Classification of covid patient image dataset using modified deep convolutional neural network system

Alagarsamy, Manjunathan; Anbalagan, Karthikram; Thangavel, Yuvaraja; Sakkarai, Jeevitha; Jenopaul, P.; Kannadhasan, S.

doi:10.11591/eei.v11i4.3290

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…Because of the absence of significant conservation markers within their sequencing, automatic recognition of fRNAs using genomic patterns was not as reliable as recognition of protein-coding RNAs. Which were exceptionally trustworthy based on analytical and technological evaluations, it provided a computerized analysis of the genomes for T. brucei and L. braziliensis in the hopes of discovering a group of preserved ncRNAs.We showed that this technique could identify a significant variety of possible ncRNAs, both identified and unknown [10][11].We look at possible Premi RNAs within prospective ncRNAs and find that the occurrence of miRNA sequences maintained across T. brucei and L. braziliensis was very improbable. We also employ a new strategy for identifying shorter regulation RNA motifs in T.brucei genomes' 5' & 3' UTRs, which are homology-independent [12][13].…”

Section: Related Workmentioning

confidence: 97%

Genome Based Computational Technique to Identify the Functional RNA in Protozoan Parasites

Pandey,

Lalitha,

Prema

et al. 2023

Recent Trends in Data Science and Its Applications

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Operative RNAs, including non-coding RNAs (ncRNAs) and cis-acting RNA elements involved in posttranscriptional genome regulation, are found based on two distinct computerized examinations of Trypanosoma genomes. Based on compliance with comparable trypanosomatidLeishmaniabraziliensis, the expected possible ncRNAs are discovered in the first analysis. Several of the expected ncRNAs are novel categories having undetermined characteristics, such that predictions have a low estimated incorrect fraud threat. This research uncovered several mechanism regulatory motifs in the subsequent research we used to develop a classification that can distinguish between actions that aren't identical. The first genomic sequence analysis of trypanosomatidfRNAs helps focus the research on practical approaches and accelerates the discovery of those elements. These functionality predictions classifiers built upon cis-acting regulation regions may potentially be used to offer homology-independent annotating for the trypanosomatid genome when combined with existing approaches.

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Section: Related Workmentioning

confidence: 97%

Genome Based Computational Technique to Identify the Functional RNA in Protozoan Parasites

Pandey,

Lalitha,

Prema

et al. 2023

Recent Trends in Data Science and Its Applications

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“…The proposed intrusion detection system, which is based on a neural network model, manages healthcare information and ensures that healthcare providers' jobs are completed effectively. The proposed system effectively manages data for all clinical, financial, research lab, outpatient care, primary care, operating room, equipment, nursing, pharmacy, radiation oncology, pathology, and other departments of healthcare [21].…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Intrusion Detection System in Hospital Management Systems

Sharma,

Begum,

et al. 2023

Recent Trends in Data Science and Its Applications

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The technological transformation has helped to simplify formerly time-consuming tasks. In this study, we will look at neural network-based intrusion detection systems in hospital management systems. This paper presents an intrusion-identifying system based on NN modeling. A hospital managing system (HMS) is a computer-based system that assists in controlling the healthcare data in the way of the effective ending of healthcare providers' jobs. An intrusion detection system (IDS) is software that identifies malicious activity on a network. A neural network (NN) is a set of algorithms that aims to determine interactions in a data set using a process that resembles how the human brain works, and the HMS is functioned in this proposed system using these NNs. In this research, K-Means algorithm is implemented to identify the intrusion in the hospital management System. KeywordsAshraf, Eman, et al. ( 2022) and Sivakumar P (2015) proposed FIDChain IDS using lightweight Artificial Neural Network in learning means to guarantee to care of health information secured in managing preservation with the advancements of blockchain platform that enable the ledge that is shared for gathering the weights in local and transmitting the developed weights in global after taking an average, that restricts poisoning attacks and delivers complete transparency at the same time immutability in a distributed system according to the negligibility. Laxminarayana, Nikhil, et al. (2022), Karnan B et al (2022), and Latchoumi TP et al (2022) investigated the IDS is trained using NNs and the concepts of quantum physics. It is suggested to use a hybrid classical-quantum neural architecture with a quantum-aided activation -Hospital Management System (HMS), Intrusion Detection System (IDS), Neural Network (NN), K-Means Algorithm

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“…The system comprises of a GSM module and Arduino controller, a set of sensors, and a mobile app or website that can be used to monitor and records the data. The wireless link between the sensors and the mobile application is created via the GSM module [15][16]. The mobile application receives the processed data from the Arduino controller after it has been sent from the sensors.…”

Section: Introductionmentioning

confidence: 99%

IoT Based Patient’s Smart Healthcare Monitoring and Recording Using GSM Module

Yuvaraj¹,

Naveen²,

N³

et al. 2023

E3S Web Conf.

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An IoT based smart healthcare monitoring system is presented in this study. A local database, sensors, a microcontroller, and a GSM module make up the suggested system. Here, we’re using an Arduino Uno to attach sensors, which then sense the environment and record data that is sent over the internet using the GSM protocol. The health of patients in isolated or rural locations without access to medical services can be tracked using this project. The importance of offering patients who were anticipated to decrease travel and direct contact with others remote patient monitoring services became even more clear when the epidemic struck. When there is an abnormal activity identified, healthcare team will get a message and they would make a call and prescribe the activities which need to be do for emergency and even it doesn’t work ambulance is immediately depart to that particular patient’s location since we have the patient’s personal info about his location and his/her relative emergency mobile number.

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Classification of covid patient image dataset using modified deep convolutional neural network system

Cited by 3 publications

References 26 publications

Genome Based Computational Technique to Identify the Functional RNA in Protozoan Parasites

Genome Based Computational Technique to Identify the Functional RNA in Protozoan Parasites

Neural Network-Based Intrusion Detection System in Hospital Management Systems

IoT Based Patient’s Smart Healthcare Monitoring and Recording Using GSM Module

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