Machine Learning Approaches for Assessing Fluoride Concentration in Drinking Water and Dental Health DOI

Nehal Kumar,

Rajesh Verma

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Год журнала: 2023, Номер unknown, С. 689 - 696

Опубликована: Ноя. 22, 2023

Fluoride in drinking water has been a significant issue recent years due to speculation about positive benefits on dental health. When fluoride levels are too high, fluorosis may develop the enamel and remain permanently. Traditional methods of determining concentration often laborious time consuming, not mention non-real time. This study introduces an ML method for predicting assessing effects tooth In this study, wide variety models, such as Decision Trees, Neural Networks, Support Vector Machines, employed analyze dataset consisting samples from various locations oral health characteristics. approach be beneficial detecting real-time identifying their impact health, since our best-performing model had accuracy 96.4%. only paves way proactive quality management, but it also helps communities anticipate avoid risks caused by changing concentrations.

Язык: Английский

Advanced IoT and Machine Learning Techniques for Effective Heart Disease Diagnosis DOI
Dankan Gowda,

M. Sathyanarayanan,

Kirti Rahul Kadam

и другие.

Опубликована: Авг. 23, 2024

Язык: Английский

Процитировано

0

IoT-Enabled Predictive Healthcare Monitoring Using Machine Learning Models DOI

N. J. Patil,

Avinash Sharma,

K. D. V. Prasad

и другие.

Lecture notes in networks and systems, Год журнала: 2024, Номер unknown, С. 165 - 175

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0

Optimizing Resource Allocation in Healthcare Facilities Through IoT and Machine Learning Predictive Analysis DOI
Dankan Gowda, Avinash Sharma, Saptarshi Mukherjee

и другие.

Lecture notes in networks and systems, Год журнала: 2024, Номер unknown, С. 177 - 187

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0

Design and Implementation of an IoT-Based Control System for Precision Food Manufacturing DOI
Dankan Gowda, Avinash Sharma, Sajja Suneel

и другие.

Lecture notes in networks and systems, Год журнала: 2024, Номер unknown, С. 333 - 343

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0

Deep Learning Perspectives on Efficient Image Matching in Natural Image Databases DOI Open Access

Et al. Mirzanur Rahman

International Journal on Recent and Innovation Trends in Computing and Communication, Год журнала: 2023, Номер 11(10), С. 172 - 184

Опубликована: Ноя. 2, 2023

With the proliferation of digital content, efficient image matching in natural databases has become paramount. Traditional techniques, while effective to a certain extent, face challenges dealing with high variability inherent images. This research delves into application deep learning models, particularly Convolutional Neural Networks (CNNs), Siamese Networks, and Triplet address these challenges. We introduce various techniques enhance efficiency, such as data augmentation, transfer learning, dimensionality reduction, sampling, amalgamation traditional computer vision strategies learning. Our experimental results, garnered from specific dataset, demonstrate significant improvements quantified by metrics like precision, recall, F1-Score, time. The findings underscore potential transformative tool for database matching, setting stage further optimization this domain.

Язык: Английский

Процитировано

0

Machine Learning and Blockchain Integration in Industrial Robotics DOI
Dankan Gowda, Pullela S. V. V. S. R. Kumar,

S B Manoj Kumar

и другие.

Advances in computational intelligence and robotics book series, Год журнала: 2023, Номер unknown, С. 346 - 369

Опубликована: Дек. 29, 2023

Machine learning and blockchain have the potential to completely change landscape of industrial robots. Learning adaptation give robots an edge in functionality independence. Blockchain, on other hand, provides a decentralized secure platform for information exchange transaction verification. In this chapter, authors look at benefits drawbacks integrating machine technology use manufacturing The study begins with brief introduction learning, blockchain, their respective robot applications. Some possibilities advantages that will be covered next sections include better data security, more transparent auditable decision making, control systems. importance collaboration between academic institutions, businesses, government agencies is emphasized order speed up process mainstreaming integration

Язык: Английский

Процитировано

0

Machine Learning Approaches for Assessing Fluoride Concentration in Drinking Water and Dental Health DOI

Nehal Kumar,

Rajesh Verma

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Год журнала: 2023, Номер unknown, С. 689 - 696

Опубликована: Ноя. 22, 2023

Fluoride in drinking water has been a significant issue recent years due to speculation about positive benefits on dental health. When fluoride levels are too high, fluorosis may develop the enamel and remain permanently. Traditional methods of determining concentration often laborious time consuming, not mention non-real time. This study introduces an ML method for predicting assessing effects tooth In this study, wide variety models, such as Decision Trees, Neural Networks, Support Vector Machines, employed analyze dataset consisting samples from various locations oral health characteristics. approach be beneficial detecting real-time identifying their impact health, since our best-performing model had accuracy 96.4%. only paves way proactive quality management, but it also helps communities anticipate avoid risks caused by changing concentrations.

Язык: Английский

Процитировано

0