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All Journal ComEngApp : Computer Engineering and Applications Journal International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Belitung Nursing Journal Jurnal Keperawatan Silampari Community Development Journal: Jurnal Pengabdian Masyarakat Indonesian Journal of Global Health research
Kurnianingsih, -
Department Of Electrical Engineering, Politeknik Negeri Semarang
Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Health Professions Languange, Linguistic, Communication & Media Nursing Public Health

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Journal : International Journal of Advances in Intelligent Informatics

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Hand–object interaction recognition based on visual attention using multiscopic cyber-physical-social system Adnan Rachmat Anom Besari; Azhar Aulia Saputra; Wei Hong Chin; Kurnianingsih Kurnianingsih; Naoyuki Kubota
International Journal of Advances in Intelligent Informatics Vol 9, No 2 (2023): July 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i2.901

Abstract

Computer vision-based cyber-physical-social systems (CPSS) are predicted to be the future of independent hand rehabilitation. However, there is a link between hand function and cognition in the elderly that this technology has not adequately supported. To investigate this issue, this paper proposes a multiscopic CPSS framework by developing hand–object interaction (HOI) based on visual attention. First, we use egocentric vision to extract features from hand posture at the microscopic level. With 94.87% testing accuracy, we use three layers of graph neural network (GNN) based on hand skeletal features to categorize 16 grasp postures. Second, we use a mesoscopic active perception ability to validate the HOI with eye tracking in the task-specific reach-to-grasp cycle. With 90.75% testing accuracy, the distance between the fingertips and the center of an object is used as input to a multi-layer gated recurrent unit based on recurrent neural network architecture. Third, we incorporate visual attention into the cognitive ability for classifying multiple objects at the macroscopic level. In two scenarios with four activities, we use GNN with three convolutional layers to categorize some objects. The outcome demonstrates that the system can successfully separate objects based on related activities. Further research and development are expected to support the CPSS application in independent rehabilitation.
Big data analytics for relative humidity time series forecasting based on the LSTM network and ELM Kurnianingsih Kurnianingsih; Anindya Wirasatriya; Lutfan Lazuardi; Adi Wibowo; I Ketut Agung Enriko; Wei Hong Chin; Naoyuki Kubota
International Journal of Advances in Intelligent Informatics Vol 9, No 3 (2023): November 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i3.905

Abstract

Accurate and reliable relative humidity forecasting is important when evaluating the impacts of climate change on humans and ecosystems. However, the complex interactions among geophysical parameters are challenging and may result in inaccurate weather forecasting. This study combines long short-term memory (LSTM) and extreme learning machines (ELM) to create a hybrid model-based forecasting technique to predict relative humidity to improve the accuracy of forecasts. Detailed experiments with univariate and multivariate problems were conducted, and the results show that LSTM-ELM and ELM-LSTM have the lowest MAE and RMSE results compared to stand-alone LSTM and ELM for the univariate problem. In addition, LSTM-ELM and ELM-LSTM result in lower computation time than stand-alone LSTM. The experiment results demonstrate that the proposed hybrid models outperform the comparative methods in relative humidity forecasting. We employed the recursive feature elimination (RFE) method and showed that dewpoint temperature, temperature, and wind speed are the factors that most affect relative humidity. A higher dewpoint temperature indicates more air moisture, equating to high relative humidity. Humidity levels also rise as the temperature rises.
Co-Authors Adi Wibowo Adnan Rachmat Anom Besari Afandi Nur Aziz Thohari Angga Wahyu Wibowo Anindya Wirasatriya Anton Satria Prabuwono Azhar Aulia Saputra Azka Khoirunnisa Dhanty Nurul Amalia Erika Lety Istikhomah Puspita Sari Ery Hadiyani Puji Astuti Fikri Nizar Gustiyana I Ketut Agung Enriko I Ketut Agung Enriko Ika Muryasari Irwan Yanwari Khalid Hammed S. Allehaibi Kuwat Santoso Lutfan Lazuardi Mardiyono Mardiyono Melyana Nurul Widyawati Melyana Nurul Widyawati Muhammad Irwan Yanwari Muttabik Fathul Lathief Naoki Masuyama Naoyuki Kubota Nisa Annisa Arfiyanti Nita Aquarista Nur Ghaniaviyanto Ramadhan Nurseno Bayu Aji Nurseno Bayu Aji Prayitno Takako Hashimoto Tri Raharjo Yudantoro Wei Hong Chin Wiktasari Wiktasari Yusuke Nojima