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Synthetic hardware performance analysis in virtualized cloud environment for healthcare organization

Tan CH, Teh YW

J Med Syst, 2013 Aug;37(4):9950.
PMID: 23709190 DOI: 10.1007/s10916-013-9950-7

The main obstacles in mass adoption of cloud computing for database operations in healthcare organization are the data security and privacy issues. In this paper, it is shown that IT services particularly in hardware performance evaluation in virtual machine can be accomplished effectively without IT personnel gaining access to actual data for diagnostic and remediation purposes. The proposed mechanisms utilized the hypothetical data from TPC-H benchmark, to achieve 2 objectives. First, the underlying hardware performance and consistency is monitored via a control system, which is constructed using TPC-H queries. Second, the mechanism to construct stress-testing scenario is envisaged in the host, using a single or combination of TPC-H queries, so that the resource threshold point can be verified, if the virtual machine is still capable of serving critical transactions at this constraining juncture. This threshold point uses server run queue size as input parameter, and it serves 2 purposes: It provides the boundary threshold to the control system, so that periodic learning of the synthetic data sets for performance evaluation does not reach the host's constraint level. Secondly, when the host undergoes hardware change, stress-testing scenarios are simulated in the host by loading up to this resource threshold level, for subsequent response time verification from real and critical transactions.
Fulltext A review of subsequence time series clustering

Zolhavarieh S, Aghabozorgi S, Teh YW

ScientificWorldJournal, 2014;2014:312521.
PMID: 25140332 DOI: 10.1155/2014/312521

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.
Smartphone Motion Sensor-Based Complex Human Activity Identification Using Deep Stacked Autoencoder Algorithm for Enhanced Smart Healthcare System

Alo UR, Nweke HF, Teh YW, Murtaza G

Sensors (Basel), 2020 Nov 05;20(21).
PMID: 33167424 DOI: 10.3390/s20216300

Human motion analysis using a smartphone-embedded accelerometer sensor provided important context for the identification of static, dynamic, and complex sequence of activities. Research in smartphone-based motion analysis are implemented for tasks, such as health status monitoring, fall detection and prevention, energy expenditure estimation, and emotion detection. However, current methods, in this regard, assume that the device is tightly attached to a pre-determined position and orientation, which might cause performance degradation in accelerometer data due to changing orientation. Therefore, it is challenging to accurately and automatically identify activity details as a result of the complexity and orientation inconsistencies of the smartphone. Furthermore, the current activity identification methods utilize conventional machine learning algorithms that are application dependent. Moreover, it is difficult to model the hierarchical and temporal dynamic nature of the current, complex, activity identification process. This paper aims to propose a deep stacked autoencoder algorithm, and orientation invariant features, for complex human activity identification. The proposed approach is made up of various stages. First, we computed the magnitude norm vector and rotation feature (pitch and roll angles) to augment the three-axis dimensions (3-D) of the accelerometer sensor. Second, we propose a deep stacked autoencoder based deep learning algorithm to automatically extract compact feature representation from the motion sensor data. The results show that the proposed integration of the deep learning algorithm, and orientation invariant features, can accurately recognize complex activity details using only smartphone accelerometer data. The proposed deep stacked autoencoder method achieved 97.13% identification accuracy compared to the conventional machine learning methods and the deep belief network algorithm. The results suggest the impact of the proposed method to improve a smartphone-based complex human activity identification framework.
Fulltext Z-Scheme Photocatalytic Systems for Solar Water Splitting

Ng BJ, Putri LK, Kong XY, Teh YW, Pasbakhsh P, Chai SP

Adv Sci (Weinh), 2020 Apr;7(7):1903171.
PMID: 32274312 DOI: 10.1002/advs.201903171

As the world decides on the next giant step for the renewable energy revolution, scientists have begun to reinforce their headlong dives into the exploitation of solar energy. Hitherto, numerous attempts are made to imitate the natural photosynthesis of plants by converting solar energy into chemical fuels which resembles the "Z-scheme" process. A recreation of this system is witnessed in artificial Z-scheme photocatalytic water splitting to generate hydrogen (H2). This work outlines the recent significant implication of the Z-scheme system in photocatalytic water splitting, particularly in the role of electron mediator and the key factors that improve the photocatalytic performance. The Review begins with the fundamental rationales in Z-scheme water splitting, followed by a survey on the development roadmap of three different generations of Z-scheme system: 1) PS-A/D-PS (first generation), 2) PS-C-PS (second generation), and 3) PS-PS (third generation). Focus is also placed on the scaling up of the "leaf-to-tree" challenge of Z-scheme water splitting system, which is also known as Z-scheme photocatalyst sheet. A detailed investigation of the Z-scheme system for achieving H2 evolution from past to present accompanied with in-depth discussion on the key challenges in the area of Z-scheme photocatalytic water splitting are provided.
A scoping review of applications of artificial intelligence in kinematics and kinetics of ankle sprains - current state-of-the-art and future prospects

Teoh YX, Alwan JK, Shah DS, Teh YW, Goh SL

Clin Biomech (Bristol, Avon), 2024 Mar;113:106188.
PMID: 38350282 DOI: 10.1016/j.clinbiomech.2024.106188

BACKGROUND: Despite the existence of evidence-based rehabilitation strategies that address biomechanical deficits, the persistence of recurrent ankle problems in 70% of patients with acute ankle sprains highlights the unresolved nature of this issue. Artificial intelligence (AI) emerges as a promising tool to identify definitive predictors for ankle sprains. This paper aims to summarize the use of AI in investigating the ankle biomechanics of healthy and subjects with ankle sprains.
METHODS: Articles published between 2010 and 2023 were searched from five electronic databases. 59 papers were included for analysis with regards to: i). types of motion tested (functional vs. purposeful ankle movement); ii) types of biomechanical parameters measured (kinetic vs kinematic); iii) types of sensor systems used (lab-based vs field-based); and, iv) AI techniques used.
FINDINGS: Most studies (83.1%) examined biomechanics during functional motion. Single kinematic parameter, specifically ankle range of motion, could obtain accuracy up to 100% in identifying injury status. Wearable sensor exhibited high reliability for use in both laboratory and on-field/clinical settings. AI algorithms primarily utilized electromyography and joint angle information as input data. Support vector machine was the most used supervised learning algorithm (18.64%), while artificial neural network demonstrated the highest accuracy in eight studies.
INTERPRETATIONS: The potential for remote patient monitoring is evident with the adoption of field-based devices. Nevertheless, AI-based sensors are underutilized in detecting ankle motions at risk of sprain. We identify three key challenges: sensor designs, the controllability of AI models, and the integration of AI-sensor models, providing valuable insights for future research.
Renin angiotensin-aldosterone system (RAAS) blockers usage among type II diabetes mellitus patients-A Retrospective Study

Ng YP, Balasubramanian GP, Heng YP, Kalaiselvan M, Teh YW, Cheong KM, et al.

Diabetes Metab Syndr, 2018 May;12(3):305-308.
PMID: 29279269 DOI: 10.1016/j.dsx.2017.12.005

AIMS: Recent data showed an alarming rise of new dialysis cases secondary to diabetic nephropathy despite the growing usage of RAAS blockers. Primary objective of this study is to explore the prevalence of RAAS blockers usage among type II diabetic patients, secondary objectives are to compare the prescribing pattern of RAAS blocker between primary and tertiary care center and to explore if the dose of RAAS blocker prescribed was at optimal dose as suggested by trials.

MATERIALS AND METHODS: This is a retrospective study conducted at one public tertiary referral hospital and one public health clinic in Sungai Petani, Kedah, Malaysia.

RESULTS: RAAS blockers in T2DM patients was found to be 65%. In primary care, 14.3% of the RAAS blockers prescribed was ARB. Tertiary care had higher utilization of ARB, which was 42.9%. In primary care setting, the most commonly used ACEI were perindopril (92.4%) followed by enalapril (7.6%), meanwhile perindopril was the only ACEI being prescribed in tertiary care. The most prescribed ARB was irbesartan (63.6%) and telmisartan (54.2%) respectively in primary and tertiary care. Overall, 64.9% of RAAS blockers prescribed by both levels of care were found to be achieving the target dose as recommended in landmark trials. Crude odd ratio of prescribing RAAS blocker in primary care versus tertiary care was reported as 2.70 (95% CI: 1.49 to 4.91).

CONCLUSION: RAAS blockers usage among T2DM patients was higher in primary care versus tertiary care settings. Majority of the patients did not receive optimal dose of RAAS blockers.