Automated Medication System for Rural and War Affected Areas

Sadhayo, Intesab Hussain; Channa, Muhammad Ibrahim; Laghari, Asif Ali

doi:10.14738/tmlai.31.798

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Machine learning and deep learning techniques plays an important role in automated diagnosis of disease in the medical field for many applications. Several researchers have used the artificial intelligence based machine learning techniques for many medical applications (Junejo et al, 2019;Memon et al, 2020;Sadhayo et al, 2015). Farook et al indicated that application of machine learning techniques in dental and orofacial health care has provided remarkable results and can be used as diagnostic tool in the clinics (Farook et al, 2021).…”

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confidence: 99%

Automated detection of orofacial pain from thermograms using machine learning and deep learning approaches

Snekhalatha

Krishnan

2021

Expert Systems

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The main objectives of this study are (i) to perform automated segmentation of facial regions from thermograms using k-means clustering algorithm and to classify the data into normal and orofacial pain (OFP) categories using various machine learning classifiers (ii) to implement the convolutional neural network (CNN) for classification of normal and OFP subjects which involves automated feature extraction and feature selection process. Fifty normal and 50 diseased cases suffering from orofacial pain were included in the study. Facial thermograms were segmented using k-means algorithm, then statistical features were extracted and classified into normal and OFP using various machine learning classifier. Further, the deep learning networks such as VGG-16 and DenseNet-121 were used for automated feature extraction and classification of facial thermograms. The facial temperature variations of 3.46%, 3.4%, and 3.27% were observed in the front, right and left side facial regions respectively between the normal and the OFP subjects. Machine learning classifiers such as support vector machine (SVM) and random forest (RF) classifier provided the highest accuracy of 99%. On the other hand, deep learning models such as modified VGG-16 achieved an average accuracy of 97% compared to modified DenseNet-121 which produced an average accuracy of 68% in classification of normal and OFP thermograms. Thus, computer aided diagnosis of facial thermography could be used as a viable screening device for a reliable identification of tooth pathology before the occurrence of structural changes and complications.

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confidence: 99%

Automated detection of orofacial pain from thermograms using machine learning and deep learning approaches

Snekhalatha

Krishnan

2021

Expert Systems

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Human profiling based on computer vision : A survey with application

Elbaghazaoui

Amnai

Fakhri

et al. 2022

2022 IEEE 3rd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS)

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Research on Statistical Characteristics Modeling of Matching Probability and Measurement Error Based on Machine Learning

Pan

2022

International Journal of Information Systems in the Service Sector

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In view of the problems of the current modeling methods for the statistical characteristics of matching probability and measurement error, the modeling method of matching probability and measurement error statistical characteristics based on machine learning is proposed. According to the requirements of total sequence matching probability and system matching times, the sequence matching probability is calculated. The measurement error is analyzed in the process of acquisition and matching, and the measurable interference parameters are obtained. According to the analysis results, the mean value of matching measurement error is standardized, and the matching probability and measurement error statistical characteristics are established sex model. The experimental results show that the matching probability and measurement error statistical model of this method has high accuracy, and has good application effect in practical application.

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Automated Medication System for Rural and War Affected Areas

Cited by 3 publications

References 9 publications

Automated detection of orofacial pain from thermograms using machine learning and deep learning approaches

Automated detection of orofacial pain from thermograms using machine learning and deep learning approaches

Human profiling based on computer vision : A survey with application

Research on Statistical Characteristics Modeling of Matching Probability and Measurement Error Based on Machine Learning

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