<span>e-Health remote monitoring systems have bloomed rapidly with a myriad of applications. This paper discusses a design of a remote monitoring device for biomedical field. Four biomedical sensors which are electrocardiography (ECG), airflow, galvanic skin response and temperature with two boards which are the e-Health Shield Board V2.0 and Arduino Uno Board are used. The results show satisfactory output for each experiment using two test subjects. The device able to achieve high accuracy where percentage of temperature difference is less than 1% compared to the commercial devices with an average power consumption of each working sensor on board is ≤9W.</span>

A novel clinical decision support system is proposed in this paper for evaluating the fetal well-being from the cardiotocogram (CTG) dataset through an Improved Adaptive Genetic Algorithm (IAGA) and Extreme Learning Machine (ELM). IAGA employs a new scaling technique (called sigma scaling) to avoid premature convergence and applies adaptive crossover and mutation techniques with masking concepts to enhance population diversity. Also, this search algorithm utilizes three different fitness functions (two single objective fitness functions and multi-objective fitness function) to assess its performance. The classification results unfold that promising classification accuracy of 94% is obtained with an optimal feature subset using IAGA. Also, the classification results are compared with those of other Feature Reduction techniques to substantiate its exhaustive search towards the global optimum. Besides, five other benchmark datasets are used to gauge the strength of the proposed IAGA algorithm.

<span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Wireless Sensor Network (WSN) is known for its autonomous sensors, where it has been found to be useful during the development of Internet of Things (IoT) devices. However, WSN is also known for its limited energy supply and memory space, as it carries small-sized batteries and memory space. Hence, a data compression approach has been introduced in this paper with the purpose of solving this particular issue. This paper focused on the performance of the Arithmetic Coding algorithm. Temperature (Temp), Sea-Level Pressure (Pressure), stride interval (Stride), and heart rate (BPM) were chosen as the dataset in this project. Based on the results, the compression ratio of Temp, Pressure, Stride, and BPM were 0.428, 0.255, 0.217, and 0.159 respectively. From this analysis, BPM produced the best compression ratio. Undeniably, the Arithmetic Coding algorithm is one of the best methods to compress real-world datasets. Hence, by using this approach, it can reduce the usage of energy and memory space.</span><table class="MsoTableGrid" style="width: 444.85pt; border-collapse: collapse; border: none; mso-border-alt: solid windowtext .5pt; mso-yfti-tbllook: 1184; mso-padding-alt: 0in 5.4pt 0in 5.4pt;" width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr style="mso-yfti-irow: 0; mso-yfti-firstrow: yes; mso-yfti-lastrow: yes; height: 63.4pt;"><td style="width: 290.6pt; border: none; border-top: solid windowtext 1.0pt; mso-border-top-alt: solid windowtext .5pt; padding: 0in 5.4pt 0in 5.4pt; height: 63.4pt;" valign="top" width="387"><p class="MsoNormal" style="margin-top: 6.0pt; text-align: justify;"><span style="font-size: 9.0pt; color: black; mso-bidi-font-style: italic;">Wireless Sensor Network (WSN) is known for its autonomous sensors, where it has been found to be useful during the development of Internet of Things (IoT) devices. However, WSN is also known for its limited energy supply and memory space, as it carries small-sized batteries and memory space. Hence, a data compression approach has been introduced in this paper with the purpose of solving this particular issue. This paper focused on the performance of the Arithmetic Coding algorithm. Temperature (Temp), Sea-Level Pressure (Pressure), stride interval (Stride), and heart rate (BPM) were chosen as the dataset in this project. Based on the results, the compression ratio of Temp, Pressure, Stride, and BPM were 0.428, 0.255, 0.217, and 0.159 respectively. From this analysis, BPM produced the best compression ratio. Undeniably, the Arithmetic Coding algorithm is one of the best methods to compress real-world datasets. Hence, by using this approach, it can reduce the usage of energy and memory space.</span></p></td></tr></tbody></table>

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