Early flood detection and rescue using bioinformatic devices, internet of things (IOT) and Android application

Khan, Rijwan; Shabaz, Mohammad; Hussain, Sarfaraj; Ahmad, Feroz; Mishra, Pranav

doi:10.1108/wje-05-2021-0269

Cited by 18 publications

(9 citation statements)

References 36 publications

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“…Using the STRING web tool, 30 seed proteins and 34 derived partners were discovered to be significantly engaged in the protein-protein interaction structure [49]. Protein motifs have a specialized purpose in chemistry or biology [50,51]. Despite the evolution, these short signature sequences persist in various proteins involved in common tasks [52].…”

Section: Discussionmentioning

confidence: 99%

Structural and Functional Characterization at the Molecular Level of the MATE Gene Family in Wheat in Silico

Debnath

Mohanta

Perveen

et al. 2022

Contrast Media & Molecular Imaging

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A series of multidrug extransporters known as the multidrug and potentially toxic extrusion (MATE) genes are found in all living things and are crucial for the removal of heavy metal ions, metalloids, exogenous xenobiotics, endogenous secondary metabolites, and other toxic substances from the cells. However, there has only been a small amount of them in silico analysis of the MATE family of genes in plant species. In the current study, the MATE gene family was characterized in silico where two families and seven subfamilies based on their evolutionary relationships were proposed. Plant breeders may use TraesCS1D02G030400, TraesCS4B02G244400, and TraesCS1A02G029900 genes for marker-assisted or transgenic breeding to develop novel cultivars since these genes have been hypothesized from protein-protein interaction study to play a critical role in the transport of toxic chemicals across cells. The exon number varies from 01 to 14. One exon has TraesCS1A02G188100, TraesCS5B02G562500, TraesCS6A02G256400, and TraesCS6D02G384300 genes, while 14 exons have only two genes that are TraesCS6A02G418800 and TraesCS6D02G407900. Biological stress (infestations of disease) affects the expression of most of the MATE genes, with the gene TraesCS5D02G355500 having the highest expression level in the wheat expression browser tool. Using the Grain interpretation search engine tool, it is found that the vast bulk of MATE genes are voiced throughout biotic environmental stresses caused by disease pests, with the genotype TraesCS5B02G326600.1 from family 1 exhibiting the greatest level of expression throughout Fusarium head blight infection by Fusarium graminearum after 4 days of infection. The researchers constructed 39 ternary plots, each with a distinct degree of expression under biotic and abiotic stress settings, and observed that 44% of the triplets have imbalanced outputs (extreme values) due to their higher tissue specificity and increased intensity.

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Section: Discussionmentioning

confidence: 99%

Structural and Functional Characterization at the Molecular Level of the MATE Gene Family in Wheat in Silico

Debnath

Mohanta

Perveen

et al. 2022

Contrast Media & Molecular Imaging

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“…Logical regression [25] is one method among many others that may be used to predict a dependent variable based on a number of independent variables. In order to determine which classes had the greatest potential for failure, a multivariate model of resource consumption was used.…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence Enabled Effective Fault Prediction Techniques in Cloud Computing Environment for Improving Resource Optimization

Abro

Shafiq

et al. 2022

Scientific Programming

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The bulk of the fault tolerance techniques that are in use today laid their primary emphasis, in the event that a virtual machine fails, on the production of clones to replace it, rather than on the early prediction of the failure itself in advance. Several of the currently used techniques give migration priority over recovery in the event that a virtual machine (VM) fails. This is due to resource constraints and concerns with server availability. Examples of algorithms with a single objective include fault tolerance, migration prediction, and simply expecting failure. Another example is fault tolerance. In this research, we are aiming to determine the most effective strategy to transition from a system that is not operating well to one that does. It is essential to be able to predict the failure of a virtual machine in a timely manner due of issues such as squandered resources, energy, and cost. Since the beginning of cloud computing, there has been an issue with the dependability of virtual computers, often known as VMs. As an integral component of a fault tolerance system, preemptive measures are an absolute need in order to guarantee the continuation of service. As a consequence of this, it is vital to work toward enhancing and emphasizing the proactive failure prediction of virtual machines. The key motivations for this are decreased periods of downtime and enhanced scalability. A technique was utilized to transfer the resources that were predicted to fail from one virtual machine (VM) to another VM in a safe manner. Using the compression strategy reduced the amount of time needed to complete the migration, and resource utilization increased. This article provides artificial intelligence that enables effective fault prediction techniques in cloud computing to improve resource optimization.

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“…IoT technology has a wide range of potential applications in emergency management, from early warning systems to real-time monitoring and response. One of the key applications of IoT in emergency management is in the area of disaster detection and warning [27][28][29][30]. IoT sensors can be used to detect natural disasters such as earthquakes, hurricanes, and floods, and to issue real-time alerts to authorities and the public [31][32][33].…”

Section: Applications Of Iot In Emergency Managementmentioning

confidence: 99%

“…Flood monitoring [27,28,58]: During floods, sensors are used to monitor the water levels and detect changes in water pressure. This information is used to issue alerts and warnings to the public and emergency responders.…”

mentioning

confidence: 99%

From Sensors to Safety: Internet of Emergency Services (IoES) for Emergency Response and Disaster Management

Damaševičius

Bačanin

Misra

2023

JSAN

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The advancement in technology has led to the integration of internet-connected devices and systems into emergency management and response, known as the Internet of Emergency Services (IoES). This integration has the potential to revolutionize the way in which emergency services are provided, by allowing for real-time data collection and analysis, and improving coordination among various agencies involved in emergency response. This paper aims to explore the use of IoES in emergency response and disaster management, with an emphasis on the role of sensors and IoT devices in providing real-time information to emergency responders. We will also examine the challenges and opportunities associated with the implementation of IoES, and discuss the potential impact of this technology on public safety and crisis management. The integration of IoES into emergency management holds great promise for improving the speed and efficiency of emergency response, as well as enhancing the overall safety and well-being of citizens in emergency situations. However, it is important to understand the possible limitations and potential risks associated with this technology, in order to ensure its effective and responsible use. This paper aims to provide a comprehensive understanding of the Internet of Emergency Services and its implications for emergency response and disaster management.

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Early flood detection and rescue using bioinformatic devices, internet of things (IOT) and Android application

Cited by 18 publications

References 36 publications

Structural and Functional Characterization at the Molecular Level of the MATE Gene Family in Wheat in Silico

Structural and Functional Characterization at the Molecular Level of the MATE Gene Family in Wheat in Silico

Artificial Intelligence Enabled Effective Fault Prediction Techniques in Cloud Computing Environment for Improving Resource Optimization

From Sensors to Safety: Internet of Emergency Services (IoES) for Emergency Response and Disaster Management

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