications, forwarding sense-actuate data from vehicles to the cloud became practically infeasible. Therefore, a new computational model called Vehicular Fog Computing (VFC) was proposed. It offloads the computation workload from passenger devices (PDs) to transportation infrastructures such as roadside units (RSUs) and base stations (BSs), called static fog nodes. It can also exploit the underutilized computation resources of nearby vehicles that can act as vehicular fog nodes (VFNs) and provide delay- and energy-aware computing services. However, the capacity planning and dimensioning of VFC, which come under a class of facility location problems (FLPs), is a challenging issue. The complexity arises from the spatio-temporal dynamics of vehicular traffic, varying resource demand from PD applications, and the mobility of VFNs. This paper proposes a multi-objective optimization model to investigate the facility location in VFC networks. The solutions to this model generate optimal VFC topologies pertaining to an optimized trade-off (Pareto front) between the service delay and energy consumption. Thus, to solve this model, we propose a hybrid Evolutionary Multi-Objective (EMO) algorithm called Swarm Optimized Non-dominated sorting Genetic algorithm (SONG). It combines the convergence and search efficiency of two popular EMO algorithms: the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Speed-constrained Particle Swarm Optimization (SMPSO). First, we solve an example problem using the SONG algorithm to illustrate the delay–energy solution frontiers and plotted the corresponding layout topology. Subsequently, we evaluate the evolutionary performance of the SONG algorithm on real-world vehicular traces against three quality indicators: Hyper-Volume (HV), Inverted Generational Distance (IGD) and CPU delay gap. The empirical results show that SONG exhibits improved solution quality over the NSGA-II and SMPSO algorithms and hence can be utilized as a potential tool by the service providers for the planning and design of VFC networks.

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How accident causation theory can facilitate smart safety management: An application of the 24Model

Lyu

Gui

Wang

et al. 2022

Process Safety and Environmental Protection

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An operative constellation rate for smart safety units using Internet of Things

Cited by 3 publications

References 18 publications

Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm

Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm

SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks

How accident causation theory can facilitate smart safety management: An application of the 24Model

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