Non-invasive discrimination between diabetic states (HBA1C<8% and HBA1C>10%) using photoplethysmography

Diabetes mellitus is a group of metabolic diseases associated with the production and/or reaction of insulin leading to hyperglycemia. Glycated hemoglobin (HbA1c) level is generally measured for hyperglycemia. The risk of developing complications depends on both the duration of diabetes and hyperglycemia. A trend of increasing arterial stiffness has been identified in type 2 diabetes. Photoplethysmographic (PPG) pulse wave provides a ‘window’ into the properties of small arteries whereas stiffening of these arteries will alter the PPG waveform. In this research, the potential of PPG in discriminating between type 2 diabetic patients at risk of having HbA1c level > 10% has been investigated. To this end, PPG signals recorded from diabetic patients with different levels of HbA1c (HbA1c level 10%) were acquired from the index finger of the right arm of 101 subjects (53 subjects with HbA1c level 10%) at a sampling rate of 275 Hz. The area under the curve of PPG (auc-PPG) was proposed in analyzing the PPG pulse contour. Results of t-test analysis show that auc-PPG is significantly larger in diabetic patients with HbA1c level 10% (p-value 10% (total 56 subjects) show that there is no significant difference in the mean value of auc-PPG between the first measurement and repeated measurement for both groups. Finally, a logistic regression model for estimating the risk of having HbA1c level > 10% among diabetic patients was estimated using data from 51 female diabetic patients. The model shows that the auc-PPG is an independent predictor for estimating the risk of having HbA1c level > 10% (p-value = 0.005) among female diabetic patients

A Study on the Correlation Between Hand Grip and Age Using Statistical and Machine Learning Analysis

Handgrip strength (HGS) is an easy-to-use instrument for monitoring people's health status. Numerous researchers in many countries have done a study on handgrip disease or demographic data. This study focused on classifying aged groups referring to handgrip value using machine learning. A total of fifty-four participants had involved in this study, ages ranging from 24 years to 57 years old. Digital Pinch Grip Analyzer had been used to measure the handgrip measurement three times to get more accurate results. The result is then recorded by Clinical Analysis Software (CAS) that is built into the analyzer. An independent t-test is used to investigate the significant factor for age group classification. The data were then classified using machine learning analysis which are Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes. The overall dataset shows that the Support Vector Machine is the most suitable classification technique with average accuracy between 5 groups of age is 98%, specificity of 0.79, the sensitivity of 0.9814 and 0.0185 of mean absolute error. SVM also give the lowest mean absolute error compared to RF and Naïve Bayes. This study is consistent with the previous work that there is a relationship between handgrip and age

A Study on the Correlation Between Hand Grip and Age Using Statistical and Machine Learning Analysis

Handgrip strength (HGS) is an easy-to-use instrument for monitoring people's health status. Numerous researchers in many countries have done a study on handgrip disease or demographic data. This study focused on classifying aged groups referring to handgrip value using machine learning. A total of fifty-four participants had involved in this study, ages ranging from 24 years to 57 years old. Digital Pinch Grip Analyzer had been used to measure the handgrip measurement three times to get more accurate results. The result is then recorded by Clinical Analysis Software (CAS) that is built into the analyzer. An independent t-test is used to investigate the significant factor for age group classification. The data were then classified using machine learning analysis which are Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes. The overall dataset shows that the Support Vector Machine is the most suitable classification technique with average accuracy between 5 groups of age is 98%, specificity of 0.79, the sensitivity of 0.9814 and 0.0185 of mean absolute error. SVM also give the lowest mean absolute error compared to RF and Naïve Bayes. This study is consistent with the previous work that there is a relationship between handgrip and age

TRIZ Inventive Solution in Solving Water Pipeline Leakage Using Accelerometer Sensor

To a developing country, sustaining a consistent water supply for industrial and domestic usages is a great challenge. In Malaysia, this can be demonstrated by the alarming rate of the Non-Revenue Water (NRW), which is &gt;30% and this is greater than the recommended NRW by the World Bank. Therefore, this paper highlights several causes that lead to this and determines the primary cause, which is water pipeline leakage. The Teoriya Resheniya Izobretatelskikh Zadatch (TRIZ), a problem-solving tool is employed to study the potential solutions. Then, the water system testbed is developed to examine the performance of the proposed solution

Identification of Risk Factors for Scoliosis in Elementary School Children Using Machine Learning

Scoliosis is an abnormal curvature of the spine and often diagnosed in childhood or early adolescence. In this study, the risk factors for scoliosis in elementary school children is investigate based on age, backpack weight and gender. There are 260 children participated in this study from aged 7 up to 12 years old. Scoliometer is used to measure the angle of trunk rotation (ATR) on Adam Forward Bending Test. Statistical analysis of analysis of variance (ANOVA) is used to determine the characteristic difference of ATR readings on the risk factors for scoliosis. Significant results with P-value less than 0.001 are found among ATR readings on a linear combination of risk factors for scoliosis of age and backpack weight. Then, the risk factors for scoliosis are classified among elementary school children using Decision Tree and K-Nearest Neighbor. The classification results shown that both Decision Tree method produced highest classification percentage up to 98.08%. This finding indicates that age and backpack weight are significant as the risk factors for scoliosis

Identification of Risk Factors for Scoliosis in Elementary School Children Using Machine Learning

Scoliosis is an abnormal curvature of the spine and often diagnosed in childhood or early adolescence. In this study, the risk factors for scoliosis in elementary school children is investigate based on age, backpack weight and gender. There are 260 children participated in this study from aged 7 up to 12 years old. Scoliometer is used to measure the angle of trunk rotation (ATR) on Adam Forward Bending Test. Statistical analysis of analysis of variance (ANOVA) is used to determine the characteristic difference of ATR readings on the risk factors for scoliosis. Significant results with P-value less than 0.001 are found among ATR readings on a linear combination of risk factors for scoliosis of age and backpack weight. Then, the risk factors for scoliosis are classified among elementary school children using Decision Tree and K-Nearest Neighbor. The classification results shown that both Decision Tree method produced highest classification percentage up to 98.08%. This finding indicates that age and backpack weight are significant as the risk factors for scoliosis

Internet of Things: A Monitoring and Control System for Rockmelon Farming

This paper describes the internet of things application in agriculture production especially for rock melon farming. In order to boost agricultural production on a commercial basis, a more systematic approach should be developed and organized to be adopted by operators to increase production and income. This work combines fertilization and irrigation in one system under protective structures to ensure that high-quality plant production and an alternative to conventional cropping systems. In addition, the use of technology for online monitoring and control is an improvement to the system in parallel with the rapid development of information technology today. The proposed system is focusing on automation, wireless, data analytics and simplified design with minimal and scalable skid size for rock melon in the Klang valley. Other than that, a monitoring and control system were developed besides, applied Internet of Thing (IoT) platform with additional user-friendly programmable farming routine Human-machine interfacing (HMI). HMI is a software interface that is capable to conduct the systems directly via autonomous cloud control. It provides time management and contributes to a more efficient workforce. The aim to develop a monitoring and controlling system using IoT for rock melon in this study was achieved. The rock melon harvesting succeeds according to the scheduled routine and the quality of yields also satisfied

A Study on the Correlation Between Hand Grip and Age Using Statistical and Machine Learning Analysis

Handgrip strength (HGS) is an easy-to-use instrument for monitoring people's health status. Numerous researchers in many countries have done a study on handgrip disease or demographic data. This study focused on classifying aged groups referring to handgrip value using machine learning. A total of fifty-four participants had involved in this study, ages ranging from 24 years to 57 years old. Digital Pinch Grip Analyzer had been used to measure the handgrip measurement three times to get more accurate results. The result is then recorded by Clinical Analysis Software (CAS) that is built into the analyzer. An independent t-test is used to investigate the significant factor for age group classification. The data were then classified using machine learning analysis which are Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes. The overall dataset shows that the Support Vector Machine is the most suitable classification technique with average accuracy between 5 groups of age is 98%, specificity of 0.79, the sensitivity of 0.9814 and 0.0185 of mean absolute error. SVM also give the lowest mean absolute error compared to RF and Naïve Bayes. This study is consistent with the previous work that there is a relationship between handgrip and age

Implementation of IoT and blockchain for temperature monitoring in COVID19 vaccine cold chain logistics

The current COVID-19 pandemic impacted globally and resulted one hundred million cases with almost two and half million death to-date. The concerns of further spread of the virus has caused the global economy to a halt when most countries globally implementing movement control. Recent approved vaccines developed by multiple fronts has shed some hoped for the recovery of the human activities back to pre-pandemic state. For Malaysia, the government has procured the vaccine from six suppliers and each vaccine require proper temperature monitoring to ensure the safety and the efficacy of the vaccines during transportation and distribution process. Effective vaccine Cold Chain Logistics (CCL) management will be required precise coordination and cooperation across multiple parties to ensure the quality of the vaccines which require temperature monitoring, distribution of records for traceability. Furthermore, the CCL process continuity is becoming critical from the handover point at Malaysia point of receipt from the manufacturer and further distribution from central storage to the two hundred vaccination center across the country. Key concerns will be the vaccines’ CCL process for remote areas in Malaysia. This paper will describe the architecture required to integrate IoT and Blockchain into the CCL management system to monitor the temperature of insulated container or cooler box. While this project may not be able to cover all the parameters it will serve an indication of the required system engineering consideration for the IoT and Blockchain application for the vaccine CCL management

Denoising of impulse noise using partition-supported median, interpolation and DWT in dental X-ray images

The impulse noise often damages the human dental X-Ray images, leading to improper dental diagnosis. Hence, impulse noise removal in dental images is essential for a better subjective evaluation of human teeth. The existing denoising methods suffer from less restoration performance and less capacity to handle massive noise levels. This method suggests a novel denoising scheme called "Noise removal using Partition supported Median, Interpolation, and Discrete Wavelet Transform (NRPMID)" to address these issues. To effectively reduce the salt and pepper noise up to a range of 98.3 percent noise corruption, this method is applied over the surface of dental X-ray images based on techniques like mean filter, median filter, Bi-linear interpolation, Bi-Cubic interpolation, Lanczos interpolation, and Discrete Wavelet Transform (DWT). In terms of PSNR, IEF, and other metrics, the proposed noise removal algorithm greatly enhances the quality of dental X-ray images