OdorTAM: Technology Acceptance Model for Biometric Authentication System Using Human Body Odor

Naaz, Sameena; Khan, Sarah; Siddiqui, Farheen; Sohail, Shahab Saquib; Madsen, Dag Øivind; Ahmad, Aqil

doi:10.3390/ijerph192416777

Cited by 3 publications

(3 citation statements)

References 67 publications

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“…A quite important aspect that underlies various theories in artificial intelligence is the color recognition system which is part of the practical implementation of artificial neural networks. In Jegede et al's research (Vadivelu et al, 2021;Naaz et al, 2022), research by taking characteristics of the human body is known as biometric authentication.…”

Section: Resultsmentioning

confidence: 99%

Texture and Flag Color Extraction in Backpropagation Neural Network Architecture

Rizki,

Defit

2024

jppipa, pendidikan ipa, fisika, biologi, kimia

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A flag is a rectangular or triangular piece of cloth or paper used as a symbol of the state, association, body, etc. or as a sign. It is often also used to symbolize a country to show its sovereignty. Along with the large number of countries, the country's flag also has many varieties and colors. The use of computers as a human aid is expected to the extent that the computer's ability can replace the limitations that humans have. Humans can recognize an object by using their eyes and brain, but if the eyes and brain cannot work properly it will hamper human work. In this research, training will be conducted on the Back Propagation Neural Network Architecture. Characteristic data for image recognition is obtained by extracting texture features and RGB color features. So that the network can recognize the flags by matching the feature data obtained from the training carried out. Characteristic data obtained from 24 data consisting of 16 training images and 8 testing images. From the results of the image network training can be identified properly, the accuracy rate of object identification is 87.50%. GUI users are able to identify flag images based on RGB text and color features.

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Section: Resultsmentioning

confidence: 99%

Texture and Flag Color Extraction in Backpropagation Neural Network Architecture

Rizki,

Defit

2024

jppipa, pendidikan ipa, fisika, biologi, kimia

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“…In the last 2 years, machine learning has been applied in various healthcare branches (Mufti et al, 2021;Fatima et al, 2022;Naaz et al, 2022;Rashid Irshad et al, 2023) among which the growing problem of AMR is one of the trending research areas (Cockerill, 1999). Traditional antimicrobial susceptibility testing procedures call for isolating bacteria from human specimens using culture techniques, after which the recovered bacteria are subjected to various antibiotic concentrations in numerous assays to determine which concentration limits growth (Wiegand et al, 2008;Nguyen et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Antimicrobial resistance and machine learning: past, present, and future

et al. 2023

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Machine learning has become ubiquitous across all industries, including the relatively new application of predicting antimicrobial resistance. As the first bibliometric review in this field, we expect it to inspire further research in this area. The review employs standard bibliometric indicators such as article count, citation count, and the Hirsch index (H-index) to evaluate the relevance and impact of the leading countries, organizations, journals, and authors in this field. VOSviewer and Biblioshiny programs are utilized to analyze citation and co-citation networks, collaboration networks, keyword co-occurrence, and trend analysis. The United States has the highest contribution with 254 articles, accounting for over 37.57% of the total corpus, followed by China (103) and the United Kingdom (78). Among 58 publishers, the top four publishers account for 45% of the publications, with Elsevier leading with 15% of the publications, followed by Springer Nature (12%), MDPI, and Frontiers Media SA with 9% each. Frontiers in Microbiology is the most frequent publication source (33 articles), followed by Scientific Reports (29 articles), PLoS One (17 articles), and Antibiotics (16 articles). The study reveals a substantial increase in research and publications on the use of machine learning to predict antibiotic resistance. Recent research has focused on developing advanced machine learning algorithms that can accurately forecast antibiotic resistance, and a range of algorithms are now being used to address this issue.

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“…The performance analysis based on the prediction of diseases is analyzed with the metrics mentioned above. The proposed approach work is compared with state-of-art works, such as HSM [8], SW-SSS [10], TDT [14], OCFA [15], and the Random Hashing Mechanism [21,42]. The comparative study is illustrated in Table 3.…”

Section: Performance Analysis Based On the Prediction Of Diseasesmentioning

confidence: 99%

An Optimization-Linked Intelligent Security Algorithm for Smart Healthcare Organizations

et al. 2023

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IoT-enabled healthcare apps are providing significant value to society by offering cost-effective patient monitoring solutions in IoT-enabled buildings. However, with a large number of users and sensitive personal information readily available in today’s fast-paced, internet, and cloud-based environment, the security of these healthcare systems must be a top priority. The idea of safely storing a patient’s health data in an electronic format raises issues regarding patient data privacy and security. Furthermore, with traditional classifiers, processing large amounts of data is a difficult challenge. Several computational intelligence approaches are useful for effectively categorizing massive quantities of data for this goal. For many of these reasons, a novel healthcare monitoring system that tracks disease processes and forecasts diseases based on the available data obtained from patients in distant communities is proposed in this study. The proposed framework consists of three major stages, namely data collection, secured storage, and disease detection. The data are collected using IoT sensor devices. After that, the homomorphic encryption (HE) model is used for secured data storage. Finally, the disease detection framework is designed with the help of Centered Convolutional Restricted Boltzmann Machines-based whale optimization (CCRBM-WO) algorithm. The experiment is conducted on a Python-based cloud tool. The proposed system outperforms current e-healthcare solutions, according to the findings of the experiments. The accuracy, precision, F1-measure, and recall of our suggested technique are 96.87%, 97.45%, 97.78%, and 98.57%, respectively, according to the proposed method.

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OdorTAM: Technology Acceptance Model for Biometric Authentication System Using Human Body Odor

Cited by 3 publications

References 67 publications

Texture and Flag Color Extraction in Backpropagation Neural Network Architecture

Texture and Flag Color Extraction in Backpropagation Neural Network Architecture

Antimicrobial resistance and machine learning: past, present, and future

An Optimization-Linked Intelligent Security Algorithm for Smart Healthcare Organizations

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