Journal Description
Green Intelligent Systems and Applications
Green Intelligent Systems and Applications (e-ISSN: 2809-1116) is an international, scientific, peer-reviewed, open-access journal on theoretical and applied sciences related to all aspects of green technologies and intelligent systems published biannually online by Tecno Scientifica.
- Open Access — free for readers and authors, with no article processing charges (APC)
- High Visibility: indexed within CrossRef, Scilit, Google Scholar and many other databases.
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 3 weeks after submission.
Articles
Articles
Effectiveness of Using Artificial Intelligence for Early Child Development Screening
by
Michael-Lian Gau,
Huong-Yong Ting,
Teck-Hock Toh,
Pui-Ying Wong,
Pei-Jun Woo,
Su-Woan Wo,
Gek-Ling Tan
Abstract This study presents a novel approach to recognizing emotions in infants using machine learning models. To address the lack of infant-specific datasets, a custom dataset of infants' faces was created by extracting images from the AffectNet dataset. The dataset was then used to train various machine learning models with different parameters. The best-performing model was evaluated on the City Infant Faces dataset. The proposed deep learning model achieved an accuracy of 94.63% in recognizing positive, negative, and neutral facial expressions. These results provide a benchmark for the performance of machine learning models in infant emotion recognition and suggest potential applications in developing emotion-sensitive technologies for infants. This study fills a gap in the literature on emotion recognition, which has largely focused on adults or children and highlights the importance of developing infant-specific datasets and evaluating different parameters to achieve accurate results.[...] Read more. Full text
Green Intell. Syst. Appl. 2023, 3(1), pp 1-13; https://doi.org/10.53623/gisa.v3i1.229
70 viewsAbstract This study presents a novel approach to recognizing emotions in infants using machine learning models. To address the lack of infant-specific datasets, a custom dataset of infants' faces was created by extracting images from the AffectNet dataset. The dataset was then used to train various machine learning models with different parameters. The best-performing model was evaluated on the City Infant Faces dataset. The proposed deep learning model achieved an accuracy of 94.63% in recognizing positive, negative, and neutral facial expressions. These results provide a benchmark for the performance of machine learning models in infant emotion recognition and suggest potential applications in developing emotion-sensitive technologies for infants. This study fills a gap in the literature on emotion recognition, which has largely focused on adults or children and highlights the importance of developing infant-specific datasets and evaluating different parameters to achieve accurate results.[...] Read more. Full text
Solar Powered Wireless Sensor Network for Water Quality Monitoring and Classification
by
Octarina Nur Samijayani,
Tyan Permana Saputra,
Hamzah Firdaus,
Anwar Mujadin
Abstract Water is essential for human being, also for animals and plants. In Indonesia, there are a lot of residential living in the riverbank which have poor water conditions. People frequenty use water from the river for daily activities. To determine the quality of water, samples are usually taken and tested in the laboratory. This method is less efficient in time and also cost. In order to determine and monitor the quality of water, this paper discuss the Wireless Sensor Network (WSN) to monitor the quality of water from a distance with the self powered sensor node. One of the issue in developing the WSN is the energy. Since this is implemented in outdoor, therefore it is possible to use solar panel to produce the energy. In this study three indicators; pH, TDS, and turbidity; were used to determine water quality based on the Indonesian Minister of Health Regulation. The results examine the WSN performance, and also the analysys of the solar energy supply for each sensor node. The WSN successfully works in detect and clasify tha water quality category and display it in the monitoring center or user. The sensors are calibrated and works with tolerable error of sensor reading of 5,1%. The WSN node is embedded with solar panel to supply the energy for node component. Therefore it able to extend the lifetime of the networks devices with renewable energy to implement the Green WSN.[...] Read more. Full text
Green Intell. Syst. Appl. 2023, 3(1), pp 14-21; https://doi.org/10.53623/gisa.v3i1.244
23 viewsAbstract Water is essential for human being, also for animals and plants. In Indonesia, there are a lot of residential living in the riverbank which have poor water conditions. People frequenty use water from the river for daily activities. To determine the quality of water, samples are usually taken and tested in the laboratory. This method is less efficient in time and also cost. In order to determine and monitor the quality of water, this paper discuss the Wireless Sensor Network (WSN) to monitor the quality of water from a distance with the self powered sensor node. One of the issue in developing the WSN is the energy. Since this is implemented in outdoor, therefore it is possible to use solar panel to produce the energy. In this study three indicators; pH, TDS, and turbidity; were used to determine water quality based on the Indonesian Minister of Health Regulation. The results examine the WSN performance, and also the analysys of the solar energy supply for each sensor node. The WSN successfully works in detect and clasify tha water quality category and display it in the monitoring center or user. The sensors are calibrated and works with tolerable error of sensor reading of 5,1%. The WSN node is embedded with solar panel to supply the energy for node component. Therefore it able to extend the lifetime of the networks devices with renewable energy to implement the Green WSN.[...] Read more. Full text
