Mobile Internet Analysis in Prevention of Negative Impacts of Information and Communication Technology in Indonesia

Nauvaldi, Muhamad Rio

doi:10.34306/itsdi.v4i2.585

Cited by 6 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the widespread use of smartphones and video surveillance equipment, coupled with the continuous advancements in artificial intelligence (AI), image quality has improved, and acquisition and collection have become more accessible, making it possible to detect air quality through AI methods such as image processing and machine learning [25], [26]. Individuals can easily capture images of their surroundings using their mobile phones and apply established air quality image recognition models to obtain information on air quality [27], [28]. This information can then inform individuals to take appropriate air pollution protection measures in a timely manner.…”

Section: • Image-based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Image-based Air Quality Prediction using Convolutional Neural Networks and Machine Learning

Hardini,

Riza Chakim,

Magdalena

et al. 2023

ATT

View full text Add to dashboard Cite

Air quality has become a major public concern due to the significant threat posed by air pollution to human health, and rapid and efficient monitoring of air quality is crucial for pollution control and human health. In this paper, deep learning and image-based models are proposed to estimate air quality. To evaluate the level of air quality, the model collects feature information from landscape photos taken by mobile cameras. To analyze public perception of air quality, researchers collected questionnaire data from 257 people. The Smartpls method allows for structural analysis to determine the influence of each variable on other variables and the extent of their contribution to the final variable of overall perception of air quality. This study aims to develop a novel approach for air quality prediction using image-based data and machine learning techniques. The research used convolutional neural networks to extract features from images and predict the air quality index. The study was conducted using a dataset obtained from a network of air quality sensors across the city. The results of the study showed that the proposed approach can provide accurate air quality predictions compared to the traditional methods. The developed model was able to capture the complex relationships between air quality and environmental factors, such as temperature and humidity. The implications of the study suggest that image-based air quality prediction can be a powerful tool for improving public health and reducing the impact of air pollution. The study's findings hold promise for a healthier future by facilitating more effective pollution management and improved air quality regulation. The study's primary novelty lies in its approach to air quality prediction by deploying convolutional neural networks to extract image features for predicting air quality indices. This application of advanced machine learning techniques to image-based data for air quality estimation marks a significant advancement.

show abstract

Section: • Image-based Methodsmentioning

confidence: 99%

“…Throughout this process, meticulous data quality control is paramount, as is the judicious selection of the optimal CNN architecture to augment model performance [28]. The strategic calibration of model training parameters significantly influences the final outcomes.…”

Section: Figure 6 3000 Sky Photos In Indonesiamentioning

confidence: 99%

Image-based Air Quality Prediction using Convolutional Neural Networks and Machine Learning

Hardini,

Riza Chakim,

Magdalena

et al. 2023

ATT

View full text Add to dashboard Cite

show abstract

“…With the increasing digitization of healthcare records and the adoption of electronic health record (EHR) systems, the vulnerability of patient data to unauthorized access and breaches has become a significant challenge. Security breaches in healthcare can have severe consequences, including identity theft, financial fraud, and compromised patient care [20].…”

Section: A Overview Of Security and Privacy Issues In Healthcarementioning

confidence: 99%

Enhancing Security and Privacy of Patient Data in Healthcare: A SmartPLS Analysis of Blockchain Technology Implementation

Handayani,

Apriani,

Mulyati

et al. 2023

itsdi

View full text Add to dashboard Cite

Security and privacy of patient data are critical concerns in the healthcare system. This research aims to investigate the impact of implementing blockchain technology on enhancing the security and privacy of patient data in healthcare. Using the SmartPLS analysis method, this study empirically examines the relationships between blockchain technology implementation and data security and data privacy within the healthcare system. The research sample consists of healthcare organizations that have implemented blockchain technology for data management. Data is collected through surveys and analyzed using SmartPLS to assess the effects of blockchain technology on data security and privacy. The findings reveal the positive influence of blockchain technology implementation on enhancing the security and privacy of patient data. The study also identifies challenges, such as scalability and interoperability, that need to be addressed for successful implementation. This research contributes to the existing literature by providing empirical evidence on the benefits and challenges of implementing blockchain technology to safeguard patient data in healthcare systems.

show abstract

Digital Transformation in Reading Behavior: A Business Model Canvas Approach for Marketing E-Books Library

Lukita,

Wali,

Sunarjo

et al. 2023

2023 11th International Conference on Cyber and IT Service Management (CITSM)

View full text Add to dashboard Cite

Mobile Internet Analysis in Prevention of Negative Impacts of Information and Communication Technology in Indonesia

Cited by 6 publications

References 21 publications

Image-based Air Quality Prediction using Convolutional Neural Networks and Machine Learning

Image-based Air Quality Prediction using Convolutional Neural Networks and Machine Learning

Enhancing Security and Privacy of Patient Data in Healthcare: A SmartPLS Analysis of Blockchain Technology Implementation

Digital Transformation in Reading Behavior: A Business Model Canvas Approach for Marketing E-Books Library

Contact Info

Product

Resources

About