This paper proposed a simple but highly accurate feature extraction method for epilepsy detection from electroencephalogram (EEG) signals. Based on the combination of Discrete Wavelet Transform (DWT) and the newly proposed features Local Binary Pattern Transition Histogram (LBPTH) and Local Binary Pattern Mean Absolute Deviation (LBPMAD), our proposed feature extraction method can efficiently extract features from EEG signals for machine learning classification of epilepsy, achieving high classification accuracy with a feature size of only 18 for each signal. Tested on the publicly available University of Bonn Epilepsy EEG Dataset using a signal length of 4097 data points (23.61 seconds), the proposed method achieved larger than 99.6% accuracy results for Support Vector Machine (SVM) and k-Nearest Neighbor (KNN) classification of ictal (set E) against any non-ictal (set A, B, C, or D) or combinations of non-ictal (set $\text{A}+\text{B}$ , set $\text{C}+\text{D}$ , or set $\text{A}+\text{B}+\text{C}+\text{D}$ ) EEG signals, which is among the best of currently published works. Our method can maintain high classification accuracy even with short input signals, achieving more than 99.1% SVM classification accuracy when input signal length is reduced to 512 data points (2.95 seconds). The high accuracy, small feature size, ability to work with short input signals and low computing requirements made the proposed method suitable for mobile, low power, and low-cost wearable medical devices.

The battery monitoring system (BMoS) is crucial to monitor the condition of the battery in supplying and absorbing the energy when operating and simultaneously determine the optimal limits for achieving long battery life. All of this can be done by measuring the battery parameters and increasing the state of charge (SoC) and the state of health (SoH) of the battery. The battery dataset from NASA is used for evaluation. In this work, the gradient vector is employed to obtain the trend of the energy supply pattern from the battery. In addition, a support vector machine (SVM) is adopted for an accurate battery accuracy index. This is in line with the use of polynomial regression; hence, points V1 and V2 are obtained as the boundaries of the normal-usage phase. Furthermore, testing of the time length distribution is also carried out on the length of time the battery was successfully extracted from the classification. All these stages can be used to calculate the rate of battery degradation during use so that this strategy can be applied in real situations by continuously comparing values. In this case, using the voltage gradient, SVM method, and the suggested polynomial regression, MAPE (%), MAE, and RMSE can be obtained against the battery value graph with values of 0.3%, 0.0106, and 0.0136, respectively. With this error value, the dynamics of the SoC value of the battery can be obtained, and the SoH problem can be resolved with a shorter usage time by avoiding the voltage-drop phase.

The use of blockchain has received great attention in its adoption as a financial instrument in cryptocurrencies. This phenomenon needs to be considered in the sense not only as a form of financial transactions but also in other fields such as health, which is also a challenge for modern society. In addition, several government policies have also supported the provision of health services as a form of improving people’s living standards in the form of insurance. In this study, we try to design the system by using UML diagram and simulate the use of DApps offered by the Vexanium Ecosystem. For example, three basic activities between patients, doctors, and insurance will be simulated in the form of the transaction ledger. This method allows us to speed up the authentication process that previously needed to be performed for a long time with bureaucracy becoming the rule in smart contracts in a matter of minutes. The evaluation of this method will then be compared with eight existing blockchain projects. The result in healthcare processes is cost savings through increased automation, speed, standardization, and efficiency. All of this can be a preliminary analysis of its application in Indonesia, particularly related to the authentication and recording of medical records.

This article proposes a new authentication method for vehicular monitoring Internet of Things (IoT) systems. Based on parallel hash chains, the proposed method is suitable for low-power and low-cost IoT devices. Encryption keys are continuously generated on identical parallel hash chains on both the IoT device and the server, removing the need to transmit secret keys over the network. The proposed method only needs two transmission handshakes for each authentication and data transmission. It does not need random number generation on the IoT device, reducing hardware requirements and potential security vulnerability. It minimizes power consumption and data transmission cost while providing mutual authentication of both the server and the device. Other merits of our proposed methods include: it provides device anonymity, allows message authentication, provides database integrity verification, is resistant to transmission loss, and is dynamically adaptable to disturbances in transmission network quality.

In the industrial era 4.0, domestic baby incubator producers are facing the challenge of free trade of foreign products that will compete in innovation with the application of IoT technology. One of the opportunities that arise consciously or unconsciously at the NICU (Neonatal Intensive Care Unit) unit in Hospitals, in general, is that there are no facilities for parents to monitor the baby’s condition inside the incubator directly. The purpose of this research project is to build a prototype of an internet-based baby incubator monitoring system based on things equipped with various sensors that will send data to the server in real-time and mobile apps for monitoring facilities for parents, including the voice of the baby. This study focused on how to develop baby incubators that can listen to baby’s crying, capture the voice, and interpret it using artificial intelligence. More than 40 (forty) voice datasets were used and successfully classified into five possible terms of the baby’s condition: burping, sleepy, hungry, uncomfortable, and pain by energy signal and spectrum analysis. The benefit of this research is as a driver of innovation for baby incubator products that will support the national medical devices manufacturing company.