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Osman Yakubu
Garden City University College

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Internet of things based vital signs monitoring system: A prototype validity test Osman Yakubu; Emmanuel Wireko
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp962-972

Abstract

The advent of the internet of things (IoT) has resulted in an upsurge in the deployment of digital health care systems enabling patients’ health conditions to be remotely monitored. This article presents an intelligent and automated IoT-based vital signs monitoring system to aid in patient care. A the oretical framework was established to guide the development of a prototype. It encompasses the patient, IoT sensors, input and storage unit and data processing, analysis and data transmission. The prototype is equipped with the capability of sensing a patient’s body temperature, heart rate, and respiration rate in real time and transmits the data to a cloud data repository for storage and analysis. Alerts are sent to caregivers using SMS, email and voice calls where urgent attention is required for the patient. The voice call isto ensure a caregiver does not miss the alert since SMS and email may not be checked on time. To ensure privacy of patients, a caregiver has to be biometrically verified by either fingerprint or facial pattern. The experimental results confirmed the accuracy of the data gathered by the prototype, privacy of patients is also guaranteed compared to other benchmark systems.
Electricity consumption forecasting using DFT decomposition based hybrid ARIMA-DLSTM model Osman Yakubu; Narendra Babu C.
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 2: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i2.pp1107-1120

Abstract

Forecasting electricity consumption is vital, it guides policy makers and electricity distribution companies in formulating policies to manage production and curb pilfering. Accurately forecasting electricity consumption is a challenging task. Relying on a single model to forecast electricity consumption data which comprises both linear and nonlinear components produces inaccurate results. In this paper, a hybrid model using autoregressive integrated moving average (ARIMA) and deep long short-term memory (DLSTM) model based on discrete fourier transform (DFT) decomposition is presented. Aided by its superior decomposition capability, filtering using DFT can efficiently decompose the data into linear and nonlinear components. ARIMA is employed to model the linear component, while DLSTM is applied on the nonlinear component; the two predictions are then combined to obtain the final predicted consumption. The proposed techniques are applied on the household electricity consumption data of France to obtain forecasts for one day, one week and ten days ahead consumption. The results reveal that the proposed model outperforms other benchmark models considered in this investigation as it attained lower error values. The proposed model could accurately decompose time series data without exhibiting a performance degradation, thereby enhancing prediction accuracy.
Co-Authors Emmanuel Wireko Narendra Babu C.