Apprehending Air Conditioning Systems in Context to COVID-19 and Human Health: A Brief Communication

Akhai, Shalom

doi:10.24321/2455.9199.202004

Cited by 11 publications

(7 citation statements)

References 9 publications

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“…The structure of COVID-19 virus is positive single strand RNA which shares genetically characteristics with SARS and Bat SARS corona viruses [2]. Every virus has a size of between 50 nm to 200 nm and comprises of some basic proteins like Spike, Envelop, Membrane and nucleocapsid which are represented by Alphabetical letters S, E, M and N respectively [3,4]. The virus envelop is created by utilizing the S, E and M proteins while as, N protein is responsible for holding the RNA genome of the virus [3].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Detection of COVID-19 using Deep Learning and Multi-Agent Framework: The DLRPET Approach

Walia,

Kaur

2024

IJACSA

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The ongoing global pandemic caused by novel coronavirus (COVID-19) has emphasized the urgent need for accurate and efficient methods of detection. Over the past few years, several methods were proposed by various researchers for detecting COVID-19, but there is still a scope of improvement. Considering this, an effective and highly accurate detection model is presented in this paper that is based on Deep learning and multi-Agent concepts. Our main objective is to develop a model that can not only detect COVID-19 with high accuracy but also reduces complexity and dimensionality issues. To accomplish this objective, we applied a Deep Layer Relevance Propagation and Extra Tree (DLRPET) technique for selecting only crucial and informative features from the processed dataset. Also, a lightweight ResNet based Deep Learning model is proposed for classifying the disease. The ResNet model is initialized three times creating agents which analyses the data individually. The novelty contribution of this work is that instead of passing the entire training set to the classifier, we have divided the training dataset into three subsets. Each subset is passed to a specific agent for training and making individual predictions. The final prediction in proposed network is made by implementing majority voting mechanism to determine whether an individual is COVID-19 positive or negative. The experimental outcomes indicated that our approach achieved an accuracy of 99.73% that is around 2% higher than standard best performing KISM model. Moreover, proposed model attained precision of 100%, recall of 99.73% and F1-score of 98.59 % respectively, showing an increase of 5% in precision, 4.73% in recall and 4.59% in F1score than best performing SVM model.

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Section: Introductionmentioning

confidence: 99%

Enhanced Detection of COVID-19 using Deep Learning and Multi-Agent Framework: The DLRPET Approach

Walia,

Kaur

2024

IJACSA

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“…The COVID-19 virus has a high incidence rate that falls between 1.5 and 3.5, making it very contagious. 9,10 As a result, it's critical to get a diagnosis promptly in order to curb the virus's propagation. This has proven to be exceedingly difficult, though, as the virus can remain dormant among people for over 5 days until symptoms appear.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent-Based Ensemble Learning Model with Feature Selection for Enhanced COVID-19 Detection

Kaur Walia

2024

JCD

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The ongoing COVID-19 pandemic has driven the attention of researchers for advanced and adaptable methods in disease detection. This research paper proposes a novel multi-agent-based model for detecting COVID-19 among patients with high accuracy. Methods:Initially, all the necessary information is attained from a COVID-19 dataset that is available on GitHub. This data is then subjected to pre-processing because it contains a lot of null, missing and redundant values. The processed dataset is then passed to the proposed Deep Learning (DL) architecture for selecting only important and effective features. In order to make the process of feature selection in DL more effective, we have implemented the Layer-wise Relevance Propagation (LRP) and the Extra Tree technique on each layer of the DL model. The LRP and Extra tree evaluate the importance of each feature at each DL layer and finally, the output of two is combined to get the final feature set. After this, data is divided into training and testing sets in the proportion of 80:20. To introduce the concept of novelty, we have divided the training dataset into three agents (data subsets) which are then passed to three base models i.e., Support Vector Machine (SVM), Logistic Regression (LR) and K-Nearest Neighbor (KNN) separately. This was not the case in traditional models wherein the entire training dataset was passed to classifiers for training purposes.Results: The outputs generated by three base models are then combined by using an ensemble learning voting mechanism to make the final prediction which determines whether a patient is COVID-19 positive or not. The efficacy of the proposed approach is validated using Python software, wherein, it outperforms traditional LR, SVM and Bernoulli NB models by attaining an accuracy of 97.5%. Conclusion:The proposed multi-agent-based model for COVID-19 detection shows promising results in terms of accuracy and efficiency. By employing advanced techniques such as deep learning, feature selection, and ensemble learning, this approach addresses key challenges in disease detection and offers a significant improvement over traditional methods.

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“…2 Many electronic types of equipment are proving to be helpful in this direction, for example, air conditioners are used in buildings. [3][4][5] Many smart gadgets are being developed for buildings that can detect people who are infected with covid-19 and thus reduce the spread by providing them and others with the indication. 6 A doorbell allows visitors to announce their presence and request entry into a building, as well as allowing the occupant to verify the identity of the visitors in order to help prevent COVID-19 invasion at any time.…”

Section: Introductionmentioning

confidence: 99%

Development of COVID-19 Detection Smart Doorbell Based on IoT

Kaur

2022

JoARMST

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A unique thermal sensor camera must be utilised to detect a person with covid 19 symptoms. A Coronavirus doorbell has been invented in this research, which can be used to uniquely identify persons using their fingerprints, and they can also be identified for COVID positive. This is an electronic based gadget made with a Raspberry Pi 3, an IR sensor, a Serial 16x2 Serial LCD, a Raspberry Pi camera, and tools for fingerprint scanning. A COVID doorbell has been obtained by combining these tools to detect an infected person based on his temperature. When a person’s temperature exceeds 104 degrees Celsius, it detects it by touching his finger with a finger print sensor, captures an image of the infected person, and alerts the person inside the house with the appropriate message. This system thus has the potential to save human lives from COVID.

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Apprehending Air Conditioning Systems in Context to COVID-19 and Human Health: A Brief Communication

Cited by 11 publications

References 9 publications

Enhanced Detection of COVID-19 using Deep Learning and Multi-Agent Framework: The DLRPET Approach

Enhanced Detection of COVID-19 using Deep Learning and Multi-Agent Framework: The DLRPET Approach

Multi-Agent-Based Ensemble Learning Model with Feature Selection for Enhanced COVID-19 Detection

Development of COVID-19 Detection Smart Doorbell Based on IoT

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